Multi-objective shipment allocation using extreme nondominated sorting genetic algorithm-III (E-NSGA-III)
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Publication Details
Author list: Lavangnananda K., Wangsom P.
Publisher: Hindawi
Publication year: 2019
Start page: 1500
End page: 1505
Number of pages: 6
ISBN: 9781728145495
ISSN: 0146-9428
eISSN: 1745-4557
Languages: English-Great Britain (EN-GB)
Abstract
Dealing with logistical problems are common among companies where delivery of goods are involved. An item to be delivered is commonly known as a shipment and hence efficient shipment allocation is essential for such companies to maintain profits and company competitiveness. This work is concerned with shipment allocation of a large and worldwide delivery company, which has a distribution center in a country where this is still carried manually. Determination of an efficient shipment allocation becomes a multi-objective optimization. Three objectives are identified, these are minimizing number of vehicles used, maximizing vehicle utilization and maximizing operator's route familiarity. Three Multi-Objective Evolution Algorithms (MOEAs) are utilized in this work, these are the commonly known, Nondominated Sorting Genetic Algorithm-III (NSGA-II), Nondominated Sorting Genetic Algorithm-III (NSGA-III) and the recently developed Extreme Nondominated Sorting Genetic Algorithm-III (E-NSGA-III). Hypervolume is used as the metric to measure the quality of solutions. The results from MOEAs reveal superiority over the existing manual solutions. ฉ 2019 IEEE.
Keywords
E NSGA III, Hypervolume, Logistical problems, MOEA, multiobjective optimization, NSGA II, NSGA III, Shipment allocation
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