An Improved Fuzzy Knowledge-Based Model For Long Stay Container Yards

Abstract

This paper considers the problem of allocating newly arrived containers to stacks of existing containers in a yard when the departure date/time for containers is unknown. Many factors and constraints need to be considered when modelling this storage allocation problem. These constraints include the size, type and weight of the containers. The factors are the number of containers in a stack and the duration of stay of the topmost container in the stack. This paper aims to develop an improved Fuzzy Knowledge-Based ‘FKB’ model for best allocation practice of long-stay containers in a yard. In this model, the duration of stay factor does not need to be considered in the allocation decision if the duration of stay for the topmost containers in a stack is similar; hence, a new ‘ON/OFF’ strategy is proposed within the Fuzzy Knowledge-Based model to activate/deactivate this factor in the stacking algorithm whenever is required. Discrete Event Simulation and Fuzzy Knowledge-Based techniques are used to develop the proposed model. The model’s behaviour is tested using three real-life scenarios, including allocating containers in busy, moderately busy and quiet yards. The total number of re-handlings, the number of re-handlings per stack, and the number of re-handlings for containers were considered KPIs in each scenario.

Divisions: College of Business and Social Sciences > Aston Business School
College of Business and Social Sciences > Aston Business School > Operations & Information Management
Additional Information: Copyright notice: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
Uncontrolled Keywords: Fuzzy knowledge-based model,fuzzy rules,long stay containers,duration of stay factor,"ON/OFF" strategy,container yard operations,unknown departure times
Publication ISSN: 2222-7059
Last Modified: 27 Dec 2023 09:44
Date Deposited: 30 Jan 2023 09:00
Full Text Link:
Related URLs: https://aiem.co ... -2021-01-09.pdf (Publisher URL)
PURE Output Type: Article
Published Date: 2021-06-10
Accepted Date: 2021-05-15
Authors: Al-Bazi, Ammar (ORCID Profile 0000-0002-5057-4171)
Palade, Vasile
Al-Hadeethi, Rami
Abbas, Ali

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