基于二层规划的供应链多阶响应周期决策模型

优化和缩短供应链多阶响应周期是供应链快速响应市场需求、缩短产品上市周期的有效途径,也是供应链管理总体目标之一.全文首先阐明了供应链多阶响应周期的概念,以核心企业作为协调中心,建立了供应链整体计划决策机制,并运用二层规划方法对供应链多阶响应周期进行了建模研究.最后用实际调研的数据作为一个算例对模型进行了数值演算和验证.结果表明,该模型能够协调管理供应链整体利益和节点企业局部利益、合理分配生产时间与物流时间,为供应链优化和缩短多阶响应周期提供了一个有效的决策工具.     

Green supply chain management: the case of the construction sector in the United Arab Emirates (UAE)

Restraining the negative environmental impacts of the construction sector constitutes one of the major challenges of the twenty-first century. However, efforts to address it have been largely fragmented. With environmental consequences of a construction project typically dispersed across its life cycle, i.e. from design through to end-of-life, greening this sector requires a supply chain wide focus inclusive of all key stages and stakeholders; also, all relevant aspects such as the nature of green practices implemented and associated drivers, barriers and performance implications need to be considered. This forms the focus of the present study where a comprehensive, green supply chain management oriented understanding of the construction sector is developed through the context of the UAE construction sector, and incorporating inputs from all key stakeholders, i.e. Developers, Architects/Consultants, Contractors and (material) Suppliers. The study contributes to improving the efficiency and effectiveness of greening of the construction sector.     

Causal factor relations of supply chain competitiveness via fuzzy DEMATEL method for Thai automotive industry

Abstract

The automotive industry is complex and global. An automotive supply chain involves multiple manufacturers and service providers in different parts of the chain across different countries. Diffusion of the global production system creates both opportunities and challenges for the supply chain itself as well as production base countries. Thailand is a production base for over 15 automotive brands. The industry contributes significantly to the Thai economy and employs approximately half a million people. To address competitiveness of the Thai automotive industry, its supply chain is studied in this paper. Ten factors affecting the competitiveness of the Thai automotive supply chain are analysed through the fuzzy DEMATEL method. The method forms a structural model that categorises the causal or effect roles of the factors, and the degree at which they impact the supply chain. This study focuses on Tier 1 and Tier 2 manufacturers of the supply chain. The results reveal that there are three common causal factors and four common effects in both tiers. The other four factors play different roles in different tiers. Managerial implications relating to these factors are also discussed and certain recommendations proposed in order to enhance the competitiveness of the Thai automotive supply chain.     

Cloud manufacturing system for sheet metal processing

Abstract

Cloud computing is changing the way industries and enterprises run their businesses. Cloud manufacturing is emerging as an approach to transform the traditional manufacturing business model, while helping the manufacturer to align production efficiency with its business strategy, and creating intelligent factory networks that enable collaboration across the whole enterprise. Many production planning and control (PPC) problems are essentially optimisation problems, where the objective is to develop a plan that meets the demand at minimum cost or maximum profit. Because the underlying optimisation problem will vary in the different business and operation phases, it is important to think about optimisation in a dynamic mechanism and in a number of interlinked sub-problems at the same time. Cloud manufacturing has the potential to offer decision support as a service and medium of communication in PPC. To solve these problems and produce collaboration across the supply chain, this paper provides an overview of the state of the art in cloud manufacturing and presents a model of cloud-based production planning and production system for sheet metal processing.     

