Product-service systems contribute to sustainable development through innovative service integration and novel customer value creation. However, the competitive advantage of sustainable product lifecycle service delivery hinges critically on the operational efficiency of service networks. This study addresses dynamic service facility location and allocation challenges in a time-varying demand environment, focusing on the strategic deployment of multiple comprehensive service centers (CSCs) and their dynamic customer allocation across planning horizons. In this study, we develop a 0–1 integer programming model and propose a novel co-evolutionary adaptive multi-objective genetic algorithm (CA-MOGA) with four key enhancements: (1) optimized chromosome representation, (2) adaptive strategy incorporation, (3) genetic operators with gene repair mechanisms, and (4) elite trans-generation migration. Through real-world case validation, CA-MOGA demonstrates significant improvements over conventional genetic algorithms in both convergence speed and solution quality. The performance and adaptability of the proposed algorithm suggest strong potential for customizable applications in solving diverse complex optimization problems.

The rapid emergence and widespread adoption of next-generation information technologies have led to a growing recognition of the need for digitalized warehouse designs. However, creating a digital replica of a physical warehouse in a virtual environment is a complex task. This research introduces a framework for designing smart warehouses based on digital twins, consisting of four key steps: (1) defining the dimensions of the digital twin, (2) establishing a digital twin framework that encompasses the physical warehouse, digital twin, and design processes, (3) implementing modularization techniques for the digital twin, and (4) operating the smart warehouse based on the digital twin. To validate the proposed framework, we present a detailed case study involving a semiconductor manufacturing plant. The results demonstrate the effectiveness of the digital twin-based smart warehouse design and its operational processes.

Industrial 4.0 (I4.0) is believed to revolutionize supply chain (SC) management and the articles in this domain have experienced remarkable increments in recent years. However, the existing insights are scattered over different sub-topics and most of the existing review papers have ignored the underground decision-making process using OR methods. This paper aims to depict the current state of the art of the articles on SC optimization in I4.0 and identify the frontiers and limitations as well as the promising research avenue in this arena. In this study, the systematic literature review methodology combined with the content analysis is adopted to survey the literature between 2013 and 2022. It contributes to the literature by identifying the four OR innovations to typify the recent advances in SC optimization: new modeling conditions, new inputs, new decisions, and new algorithms. Furthermore, we recommend four promising research avenues in this interplay: (1) incorporating new decisions relevant to data-enabled SC decisions, (2) developing data-enabled modeling approaches, (3) preprocessing parameters, and (4) developing data-enabled algorithms. Scholars can take this investigation as a means to ignite collaborative research that tackles the emerging problems in business, whereas practitioners can glean a better understanding of how to employ their OR experts to support digital SC decision-making.

Extended warranty services have become increasingly important for both manufacturers and retailers, offering avenues for new profit sources and growth opportunities. Focusing on the multiple effects of product quality, this study develops a two-period supply chain decision model to analyze the effects of product quality, pricing, and inventory management in the context of extended warranty services. Using a Stackelberg dynamic game model, this study examines the interaction between a manufacturer and a dominant retailer who provides extended warranties. The results indicate significant differences in optimal decisions between centralized and decentralized supply chains, especially concerning pricing and inventory control. Introducing a “quality cost-sharing” contract enhances product quality and improves coordination, leading to increased profits for both the manufacturer and the retailer. Numerical simulations confirm that the cost-sharing contract effectively balances product quality improvements with supply chain profitability.

The greenhouse gas emissions due to the energy use in production and distribution in a supply chain are of interest to industries aiming to achieve decarbonization. The industry subjected to carbon regulations require recycling and reusing materials to promote a circular economy through a closed-loop supply chain (CLSC). In this research, we propose a two-stage stochastic model to design the CLSC under a carbon trading scheme in the multi-period planning context by considering the uncertain demands and carbon prices. We also provide a four-step solution procedure with scenario reduction that enables the proposed model to be solved using popular commercial solvers efficiently. This solution makes the proposed model distinguished from the existing models that assume the firms can purchase or sell carbon credits without quantity limitation. The application of the proposed model is demonstrated via simulation-based analysis of the aluminum industry. The results that the proposed stochastic model generates a network with capacity redundancy to cope with the varying customer demands and carbon prices, while only a slight increase in cost and emission is observed compared with the deterministic model. Furthermore, using scenario reduction, the model solved with 80% of the scenarios share the same CLSC network configuration with the model with full scenarios, while the deviation of the total costs is less than 0.53% and the computational burden can be diminished by more than 40%. This research is expected to be useful to solve optimization problems facing large-scale scenarios with known occurrence probabilities aiming for energy conservation and emissions reduction.

