Asia-Pacific Journal of Operational Research

New technology innovations and operational research (OR) models can enhance the efficiency and competitiveness of e-commerce platforms. The rapid expansion of e-commerce has led to increased complexity in operations, including dynamic pricing, inventory management, last-mile delivery, and fraud detection. This special issue features nine articles exploring how advanced OR models, such as game-theory-based approaches, machine learning, and big data analytics, offer solutions to these challenges by enabling platforms to make data-driven decisions. As e-commerce evolves, these technological advancements, combined with OR models, will continue to drive platform innovation, improve efficiency, and strengthen resilience in the face of disruptions and fluctuating market demands.

Some prominent e-commerce giants, such as Amazon and JD.com, have established their own logistics service and shared it with other merchants. In this paper, we study the channel (reselling channel or agency channel) and logistics service (platform’s logistics or third-party logistics) selection strategies in an e-commerce supply chain composed of a limited capital supplier and a platform within the consideration of the supply chain finance. We show that the supplier’s channel and logistics service selection strategies depend on the cost coefficient of the platform’s logistics service and the production cost. Interestingly, the supplier prefers the reselling channel rather than the agency channel with the platform’s logistics service. This explains why the merchants in JD.com rarely use JD.com logistics in the agency channel. From the perspective of the platform, the platform may prefer the supplier to use third-party logistics service to deliver goods in the agency channel particularly when the cost coefficient of the platform’s logistics service is relatively large. Furthermore, the platform will put in more effort level aimed at enhancing the logistics service in the reselling channel.

The decision of sourcing channels is a critical strategy for online retail platforms (ORs) when they encroach into the product market of the third-party retailers (3PRs). Whether the products of the OR are provided by the common supplier or the one different from that of the 3PR leads to different supply chain structures. This paper focuses on the sourcing channel decision of an encroaching OR given the sourcing channel of a 3PR. The OR may source either from the common supplier, i.e., the same supplier as that of the 3PR, or from a different supplier, i.e., one different from that of the 3PR. This paper develops two game-theoretical models to examine the sourcing channel decision of an OR in quantity competition with a 3PR on the platform. The results show that the OR’s preference for the sourcing channel is significantly affected by the production cost, product substitutability and commission rate. By comparing the performances of the OR under these two sourcing channels, we find that when the production cost of the different supplier is very low, or when the product substitutability of the different supplier is low, or when the commission rate is either very low or very high, the OR prefers to source from the different supplier. Otherwise, the OR tends to source from the common supplier. We also find that the OR’s encroachment into the sales market of a 3PR by sourcing from the common supplier will always hurt the 3PR, but the platform encroachment by sourcing from the different supplier will not necessarily hurt, especially when the supplier selected by the OR has a significant disadvantage in production cost compared to those of the 3PR. Furthermore, when the OR encroaches by sourcing from the common supplier, it becomes the largest retailer in the market; otherwise, the 3PR is the largest retailer in that market. Our results also indicate that platform encroachment by different sourcing can not only expand the sales market, but also weaken the negative effects of platform encroachment on the profit of the 3PR.

High volatility in customer demand orders during peak and off-peak periods is a great challenge for instant delivery. In this paper, considering the rider familiarity with different areas and the learning effect, we establish two models for different rider assignment strategies: Maximum efficiency model during the peak period and Training familiarity model during the off-peak period. Meanwhile, a hybrid algorithm parallel genetic algorithm and a large-scale neighborhood search (PGA-LNS) is designed to solve the models. The results of two comparative experiments and 50-cycle peak and off-peak alternating experiments show that adopting the Maximum efficiency model in the peak period and the Training familiarity model in the off-peak period is beneficial for instant delivery to achieve overall flexibility, stability, and delivery efficiency.

The meteoric rise of live streaming e-commerce, an emergent form of electronic commerce, is accompanied by frequent occurrences of “live streaming mishaps”. Live streaming’s visual nature offers a more varied and intuitive method for showcasing traceable products, thereby enhancing customer engagement and contracting customers. However, due to the diverging interests, conflicts and contradictions among the stakeholders in traceable products persist, introducing challenges and complexities to its management. We develop an evolutionary game-theoretical model involving three key entities: the platform, anchors (live stream hosts), and consumers. Through the establishment of a payment matrix, we analyze their interactive behaviors and equilibrium states. Additionally, we examine the impacts of key factors on the system and find that platforms and anchors play a pivotal “dual-core” role in traceable product management within the live streaming e-commerce landscape. Moreover, factors such as market attention, anchor characteristics, and consumer dissatisfaction emerge as critical determinants for effective traceable product management. This research contributes valuable insights into enhancing coordination of traceable products in live streaming e-commerce. It offers perspectives on the evolutionary dynamics and strategies for selecting traceable products by platforms, broadcasters, and consumers.

