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The purpose of this paper is to propose a concept of cloud auction robot (CAR) and its execution platform for transforming perishable food supply chain management. A new paradigm of goods-to-person auction execution model is proposed based on CARs. This paradigm can shift the management of traditional manual working to automated execution with great space and time saving. A scalable CAR-enabled execution system (CARES) is presented to manage logistics workflows, tasks and behavior of CAR-Agents in handling the real-time events and associated data.

An Internet of Things enabled auction environment is designed. The robot is used to pick up and deliver the auction products and commends are given to the robot in real-time. CARES architecture is proposed while integrating three core services from auction workflow management, auction task management, to auction execution control. A system prototype was developed to show its execution through physical emulations and experiments.

The CARES could well schedule the tasks for each robot to minimize their waiting time. The total execution time is reduced by 33 percent on average. Space utilization for each auction studio is improved by about 50 percent per day.

The CAR-enabled execution model and system is simulated and verified in a ubiquitous auction environment so as to upgrade the perishable food supply chain management into a new level which is automated and real-time. The proposed system is flexible to cope with different auction scenarios, such as different auction mechanisms and processes, with high reconfigurability and scalability.

This research explores mitigating carbon emissions and integrating sustainability in e-commerce logistics by optimizing the multi-depot pollution routing problem with time windows (MDPRPTW). A proposed model contrasts non-collaborative and collaborative decision-making for order assignment among logistics service providers (LSPs), incorporating low-carbon considerations.

The model is substantiated using improved adaptive large neighborhood search (IALNS), tabu search (TS) and oriented ant colony algorithm (OACA) within the context of e-commerce logistics. For model validation, a normal distribution is employed to generate random demand and inputs, derived from the location and requirements files of LSPs.

This research validates the efficacy of e-commerce logistics optimization and IALNS, TS and OACA algorithms, especially when demand follows a normal distribution. It establishes that cooperation among LSPs can substantially reduce carbon emissions and costs, emphasizing the importance of integrating sustainability in e-commerce logistics optimization.

This paper proposes a meta-heuristic algorithm to solve the NP-hard problem. Methodologies such as reinforcement learning can be investigated in future work.

This research can help logistics managers understand the status of sustainable and cost-effective logistics operations and provide a basis for optimal decision-making.

This paper describes the complexity of the MDPRPTW model, which addresses both carbon emissions and cost reduction. Detailed information about the algorithm, methodology and computational studies is investigated. The research problem encompasses various practical aspects related to routing optimization in e-commerce logistics, aiming for sustainable development.

Workload contribution calculation approaches in the existing literature overestimate or underestimate indirect workload, which increases both workload fluctuation and shop floor throughput performance. This study optimizes a Corrected Aggregate Workload (CAW) approach to control the workload contribution of workstations and Work In Progress (WIP) levels, thereby improving the shop floor throughput performance.

This study adopts simulation experiment by SimPy, and experimental factors are: (1) two workload contribution methods (CAW method and considering Position Corrected Aggregate Workload [PCAW] method); (2) two release methods (LUMS COR release and immediate release); (3) eleven workload norms for LUMS COR release (from 7- to 15-time units), and infinite workload norm for immediate release; and (4) two dispatching rules (First Come First Served, FCFS and Operation Due Date, ODD). Each scenario is replicated 100 times, and for each replication data are collected for 10,000 time units, being the warm-up period set to 3,000-time units.

The results of this study confirm that the PCAW calculation method outperforms the CAW method, especially during higher workload norm levels. The PCAW method is considered the better solution in practice due to its excellent performance in terms of percentage tardiness and mean tardiness time. The efficient workload contribution approach, as discussed in this study, has the potential to offset delivery performance loss that results from throughput performance loss.

This study proposes a novel approach that considers the workstations’ position in the routing of the job and the position of jobs CAW method. The results demonstrated that it allows shop floor throughput time to be short and feasible. It controls WIP by workload contribution of workstations, resulting in a lean shop floor. Therefore, workload contribution calculation is of particular significance for high-variety Make-To-Order (MTO) companies.

Blockchain as a disruptive technology has revolutionized many industries, such as finance, logistics and manufacturing sectors. This paper aims to explore Blockchain applications in construction to effectively manage project workflows and uplift construction efficiency.