智能工厂需要智能物流系统的支撑,尤其对立体工厂而言,物流系统往往是制约整个智能工厂生产系统能力和效率的主要瓶颈之一.通过仿真技术对智能工厂物流系统进行分析,针对智能工厂的立体物流结构特征,提出了两种优化方案,即分楼层单独优化和多楼层协同优化,并基于Anylogic仿真平台构建了立体物流仿真模型,设计了多种逻辑结构以确保货运电梯和自动引导车(Automated Guided Vehicle,AGV)顺利运行.通过模拟多场景下的物流活动,对不同情境下的AGV数量和利用率以及暂存区物料堆积量等指标进行了分析.结果表明,分楼层单独优化方案可以减少AGV数量,而多楼层协同优化方案能够提高AGV利用率.

This paper proposes one centralized and three decentralized models (manufacturer-dominated, retailer-dominated, and collector-dominated) for a closed-loop supply chain with fuzzy demand and different quality levels for second-hand products. Optimal pricing, collection ratios, and profit allocations for each model are determined through a combination of a Stackelberg game and a fuzzy cut-set method. The model is also successfully applied to a case of automobile starter. Results indicate that the centralized model can gain the highest expected overall profit if the cost coefficient of collection effort and the difference between unit mean transferred price and unit classification expense of recycled product are greater than the corresponding thresholds. For the three decentralized models, optimal prices of new products rise with the ratio of used products of different quality levels. However, the optimal prices of new and remanufactured products and the optimal collection ratios drop with the cost coefficient of the collection effort. Additionally, a counterintuitive result is identified. The optimal collection ratio reaches a maximum in the manufacturer-dominated model rather than the collector-dominated model. Two important management implications are derived from the results: First, all supply chain members should build strategic partnerships following a centralized model to maximize the expected overall profit; Second, the manufacturers should strive for raising the remanufacturing rate of recycled products to gain a maximum expected overall profit and a satisfactory collection ratio of recycled products.

Due to rapid urbanization in developing countries, it is necessary for respective governments to seek new approaches to providing sustainable fresh food and clean energy supplies. The implementation of photovoltaic (PV) systems in hydroponic farms offers an innovative solution to shortages of energy and fresh food in urban areas. These shortages have become increasingly serious during the Covid-19 lockdown periods. This paper proposes an approach to analyzing the impacts of the PV system subsidy schemes on solar-assisted hydroponic farm (SAHF) design and planning, in terms of the profit and outputs of the SAHF and the effectiveness of the subsidies. The subsidy schemes considered include the improving electricity tariff (IET), feed-in tariff (FIT) scheme, and investment co-funding (ICF) scheme. A quadratic programming model is developed to optimize the type and capacity of the PV system simultaneously. The proposed model can be solved by most commercial solvers using the linearization approach proposed in this study. A case study in Qatar is analyzed and incentive thresholds that promise PV system adoption profitability for SAHFs under IET, full FIT, surplus FIT, and ICF subsidies are identified. The thresholds are 0.0425 $/kWh, 0.0063 $/kWh, 0.027 $/kWh, and 14.90%, respectively. The subsidy conditions that lead to optimum benefits for hydroponic farmers and the government are identified via sensitivity analysis. Our method helps policymakers to optimize subsidy levels and therefore reduces subsidy inefficiency. For hydroponic farmers, it is profitable to take full advantage of the available space to enlarge their PV systems if the surplus electricity can be sold to the grid or other entities.

