A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty

• Published in: International journal of production economics Vol. 134; no. 1; pp. 28 - 42
• Main Authors: Mirzapour Al-e-hashem, S.M.J., Malekly, H., Aryanezhad, M.B.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.11.2011; Elsevier; Elsevier Sequoia S.A
• Series: International Journal of Production Economics
• Subjects: Aggregate production planning; Aggregate production planning Robust multi-objective optimization Uncertainty Supply chain; Cost engineering; Demand; Integer programming; Marketing; Mathematical models; Optimization algorithms; Production planning; Robust multi-objective optimization; Shortages; Studies; Supply chain; Supply chain management; Supply chains; Uncertainty; Supply chain; Aggregate production planning; Robust multi-objective optimization; Uncertainty
• DOI: 10.1016/j.ijpe.2011.01.027
• ISSN: 0925-5273

Manufacturers need to satisfy consumer demands in order to compete in the real world. This requires the efficient operation of a supply chain planning. In this research we consider a supply chain including multiple suppliers, multiple manufacturers and multiple customers, addressing a multi-site, multi-period, multi-product aggregate production planning (APP) problem under uncertainty. First a new robust multi-objective mixed integer nonlinear programming model is proposed to deal with APP considering two conflicting objectives simultaneously, as well as the uncertain nature of the supply chain. Cost parameters of the supply chain and demand fluctuations are subject to uncertainty. Then the problem transformed into a multi-objective linear one. The first objective function aims to minimize total losses of supply chain including production cost, hiring, firing and training cost, raw material and end product inventory holding cost, transportation and shortage cost. The second objective function considers customer satisfaction through minimizing sum of the maximum amount of shortages among the customers’ zones in all periods. Working levels, workers productivity, overtime, subcontracting, storage capacity and lead time are also considered. Finally, the proposed model is solved as a single-objective mixed integer programming model applying the LP-metrics method. The practicability of the proposed model is demonstrated through its application in solving an APP problem in an industrial case study. The results indicate that the proposed model can provide a promising approach to fulfill an efficient production planning in a supply chain.