Global genetic algorithm solution for vehicle routing problem in a specific quadratic assignment problem

Mourad Zirour (P39791)

Please use this identifier to cite or link to this item: https://ptsldigital.ukm.my/jspui/handle/123456789/499885

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DC Field	Value	Language
dc.contributor.advisor	Liong Choong Yeun, Prof. Madya Dr.
dc.contributor.author	Mourad Zirour (P39791)
dc.date.accessioned	2023-10-13T09:35:48Z	-
dc.date.available	2023-10-13T09:35:48Z	-
dc.date.issued	2016-05-09
dc.identifier.other	ukmvital:85405
dc.identifier.uri	https://ptsldigital.ukm.my/jspui/handle/123456789/499885	-
dc.description	The Quay Management Problem (QMP) consists of assigning customers to loading positions and satisfying their demands from storage sections. The management of this process is very important in order to ensure optimal use of company resources. In this work the QMP has been formulated as a Quadratic Assignment Problem (QAP) that has been extended to manage the movement of the lifting trucks as a Vehicle Routing Problem (VRP). The problem has been formulated using mixed variables of binary and integers. A new mathematical model has been introduced covering both QMP for assigning positions and VRP for finding collection routes. The model is implemented with the aim of providing optimal loading positions for the customer trucks, sections from where the products should be sourced, and trips for the lifting trucks to serve customers in the loading positions. The QAP and the VRP are well known NP-hard problems. Therefore heuristic approaches have to be used for solving medium- and large-scale problems. The approach adopted is a global Genetic Algorithm solution which uses a combination of a Greedy Algorithm and an enhanced Genetic Algorithm (eGA). The Greedy Algorithm has been used to provide an effective initial solution that helps the GA to go faster to the neighbourhood of the best solution. It is based on location of the nearest free position to the storage section of the demanded products. The GA is used to find and improve the solutions and finally the eGA is applied to refine the best solution of GA. Five cases of the QMP problem have been used to test the model. The first two cases consists of seven loading positions, seven customers and 10 lifting trucks with capacity of 11 units. For the third and the fourth cases, the number of customers has been reduced to five while still have seven loading positions, and 10 lifting trucks. The last case assumes that there are 15 positions, 15 customers and 20 lifting trucks. The initial plan of comparing the solutions by using Lingo, was not possible. The problem proved too large for Lingo which can only solve 2-customer instances. Thus, the five cases of the problem have been solved by using the customised application. The solutions using GA prove that the algorithm converged. The eGA gives better solutions in a shorter computational time and converged rapidly to the best solution in all the five cases tested. Based on previous works, it can be concluded that the application will be very helpful in quay management for decision makers.,Certification of Master's/Doctoral Thesis" is not available
dc.language.iso	eng
dc.publisher	UKM, Bangi
dc.relation	Faculty of Science and Technology / Fakulti Sains dan Teknologi
dc.rights	UKM
dc.subject	Quadratic assignment problem
dc.subject	Global Genetic Algorithm
dc.subject	Vehicle routing
dc.subject	Dissertations, Academic -- Malaysia
dc.title	Global genetic algorithm solution for vehicle routing problem in a specific quadratic assignment problem
dc.type	Theses
dc.format.pages	221
dc.identifier.barcode	002605(2017)
Appears in Collections:	Faculty of Science and Technology / Fakulti Sains dan Teknologi

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