Integrating multi-dynamic virtual cellular manufacturing systems into multi-market allocation and production planning

Abstract

This article presents a two-stage approach to solve a multi-dynamic virtual cellular manufacturing system in multi-market allocation and production planning. The first stage identifies the plant locations and grouping parts–machines–workers’ assignment to virtual cells, while the second stage determines the production volume for each plant, the allocated amounts to each market and the operations’ allocation to the virtual cells. The goal of the proposed mathematical model is to minimize total expected cost consisting of holding, outsourcing, inter-cell material handling, external transportation, fixed cost for producing each part in each plant as well as machine and labour salaries. It is assumed that the parts’ processing times on each machine with workers as well as the products’ demands are stochastic and described by discrete scenario enrichment using the sample average approximation and Latin hypercube sampling techniques. To solve such a stochastic model, an efficient method based on the genetic algorithm is then utilized. Also, Taguchi method is employed so as to evaluate the effects of different operators and parameters on the performance of genetic algorithm. Finally, computational experiments on randomly generated test problems are presented to demonstrate the performance and power of developed model in handling uncertainty.

Review of Responsiveness and Sustainable Concepts in Cellular Manufacturing Systems

“…Aalaei et al [23] proposed a stochastic model to assign parts and machine workers to multi-dynamic virtual cells. The approach proposed minimizes the costs of holding, outsourcing, inter-cell material handling, and external transportation, and fixed the costs for producing each part.…”

Cell Production System Design: A Literature Review

“…Chen et al, considered the synchronization of order production production with the dynamic entry into a flowshop problem [8]. Alaei et al, discussed a dynamic cell production system under discrete scenarios with random product demand [9]. Xue and Offodile used a hierarchical production planning model to solve the problem of cell reconfiguration with different needs in different periods [10].…”

Modeling the Trade-off between Manufacturing Cell Design and Supply Chain Design

“…The effect of new factors such as human resource allocation in the integrated model has been shown in the proposed model. Aalaei et al [13] developed a two-stage approach to solve a multidynamic virtual cellular manufacturing system in multimarket allocation and production planning. For the purpose of minimizing the cost of holding and outsourcing, outbound transportation, machines and removal, hiring, firing, and salary workers, Aalaei and Davoudpour [14] presented a new mathematical model for integration of dynamic cellular manufacturing into the supply chain system.…”