A Socio-Economic Fuzzy Linear Aggregate Production Planning Model Solved via the Fuzzy Ranking Method

Abstract Uncertainty is inherent to aggregate production planning (APP) due to the dynamic nature of the manufacturing environment. APP delineates the economic and social performance of the production system by determining the production plan and workforce level to achieve demand satisfaction. This work develops a fuzzy linear single objective model aiming for a trade-off between minimizing the total production costs and change in workforce level and maximizing customer satisfaction and capacity utilization. To this end, two types of penalty costs are introduced: the cost of unmet demand and the cost of unutilized capacity. The fuzzy ranking method is used to solve the proposed model. Numerical instances considering a single product family reflecting different industrial cases are solved for different planning horizons. Furthermore, different APP strategies were investigated in various industrial contexts. The mixed APP strategy resulted in a more robust and effective production plan, which is especially advantageous for industries dealing with fluctuating demands, cost swings, or seasonal variations. However, the choice of the APP strategy depends on the planning horizon length and the type of industry. Future research may include actual case studies with multiple product families, consideration of backordering, environmental impact, and other forms of uncertainty.