32,781 research outputs found

    An optimization model for selecting a product family and designing its supply chain

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    International audienceWhen designing a new family of products, designers and manufacturers must define the product family and its supply chain simultaneously. At the very first step of the design process, designers propose various solutions for the set of variants of a product family and their bill-of-materials. The second step is to select some of these variants while choosing the architecture of the supply chain. A mixed integer linear programming model is investigated that optimizes the operating cost of the resulting supply chain while choosing the product variants. This work is applied to the problem of an automotive supplier

    Decision support for build-to-order supply chain management through multiobjective optimization

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    This is the post-print version of the final paper published in International Journal of Production Economics. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.This paper aims to identify the gaps in decision-making support based on multiobjective optimization (MOO) for build-to-order supply chain management (BTO-SCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting MOO techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted. Future research directions to better exploit the decision support capabilities of MOO are proposed. These include: reformulation of the extant optimization models with a MOO perspective, development of decision supports for interfaces not involving manufacturers, development of scenarios around service-based objectives, development of efficient solution tools, considering the interests of each supply chain party as a separate objective to account for fair treatment of their requirements, and applying the existing methodologies on real-life data sets.Brunel Research Initiative and Enterprise Fund (BRIEF

    A Methodology for Assessing Eco-efficiency in Logistics Networks

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    Recent literature on sustainable logistics networks points to two important questions: (i) How to spot the preferred solution(s) balancing environmental and business concerns? (ii) How to improve the understanding of the trade-offs between these two dimensions? We posit that a complete exploration of the efficient frontier and trade-offs between profitability and environmental impacts are particularly suitable to answer these two questions. In order to deal with the exponential number of basic efficient points in the frontier, we propose a formulation that performs in exponential time for the number of objective functions only. We illustrate our findings by designing a complex recycling logistics network in Germany.Eco-efficiency;Environmental impacts;Profitability;Recycling logistics network

    Optimal formation of supplier networks for product design and production phases to realize an evolving product family

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    Due to rapid changes in customer requirements and vast improvements in technology, many product development companies have identified strategies like time-to-market (TTM) compression and product family development as critical for attaining success in today\u27s hyper-competitive markets. Compressing the TTM, to a large extent, is dependent on the suppliers and the project execution skills of the integrator companies. This study presents a methodology for selecting suppliers for two significant phases of the product realization process, namely, product design and production. The proposed methodology uses a two-stage approach for supplier selection where suppliers for product design are selected in the first stage and suppliers for production are selected in the second stage. These suppliers cater to the evolving customer requirements over a given planning horizon. Apart from using traditional supplier selection metrics such as cost and time, this study also considers the inter-supplier and supplier-integrator communication effectiveness --Abstract, page iv

    A Stackelberg game theoretic model for optimizing product family architecting with supply chain consideration

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    Planning of an optimal product family architecture (PFA) plays a critical role in defining an organization's product platforms for product variant configuration while leveraging commonality and variety. The focus of PFA planning has been traditionally limited to the product design stage, yet with limited consideration of the downstream supply chain-related issues. Decisions of supply chain configuration have a profound impact on not only the end cost of product family fulfillment, but also how to design the architecture of module configuration within a product family. It is imperative for product family architecting to be optimized in conjunction with supply chain configuration decisions. This paper formulates joint optimization of PFA planning and supply chain configuration as a Stackelberg game. A nonlinear, mixed integer bilevel programming model is developed to deal with the leader–follower game decisions between product family architecting and supply chain configuration. The PFA decision making is represented as an upper-level optimization problem for optimal selection of the base modules and compound modules. A lower-level optimization problem copes with supply chain decisions in accordance with the upper-level decisions of product variant configuration. Consistent with the bilevel optimization model, a nested genetic algorithm is developed to derive near optimal solutions for PFA and the corresponding supply chain network. A case study of joint PFA and supply chain decisions for power transformers is reported to demonstrate the feasibility and potential of the proposed Stackelberg game theoretic joint optimization of PFA and supply chain decisions
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