Mathematical Models for Supply Chain Management

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 management 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 transportation routing, distribution networks, or warehouse operations. Mathematical modeling approaches that are usually considered in supply chain problems include linear programming, mixed-integer/integer linear programming, nonlinear programming, multiobjective programming, fuzzy mathematical 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