Abstract
A hybrid genetic-gravitational search algorithm for a multi-objective flow shop scheduling problem
Highlights
Production scheduling is the process of allocating limited resources, which include the manpower, machines or utilities, with respect to various products, in a limited time (Pinedo, 2008)
Genetic algorithm (GA) is used to select two appropriate dispatching rules to combine as a weighted multi-attribute function, while the Gravitational Search Algorithm (GSA) is used to optimize the contribution weightage of each rule in each stage of the flow shop
Genetic algorithm (GA) is used to select two appropriate dispatching rules to combine as a weighted multi-attribute function with prioritize index to trade with the multi-criteria environment, while the GSA is used to optimize the contribution weightage of each rule in each stage of the flow shop based on the selected rules and the prioritize index of the objectives functions
Summary
Production scheduling is the process of allocating limited resources, which include the manpower, machines or utilities, with respect to various products, in a limited time (Pinedo, 2008). This process involves a search for job order and job sequencing to obtain an optimal schedule with the highest utilization and efficiency. A great number of semiconductor industries implement flexible flow shop in a wide range of production processes in order to gain flexibility in scheduling and control. Many semiconductor industries are facing high tardiness and high makespan scheduling problem. It will leave a significant impact on
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