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POLITECNICO DI MILANO
School of Industrial and Information Engineering
Master of Science in Management Engineering
ARTIFICIAL INTELLIGENCE APPLICATIONS
IN E-COMMERCE LOGISTICS, A
SYSTEMATIC LITERATURE REVIEW
Supervisor: Riccardo Mangiaracina
Co-supervisor: Arianna Seghezzi
Alessandro Muzzin
Academic Year: 2019/2020
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Index
Index ... 2 Table Index ... 3 Figure Index ... 3 Executive Summary ... 4 1.Introduction ... 4 2. Methodology ... 53. Classification by logistic process impacted ... 6
4. Classification by objective ... 7
5. Conclusions... 8
1.Introduction ... 9
1.1 E-commerce complexities ... 11
1.2 Artificial Intelligence ... 13
2.Objectives and methodology ... 16
2.1. Material selection... 17
2.2. Descriptive Analysis ... 19
2.2.1. Year of publication ... 19
2.2.2. Country ... 20
2.2.3. Study type and source ... 22
2.3 Category selection ... 25
2.4 Material evaluation ... 27
3. Classification by logistic process impacted ... 28
3.1. Order Picking ... 30
3.1.1. Storage Location Assignment ... 33
3.1.2. Order Batching ... 34
3.1.3. Picker Routing ... 34
3.2. Order Packing ... 36
3.3. Order Delivery ... 37
3.3.1. Delivery Task Assignment ... 38
3.3.2. Vehicle Routing ... 39
3.3.3. Physical Exchange ... 40
3.4. Plan ... 42
3 3.4.2 Network design ... 43 3.5. Return ... 44 4. Classification by objective ... 48 4.1. Warehousing applications ... 48 4.2. Delivery applications ... 51
5.Conclusions and discussion ... 60
References ... 63
Table Index
Table 1 – Differences between traditional and e-commerce orders ... 12Table 2 – Classification of papers and sources ... 22
Table 3 – Manual vs automated picking systems ... 30
Table 4 – Classification of papers by impacted process and sub-process ... 46
Table 5 – Papers classified by objective and related cost item... 57
Figure Index
Figure 1 – Distribution of papers by year of publication ... 19Figure 2 – Distribution of papers by country/area ... 21
Figure 3 – The SCOR model and its adaptation to e-commerce logistics ... 29
Figure 4 – Subdivision of order picking in sub-processes ... 33
Figure 5 – Subdivision of order delivery in sub-processes ... 37
Figure 6 – Final framework of identified processes and sub-processes ... 45
Figure 7 – Identified categories for warehousing applications along the objective axis ... 49
Figure 8 – Objectives of warehousing applications and relationship with cost items ... 50
Figure 9 – Cost factors and cost components of last-mile delivery by Mangiaracina ... 52
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Executive Summary
1.Introduction
E-commerce is booming across the world and the growth rates experienced in recent years are expected to continue in the upcoming future. One of the major bottlenecks affecting the development of the e-commerce business is order fulfillment. Mainly due to e-commerce orders being fundamentally different from traditional orders in terms of characteristics and requirement, the handling complexity introduced by e-commerce is now the major blocker to its development. In this context, Artificial Intelligence is expected to improve the management of logistic operations by increasing process efficiency.
What makes Artificial Intelligence particularly relevant today, is not the technology itself, which is in fact everything but recent, but rather its current applicability to the operations world. The availability of large volumes of data, made possible by IoT and digitization of processes, combined with the increased computation capacity made available through Cloud computing, created the perfect conditions for a more practical use for algorithms devised decades ago.
The scope of this research is hence to conduct a systematic literature review of Artificial Intelligence applications focused on supporting e-commerce logistic operations. The objective of this work is hence to collect and review existing literature, and to structure it by classifying material according to multiple dimensions in order to provide the reader with a comprehensive view. In doing so, the author is guided by two research questions:
(RQ1) Which are the main logistic processes impacted by Artificial Intelligence in the
context of e-commerce operations?
(RQ2) Which are the main benefits that e-commerce practitioners could expect from
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2. Methodology
The review followed four main phases: material collection, descriptive analysis, category selection, and material evaluation.
Material selection itself is a multi-step process, which started by combining the proper set of keywords into a query, for interrogating a research database. A list of inclusion/exclusion criteria were then applied in order to filter the results and guarantee relevance to the research scope. The filtering process led to the corpus review of 51 unique papers.
Descriptive analysis has been carried out in order to profile the research corpus, understanding when and where the topic under examination has received research attention. Year-of-publication analysis indicated that the topic is gaining popularity among researchers as the number of papers increases every year. By-country analysis showed that China is the country where most attention has been given to the topic, coherently with the amount of capital invested in Artificial Intelligence and the size of e-commerce Chinese market.
After identifying the two classification dimensions, which were defined in order to answer to the two research questions, category selection has been carried out. The choice of the categories has been conducted following a mixed deductive and inductive approach. Frameworks from extant literature have been used as starting points and then adjusted according to the peculiarities of the topic under examination.
Papers have been read and examined, and each paper has been assigned to one (or more) category per dimension. In doing so, two classification frameworks have been generated. In order to appreciate the inner diversity within categories, some have been further broken down into 2nd-level categories, hence generating multi-level classification.
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3. Classification by logistic process impacted
In order to answer RQ1, papers have been classified according to the logistic process(es) impacted by the proposed Artificial Intelligence application. In doing so, a framework collecting the most common logistic processes has been created.
Starting from the SCOR model, five macro-processes have been identified (Figure 3): order picking, order packing, order delivery, return management and planning activities. These categories have been further divided into sub-processes. For example, papers dealing with order picking have been divided according to the main leverages used to shorten picker travelled distance: storage location assignment, order batching and picker routing.
Whilst defining 1st and 2nd level categories, the framework generated (Figure 6) provides structure and order to the axis under examination, presenting a sequence of activities typical of e-commerce logistics.
Classification along this axis showed that the processes that received the greatest deal of attention are picker routing and vehicle routing. At a glance, these seem to be the processes mostly impacted by Artificial Intelligence. Unsurprisingly, the processes object of many applications are also the ones which traditionally entail the greater complexity, and which mostly contribute to total logistics cost.
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4. Classification by objective
In order to answer RQ2, material has been classified according to the objective(s) of each application. Similarly to what was done for the first dimension, the categories generated two-level frameworks, characterized by cost items -i.e. labor cost- and operational objectives -i.e. picker travelled distance reduction-. The frameworks point out the relationships between operational objectives and cost items so that every paper is classified for what is its ultimate goal (cost item) and how it is achieved (operational objective).
The framework for warehousing applications can be found in Figure 8, the framework for delivery systems instead, originated from literature (Mangiaracina, et al., 2019) and can be found in Figure 9.