Sustainable supply chain management and the transition towards a circular economy: Evidence and some applications

In the last decades, green and sustainable supply chain management practices have been developed, trying to integrate environmental concerns into organisations by reducing unintended negative consequences on the environment of production and consumption processes. In parallel to this, the circular economy discourse has been propagated in the industrial ecology literature and practice. Circular economy pushes the frontiers of environmental sustainability by emphasising the idea of transforming products in such a way that there are workable relationships between ecological systems and economic growth. Therefore, circular economy is not just concerned with the reduction of the use of the environment as a sink for residuals but rather with the creation of self-sustaining production systems in which materials are used over and over again.Through two case studies from different process industries (chemical and food), this paper compares the performances of traditional and circular production systems across a range of indicators. Direct, indirect and total lifecycle emissions, waste recovered, virgin resources use, as well as carbon maps (which provide a holistic visibility of the entire supply chain) are presented. The paper asserts that an integration of circular economy principles within sustainable supply chain management can provide clear advantages from an environmental point view. Emerging supply chain management challenges and market dynamics are also highlighted and discussed.     

A harmony search-based memetic optimization model for integrated production and transportation scheduling in MTO manufacturing

This paper investigates an integrated production and transportation scheduling problem in an MTO supply chain. A harmony search-based memetic optimization model is developed to handle this problem, in which certain heuristic procedures are proposed to convert the investigated problem into an order assignment problem. A novel improvisation process is also proposed to improve the optimum-seeking performance. The effectiveness of the proposed model is validated by numerical experiments. The experimental results show that (1) the proposed model can solve the investigated problem effectively and that (2) the proposed memetic optimization process exhibits better optimum-seeking performance than genetic algorithm-based and traditional memetic optimization processes.     

Coordinating contracts for a financially constrained supply chain

We consider a financially constrained supply chain in which a supplier (leader) sells products to a retailer (follower) who has no access to bank financing due to her low credit rating. However, the supplier can borrow from a bank and offer trade credit to the retailer to alleviate her financial constraint. Failure to pay off a bank loan or trade credit incurs a variable default cost. We analyze the centralized version of the supply chain to obtain new coordination requirements. We then examine whether revenue-sharing, buyback, and all-unit quantity discount contracts can coordinate our supply chain. We show that the all-unit quantity discount contract fails to coordinate. However, the revenue-sharing and buyback contracts can coordinate the supply chain, but only when the supply chain has a sufficient total working capital. Moreover, they cannot allocate profit flexibly unless the supplier has a large enough working capital. Finally, we design a generalized revenue-sharing contract that coordinates the supply chain with flexible profit allocation, and also show by numerical examples its superiority over the revenue-sharing and buyback contracts.     

Evaluation of multi-objective optimization approaches for solving green supply chain design problems

This paper evaluates the applicability of different multi-objective optimization methods for environmentally conscious supply chain design. We analyze a case study with three objectives: costs, CO₂ and fine dust (also known as PM – Particulate Matters) emissions. We approximate the Pareto front using the weighted sum and epsilon constraint scalarization methods with pre-defined or adaptively selected parameters, two popular evolutionary algorithms, SPEA2 and NSGA-II, with different selection strategies, and their interactive counterparts that incorporate Decision Maker׳s (DM׳s) indirect preferences into the search process. Within this case study, the CO₂ emissions could be lowered significantly by accepting a marginal increase of costs over their global minimum. NSGA-II and SPEA2 enabled faster estimation of the Pareto front, but produced significantly worse solutions than the exact optimization methods. The interactive methods outperformed their a posteriori counterparts, and could discover solutions corresponding better to the DM preferences. In addition, by adjusting appropriately the elicitation interval and starting generation of the elicitation, the number of pairwise comparisons needed by the interactive evolutionary methods to construct a satisfactory solution could be decreased.     

A hybrid approach to configure eco-efficient supply chains under consideration of performance and risk aspects

Formal models that support multi-criteria decision making represent a strongly growing area in sustainable supply chain management research. However, uncertainties and risks are seldom considered in quantitative models for green supply chain (SC) design. The paper at hand suggests a hybrid approach to configure an eco-efficient SC for a new product under consideration of economic and environmental risks. Discrete-event simulation is applied to assess the financial, operational and environmental performance of different SC configuration options while the value-at-risk concept is adapted to evaluate related SC risks. The analytic hierarchy process is employed to solve the resulting multi-criteria decision problem of choosing the best option. The approach is illustrated at a case example of a fast moving consumer goods manufacturer.