Hybrid Metaheuristics for Solving a Fuzzy Single Batch-Processing Machine Scheduling Problem

This paper deals with a problem of minimizing total weighted tardiness of jobs in a real-world single batch-processing machine (SBPM) scheduling in the presence of fuzzy due date. In this paper, first a fuzzy mixed integer linear programming model is developed. Then, due to the complexity of the problem, which is NP-hard, we design two hybrid metaheuristics called GA-VNS and VNS-SA applying the advantages of genetic algorithm (GA), variable neighborhood search (VNS), and simulated annealing (SA) frameworks. Besides, we propose three fuzzy earliest due date heuristics to solve the given problem. Through computational experiments with several random test problems, a robust calibration is applied on the parameters. Finally, computational results on different-scale test problems are presented to compare the proposed algorithms

A hybrid imperialist competitive algorithm for the flexible job shop problem

Flexible job shop scheduling problem (FJSP) is one of the hardest combinatorial optimization problems known to be NP-hard. This paper proposes a novel hybrid imperialist competitive algorithm with simulated annealing (HICASA) for solving the FJSP. HICASA explores the search space by using imperial competitive algorithm (ICA) and use a simulated annealing (SA) algorithm for exploitation in the search space. In order to obtain reliable results from HICASA algorithm, a robust parameter design is applied. HICASA is compared with the widely-used genetic algorithm (GA) and the relatively new imperialist competitive algorithm (ICA). Experimental results suggest that HICASA algorithm is superior to GA and ICA on the FJSP