The classification of papers along this axis led to two main results. First, the vast majority of applications aim at a cost reduction through improvements in efficiency, suggesting that logistics is still perceived mainly as a cost center rather than a source of added value. Second, the greatest opportunities for cost reduction are sought in labor cost.
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5. Conclusions
Artificial Intelligence is considered a powerful tool that can support logisticians tackle the challenges brough about by e-commerce. The main processes which are impacted by its implementation seem to be order picking and order delivery, which entail a great deal of complexity and greatly contribute to total logistics cost.
On the other hand, the main research gap identified is the lack of papers focused on return management. Although reverse logistics is gaining increasing relevance in the industry, the scarcity of applications on this matter must be investigated and could be the object of future research.
To the best of the author’s knowledge, this work is the first attempt to gather and structure existing knowledge on the topic of Artificial Intelligence in e-commerce logistics. Similar research has been carried out -i.e. Machine Learning for logistics in (Woschank, et al., 2020)- but none with the same research boundaries as this one. The contribution of this work lies in the answers to the research questions as well as in the frameworks generated, which might serve future research on the topic.
The main limitation is given by the limited number of papers constituting the corpus of the review. Considering the publication trend, it is reasonable to assume that a similar work conducted a few years from now, would rely on a much wider research body, and would hence generate deeper and more accurate knowledge.
Moreover, the frameworks generated must not be considered as exhaustive and omni-appliable. The sequence of processes and the cost items vary industry by industry, depending on the product typology and other variables. Hence, the frameworks should be considered as generals, which must then be adapted to the specifics of every case.
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1.Introduction
commerce is booming and online initiatives are proliferating across the world. E-commerce sales in 2019 surpassed 3.53 trillion USD with an estimated 1.92 billion people having purchased goods or services online during the same year (www.statista.com), and the growth rate of recent years is expected to continue as e-commerce gains more and more popularity across the globe.
Order fulfillment along supply chains has been identified as one of the major bottlenecks affecting the development of the global e-business (Joong-Kun Cho, et al., 2008) (Wang, et al., 2014). This boils down to the fact that e-commerce orders are fundamentally different from traditional orders in terms of order characteristics and requirements. They are more fragmented, smaller in size, and demand a greater delivery accuracy and timeliness (Leung, et al., 2016). Although the challenges of e-order fulfillment have received great concern by both industry practitioners and researchers in recent years, the increased complexity and dynamism of the handling requirements of e-commerce orders have worsened the logistics industry’s headaches (Leung, et al., 2018).
Nowadays, e-fulfillment centers must be able to pick and pack single items and small volume orders and deliver them in small parcel shipments at high frequency to dispersed consumers. In this context, traditional order fulfillment might not be able to fully meet the requirements of e-commerce orders (Leung, et al., 2017).
With logistic capabilities being a critical element for superior firm performance in handling e-commerce business, it is essential for decision makers to have decision support for making prompt decisions within the warehousing and transportation operations (Leung, et al., 2017).
However, there is a widespread call for innovative decision support systems, as without consideration of the differences in the nature and handling requirements between e-commerce orders and traditional logistic orders, previous expert systems might not be applicable to the scenario of today’s e-commerce order handling process (Leung, et al., 2018).
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In this context, Artificial Intelligence applications are widely expected to improve the management of logistic operations, including e-commerce fulfillment center (Min, 2010). Nonetheless, research analyzing successful AI applications that could provide invaluable lessons for organizations embarking on their AI journey is still lacking (Pettey, 2018) (Duan, et al., 2019).
Hence, the scope of this research is to conduct a systematic literature review of Artificial Intelligence applications specifically focused on supporting e-commerce logistic operations, answering to the call highlighted by (Duan, et al., 2019), asking for more research that could provide a richer understanding of actual AI applications using empirical research methods.
The objective of this work is to collect and review existing literature on the abovementioned topic, and to structure it by classifying material according to multiple dimensions in order to provide the reader with a comprehensive view. In doing so, the author aims at answering two questions:
> (RQ1) Which are the main logistic processes impacted by Artificial Intelligence in the
context of e-commerce operations?
> (RQ2) Which are the main benefits that e-commerce practitioners could expect from
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1.1 E-commerce complexities
Traditionally, warehouses served as distribution centers for physical retailer networks’. Points of sale periodically issue replenishment orders according to predefined policies in terms of timwindows, lot-size and other characteristics. With the advent of e-commerce, logistic service providers are faced with increased complexities in order fulfillment and delivery operations.
The ever-increasing sales volumes of e-commerce, gave rise to a new generation of warehouses (often referred to as fulfillment centers) which typically face the following requirements (Boysen, et al., 2018):
> Small orders: Larger number of orders which come with fewer order lines, each
demanding only very few items. This is especially true in the Business-to-consumer (B2C) segment.
> Varying workloads: According to the nature of the products sold, many online
retailers face volatile and seasonal demand, increasing the need for flexibility in warehousing and delivery capacities. Fluctuating demand is also harder to forecast and adjust to.
> Large assortment: Items offered on websites do not consume costly storage space
in store shelves. In addition, niche products account for a much larger proportion of sales in e-commerce than they do in physical stores, making wide product range a key success factor for e-platforms. Hence, e-commerce has brought about the enlargement of SKUs, a phenomenon also known under the term “the long tail” (Brynjolfsson, et al., 2003)
> Tight delivery schedules: Next-day or even same-day deliveries are a common
promise among online retailers, especially in the B2C segment which also increases the number and the dispersion of delivery locations.
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Table 1 summarizes the sources of operational complexities brought about by electronic commerce.
Table 1 – Differences between traditional and e-commerce orders
Traditional orders E-commerce orders
Number of orders Low and predictable High and fluctuating
Order size Medium-Large Small
Delivery locations Few Many
Delivery lead time Tight Tighter
Assortment Medium Large
What is more, the increasing service level is forcing the products close to the customers, where storage space is ra re and costly, in order to operate distribution promptly and efficiently (Boysen, et al., 2018).This trend, combined with the enlargement of SKUs previously mentioned, poses an additional challenge for logisticians in terms of storage space, further increasing the importance of storage space utilization.
It must be noted that the impact of each individual factor varies according to the geographical area and the products offered. In online grocery retailing, for instance, order size is quite large in terms of order lines. In the furniture industry for example, delivery lead time is not such a competitive leverage as in other markets, hence e-retailers are not forced to extremely tight delivery schedules.
In the last two decades, research has been carried out on the impact of e-commerce and its consequences on logistic operations, more in-depth information can be found in (Delfmann, et al., 2002), (Joong-Kun Cho, et al., 2008) and (Boysen, et al., 2018).
