A heuristic algorithm for optimal fleet composition with vehicle routing considerations

This paper proposes a fast heuristic algorithm for solving a combined optimal fleet composition and multi-period vehicle routing problem. The aim of the problem is to determine an optimal fleet mix, together with the corresponding vehicle routes, to minimize total cost subject to various customer de...

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Bibliographic Details
Published in:Optimization methods & software Vol. 31; no. 2; pp. 272 - 289
Main Authors: Mardaneh, E., Lin, Q., Loxton, R.
Format: Journal Article
Language:English
Published: Abingdon Taylor & Francis 03.03.2016
Taylor & Francis Ltd
Subjects:
Algorithms
Computer programs
cross entropy
Dynamic programming
fleet composition
golden section method
Heuristic
Heuristic methods
Mathematical models
Optimization
Production scheduling
Route selection
Solvers
Transportation
vehicle routing
Vehicles
ISSN:1055-6788, 1029-4937
Online Access:Get full text
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Summary:This paper proposes a fast heuristic algorithm for solving a combined optimal fleet composition and multi-period vehicle routing problem. The aim of the problem is to determine an optimal fleet mix, together with the corresponding vehicle routes, to minimize total cost subject to various customer delivery requirements and vehicle capacity constraints. The total cost includes not only the fixed, variable, and transportation costs associated with operating the fleet, but also the hiring costs incurred whenever vehicle requirements exceed fleet capacity. Although the problem under consideration can be formulated as a mixed-integer linear program (MILP), the MILP formulation for realistic problem instances is too large to solve using standard commercial solvers such as the IBM ILOG CPLEX optimization tool. Our proposed heuristic decomposes the problem into two tractable stages: in the first (outer) stage, the vehicle routes are optimized using cross entropy; in the second (inner) stage, the optimal fleet mix corresponding to a fixed set of routes is determined using dynamic programming and golden section search. Numerical results show that this heuristic approach generates high-quality solutions and significantly outperforms CPLEX in terms of computational speed.
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ISSN:1055-6788
1029-4937
DOI:10.1080/10556788.2015.1062890