Collaborative truck–drone delivery is a crucial model of drone involvement in urban logistics, addressing drone limitations in load capacity and endurance. However, regional constraints, including damage, blockades, pollution, and epidemics, pose routing challenges for trucks and drones. This study integrates regional restrictions into the heterogeneous truck–drone routing problem, presenting a mixed-integer programming model for cost minimization. To tackle complexity, we introduce an enhanced gray wolf optimization algorithm (EGWO), which improves the initial solution through partition scanning and a heuristic insertion algorithm. EGWO effectiveness is confirmed through enhancements in the standard test library. On average, the heterogeneous truck–drone model achieves a 28.31% cost reduction compared to the single-type truck delivery model. Moreover, deep insights into the impacts of multi-type trucks, the number of no-fly zones and the number of restricted traffic zones on the performance of the heterogeneous truck–drone system are discussed.

In conjunction with the online sales channel, an increasing number of manufacturers are launching showrooms to address consumer fit uncertainty. Our study examines showroom deployment by manufacturers under two scenarios: manufacturer-operated showroom and showroom-operated in collaboration with an independent operator. We consider two types of consumers’ anticipated regret — “mismatch regret” and “high-cost regret” — and develop two game models wherein either the manufacturer or the independent operator determines the optimal pricing strategies. From the derived equilibria, we obtain three key findings: (1) Regardless of whether the showroom is launched by the manufacturer or an independent operator, there are two pricing strategies: unified pricing when the return cost for consumers is relatively low and differential pricing when the cost is high. (2) Under the differential pricing strategy, the optimal price in the showroom increases with the intensity of consumers’ mismatch regret and decreases with the intensity of consumers’ high-cost regret. Moreover, a showroom that collaborates with an independent operator induces a lower price in the showroom than under the scenario of a manufacturer-operated showroom. (3) Consumers’ mismatch regret has a promoting effect on showroom deployment.

With e-commerce rapidly developing, the Online To Offline (O2O) business model requests high efficiency for order allocation and last-mile delivery. Focusing on the challenges associated with online, same-day, and large-scale order allocation and distribution, we formulate an online dynamic vehicle routing problem with pickup and delivery (ODVRPPD), considering the uncertainty of dynamic orders and sustainability of online reassignments to improve the Quality of Experience (QoE). A novel social behavior whale optimization algorithm (SBWOA) with state machine formulation is proposed to solve this problem and express the order closed-loop fulfillment procedure. Inspired by the social behaviors and sonar communication of whale swarms, we propose SBWOA with a double-zone coding (DZC) scheme and affinity propagation clustering (AP clustering). DZC could make real-coding optimization algorithms be used in integer-coding VRPPDs. SBWOA uses AP clustering for the pickup and delivery locations to minimize delivery distance without specifying the initial clustering center and the number of clusters. Additionally, we use the real order data from Alibaba Cloud to construct 11 test problems (including a multi-day test problem with 12925 tasks and 990 vehicles). SBWOA outperforms four compared algorithms. Moreover, the extensive experimental results demonstrate the feasibility and adaptability of our model and SBWOA.

Supply disruption is one of the crucial risks faced by supply chains in the changing external environment. To mitigate supply disruption risk and enhance resilience, product upgrading with Research and Development (R&D) investment is widely adopted by firms. In this paper, we establish a game-theoretic model to study the operation decisions and product upgrading strategies for a supply chain. Specifically, Firm 1 faces supply disruption risks when selling a normal product with a key component sourced from an unreliable supplier. To achieve supply resilience, the firm has an incentive to replace the unreliable supplier with product upgrading supported by R&D. It has an option to make high or low investments in R&D projects, indicating a high or low success rate. Our findings reveal that Firm 1 prefers to make investments in R&D for the upgraded product at a high investment level when the additional production cost of the upgraded product is relatively low or both the upgrading cost and supply disruption risk are relatively high. Extending our model to a duopoly scenario, we investigate the impact of competition on the product upgrading strategy selection. It is shown that a firm with a sufficiently large market share can drive its competitor out of the market by making R&D investments. Compared to the monopoly scenario, Firm 1 has a stronger incentive to upgrade the product under the duopoly scenario if its market share is relatively low. Finally, we identify the conditions where consumers get benefits from product upgrading. Our findings shed light on the importance of product upgrading strategy in a volatile business environment and provide insights into how firms can effectively mitigate supply disruption risk through R&D investments.

Subscription pricing strategies are increasingly adopted by crowdsourcing delivery platforms, allowing subscribers to pay a fixed upfront fee for unlimited future services. This paper explores the impact of demand heterogeneity and transaction costs on the comparison between subscription pricing and pay-per-transaction pricing. We construct and solve models to obtain optimal service prices, delivery commissions, and profits under both strategies. Our findings indicate that the subscription strategy is more effective at attracting consumers compared to the pay-per-transaction strategy. Specifically, the subscription strategy can lower commission rates and reduce the number of deliverers required, while generating higher surplus for both consumers and deliverers when transaction costs are high. Moreover, the subscription strategy maintains a profit advantage when demand heterogeneity is low and transaction costs are high. This study offers valuable insights into pricing decisions, strategy selection, and operational performance optimization, aiding delivery service platforms in better targeting consumers and products.