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Editorial Mathematical Models for Supply Chain Management Young Hae Lee, 1 Paulina Golinska-Dawson, 2 and Jei-Zheng Wu 3 1 Department of Industrial and Management Engineering, Hanyang University, Ansan 15588, Republic of Korea 2 Faculty of Engineering Management, Poznan University of Technology, 60965 Poznan, Poland 3 Department of Business Administration, Soochow University, Taipei 10048, Taiwan Correspondence should be addressed to Young Hae Lee; [email protected] Received 20 March 2016; Accepted 31 March 2016 Copyright © 2016 Young Hae Lee et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1. Introduction Business leaders today are facing many challenges including cost, tax laws, skills, material availability, and new market entry and others have driven organizations to redesign and reconfigure their supply chains continually. As supply chains become globalized the complexities to manage and control those globalized supply chains will also increase and that usually results in inadequate existing experience and intuition. Therefore, to overcome that inadequacy the use of mathematical models becomes necessary. The attention towards the use of mathematical models to optimize supply chain has been increasing, mainly because of their lower cost and greater capability. In supply chain man- agement the use of mathematical modeling is not specific to any particular level; those can be used at any level (strategic, tactical, or operational), considering the factors like trans- portation routing, distribution networks, or warehouse oper- ations. Mathematical modeling approaches that are usually considered in supply chain problems include linear program- ming, mixed-integer/integer linear programming, nonlinear programming, multiobjective programming, fuzzy mathe- matical programming, stochastic programming, heuristics algorithms, and metaheuristics and hybrid models. The articles in this special issue speak of development of advance mathematical modeling approached to tackle complex global supply chain management challenges. The following sections briefly explain the outcome of published articles in this special issue. The next section is divided into several key research areas based on publications. 2. Supply Chain Quality Management H. J. Kim et al. proposed strategies for improving supply chain quality in buyer-supplier relations. This study ana- lyzes the efficiency of supply chain quality management (SCQM) by grouping buyers and suppliers and conducting an empirical analysis of aspects affecting SCQM efficiency from the supplier’s perspective. Another paper under this research umbrella is published by M. S. Memon et al. for increasing product safety and quality. They analyzed impact of traceability optimization on supply chain operational efficiency and shareholder profit under adverse recall crisis.
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