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1.2 Artificial Intelligence
The birth of the discipline of Artificial Intelligence dates back to 1956, when the expression was first used by American computer scientist John McCarthy. In the following sixty-five years, knowledge about intelligent computer systems evolved and many definitions of Artificial Intelligence have been given ever since. Nowadays, Artificial Intelligence is a broad term which is often used unproperly. For the continuation of this work it is important to provide the reader with a clear definition of Artificial Intelligence, which will then be used in the phase of paper selection to discriminate between Artificial Intelligence applications and other techniques not relevant to this research.
In the context of the Fourth Industrial Revolution (Industry 4.0), which comprises the digital transformation of logistic processes (Smart Logistics), Artificial Intelligence is often defined as the science and engineering of intelligent machines with a special focus on intelligent computer programs (McCarthy, 2007).
This definition shifts the focus towards the question: What characterizes an intelligent
machine?
The three components of Artificial Intelligence are considered to be: data, algorithms, and robotics. Among these, algorithms serve as the core building block that constitute Artificial Intelligence (Grover, et al., 2020), hence the question is What characterizes
intelligent algorithms?
Algorithms are considered “intelligent” if they have two distinctive features: a knowledge base extrapolated from data, and an inference engine capable of generating new knowledge. These are also commonly referred to as knowledge-based systems. In other words, Intelligent algorithms are capable of extrapolating knowledge from data through the process of learning, and subsequently use the acquired knowledge to mimic human behavioral patterns and to create further knowledge relevant to problem-solving (Min, 2010).
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Machine Learning is considered as an integral part of Artificial Intelligence, which refers to the automated detection of meaningful patterns in datasets (Shalev-Shwartz & Ben-David, 2014). Moreover, Deep Learning, is defined as a sub-class of Machine Learning that explores multiple layers of non-linear information processing for features extraction and transformation (Diez-Olivan, et al., 2019)
For further information about Artificial Intelligence, its definition, and its branches, see the seminal paper by John McCarthy “What is Artificial Intelligence?” (McCarthy, 2007). According to the recently updated International Data Corporation (IDC) Worldwide Artificial Intelligence Systems Spending Guide, spending on Artificial Intelligence systems will reach 97.9 billion USD in 2023, compared to the 37.5 billion USD spent in 2019. The compound annual growth rate (CAGR) for the 2018-2023 forecast period will be 28.4%.
Accordingly, Artificial Intelligence is now a buzzword which triggers speculation about innovation and futuristic technologies. However, intelligent algorithms currently in use have been devised decades ago and have been around for a while. Neural networks for instance, have been invented in 1943, with multi-layers networks being introduced in 1965. Ant-Colony-Optimization was proposed in 1992. Very popular K-means clustering techniques have also been present in literature since the 1960s. As a matter of fact, what makes Artificial Intelligence especially relevant today, is not the technology itself, but rather the current conditions which allow for this technology a more feasible use. In the past decade, OSCM activities have become more networked, resulting in the generation of a huge volume of real-time data, referred to as ‘Big Data’ (Chen, et al., 2015). Such data generation in supply chain networks is the result of advanced networking technologies, including embedded sensors, tags, tracks, barcodes, Internet of Things (IOTs), radio-frequency identification (RFID) tags, and several smart devices that capture such data (Gunasekaran, et al., 2017).
The recent advent of IoT, Big-data and Cloud Computing created favorable conditions for the effective employment of intelligent algorithms which can now rely on large datasets for generating a wide knowledge base.
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Other systematic literature reviews have been proposed on similar topics. However, to the best of the author’s knowledge, there seems to be a research gap on the specific topic identified by the research boundaries. The most similar work can be found in (Woschank, et al., 2020), which provides an overview of Artificial Intelligence in Logistics through a systematic literature review. What differentiates this research is the specific focus on e-commerce logistics rather than general logistic operations and the emphasis on practical applications rather than conceptual frameworks.
The remainder of this work is organized as follows: Section 2 presents research objectives and methodology utilized in every step. Section 3 contains the classification of papers along the first dimension (classification by logistics process). Section 4
presents the classification along the second dimension (classification by objective). Finally, in Section 5 conclusions are drawn, research gaps and directions for future research are identified.
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2.Objectives and methodology
In the context described in the introduction, this work aims to provide a systematic analysis and classification of the extant body of research on Artificial Intelligence applied to e-commerce logistics, to outline major gaps in the extant literature and consequently, to propose directions for future research, which may be of interest for academics and industry practitioners. In order to achieve these objectives, the intermediate aim of this work is to develop a framework of existing knowledge by classifying the extant body of research according to multiple dimensions, and then to find potential research gaps as well as areas which received more attention. In doing so, this work addresses two research questions:
> RQ1. What are the processes of e-commerce logistic operations mostly impacted by
the advent of Artificial Intelligence? – to identify and discuss those parts of the e-commerce supply chain which are the object of Artificial Intelligence implementation projects.
> RQ2. What are the main benefits that Artificial Intelligence implementation is
expected to bring to e-commerce logistics? – to identify and discuss the most common objectives of Artificial Intelligence implementation projects in this context. Based on the recommendations in the methodological paper by (Seuring & Gold, 2012), this review followed four main phases:
> Phase 1 (material collection): to retrieve and select papers from extant literature, creating the corpus of the review.
> Phase 2 (descriptive analysis): to analyze the contributions based on their main descriptive characteristics - i.e. year of publication, country, title of the journal/conference -
> Phase 3 (category selection): to first define the dimensions/axis and then the categories used for classification in the content analysis.
> Phase 4 (material evaluation): to review and classify the papers based on the previously defined dimensions and categories.
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2.1. Material selection
Being one of the most complete scientific research databases, the search engine Scopus has been used as main source for paper collection. During the first phase of the process, keywords used were “ARTIFICIAL INTELLIGENCE”, “MACHINE LEARNING”,” DEEP
LEARNING”, “E-COMMERCE”, “LOGISTICS”, “WAREHOUS*”, “TRANSPORT*”, “DELIVERY”.
Several queries, combining those keywords have been tried in order to get the largest number of papers possible. An effective query would include one keyword relating to Artificial Intelligence and its branches, in order to make sure that results were limited to Artificial Intelligence solutions. The keyword “E-COMMERCE” shall always be included for ensuring relevance with the research objective. Lastly, one keyword from the last group (“LOGISTICS”, “WAREHOUS*”, “TRANSPORT*”, “DELIVERY”) must be included in order to rule out all those applications related to the digital side of e-commerce, as for example Machine Learning classification techniques for fake reviews detection or techniques for customized purchase suggestions on the retailers’ platforms.
Search was limited to English language material but given the novelty of the topic, grey literature such as conference proceedings was included in addition to peer-reviewed black literature.
