Fuzzy Multi-Objective, Multi-Period Integrated Routing–Scheduling Problem to Distribute Relief to Disaster Areas: A Hybrid Ant Colony Optimization Approach

This paper explores a multi-objective, multi-period integrated routing and scheduling problem under uncertain conditions for distributing relief to disaster areas. The goals are to minimize costs and maximize satisfaction levels. To achieve this, the proposed mathematical model aims to speed up the...

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Bibliographic Details
Published in:Mathematics (Basel) Vol. 12; no. 18; p. 2844
Main Authors: Niksirat, Malihe, Saffarian, Mohsen, Tayyebi, Javad, Deaconu, Adrian Marius, Spridon, Delia Elena
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.09.2024
Subjects:
Algorithms
Ant colony optimization
Delivery scheduling
Disaster relief
Disasters
Distribution costs
Fuzzy algorithms
Fuzzy logic
fuzzy multi-objective integer programming problem
Fuzzy systems
Genetic algorithms
Integer programming
Iran
Linear programming
Logistics
Mathematical analysis
Mathematical models
Mathematical optimization
Mathematical research
Methods
multi-objective ant colony system
multi-period integrated vehicle routing and scheduling
Multiple objective analysis
Optimization algorithms
Optimization techniques
Performance evaluation
Planning
Resource allocation
Scheduling
Scheduling (Management)
Simulated annealing
simulated annealing algorithm
Unmanned aerial vehicles
Vehicles
Iran
ISSN:2227-7390, 2227-7390
Online Access:Get full text
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Summary:This paper explores a multi-objective, multi-period integrated routing and scheduling problem under uncertain conditions for distributing relief to disaster areas. The goals are to minimize costs and maximize satisfaction levels. To achieve this, the proposed mathematical model aims to speed up the delivery of relief supplies to the most affected areas. Additionally, the demands and transportation times are represented using fuzzy numbers to more accurately reflect real-world conditions. The problem was formulated using a fuzzy multi-objective integer programming model. To solve it, a hybrid algorithm combining a multi-objective ant colony system and simulated annealing algorithm was proposed. This algorithm adopts two ant colonies to obtain a set of nondominated solutions (the Pareto set). Numerical analyses have been conducted to determine the optimal parameter values for the proposed algorithm and to evaluate the performance of both the model and the algorithm. Furthermore, the algorithm’s performance was compared with that of the multi-objective cat swarm optimization algorithm and multi-objective fitness-dependent optimizer algorithm. The numerical results demonstrate the computational efficiency of the proposed method.
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ISSN:2227-7390
2227-7390
DOI:10.3390/math12182844