Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.783232
Title: Development of a constrained fuzzy knowledge-based optimisation system for the management of container yard operations in the logistics industry
Author: Abbas, A.
ISNI:       0000 0004 7968 8281
Awarding Body: Coventry University
Current Institution: Coventry University
Date of Award: 2018
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Abstract:
Owing to the many uncertainties involved, the management of container yard operations is very challenging. The storage of containers is one of those operations that require proper management to achieve efficient utilisation of the yard, short handling time and a minimum number of re-handlings. The aim of this study is to develop a fuzzy knowledge based optimisation system named 'FKB_GA' for the management of container yard operations that takes into consideration factors and constraints that exist in real-life situations. One of these factors is the duration of stay of a container in each stack. Because the duration of stay of containers stored with pre-existing containers varies dynamically over time, an 'ON/OFF' strategy is proposed to activate or deactivate the duration of stay factor in the estimation of departure time if the topmost containers for each stack have been stored for a similar time period. A Genetic Algorithm model based Multi-Layer concept is developed which identifies the optimal fuzzy rules required for each set of fired rules to achieve a minimum number of container re-handlings when selecting a stack. The system was coded using Visual Basic for Applications (VBA) in MS Office Excel. An industrial case study is used to demonstrate the applicability and practicability of the developed system. The proposed system has the potential to produce more effective storage and retrieval strategies, by reducing the number of re-handlings of containers. The performance of the proposed system is assessed by comparing with other storage and retrieval techniques including Constrained-Probabilistic Stack Allocation "CPSA" and Constrained-Neighbourhood Stack Allocation "CNSA".
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.783232  DOI: Not available
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