Abstract
The objective is to investigate a well-known scheduling problem, namely the Permutation Flow-Shop Scheduling with the makespan as the objective function to be minimized. Various techniques, ranging from the simple constructive algorithms to the state-of-the-art techniques, such as Genetic Algorithms (GA), have been cited in the pertinent literature to solve this type of scheduling problem. A new GA-based solution methodology was developed and implemented. In this context, the performance of a stand-alone genetic algorithm (referred to as the non-hybrid genetic algorithm) and a novel hybridized genetic algorithm amalgamated with an iterative greedy algorithm were studied. The parameters of the hybrid and the non-hybrid genetic algorithms were tuned using a Full Factorial Experimental Design and Analysis of Variance. The performance of the properly tuned hybridized GA-based algorithm was examined on the existing standard benchmark problems of Taillard and it was shown that the proposed hybridized genetic algorithm performs very well on the benchmark problems.
Highlights
In Partial Fulfillment o f the Requirements for the Degree of Master o f Applied Science in Industrial Systems Engineering
AVIS: L'auteur a accord&e une licence non exclusive permettant a la Bibliotheque et Archives Canada de reproduire, publier, archiverr, sauvegardeer, conserver, transmettre au public par telecommunication ou par
The Permutation Flow-Shop Scheduling Problem (PFSP) is conventionally labeled as n/mm//P/CCmmaaxx, where n represents the number o f joobs, m represents the number of machines, and PP denotes that the given machine environment is permutation flow-shopp
Summary
The flow-shop scheduling problem with the makespan as the objective function to be minimized is given the notation n/mm//F/CCmmaaxx, where n represents the number o f joobbs, m represents the number o f machines, and FF denotes that the given machine environment is flow-shop. The Permutation Flow-Shop Scheduling Problem (PFSP) is conventionally labeled as n/mm//P/CCmmaaxx, where n represents the number o f joobs, m represents the number of machines, and PP denotes that the given machine environment is permutation flow-shopp. In the general form o f flow-shop scheduling (where the permutation o f joobbs is allowed to be different on each machine) one might obtain a slightly better makespan at the cost of much higher computation time. Johnson proposed a constructive algorithm which can find the optimal solution for the two-machine permutation flow-shop scheduling problem (2/mm//PP/CCmm„ax,)). The scheduler can specify a route for each joob, and different joobbs are allowed to have different routes
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