These searching criteria explained above can be summarized in the query:
TITLE-ABS-KEY (("ARTIFICIAL INTELLIGENCE" OR "MACHINE LEARNING" OR "DEEP LEARNING") AND ("E-COMMERCE") AND ("LOGISTICS" OR "WAREHOUS*" OR
"DELIVERY" OR "TRANSPORT*")) AND (LIMIT-TO (LANGUAGE, "English"))
The search generated by this query resulted in a preliminary corpus of 229 unique papers. Abstracts have been examined in order to assess relevance for the scope of the research and papers have been filtered according to relevance with the research boundaries. The systematic literature review requires a well-defined set of criteria in order to distinguish those papers which fall within the research boundaries from those which do not. Research boundaries in turn are strictly linked to the research objectives. As explained in the previous paragraph, the objective of this research is to collect and
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structure existing knowledge on Artificial Intelligence applications in the e-commerce logistic operations, with a specific focus on intelligent algorithms which support the decision makers operating in e-commerce logistics.
In accordance with this objective, following is the list of exclusion criteria applied for filtering:
> Papers not purely focused on the logistic side of operations but rather on industrial processes in general - i.e. manufacturing or assembly operations - were excluded
> Papers without a specific mention to e-commerce – i.e. general logistics application not focused on the e-commerce business - were excluded
> Papers without a specific focus on logistic operations – i.e. papers dealing with non-logistical aspect of e-commerce - were excluded
> Applications which did not employ Artificial Intelligence algorithms, as defined in the introduction, were excluded.
> Only case studies, analytical models and more generally, research on practical applications were considered. Literature reviews, conceptual frameworks and similar works were hence excluded.
At the end of this initial screening process, the 93 remaining papers have been fully read in order to further assess their relevance by re-applying the abovementioned set of criteria.
During this phase, in order to enlarge the corpus of the review, an approach commonly referred to as “Snowballing” technique was applied, which consists in searching for relevant material among backward and forward bibliography and applying the same exclusion criteria to new potential material.
Finally, the multi-step process described, resulted in 51 studies which represent the final corpus of the review.
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2.2. Descriptive Analysis
In order to make sense of the findings and to structure existing knowledge, papers have been classified according to multiple dimensions, but before doing so, a descriptive analysis of the corpus has been carried out in order to profile the research corpus, understanding when and where the topic under examination has received research attention. In order to do so, year of publication, country of origin and source type are presented.
2.2.1. Year of publication
Four papers (7.84%) were published up until 2014, three papers (5.88%) were published in 2015, five papers (9.80%) were published in 2016, four papers (7.84%) were published in 2017, eight papers (15.69%) were published in 2018, twelve papers (23.53%) were published in 2019 and fifteen papers (29.41%) were published in 2020 and beginning of 2021. More than half of the studies in the corpus (52.94%) were published in the last two years alone. These figures are illustrated in Figure 1 below.
Figure 1 – Distribution of papers by year of publication
The increasing research interest that this topic is receiving matches the expectations and the investment trends in Artificial Intelligence technologies. Investments in Artificial
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Intelligence startups worldwide has grown from 4.25 billion USD in 2014 to 26.58 billion USD in 2019, with an impressive compound annual growth rate CAGR slightly above 44% over the considered timespan (data from www.statista.com).
A similar trend is found in the e-commerce business. For instance, US e-commerce sales accounted for 9.7% of the total in 2014, and this ratio has more than doubled in just six years with 21.3% in 2020 (www.digitalcommerce360.com).
Given the increasing financial flows, both in the form of investments for Artificial Intelligence technologies, and of revenues for e-commerce businesses, it comes with no surprise that the topic has received increasing attention by researchers and industry practitioners. If this line of reasoning is pushed further, assuming a correlation between financial flows and research interest, it is reasonable to assume that a similar research to this one, carried out two years from now would rely on a much wider research body, as both e-commerce and Artificial Intelligence are expected to keep on growing at impressive rates in the foreseeable future.
2.2.2. Country
As for the country where studies in the review originated, China leads the ranking with 30 papers (51.72%) counting at least one author from Chinese academic institutions (that is including those in Taiwan and Hong Kong territories). Europe follows with a total of eleven papers (18.97%), with two from France, two from The Netherlands, two from Italy, one from Germany, Poland, Croatia, Bosnia, and The United Kingdom, respectively. Studies carried out, fully or partially, in Asian countries other than China add up to nine (15.52%), with two papers from Indian, and one from South Korea, Thailand, Singapore, Palestine, Jordan and Asian Turkey, respectively. Finally, five papers (8.62%) are authored or co-authored by academics working in the USA, and three paper (5.17%) come from other non-Asia countries (Canada, Colombia, and Morocco).
It must be noted that seven papers in the corpus come from cross-border collaboration between institutions in different countries (more specifically China-France in (Zou, et al., 2017), China-Singapore in (Zhang, et al., 2021), South Korea-USA in (Kang, et al., 2019), Jordan-United Kingdom in (Al-nawayseh, et al., 2013), USA- Canada in (Ardjmand, et al.,
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2018) and China-USA in (Cheng, et al., 2015) and (Ren, et al., 2020)), therefore the ratio between number of papers and related percentage of the total differs from the previous analysis as the total amount of national contributions is 58 instead of 51.
Figure 2 illustrates the distribution of papers in the review by their country of origin.
Figure 2 – Distribution of papers by country/area
As highlighted through the remarks made while examining year of publication of studies, research interest in the topic seems to be related to financial flows. This correlation also applies to geographical scope. Indeed, China is both the largest e-commerce market in the world (www.Emarketer.com) as well as the biggest investor in Artificial Intelligence technology (www.ifc.org). China’s progress with Artificial Intelligence is largely the results of strong and direct government support for the technology, leadership from Chinese tech industry giants, and a robust venture capital community. In the light of this, the reader should not be surprised by the large proportion of research coming from the country.
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2.2.3. Study type and source
Out of the 51 papers constituting the review corpus, 25 studies (49.02%) are conference proceedings, whereas 26 (50.98%) are journal articles, confirming that the topic discussed has recently gaining popularity, as a relevant portion of the literature has not made it from grey to black peer-reviewed journals yet.
