A new hybrid algorithm of simulated annealing and simplex downhill for solving multiple-objective aggregate production planning on fuzzy environment
This paper work demonstrated an interactive Fuzzy Based Genetic Algorithm (FBGA) approach for solving a two products & two periods aggregate production planning (APP) with some vulnerable managerial constraints like imprecise demands, variable manufacturing costs etc. 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 in lieu of some strong resource constraints. Another important purpose of this study is to derive & observe the variations along with the scope of the imprecise total cost, maximizing the possibility of obtaining lower total costs and also minimizing the risk of obtaining higher total costs. Here the authors employed different unique genetic algorithm parameters scrupulously for solving nondeterministic polynomials problems like APP problems. For reinforcing & accelerating the decision making for the decision maker a case study was considered in a Ready Made garment company in Bangladesh. Consequently, the proposed FBGA approach yields an efficient APP compromise solution and could be efficient for large scale problems.