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

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

Multi crteria decision making and its applications : a literature review

This paper presents current techniques used in Multi Criteria Decision Making (MCDM) and their applications. Two basic approaches for MCDM, namely Artificial Intelligence MCDM (AIMCDM) and Classical MCDM (CMCDM) are discussed and investigated. Recent articles from international journals related to MCDM are collected and analyzed to find which approach is more common than the other in MCDM. Also, which area these techniques are applied to. Those articles are appearing in journals for the year 2008 only. This paper provides evidence that currently, both AIMCDM and CMCDM are equally common in MCDM

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

This paper aims to identify the gaps in decision-making support based on multiobjective optimization for build-to-order supply chain management (BTOSCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting multiobjective optimization (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 and future research directions to better exploit the decision support capabilities of MOO are proposed

Joint Decision on Integrated Supplier Selection and Stock Control of Inventory System Considering Purchase Discount

Stock control and supplier selection are two vital parts on supply chain and management. Integrating these parts by solving them simultaneously is a good idea for cost reducing. Furthermore, if some suppliers give a purchase discount for their product, how to determine the optimal strategy is interesting. In this paper, we propose a mathematical model in a mixed integer quadratic programming with piecewise objective function to determine the optimal strategy for integrated supplier selection and stock control of multiproduct inventory system considering purchase discount. This stock control refers to bring the stock level of each product to a reference level given by decision maker. We formulate the model in two environments which are deterministic where all parameters are known and stochastic where the demand is random. Two numerical experiments are performed to evaluate the model. From the results, the optimal strategy was generated i.e. the optimal product volume purchased from each supplier and the stock level of each product follows the reference level

A multiobjective optimization model for optimal supplier selection in multiple sourcing environment

Supplier selection is an important concern of a firm’s competitiveness, more so in the context of the imperative of supply-chain management. In this paper, we use an approach to a multiobjective supplier selection problem in which the emphasis is on building supplier portfolios. The supplier evaluation and order allocation is based upon the criteria of expected unit price, expected score of quality and expected score of delivery. A fuzzy approach is proposed that relies on nonlinear S-shape membership functions to generate different efficient supplier portfolios. Numerical experiments conducted on a data set of a multinational company are provided to demonstrate the applicability and efficiency of the proposed approach to real-world applications of supplier selectio

Probabilistic Programming with Piecewise Objective Function for Solving Supplier Selection Problem with Price Discount and Probabilistic Demand

In this article, a supplier selection problem with price discount and probabilistic demand was solved by formulating a new probabilistic programming model with a piecewise objective function. The proposed model was able to be used by the decision-maker to calculate the optimal decision involving the appropriate raw material quantity to be ordered from each supplier to have minimal total procurement cost. A numerical experiment was conducted with some randomly generated data and the results showed the supplier selection problem was solved by the proposed model and the optimal decision value is achieved

Single Item Supplier Selection and Order Allocation Problem with a Quantity Discount and Transportation Costs

In this paper, we address a single item supplier selection, economic lot-sizing, and order assignment problem under quantity discount environment and transportation costs. A mixed-integer nonlinear program (MINP) model is developed with minimization of cost as its objective, while lead-time, the capacity of the supplier and demand of the product are incorporated as constraints. The total cost considered includes annual inventory holding cost, ordering cost, transportation cost and purchase cost. An efficient and effective genetic algorithm (GA) with problem-specific operators is developed and used to solve the proposed MINP model.  The  model is illustrated through a numerical example and the results show that the GA can solve the model in less than a minute. Moreover, the results of the numerical illustration show that the item cost and transportation cost are the deciding factors in selecting suppliers and allocating orders. Keywords: Supplier selection, Economic Order Quantity, Order allocation, Mixed-integer nonlinear programming

An Environmentally Conscious Multi-Objective Weber Problem for Green Location and Distribution Planning: A Fuzzy Weighted Additive Approach

In this study, a multi-objective Weber (p-median) problem is treated in order to determine the location of the warehouses to be opened and the distribution plans of products. The company carries out the distribution with three types of vehicles differing in unit transportation cost, carbon emission and velocity. Three conflicting objectives are aimed to be minimized, i.e.; the demand weighted total transportation cost, the total delivery time and the total carbon. We adopted a fuzzy weighted additive approach to deal with the multi-objective optimization function, in which the weights of each individual objective function are determined by Analytic Hierarchy Process

An Environmentally Conscious Multi-Objective Weber Problem for Green Location and Distribution Planning: A Fuzzy Weighted Additive Approach

In this study, a multi-objective Weber (p-median) problem is treated in order to determine the location of the warehouses to be opened and the distribution plans of products. The company carries out the distribution with three types of vehicles differing in unit transportation cost, carbon emission and velocity. Three conflicting objectives are aimed to be minimized, i.e.; the demand weighted total transportation cost, the total delivery time and the total carbon. We adopted a fuzzy weighted additive approach to deal with the multi-objective optimization function, in which the weights of each individual objective function are determined by Analytic Hierarchy Process

Multidemand Multisource Order Quantity Allocation with Multiple Transportation Alternatives

This paper focuses on a multidemand multisource order quantity allocation problem with multiple transportation alternatives. To solve this problem, a bilevel multiobjective programming model under a mixed uncertain environment is proposed. Two levels of decision makers are considered in the model. On the upper level, the purchaser aims to allocate order quantity to multiple suppliers for each demand node with the consideration of three objectives: total purchase cost minimization, total delay risk minimization, and total defect risk minimization. On the lower level, each supplier attempts to optimize the transportation alternatives with total transportation and penalty costs minimization as the objective. In contrast to prior studies, considering the information asymmetry in the bilevel decision, random and fuzzy random variables are used to model uncertain parameters of the construction company and the suppliers. To solve the bilevel model, a solution method based on Kuhn-Tucker conditions, sectional genetic algorithm, and fuzzy random simulation is proposed. Finally, the applicability of the proposed model and algorithm is evaluated through a practical case from a large scale construction project. The results show that the proposed model and algorithm are efficient in dealing with practical order quantity allocation problems