Semi-structured in-depth interviews were conducted with various stakeholders from a public infrastructure project to analyze Blockchain applications in the construction industry. Eleven key construction workflows are selected to identify and discover the necessity and significance of Blockchain applications in the construction industry. They are related to construction program, daily operations and payment. Based on workflow analysis, several issues are discussed to design a development roadmap of Blockchain in the construction industry.

The findings show Blockchain can improve construction efficiency, reduce paper-based manual operation and address the fragmentation and communication issue. However, the development of Blockchain in the construction industry is still impeded by some challenges. Traditional work modes might be existing even though Blockchain-enabled systems are introduced, which may increase the workload of construction practitioners. Thus, a feasible and practical development roadmap is proposed to instruct implementation of Blockchain in construction.

This research systematically analyzes the development of Blockchain in the construction industry from several existing workflows, based on a real-life case study. Several considerations are given to provide referential value for applying Blockchain in the construction industry, including opportunities and challenges, cost–benefit analysis, obstacles and possible solutions, as well as development strategies with a prototype system.

Information sharing and coordination between buyer and vendor have been considered as useful strategies to improve supply chain performance. The debate is about what information to share and how to share most cost‐effectively to maximize the mutual benefits of the supply chain as a whole and the individual business players. Proposes a systematic framework for investigating the impacts of sharing production information on the supply chain dynamic performance. This framework supports supply chain researches to study impacts of information sharing under various scenarios. Examines, under the framework, an inventory allocation problem in an arborescent distribution supply chain with two distribution channels competing for the same source of supply. Finds that the levels of benefits by sharing information vary with different players involved in the supply chain. Suggests some guidelines to balance the benefits in a supply chain in order to motivate information sharing.

Global manufacturers have faced unprecedented cost pressures in China because of Chinese currency appreciation, rising labour costs, higher oil prices and reduced value‐added tax rebates. This paper aims to reassess the decision of operating global manufacturing facilities in China.

A mixed integer programming model is developed for a typical global manufacturing supply chain that includes production in the Pearl River Delta region and trade in Hong Kong. A case study with a footwear product is used to illustrate model application and present detailed analyses.

The modelling results affirm the need of relocating labour‐intensive production that mainly competes on low costs. Nevertheless, coastal China offers considerable benefits from industrial clustering and a logistics advantage in comparison with inland China and Asian countries where labour costs are still relatively low. Hong Kong remains a robust location choice for trade operations because of its favourable tax policies.

Retaining production in China faces high risks from Chinese currency appreciation, while relocation to lower‐cost Asian countries is more vulnerable to risks from high oil prices. An intermediate trade operation in Hong Kong can be used to hedge against risks from unfavourable tax policy changes at manufacturing locations.

China has risen to an important position in global manufacturing because of its cost advantages. This paper analyzes the new phenomenon of dramatically increasing cost pressures in China. It develops a first‐of‐its‐kind supply chain configuration model for the popular front‐shop‐back‐factory business model in China.

Rising production costs in coastal China have caused the popularity of the “China plus one” strategy, and the hike in oil prices favors near‐shore outsourcing to reduce transportation costs. Taking into consideration supply chain strategy, this paper aims to investigate the impacts of these major business environment changes on manufacturing outsourcing in China.

Considering costs and lead times, a bi‐objective integer programming model is developed to determine optimal supply chain configuration decisions under various business environment scenarios. A case study with a family of footwear products is used to illustrate model application and present detailed analyses.

Impacts of factors could be non‐intuitive. Partial relocation may be better than relocating all manufacturing steps to lower‐cost regions. Coastal China is still attractive for products with short order lead times, because of its formation of industrial clusters and efficient logistics services.

Only tangible factors including costs and lead times are evaluated; however, they offer valuable insight for the strategic decision of selecting outsourcing locations.

The analyses suggest differentiated outsourcing strategies depending on product and demand characteristics. The trend of moving manufacturing away from China is exaggerated. Coastal China has established industrial clusters and efficient logistics support to enable supply chain responsiveness; while inland China maintains a low cost comparable with competing Asian countries.

Existing studies on the research phenomenon focused on cost factors alone. This paper analyzes lead times as well. The study also introduces the network perspective into the research of offshore manufacturing outsourcing.

