Decision support system for a multi-period and multi-product aggregate production planning

Abstract

In the hierarchical production planning system, Aggregate Production Planning (APP) falls between the broad decisions of long-range planning and the highly specific and detailed short-range planning decisions. At the very first portion of this study develops an interactive Possibilistic Linear Programming (PLP) approach for solving the multiproduct, multi-period aggregate production planning (APP) with imprecise forecast demand, related operating costs, and capacity. The proposed approach attempts to minimize total costs with reference to inventory levels, labor levels, overtime, subcontracting and backordering levels, and labor, machine and warehouse capacity. This proposed approach used the strategy of simultaneously minimizing the most possible value, the most pessimistic value & also most optimistic value of the imprecise total costs. This thesis paper work also demonstrated an interactive Fuzzy Based Genetic Algorithm (FBGA) approach. Here the author employed different unique genetic algorithm parameters scrupulously for solving nondeterministic polynomials problems like APP problems. On the later portion of this study develops an interactive Multi- Objective Genetic Algorithm (MOGA) approach for solving the multi-product, multiperiod aggregate production planning (APP) with forecasted demand, related operating costs, and capacity. Here several genetic algorithm parameters are considered for solving NP-hard problem (APP problem) and their relative comparisons are focused to choose the most auspicious combination for solving multiple objective problems. For reinforcing & accelerating the decision making for the decision maker a case study was considered in a Ready Made garments manufacturer in Bangladesh named as Comfit Composite Knit Limited (CCKL). A total of five years previous data were collected from the concerned CCKL Company. The data were predominantly from sewing section of different floors. The demand data were uncertain in nature since the buyers are sophisticated to change their order volume within short notice. The manpower used & the Standard Minute Value calculations are left behind this thesis work. All the mathematical measures to calculate the sewing or machining time per unit garment are not covered in this paper because all those were directly collected from the factory representatives. At the bottom portion of this thesis paper there employed a Fuzzy Based Particle Swarm Optimization (FBPSO) approach to choose the most auspicious algorithm for successful APP problems. Two variants of PSO were targeted & solved by using C languages. In nutshell a total of four very recent & potential heuristic algorithms are developed & used in this thesis paper so that the researchers as well as the manufacturer can have the guideline to implement successful aggregate production planning problem.