Twenty-seven papers (52.94%) come from journals or conferences on the topic of Information Technology, with 14 (27.45%) sources specifically focused on Artificial Intelligence – i.e. AAAI Conference on Artificial Intelligence in (Agussurja, et al., 2016)- whereas the remaining 13 (25.49%) are found in journals and conference focused on other branches of the discipline of Information Technology (IT) – i.e. International
Conference on Data Mining and Big Data in (Kretzschmar, et al., 2016) - or on IT in
general – i.e. Journal of Internet Technology in (Hu, et al., 2020) -
Thirteen papers (27.45%) are found in sources which deal with operational topics such as manufacturing, transportation, or supply chain management as for example
International Journal of Logistics Research and Applications in (Bindi, et al., 2009). Only
four papers (7.84%) come from sources focused on operational applications of Information technologies, such as Computers & Operations Research in (Li, et al., 2016). Table 2 displays the papers in the corpus along with the year of publication, the country, and the related source.
Table 2 – Classification of papers and sources
No. Ref. No. Author(s) and Year Country Source
1 5 (Bindi, et al., 2009) Italy International Journal of Logistics Research and Applications
2 77 (Zhang, et al., 2010) China International Conference on E-Business and E-Government
3 10 (Chen, et al., 2013) China International Conference on Business Information Systems
4 3 (Al-nawayseh, et al., 2013) Jordan, United Kingdom International Journal of Decision Support System Technology
5 28 (Hu, et al., 2015) China International Journal of Clothing Science and Technology
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No. Ref. No. Author(s) and Year Country Source
7 13 (Cheng, et al., 2015) China, United States International Journal of Production Economics
8 11 (Kretzschmar, et al., 2016) Germany International Conference on Data Mining and Big Data
9 34 (Li, et al., 2016) Netherlands Computers & Operations Research
10 41 (Li, et al., 2016)b Netherlands Transportation Research Part C: Emerging Technologies
11 42 (Agussurja, et al., 2016) Singapore AAAI Conference on Artificial Intelligence
12 2 (Chen, et al., 2016) China Journal of Intelligent Manufacturing
13 39 (Leung, et al., 2017) China International Conference on Management of Engineering and
Technology
14 78 (Zou, et al., 2017) China, France International Journal of Production Research
15 72 (Yadav & Narasimhamurthy, 2017) India International Conference on Advances in Pattern Recognition 16 62 (Srivilas & Cherntanomwong, 2017) Thailand International Electrical Engineering Congress
17 79 (Leung, et al., 2018) China Expert Systems with Applications
18 38 (Joshi, et al., 2018) India International Conference on Advances in Social Networks
Analysis and Mining
19 35 (Nazari, et al., 2018) Palestine Proceedings of Advanced Neural Information Process
20 31 (Žunić, et al., 2018) Bosnia Symposium on Neural Networks and Applications
21 16 (Lai, et al., 2018) China International Conference on Technologies and Applications of
Artificial Intelligence
22 50 (De Santis, et al., 2018) Italy European Journal of Operational Research
23 73 (Yetis & Karakose, 2018) Turkey International Artificial Intelligence and Data Processing Symposium
24 4 (Ardjmand, et al., 2018) United States, Canada International Journal of Production Economics
25 12 (Chen, et al., 2019) China Complexity in Manufacturing Processes and Systems
26 71 (Xin, et al., 2019) China International Conference on Industrial Artificial Intelligence
27 54 (Qiu, et al., 2019) China International Conference on Artificial Intelligence and Security
28 29 (Ji, et al., 2019) China Mathematical Problems in Engineering
29 74 (Yu, et al., 2019) China IEEE Transactions on Intelligent Transportation Systems
30 69 (Wu & Su, 2019) China International Conference on Ubiquitous Computing and
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No. Ref. No. Author(s) and Year Country Source
31 68 (Wu & Wu, 2019) China AAAI Conference on Artificial Intelligence
32 26 (Hsiao & Chang, 2019) China Journal of Enterprise Information Management
33 60 (Shi, et al., 2019) China
International Conference on Knowledge-Based and Intelligent Information & Engineering Systems
34 8 (Kang, et al., 2019) South Korea, United States Computers & Industrial Engineering
35 1 (Cano, et al., 2019) Colombia International Conference on Business Information Systems
36 33 (Afric, et al., 2019) Croatia International Conference on AI and Mobile Services
37 36 (Leung, et al., 2020) China Advanced Engineering Informatics
38 65 (Wang, et al., 2020) China International Conference on Artificial Intelligence and
Electromechanical Automation
39 40 (Leung, et al., 2020)b China Industrial Management & Data Systems
40 44 (Liu, et al., 2020) China PLoS ONE
41 28 (Hu, et al., 2020) China Journal of Internet Technology
42 20 (Feng, 2020) China Soft Computing
43 45 (Liu, 2020) China IEEE Access
44 32 (Ren, et al., 2020) China, United States Transportation Research Part E: Logistics and Transportation Review
45 25 (Rhiat, et al., 2020) France International Conference on Optimization-Driven
Architectural Design
46 55 (Ouadi, et al., 2020) Morocco International Conference on Ambient Systems, Networks and
Technologies
47 56 (Tarapata, et al., 2020) Poland International Conference on Optimization-Driven
Architectural Design
48 51 (Snoeck, et al., 2020) United States Transportation Research Procedia
49 63 (Kang, et al., 2020) China International Conference on Artificial Intelligence, Automation and Control Technologies
50 61 (Guo & Yang, 2020) China International Conference on Artificial Intelligence and Advanced Manufacture 51 75 (Zhang, et al., 2021) China, Singapore International Journal of Information Management
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2.3 Category selection
The content analysis of the corpus, as well as the development of the framework, was developed based on a twofold perspective. First, papers have been classified according to which logistic process(es) was the object of the application at hand. Second, papers have been distinguished according to the objective of the model/application as declared by the authors in the introduction, or by the improvements achieved as observed in the conclusions of the paper.
The choice of these two classification dimensions was coherent with the two research questions presented, as mapping papers along the first axis allows to answer RQ1¸ whereas the second axis tackles RQ2.
Following to the definition of the axis, classification categories have been selected. The choice of the categories has been conducted following a mixed deductive and inductive approach. The deductive “a priori” approach is beneficial when attempting to classify dispersed and diverse contributions, in a broad and novel research field where no previous attempts had been made in this direction (Seghezzi, et al., 2021). On the other hand, the inductive approach is useful for taking into account the characteristics of the topic under examination.
Translated into action, this mixed approach means that when defining the categories, classification frameworks from extant literature have been used as a starting point and were then adjusted according to the peculiarities of Artificial Intelligence in e-commerce logistic operations.
For instance, in order to classify along the first axis (classification by process), it was necessary to first define the list of all logistic processes. In doing so, the SCOR (Supply-Chain Operations Reference) model was used as a starting point, and then work from other authors such as (Lim, et al., 2018) was used to adapt the processes identified in the SCOR model to the specifics of e-commerce operations.
When classifying along the second axis (classification by objective), it was necessary to select the level of depth of the categories, or in other words, the level of specificity of KPIs selected as classification categories. In order to highlight the individual differences
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among different papers in the corpus, selected categories are low-level, high-specificity KPIs. By doing so, it is easier to distinguish and appreciate the diversity within the review corpus.
