A single machine batch processing problem with release dates to minimize the total completion time (1|rj,batch| ƩCj) is investigated in this research. An original mixed integer linear programming (MILP) model is proposed to optimally solve the problem. Since the research problem at hand is shown to be NP-hard, several different meta-heuristic algorithms based on tabu search (TS) and particle swarm optimization (PSO) are used to solve the problem. To find the most performing heuristic optimization technique, a set of test cases ranging in size (small, medium, and large) are randomly generated and solved by the proposed meta-heuristic algorithms. An extended comparison analysis is carried out and the outperformance of a hybrid meta-heuristic technique properly combining PSO and genetic algorithm (PSO-GA) is statistically demonstrated
Single machine batch processing problem with release dates to minimize total completion time
Costa A.
2018-01-01
Abstract
A single machine batch processing problem with release dates to minimize the total completion time (1|rj,batch| ƩCj) is investigated in this research. An original mixed integer linear programming (MILP) model is proposed to optimally solve the problem. Since the research problem at hand is shown to be NP-hard, several different meta-heuristic algorithms based on tabu search (TS) and particle swarm optimization (PSO) are used to solve the problem. To find the most performing heuristic optimization technique, a set of test cases ranging in size (small, medium, and large) are randomly generated and solved by the proposed meta-heuristic algorithms. An extended comparison analysis is carried out and the outperformance of a hybrid meta-heuristic technique properly combining PSO and genetic algorithm (PSO-GA) is statistically demonstratedFile | Dimensione | Formato | |
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