Commonly shared logistics services help manufacturing companies to cut down redundant logistics investments while enhance the overall service quality. Such service-sharing mode has been naturally adopted by group companies to form the so-called headquarter-managed centralized distribution center (HQ-CDC). The HQ-CDC manages the common inventories for the group’s subsidiaries and provides shared storage services to the subsidiaries through appropriate sizing, pricing and common replenishment. Apart from seeking a global optimal solution for the whole group, the purpose of this paper is to investigate balanced solutions between the HQ-CDC and the subsidiaries.

Two decision models are formulated. Integrated model where the group company makes all-in-one decision to determine the space allocation, price setting and the material replenishment on behalf of HQ-CDC and subsidiaries. Bilevel programming model where HQ-CDC and subsidiaries make decisions sequentially to draw a balance between their local objectives. From the perspective of result analysis, the integrated model will develop a managerial benchmark which minimizes the group company’s total cost, while the bilevel programming model could be used to measure the interactive effects between local objectives as well as their final effect on the total objective.

Through comparing the numerical results of the two models, two major findings are obtained. First, the HQ-CDC’s profit is noticeably improved in the bilevel programming model as compared to the integrated model. However, the improvement of HQ-CDC’s profit triggers the cost increasing of subsidiaries. Second, the analyses of different sizing and pricing policies reveal that the implementation of the leased space leads to a more flexible space utilization in the HQ-CDC and the reduced group company’s total cost especially in face of large demand and high demand fluctuation.

Several classical game-based decision models are to be introduced to examine the more complex relationships between the HQ-CDC and the subsidiaries, such as Nash Game model or Stackelberg Game model, and more complete and meaningful managerial implications may be found through result comparison with the integrated model. The analytical solutions may be developed to achieve more accurate results, but the mathematical models may have to be with easier structure or tighter assumptions.

The group company should take a comprehensive consideration on both cost and profit before choosing the decision framework and the coordination strategy. HQ-CDC prefers a more flexible space usage strategy to avoid idle space and to increase the space utilization. The subsidiaries with high demand uncertainties should burden a part of cost to induce the subsidiaries with steady demands to coordinate. Tanshipments should be encouraged in HQ-CDC to reduce the aggregate inventory level as well as to maintain the customer service level.

The proposed decision frameworks and warehousing policies provide guidance for the managers in group companies to choose the proper policy and for the subsidiaries to better coordinate.

This research studies the services sharing on the warehouse sizing, pricing and common replenishment in a HQ-CDC. The interactive decisions between the HQ-CDC and the subsidiaries are formulated in a bilevel programming model and then analyzed under various practical scenarios.

To propose and develop a cost‐effective approach to developing and implementing customized electronic business solutions in a do‐it‐yourself (DIY) fashion.

The proposed DIY approach is based on the concept of “portalets” that are used as building blocks to develop e‐business solutions with little or varying degree of customization efforts. Portalets form a platform for deriving the software (e‐business) product lines.

This paper not only discusses the necessity but also demonstrates the feasibility of the proposed DIY e‐business approach. New challenges associated with the approach are highlighted.

It is a critical and incremental task to develop portalets building blocks and identify their interfaces and standardize their customization points in terms of backend, middleware and client components.

The study identifies a suite of tools and techniques for building and implementing EBS in order for small and medium sized enterprises (SMEs) to significantly reduce the time and effort to an affordable level while capturing the unique business logics into the solutions.

The DIY e‐business approach represents a unique alternative for SME companies to adopt and implement EBS to best suit their operational characteristics.

The study aims to provide a comprehensive review of digital twin (DT) literature and examine how various industrial sectors utilize the potential of DT.

This study’s systematic literature review (SLR) and bibliometric analysis focus on utilizing DT in the supply chain (SC) and its applications across various industries between 2017 and 2024. The use of DT for information management and risk management in SCM, which have been investigated in many sectors, is the primary focus of this article. The article also examines the various digital technologies used in digital twin literature.

The following are the main conclusions drawn from the research on digital twins and their implementation: Digital twins have been studied to improve visibility, traceability, resilience, risk identification and assessment, information sharing and decision-making in SC of various sectors. According to the literature review, most research was conducted in the manufacturing industry. Also, the integration of DT with digital technologies (like AI, BD, AI, ML and CPS) in SC has been explored less.

A multisectoral examination has been done to identify any needs or requirements and unknown areas of study and make recommendations for future directions for study on the interface between SC and DT.