Similarly to what was done for the first-dimension categories, frameworks from extant literature have been used as starting point for defining the categories, such as the one developed by (Mangiaracina, et al., 2019) which identifies the cost factors in a last-mile delivery system. Later, these frameworks have been adjusted according to the peculiarities of the topic under examination, for instance by adding a new category or by removing one which had no equivalent in this context.
For both axes, some categories have been further broken down into 2nd-level categories. This was done for categories gathering a multitude of papers under the same label, which however presented individual differences worth being noted. For example, during classification along the process-dimension, some processes (1st-level categories) are further broken down into sub-processes (2nd-level categories) in order to guarantee internal homogeneity of the categories.
More in detail presentation of categories selected for both dimensions will be found in the content analysis section (Section 3 and Section 4 respectively).
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2.4 Material evaluation
After defining the dimensions and the categories, papers were read one by one, and each paper was assigned to one category per dimension. If applications in the corpus involved more than one process, as broken down into categories, the paper under examination would be labelled with all the related categories.
Regarding the evaluation along the second dimension, only objectives clearly defined “a
priori” as applications goals, as well as improvement highlighted in the conclusions were
considered as objectives. The author of this study has not made any deduction on goals sought or improvements achieved which were not clearly mentioned by the original authors.
Following, in order to provide the reader with a comprehensive understanding of the content, papers have been classified accordingly to two dimensions coherent with the two research questions presented in the introduction.
Classifying the papers according to the logistic process(es) impacted by the application proposed allows to answer the RQ1. The second dimension, on the other hand, allows to answer the RQ2 by understanding which are the main benefits that Artificial Intelligence is expected to bring about for e-commerce logistic operations.
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3. Classification by logistic process impacted
In order to answer RQ1 by understanding which processes are most likely to be impacted by Artificial Intelligence implementations, papers have been classified according to the logistic process(es) impacted by the studies under examination.
In order to identify the set of categories later used for classification, it is necessary to define a list of the main processes constituting the e-commerce fulfillment supply chain. The SCOR model, developed and endorsed by the Supply Chain Council, distinguishes five main management processes:
> Source > Make > Delivery > Return > Plan
In the context of e-commerce order fulfillment operations, (Lim, et al., 2018) identified the equivalents of the first three phases in Order picking, Order packing and Order
delivery. Classification of papers has hence been conducted by using these three
macro-categories in addition to Return and Plan as a starting point. The adaptation of the SCOR model to the specifics of e-commerce fulfillment operations is visually presented in Figure 3.
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Figure 3 – The SCOR model and its adaptation to e-commerce logistics
These five processes are utilized as 1st-level categories which, as explained in the methodology section, later might be divided into 2nd-level categories in order to highlight the various sub-processes part of the same process/macro-category.
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3.1. Order Picking
Order picking is the process of retrieving products from the storage area in response to specific customer requests (Bindi, et al., 2009). Different picking systems, mainly depending on industry characteristics, are adopted in the market and extensive research has been conducted on the appropriate picking system to employ. Regardless of the configuration, may it be parts-to-picker, picker-to-parts, pick-to-sort… picking is certainly the most impacting warehouse activity generating more than half of the warehouse expenses (Van den berg & Zijm, 1999), (De Koster & Roodbergen, 2007). Given the advent of e-commerce, the relevance of order picking has grown even more (Zhang, et al., 2018) further increasing logisticians’ headaches.
In accordance with the aforementioned importance, a relevant portion of the literature in the corpus deals with picking operations. Twenty papers (35.08%) in the corpus of the review regard picking activities. Instances involve different picking systems, from manual picker-to-parts systems to highly automated parts-to-picker systems.
Table 3 summarizes the first step of classification, by distinguishing the twenty picking-related papers into manual picking and automated picking.
Table 3 – Manual vs automated picking systems
No. Ref. No. Author(s) and Year Picking system
1 5 (Bindi, et al., 2009) Manual picking
2 10 (Chen, et al., 2013) Manual picking
3 14 (Chen, et al., 2015) Manual picking
4 13 (Cheng, et al., 2015) Manual picking
5 11 (Chen, et al., 2016) Manual picking
6 39 (Leung, et al., 2017) Manual picking
7 78 (Zou, et al., 2017) Automated picking
8 62 (Srivilas & Cherntanomwong, 2017) Manual picking
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No. Ref. No. Author(s) and Year Picking system
10 79 (Žunić, et al., 2018) Manual picking
11 16 (De Santis, et al., 2018) Manual picking
12 4 (Ardjmand, et al., 2018) Manual picking
13 12 (Chen, et al., 2019) Manual picking
14 71 (Xin, et al., 2019) Manual picking
15 54 (Qiu, et al., 2019) Manual picking
16 8 (Cano, et al., 2019) Manual picking
17 36 (Leung, et al., 2020) Manual picking
18 65 (Wang, et al., 2020) Automated picking
19 25 (Guo & Yang, 2020) Manual picking
20 75 (Zhang, et al., 2021) Automated picking
The varying workload described in the Introduction as one of the sources of complexity for e-commerce logistics requires fulfillment centers to be flexible. Excessive automation in picking operations decreases flexibility as opposed to traditional picker-to-parts systems where adaptation to varying workload is easily achieved by adding or removing pickers. In the light of this, most studies concern manual picker-to-parts systems: seventeen applications out of twenty (85.00%) are in the context of traditional manual picking system.
On the other hand, robotic parts-to-picker systems can approximately double picking rates (Wulfraat, 2012) and are hence gaining popularity, especially among very large e-retailers such as Alibaba and Amazon, or in specific industries due to product characteristics. Although in minor proportion, researchers explored the benefits of Artificial Intelligence algorithms in robotic parts-to-picker systems as well. Intelligent algorithms can increase picking robots efficiency by improving task assignment, that is which vehicle to assign to a picking task and subsequently, matching the vehicle to the picker depending on the picker’s queued workload such as in (Wang, et al., 2020) and (Zhang, et al., 2021). Within these systems, also known as Robotic Mobile Fulfillment Systems, optimization can be achieved by matching retrieval task (e.g. shelf to retrieve)
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to robot, and robot to workstation such as in (Zou, et al., 2017). Further optimization can be achieved through algorithms that allow dynamic adjustments in the number of order retrievals for each picking tour based on the pending orders and on the overall warehouse workload as in (Wang, et al., 2020).
For what concern manual picking, in order to point out the differences among the seventeen applications within this cluster, papers have been further classified into 2nd -level categories according to manual picking sub-processes -e.g. portions of the manual picking process - they deal with.