燃气轮机研制是一个复杂的系统工程,需要众多不同类型、不同性质和不同研制合作方式的供应商共同完成,对研制项目管理而言,如何对异质供应商绩效进行评价是一个亟待解决的问题.针对此问题,首先将供应商按照特性分为技术供应商和物料供应商,然后构建了包括质量、成本、交付、技术和合作服务在内的指标体系,并提出了基于网络分析法(Analytic Network Process,ANP)和逼近理想解排序法(Technique for Order Preference by Similarity to an Ideal Solution,TOPSIS)的异质供应商绩效评价方法,通过ANP确定各级指标权重,借助TOPSIS消除指标量纲差异,最后应用示例分析,验证了该方法对复杂装备研制中异质供应商绩效评价的可行性和有效性.

The COVID-19 pandemic and locust swarm outbreaks pose a significant threat to global food systems, causing severe disruptions in both local and international food supplies from farm to fork. The main objective of this study is to understand and identify the disruptions during the crises and create a map of how resilience can be established to recover and sustain the food supply chain (FSC) functions as well as food security. The detrimental impacts of the compound crises on the FSC are explored and the effects of the affected areas are estimated under optimistic and pessimistic scenarios. As a response to the disruption caused by the crisis in FSCs, reactive and proactive solutions are proposed to develop resilience at the food sector level. In the short term, the reactive solutions, consisting of smoothing the food demand, supply and delivery, and food production and processing, can be borrowed. In the long term, the proactive solutions can be conducted by developing multi-level short intertwined FSCs. Our comprehensive investigation of the resilience elements in diverse operations and potential strategies should contribute to the improvement of FSC resilience in the face of ongoing and growing threats.

This study draws a holistic view of the supply chain with three service strategies – namely, after-sales service; maintenance, repair and operations; and the product-service system – by analysing over 71 articles in this field. In this investigation, the content analysis is used to scrutinize the research to establish the knowledge, reveal the research opportunities and propose research strategies. The supply chain for product-service system depends more heavily on the implementation of the cooperation, collaboration and integration principles in operation. However, a complete analysis reveals that those crucial principles did not embody in current literature. Current research also failed to discuss the service operation decisions by modelling the intricate relations among them in a broad product-service supply chain context. Thus, the future research directions include developing service demand forecasting models; combining the time-series methods and the causal methods; establishing service resource planning models; addressing the relations between service deliverables, service capacity and the service resource; and strengthening the quantitative evaluation of the product-service supply chain performance with a systemic view. Moreover, by addressing the principles of cooperation, collaboration and integration, the added values of this review are the proposed research strategies for integrated forecasting of product services, integrated product-service resource allocation and scheduling, and systematic performance evaluation of the product-service supply chain. The findings and the proposed research strategies develop an in-depth understanding of product-service supply chain applications and serve as a basis for future research.

Sustainable supply chain management plays an important role in company’s sustainability success. As the frequently emerging disruptions may harm supply chain’s sustainable performance, managers need to design an effective sustainable supply chain (SSC) resilience solution to mitigate different risks. In addition, it is critical to integrate customer requirements (CRs) in SSC resilience solution design as SSC’s performance is highly determined by customer satisfaction (CS) in the customer-oriented market. This article proposes an integrative approach to achieve an effective design of SSC resilience solution, taking both CRs and risk factors (RFs) into consideration. In this study, the Kano model, decision-making and trial evaluation laboratory (DEMATEL) are integrated with quality function deployment (QFD) through nonlinear programming to guide companies’ design. Kano model is employed to analyse CRs qualitatively and quantitatively to determine each CR’s priority and CS. DEMATEL is adopted to determine the RFs’ interrelationships. The results obtained with the Kano model and DEMATEL are incorporated into QFD to transform CRs into RFs, and then into resilience measures. Furthermore, a nonlinear optimisation model is formulated to achieve the optimal resilience solution with maximised CS and risk mitigation under minimum cost investment. Finally, a case study is conducted to verify the methodology.

