Multi-Objective Mixed-Integer Linear Programming for Dynamic Fleet Scheduling, Multi-Modal Transport Optimization, and Risk-Aware Logistics

Transportation planning is a complex process that aims to achieve the maximum level of effectiveness in terms of costs, usage of transport resources, reliability of deliveries, and minimizing the negative impact on the environment. Most traditional models focus on cost minimization at the expense of...

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Bibliographic Details
Published in:Sustainability Vol. 17; no. 10; p. 4707
Main Authors: Mohamed Alshabibi, Nawaf, Matar, Al-Hussein, H. Abdelati, Mohamed
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.05.2025
Subjects:
Case studies
Compliance
Cost control
Cost reduction
Decision making
Deep learning
Efficiency
Emission standards
Emissions control
Energy consumption
Linear programming
Literature reviews
Logistics
Logistics services
Maritime industry
Multiple criteria decision making
Planning
Route optimization
Scheduling
Sustainability
Technology application
Traffic congestion
Transportation schedules
Variables
Vehicles
Egypt
ISSN:2071-1050, 2071-1050
Online Access:Get full text
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Summary:Transportation planning is a complex process that aims to achieve the maximum level of effectiveness in terms of costs, usage of transport resources, reliability of deliveries, and minimizing the negative impact on the environment. Most traditional models focus on cost minimization at the expense of risk, road dynamics, and emissions constraints. In contrast, the current paper presents a mixed-integer linear programming (MILP) model for scheduling fleets, selecting transportation modes in multiple modes of transportation, and meeting emissions regulation requirements according to dynamic transportation requirements. Risk-aware routing and taking the factor of congestion and CO2 emission limits proposed by the government into consideration, this model can offer a more efficient and flexible optimization strategy. From the case study, we observe the significant result that the proposed model achieves, a 23% reduction in transport costs, a 25% improvement in fleet use, a 33.3% decrease in the delivery delay, and a 24.6% decrease in CO2 emissions. The model dynamically delivers shipments utilizing both road and rail transportation and improves mode choice by minimizing idle vehicle time. This is confirmed through sensitivity analysis which addresses factors such as traffic congestion, changing fuel prices, and changing environmental standards.
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ISSN:2071-1050
2071-1050
DOI:10.3390/su17104707