Using Distributed Computing To Improve The Performance Of Genetic Algorithms For Job Shop Scheduling Problems

Scheduling problems arise in diverse areas such as flexible manufacturing systems, logistics, and network systems and so on. A job shop scheduling problem (JSP) is among the toughest scheduling problems due to the huge number of possible solutions for every problem. There is no efficient conventional optimization algorithm that can guarantee an optimal solution in polynomial time. Due to this inherent intractability, heuristic procedures such as genetic algorithms offer an attractive way of solving these problems. Even with genetic algorithms, larger job shop problems lead to highly time consuming computations due to the vast solution space. This research tries to use the principles of distributed computing to improve solution time in case of such large problems. This document proposes software developed in Java programming language to solve job shop scheduling problems using genetic algorithms by simultaneously utilizing the processing capabilities of several networked computers. The software is based on the client-server model, where the server distributes the computationally intensive task of crossover, mutation and makespan calculation for chromosomes to remote clients. Testing has been done to determine whether this approach is useful in reducing computation time in case of different sizes of job shop scheduling problems and different types of hardware configuration. Results of the testing are discussed at the end of the document.

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