Abstract
This study and its companions (Part II, Part III) w ill concentrate on optimizing a class of supply cha in problems known as Multi-Commodities Consumer Supply Chain (MCCSC) problem. MCCSC problem belongs to Production-Distribution (P-D) planning c ategory. It aims to determine facilities location, consumers’ allocation and facilities configuration to minimize total Cost (C T) of the entire network. These facilities can be manufacturer units (MUs), Distrib ution Centres (DCs) and Retailers/End-users (REs) b ut not limited to them. To address this problem, three major tasks should be undertaken. At the first pla ce, a Mixed Integer Non-Linear Programming (MINP) mathematical model is developed. Then, system’s behaviors under different conditions will be observ ed using a simulation modeling tool. Finally, the m ost optimum solution (minimum C T) of the system will be obtained using a multi-obje ctive optimization technique. Due to the large size of the problem and the uncertainties in finding the most optimum solu tion, integration of modeling and simulation methodologies is proposed followed by developing new approach known as GASG. It is a genetic algorithm on the bas is of granular simulation which is the subject of t he methodology of this research. In part II, MCCSC is simulated using Discrete-Event Simulation (DES) device within an integrated environment of SimEvents and Simulink of MATLAB ® software package followed by a comprehensive case study to examine the given strategy. Also, the effect of genetic oper ators on the obtained optimal/near optimal solution by th e simulation model will be discussed in part III.
Highlights
Chain (SC) is defined as an effective coordination and integration of activities undertaken by several infrastructures; such as suppliers, manufacturers, distributors and retailers; from the procurement of raw material to the distribution of final products to the customer (Beamon, 1998; Shapiro, 2007; Gupta and Maranas, 2003)
Despite the fact that this study has focused on integration of modeling and simulation in P-D systems, the solutions of phases were obtained independently
Out of the existing methodologies in addressing Multi-Commodities Consumer Supply Chain (MCCSC) problems, GASG was selected due to its integrated, appropriate and effective nature. This methodology can be used for optimization of single or multi-objective P-D problems in a controlled environment
Summary
Chain (SC) is defined as an effective coordination and integration of activities undertaken by several infrastructures; such as suppliers, manufacturers, distributors and retailers; from the procurement of raw material to the distribution of final products to the customer (Beamon, 1998; Shapiro, 2007; Gupta and Maranas, 2003). Many studies have been conducted and a large number of algorithms and methodologies have been developed (Papageorgiou, 2009; Fahimnia et al, 2012) It is still a flourishing research area. An integrated P-D problem typically consists of Manufacturing Units (MU), Distribution Centres (DC), Warehouses (W) and Retailers (R) It mainly deals with simultaneous management of the information flows as well as optimization of the decision variables of various functions to obtain the best output. Due to the extent of the work and to facilitate presentation, this study is divided into three parts as bellow
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