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As a multi-criteria decision-making (MCDM) problem, supplier selection plays a key role in achieving the objectives of a supply chain system. Multiple strategic, operational, quantitative, and qualitative criteria influence the supplier selection process. A wide spectrum of criteria have been introduced, classified, and used by researchers and practitioners to evaluate the suppliers' performance; however, measuring and employing all of these criteria is impractical in real-world scenarios due to the budget, time, and information limitations. In this study, a decision support system (DSS) is developed, which helps managers to select a set of most effective criteria for the supplier selection process. This DSS is a threefold integration of MCDM and simulation and optimization. In this framework, the MCDM module incorporates a combination of criteria to select the suppliers. Then, a simulation model is used to evaluate the performance of the supply chain system considering the selected suppliers. Based on the simulation results, a multi-objective metaheuristic algorithm is utilized to find the ideal combinations of the criteria to maximize the supply chain system performance. [ABSTRACT FROM AUTHOR]

Discrete-event simulation is a decision support tool which enables practitioners to model and analyze their own system behavior. Although simulation packages are capable of mimicking most tasks in a real-world system, there are some decision-making activities, which are beyond the reach of simulation packages. The Application Programmers Interface (API) of SIMIO provides a wide range of opportunities for researchers to develop their own logic and apply it during the simulation run. This paper illustrates how to deploy MATLAB, as a computational tool coupled with SIMIO, as a simulation package by using a new user-defined step instance named “CallMATLAB” . A manufacturing system case study is introduced where the CallMATLAB step instance is used to create an Iterative Optimization-based Simulation (IOS) model. This model is created to evaluate the performance of different optimizers. The benefits of this hybridization for other industries, including healthcare systems, supply chain management systems, and project management problems are discussed. [ABSTRACT FROM AUTHOR]