Warehouse cargo space allocation helper

Pao Yue-kong Library Electronic Theses Database

Warehouse cargo space allocation helper

 

Author: Tsao, Chi-wai
Title: Warehouse cargo space allocation helper
Degree: M.Sc.
Year: 1997
Subject: Cargo handling -- Data processing
Genetic algorithms
Hong Kong Air Cargo Terminals Limited
Hong Kong Polytechnic University -- Dissertations
Department: Multi-disciplinary Studies
Pages: iv, 53, 46 leaves : ill. ; 30 cm
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b1398769
URI: http://theses.lib.polyu.edu.hk/handle/200/2945
Abstract: In this report, I investigate the use of two genetic algorithm based heuristics to solve the bin packing problem. The intractability of this problem is a motivation for the pursuit of heuristics that produce approximate optimized solutions. The bin packing problem (BPP) belongs to the class of NP-hard problems. Given a finite set of elements E = {e1, ..., en} with associated weights W = { w1, ..., wn} such that 0 <= wi <= w*. Partition E into N subsets such that the sum of weights in each partition is at most w* and that N is the minimum. The Genetic Algorithm (GA) maintains a population of strings (chromosomes) that encode candidate solutions to a problem. These strings are the analog of chromosomes in natural evolution to solve problems and establish a optimized solution. The Grouping Genetic Algorithm (GGA) is a hybrid Genetic Algorithm specifically modified to suit the structure of grouping problems. Firstly, I will describe in details the definition for BPP. Moreover, I will use a straightforward fitness function to which a graded penalty term is added to penalize some unfeasible cases. Secondly, I will describe the presentation for encoding the BPP using genetic algorithm. Its crossover and mutation operators will also be shown. And then explain the problems for solving BPP. Next, a new presentation for encoding the BPP using grouping genetic algorithm will be introduced. Its crossover and mutation operators will be investigated and compared with classic genetic algorithm. Afterwards, I will present my program analysis, design and implementation. Finally, the experiment will be performed for analysis. I compare the results and conclude with some observations, and suggest the use of grouping genetic algorithm for bin packing problem in my application.

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