Petersen pointed out that order service time can be represented as a positive correlation function of one picker’s travel distance (Petersen, 1997), and more generally, the order picking efficiency largely depends on the distance pickers have to travel (Bindi, et al., 2009). Therefore, the majority of picking-related research can be summed up as a search for picker’s travel distance minimization. (De Koster & Roodbergen, 2007) have proposed four ways to reduce the travel distance of order picking operations:
> Warehouse area division > Storage location assignment > Order batching
> Picker routing
With the exception of Warehouse area division, which is a strategical leverage and in the context of this classification, falls under the Plan category of the SCOR model, papers dealing with manual picking operations have been further sorted according to these four leverages. The two-level classification of Order Picking is depicted in Figure 4.
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Figure 4 – Subdivision of order picking in sub-processes
3.1.1. Storage
Location Assignment
The storage location assignment problem (also referred to as location-allocation problem), consists in where and how to locate incoming products in the warehouse (Bindi, et al., 2009). This problem has particular relevance in e-commerce fulfillment centers given the wide SKUs range and the need for optimal space utilization and operational efficiency.
Many procedures have been proposed by researchers to help logistic operators place incoming items. Artificial Intelligence, in the form of Machine Learning clustering techniques and association rules, has brought opportunities for data exploitation and optimization. The benefits of an intelligent storage location assignment system are twofold, on the one hand pickers’ travelled distance is reduced, on the other hand storage space requirement is reduced by optimizing SKUs turnover within the picking area. In the light of this, Artificial Intelligence applications are receiving researchers’ attention.
One example can be found in (Bindi, et al., 2009) where the authors show how under certain conditions (namely, high utilization rate) correlation-based storage policies outperform more basic approaches.
Mobile-rack warehouses are gaining popularity among e-retailers in order to increase space utilization. (Qiu, et al., 2019) proposed a model which employs clustering techniques and association rules based on historical orders data to assign the proper location considering the specifics of a mobile-rack warehouse.
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However, the wide product range typical of e-commerce might lead to weak correlation among SKUs and low support value of association rules. By grouping together multiple items based on text clustering into classes, (Xin, et al., 2019) proposed a model which analyzes the correlation among classes rather than items overcoming the issue of weak correlation brought about by wide assortment.
3.1.2. Order Batching
A few papers investigate the possibilities offered by pick-to-sort systems proposing intelligent algorithms for improving the so-called order batching. In fulfilling e-orders, which are smaller in size compared to traditional orders, batching represents a valid opportunity to reduce picking inefficiencies.
Algorithms capable of efficiently grouping orders together for processing, such as what proposed by (Leung, et al., 2017), proved to improve operational efficiency by diminishing picker’s travelled distance. Further research incorporated timely batch release (also referred to as wave-triggering), supporting operators in deciding when to release a picking wave. Wave triggering works by considering the trade-off between order processing time, which increases as orders are left pending, and operational efficiency, which increases as more pending orders can be better grouped together. Examples of batching models incorporating timely batch release can be found in (Leung, et al., 2018) and (Leung, et al., 2020). Other examples of order batching algorithms in a wave-based picking system can be found in (Ardjmand, et al., 2018), (Chen, et al., 2015) and (Cheng, et al., 2015).
3.1.3. Picker Routing
Among the leverages that influence picking efficiency in a picker-to-parts warehouse, picker routing, that is the sequence of locations to visit within a picking tour, is considered to have the highest flexibility (Chen, et al., 2013). Extensive research has been conducted on streamlining the picker routing and different routing policies -i.e.
return, largest-gap, mid-point... - have been conceived. These heuristics are commonly
applied because of their simple implementation (De Koster & Roodbergen, 2007). Nowadays, the tremendous developments of automated warehousing equipment as well as warehouse management information systems provide a great opportunity to
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implement more efficient dynamic order picker routing algorithms which are dynamically adjusted according to the real time information in the warehouse management system (Chen, et al., 2013). This opportunity is confirmed by the large presence of papers in the review which deal with this matter. Picker routing is the object, or one of the objects of 11 papers in the corpus (21.57%).
Examples of such initiatives are found in (Chen, et al., 2013) and (Chen, et al., 2016), in which the routing does not follow a pre-determined policy but rather adjusts according to real-time data, also considering aisle congestion as a variable. These two applications consider congestion “a priori”, by computing whether two or more pickers will end up in the same aisle/location around the same time. The model proposed by (Srivilas & Cherntanomwong, 2017) instead, is able to observe congestion happening through the support of a Visible Light Communication system and to adjust the routing accordingly. Other examples of intelligent algorithms for picker routing which proved to outperform common heuristic policies are found in (De Santis, et al., 2018), (Cano, et al., 2019) and (Guo & Yang, 2020).
With the advent of e-commerce and the enlargement of SKUs, reducing picking aisles width is a tempting alternative for increasing the space utilization and shortening distance travelled by the pickers (Gue, et al., 2006). However, ultranarrow aisles lead to access restriction and congestion. (Chen, et al., 2019) developed a problem-specific picker routing algorithm which takes into account the novelties brought about by ultranarrow aisles adopted in e-fulfillment centers.
As opposed to other decision support systems which consider picker routing as separated from previous activities, (Chen, et al., 2015) and (Cheng, et al., 2015) developed models which integrate order batching and picker routing by taking both decisions at the same time. Through a genetic algorithm in the former, and particle swarm optimization in the latter, these systems determine the optimal routing policy for every batch independently.
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3.2. Order Packing
Packing, that is the activity of putting one or more items into boxes for shipping, is typically characterized by low levels of automation. This is mainly due to the complexities caused by constraints such as fragility, orientation, weight, and volume. Hence, packing operations still heavily rely on human intuition, especially in e-commerce fulfillment centers, where every order is unique in its characteristics.
The case study on Alibaba’s Smart Warehouse by (Zhang, et al., 2021) describes an example of intelligent 3D-packing algorithm, which is utilized both for item positioning onto pallets for storage, and for suggesting the appropriate box to the human packer. This can be regarded as a decision-support system which helps identifying the appropriate containers, leading to reduction of costs for material, increased utilization rate during transportation and minimization of natural resources consumption.
Some authors pushed it further, investigating the possibility of using Artificial Intelligence algorithms to support packing robots. The prospect of packing robots in e-commerce would bring tremendous benefits in terms of efficiency and human labor reduction. (Rhiat, et al., 2020) conducted a series of experiments testing robot packing capabilities and introduced the idea of packing robots learning from simulations through a Deep Learning model.
