A new manufacturing resource allocation method for supply chain optimization using extended genetic algorithm

In distributed manufacturing environments, the real competitive edge of an enterprise is directly related to the optimization level of its supply chain deployment in general, and, in particular, to how it allocates diverse manufacturing resources optimally. This is faced with increasing challenges caused by the conflicting objectives in manufacturing integration over distributed manufacturing resources. This paper presents a new manufacturing resource allocation method using extended genetic algorithm (GA) to support the multi-objective decision-making optimization for supply chain deployment. A new multi-objective decision-making mathematical model is proposed to evaluate, select, and sequence the candidate manufacturing resources allocated to sub-tasks composing the supply chain, by dealing with the trade-offs among multiple objectives including similarity, time, cost, quality, and service. An extended GA approach with problem-specific two-dimensional representation scheme, selection operator, crossover operator, and mutation operator is proposed to solve the mathematical model optimally by designing a chromosome containing two kinds of information, i.e., resource selection and resource sequencing. A case study is carried out to demonstrate the effectiveness and efficiency of the proposed approach.