Development of Evolutionary Algorithm for Job Shop Scheduling Problems
Evolutionary Algorithms for Solving Mono and Multi Objective Job Shop Scheduling Problems
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Manufacturing resources have finite capacity. This means that it is necessary to schedule production regularly, although the scope of this activity is not limited to the manufacturing industry. Scheduling of Jobs and resources on a shop floor is an ever-green optimization problem. Significant amount of literature has been available in Operations Research field. Job Shop Scheduling Problem (JSSP) is allocation of n jobs on m machines to complete processing of all jobs in a minimum possible time i.e., makespan. A thorough research survey indicate that Music Based algorithm as Metaheuristic algor...
Manufacturing resources have finite capacity. This means that it is necessary to schedule production regularly, although the scope of this activity is not limited to the manufacturing industry. Scheduling of Jobs and resources on a shop floor is an ever-green optimization problem. Significant amount of literature has been available in Operations Research field. Job Shop Scheduling Problem (JSSP) is allocation of n jobs on m machines to complete processing of all jobs in a minimum possible time i.e., makespan. A thorough research survey indicate that Music Based algorithm as Metaheuristic algorithms has not been applied to JSSP and the authors have applied MBHS algorithms as most successful algorithms for JSSPs. The JSSP has been attempted by several direct, indirect methods, procedures, and algorithms in the literature. This book presents a new paradigm of Invasive Weed Optimization which mimics the process of weed colonization and distribution to solve JSSPs. The algorithm had shown encouraging and promising outputs on standard bench marking problems.
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