Manufacturing lot size optimization under demand uncertainty: A stochastic goal programming approach

Abstract

Optimization has become a standard phenomenon in the majority of organizations and establishments. Many Manufacturing companies operate under uncertainties which affect the system performance. Product demand is one of the common kinds of uncertainty that characterizes production environments. One of the challenges faced by manufacturing companies that use cost analyses is product demand uncertainty that often affects the manufacturing system performance and decision making. Manufacturing Lot size problems are normally related to proficient production planning of a given product. If a manufacturing firm wants to compete within the market, it must make the right decisions regarding lot-sizing problems and this can be a critical decision for any manufacturer. In this paper, an optimization model for the manufacturing lot size was developed using Markov chains in conjunction with stochastic goal programming. The goal constraints, deviation variables, priorities and objective function were defined to determine the over-achievement or underachievement of the manufacturing lot size for aggregate production planning, the different states of demand for the product being represented by states of a Markov chain. The model was solved using the linear programming solver in MATLABTM to determine the quantity of product plan for manufacturing within the first quarter of the year when demand changes from one state to another