Comparison Between Several Fuzzy Dynamic Heuristic- Based Programming of Scheduling Weighted Job on Single Machine

In this paper, dynamic programming for sequencing weighted jobs on a single machine to minimizing total tardiness is focused, to significance of fuzzy numbers field, and importance of that for decision makers who are facing on uncertain data, combination of dynamic programming and fuzzy numbers is applied. A random scheduling problem with fuzzy processing times is given and solved. In addition, algorithm consuming time during solving same category problem and different sizes are analyzed that for large problem CPU time usage is extremely unaffordable. Therefore demonstration of near-exact heuristic method appears. In this paper sufficient discussion around solving this kind of problems and their algorithm analysis and a combination between Dynamic programming and Several Meta-heuristics as a newly born method is proposed that stand on DP performance and Artificial Intelligence search power, and finally comparison has been held. Therefore some most famous Meta-heuristic method such as Tabu Search (TS), Simulated Annealing (SA), Genetic Algorithm (GA), and Ant Colony System (ACO) has been hybridized with Traditional Dynamic Programming (DP), and Evenly a Comparison on methods result thoroughly is held.