Optimization of Production Development in Supply Chains By Employing Functional Optimization Algorithms

Abstract

Production planning and control (PPC)’s managerial function determines the quantity of production outputs, expected inventory and staff and other resources available for the planning cycle. Producing in a supply chain is a complex and demanding operation, as the whole supply network and the local production units must be optimized. In this article, the concept of supply chain production strategies for many industries is explored with functions optimization algorithms using two theoretical models. Evolutionary computing is an effective training and classification system based on process advances like biological genetics and natural selection; is an efficient method; rather than having the combinational explosion problems resulting from disregarding the inherent knowledge within the search space, it is more likely to be the global optimal solution. Within this analysis, the two optimizations are analyzed and compared: centralized and decentralized.

Keywords: Global-local optimization, Production planning and control (PPC), Functional optimization algorithms, centralized decentralized models.

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