Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Multi-disciplinary Studies | en_US |
| dc.creator | Tsao, Chi-wai | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/2945 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | - |
| dc.rights | All rights reserved | en_US |
| dc.title | Warehouse cargo space allocation helper | en_US |
| dcterms.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. | en_US |
| dcterms.extent | iv, 53, 46 leaves : ill. ; 30 cm | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 1997 | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.educationalLevel | M.Sc. | en_US |
| dcterms.LCSH | Cargo handling -- Data processing | en_US |
| dcterms.LCSH | Genetic algorithms | en_US |
| dcterms.LCSH | Hong Kong Air Cargo Terminals Limited | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b13987690.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 2.98 MB | Adobe PDF | View/Open |
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