A genetic algorithms approach to material handling hoist scheduling with time window constraints

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A genetic algorithms approach to material handling hoist scheduling with time window constraints

 

Author: Chan, Siu-cheung
Title: A genetic algorithms approach to material handling hoist scheduling with time window constraints
Degree: M.Sc.
Year: 1998
Subject: Electroplating -- Automation
Production scheduling -- Mathematical models
Genetic algorithms
Process control -- Mathematical models
Combinatorial optimization
Hong Kong Polytechnic University -- Dissertations
Department: Dept. of Computing
Pages: iii, 52 leaves : ill. ; 29 cm
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b1439160
URI: http://theses.lib.polyu.edu.hk/handle/200/5220
Abstract: This paper describes a genetic algorithms approach to job shop optimization problem - material handling hoist scheduling with time window constraints. The interaction of process precedence relationship and floating time windows bring significant complexity to this combinatorial optimization problem. This paper begins with a brief introduction of the problem domain - the facility layout and the material handling hoist operations; followed by a description of previous approach in problem formulation and solving; the benchmark problem which has ever been studied; why and how genetic algorithms can be used heuristically to solve this problem. A new crossover operator, called the intervention crossover, has been constructed to facilitate the genetic search. The genetic algorithms development strategy and development process are also presented as reference for constrained combinatorial optimization problems of similar nature. Former research on material handling hoist scheduling, genetic algorithms and its applications, and genetic algorithms programming have been referred. The previous researchers have focused on operational research techniques, mainly linear/integer programming, to search feasible solutions for the scheduling problem but the process is tedious and time-consuming. Many other researchers have demonstrated that genetic algorithms is a powerful tool for solving constrained combinatorial optimization problems. This research attempts to tackle the scheduling problem in a new, and hopefully more efficient, alternative approach. This paper extends the research work of Shapiro (1988) [26], and Lei L. & Wang T.J. (1991 [19] and 1994 [20]) using a genetic algorithms approach.

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