Except these two instances, there is a lack of focus on packing activities. Although packing can be, especially in certain industries, an activity with relevant impact on the overall workload of the warehouse, the lack of research on this matter indicates that researchers do not foresee significant improvements achievable through Artificial Intelligence. This makes sense, considering the low level of decision-making that the activity entails. As opposed to picking activities examined before, the only decision variables when packing are the choice of the proper container, which is already fairly common in the packing module of many Warehouse Management Systems (WMS), and how to stack items onto the selected container, which is trivial in many cases with few items per order.
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3.3. Order Delivery
One of the major challenges brought by e-commerce is the so-called last-mile delivery due to challenging target levels, small dimension of orders and the high dispersal of destinations (Macioszek, 2017). Unsurprisingly, last-mile delivery is considered to be the most expensive and least efficient of all logistics process (Gevaers, et al., 2011) and its efficiency has recently been receiving growing attention by scholars (Mangiaracina, et al., 2019).
In the light of this, it comes with no surprise that delivery applications constitute the largest portion of the review corpus, with 23 papers (45.10%). As was previously done for Order picking applications, in order to better appreciate the inner diversity within the Order Delivery macro-category, papers have been further divided into three 2nd-level categories according to three sub-processes:
> Delivery Task Assignment, that is the process that leads to assigning a delivery task
to a specific vector, be it a vehicle within a fleet or a human agent within a platform.
> Vehicle Routing, that is finding the optimal sequence of locations in a delivery tour. > Physical Exchange: product delivery including every activity involving physical
interaction with the customer.
The two-level classification of order delivery applications is depicted in Figure 5:
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3.3.1. Delivery Task Assignment
Assigning the delivery task to the appropriate vehicle within the fleet is a powerful tool for optimization and it is becoming especially relevant as volumes surge, complexities increase, and fleets’ size rise. Within the corpus of this review, eight papers (34.78% of delivery applications) deal with such complex process, confirming its relevance.
In order to reduce delivery cost, innovative solutions which involve external players started to attract researchers’ and practitioners’ interest. Intelligent and dynamic delivery task assignment is crucial for making these solutions economically sustainable for the players involved. (Agussurja, et al., 2016) proposed a model for collaborative delivery among logistic service providers proving that with fair profit allocation and intelligent task assignment, all players can benefit from collaboration by reducing their costs. Other papers present intelligent systems for task assignment within crowdsourced delivery systems, may they involve private citizens as vectors as in (Kang, et al., 2019) or taxi drivers in the so-called share-a-ride-problem (SARP) as in (Li, et al., 2016) and (Li, et al., 2016)b. In all cases the algorithm that matches each delivery task to a vector is of great importance in order to ensure operational efficiency and thus, economic sustainability of the initiative.
Delivery task assignment has also been studied in the context of innovative delivery vehicles. (Wu & Su, 2019) utilized Ant Colony Optimization, a popular Artificial Intelligence algorithm based on biomimics, to assign delivery tasks within a fleet of Unmanned Aerial Vehicles (UAV) in an urban network with multiple depots. Another example of task assignment algorithm for UAVs can be found in (Kang, et al., 2020), in which the authors utilized a decision tree for building the decision support system. (Kretzschmar, et al., 2016) trained a machine learning model to accurately predict the range of delivery electric vehicles, allowing for prompt delivery task assignment to the most suitable unit within the e-fleet, considering for the battery life estimates.
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3.3.2. Vehicle Routing
In case multiple tasks are assigned to one vector, it comes the problem of defining the optimal order in which these tasks should be completed. In other words, the optimal sequence of delivery locations must be defined in order to minimize the path length. This problem is referred to as Vehicle Routing Problem (VRP) and can be considered as an extension of the famous Travelling Salesman Problem. Over the past decades, the VRP has received great attention due to its pervasive relevance in the business world and has been studied in numerous variants differing in the set of constraints or in the objective function. The VRP is especially relevant in the e-commerce industry, as delivery points are large in number and highly dispersed (see Introduction). Researchers have explored the opportunities offered by Artificial Intelligence in order to achieve further optimization and unsurprisingly, the VRP constitutes a relevant portion of the review corpus. VRP is the object of eleven papers (47.82% of delivery applications, 21.56% of the total), thus making delivery sequencing, the most widely represented process in the review.
Generic examples of intelligent algorithms for solving the VRP for e-commerce deliveries can be found in (Zhang, et al., 2010), (Nazari, et al., 2018), (Shi, et al., 2019), (Yu, et al., 2019), (Hu, et al., 2020).
In addition, as mentioned before, the VRP gets studied in all sorts of variants, with differences in the set of constraints, the objective function or more in general, in the problem setting. E-retailers are fighting to capture increasing shares of the market by offering additional services such as free returns and timely-arranged deliveries. (Hu, et al., 2015) proposed a version of the VRP specific for the apparel industry, considering additional complexity generated by the try-on service, that is the possibility for customers to try their purchases upon delivery. Another example of a variant of the VRP is studied in (Afric, et al., 2019), in which the authors present an algorithm for routing optimization under the condition of multiple delivery locations from which to choose. On the one hand this creates an opportunity for further routing optimization, on the other hand it further increases the level of complexity of the problem.
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Customer density plays a major role in determining last-mile delivery total cost (Mangiaracina, et al., 2019). In the light of this, particular attention has been given to the VRP for rural areas, where low customer density poses an ulterior challenge in the search for efficiency. Examples can be found in (Liu, 2020) and (Feng, 2020), where both authors present their models for serving customers in rural China.
Another significant cost factor in the last-mile delivery is the probability of failed delivery (Mangiaracina, et al., 2019) and researchers have recently been exploring the possibilities offered by Artificial Intelligence for decreasing the occurrence of such events. (Snoeck, et al., 2020) proposed an algorithm capable of inferring constrained customers – e.g. those customers who, based on historical orders and user data, are likely to be unavailable within defined time-windows - and subsequently to generate the optimal vehicle routing considering these constraints. This is another example of a variation in the set of constraints which defines the problem and that, if not considered, lead to inadequacies.
3.3.3. Physical Exchange
The last group of paper deals with Artificial Intelligence applications in the very delivery operation, meaning the physical exchange between the deliveryman and the customer. This category includes those applications influencing not only the company-side but also the customer-side of the delivery.
(Tarapata, et al., 2020) devised a Machine Learning algorithm, capable of identifying errors in the address provided upon electronic order emission and able to convert user personal data into precise coordinates for successful and smooth delivery. By doing so, delivery automation increases, and time spent in communication between the deliveryman and the customer, which usually happens by phone, is reduced. With a similar purpose in mind, (Wu & Wu, 2019) trained a Deep Learning model capable of providing the customers with precise expected time of arrival (ETA) which outperforms popular ETA prediction systems in the market. The underlying assumption is that delivery visibility will increase not only customer satisfaction, but also the chances of a flawless exchange.
