Overview

This thesis addresses two different models of job shop scheduling applications that are associated with multiple routings. The first application is the well known scheduling model, generally addressed in the literature as Flexible job shop scheduling problem (FJSP), which belongs to the category of toughest NP-hard problems. The second scheduling model addressed in this thesis is based on the production environment of a capital goods industry in which the components of different products are processed and assembled in an assembly job shop type environment. The processing operations on all components in the model also consider alternative routing option. This problem is, therefore, addressed as assembly job shop scheduling associated with multiple routings (AJSP), which is much more complex than FJSP. To solve such NP-hard problems, heuristic approaches have emerged as a promising alternative to the mathematical approaches. Three population based search heuristics, a Genetic Algorithm (GA), an Ant Colony Optimization (ACO) algorithm and a Particle Swarm Optimization (PSO) algorithm, are proposed to evolve optimal schedules for both the models. The performance of the three population based search heuristics for FJSP are tested with various benchmark instances for minimum makespan time criterion and evaluated by comparing their solutions with the lower bound solution (LB), best known solution (BKS) and the solution obtained with constraint programming formulation (CPF) for the problem solved using ILOG Solver. The performance comparison reveals that the proposed algorithms outperformed CPF and are competent with the existing approaches. The proposed algorithms are, therefore, effective tools for solving FJSP instances. The performance of the three population-based search heuristics for AJSP are tested with various problem instances for minimum total tardiness cost criterion and the results obtained are compared with the results of CPF solved using ILOG Solver. The performance comparison reveals that the proposed heuristics perform better than the CPF.

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