The COVID-19 pandemic has caused considerable damage to various industries worldwide. Availability and supply of a wide range of raw materials, intermediate goods, and finished products have been seriously disrupted. Global supply chains (GSCs), which had shown a high level of robustness and resiliency against several disruptions in recent decades, are genuinely compromised. Using a critical reading and a causal analysis of facts and figures, this article aims to investigate the COVID-19 impacts on the effectiveness and responsiveness of GSCs and to propose a set of managerial insights to mitigate their risks and enhance their resilience in various industrial sectors. The study showed that the COVID-19 pandemic has resulted in unprecedented disruptions to the mechanics of most GSCs such as pharmaceuticals, food, electronics, automotive industry, etc. Unlike previous major disruptions, COVID-19 has adversely affected GSCs throughout all their stages with major turbulences in manufacturing, processing, transport, and logistics, as well as significant shifts in demand. The analysis pinpointed that enhancing the supply chain resilience is the main key driver to reducing vulnerability in disruptive times. Furthermore, the analysis indicated that the post-COVID-19 GSCs will tend to be shorter through revamped strategies focusing more and more on relocations and back-shoring.

通过规划绿色生产调度实现了时间、经济和能耗三者的协同优化.以柔性作业车间为背景,结合分时电价政策,构建了设备不同工作状态下的设备能耗成本计算模型;同时兼顾碳排放与订单交付等绿色生产车间管理要求,建立了包括最小化碳排放、能耗成本和最大完工时间在内的柔性作业车间绿色调度多目标优化模型;为避免算法过早陷入"早熟"并保持种群多样性,采用基于动态控制参数和改进精英保留策略的快速非支配排序遗传算法(NSGA-Ⅱ)进行求解;最后,通过具体算例验证了所建立模型的可行性与改进算法的优越性.

Carbon footprinting of products and services is getting increasing attention due to the growing emphasis on carbon related policies in many countries. As a result, many enterprises are focusing on the design of green supply chains (GSCs) with research on supply chains (SCs) focused not only on cost efficiency, but also on its environmental consequences. The review presented in this paper focuses on the implications of carbon policies on SCs. The concept of content analysis is used to retrieve and analyze the information regarding drivers (carbon policies), actors (for example, manufacturers and retailers), methodologies (mathematical modeling techniques), decision-making contexts (such as, facility location and order quantity), and emission reduction opportunities. The review shows a lack of emissions analysis of SCs that face carbon policies in different countries. The research also focuses on the design of carbon policies for emissions reduction in different operating situations. Some possible research directions are also discussed at the end of this review.

The global photovoltaic (PV) industry has been developing rapidly in recent years. We have to face the dilemma of waste PV modules at the end of their life. It is important for government to construct recycling system to collect waste PV modules in the future. This paper analyses the three recycling models under Extended Producer Responsibility (EPR) from the perspective of reverse supply chain. The Government Subsidy is taken into account in the recycling model as an incentive factor. This paper examines the level of the PV producer’s recycling responsibility which is expressed by the recycling proportion of producers. The results show that there are some general rules about recycling responsibility by comparing the results of different models. What’s more, a coordinating way is designed in mixed recycling model within a certain recycling scale range in order to cut down the producer’s recycling cost. Our research shows that the coordinating way is a great method with practical value when it is difficult for the government to implement a complete EPR system.

This paper investigates the capacitated green vehicle routing problem (GVRP) with time-varying vehicle speed and soft time windows. The GVRP is developed as a multi-objective mixed integer nonlinear programming (MINLP) model that incorporates a fuel consumption calculation algorithm. The proposed model considers the vehicle load and capacity as well as time-varying speed in order to account for traffic congestion. An improved non-dominated sorting genetic algorithm (NSGA-II) with adaptive strategies and greedy strategies is developed to solve the GVRP. The results of numerical experiments show that the consumption of fuel in a supply chain can be decreased sharply without any significant loss in customer satisfaction. The proposed NSGA-II has a better capability and efficiency than the original NSGA-II. Our experiments also indicate that the proposed model outperforms most of the best-known solutions obtained from the traditional modeling approaches.

制造业绿色化是中国制造2025的战略方向,装备制造行业的绿色发展是我国制造业转型升级的内在要求,文中从供应链的角度,对复杂装备全寿命周期的绿色绩效管理进行研究.基于复杂装备供应链的结构和管理特征,通过借鉴供应链运作参考模型的基本结构,构建了以计划层、运作层和支撑层及其内在关联关系为核心的复杂装备绿色供应链管理模型,为复杂装备绿色供应链的构建、管理和优化提供参考.