A simulated annealing approach for the multi-periodic rail-car fleet sizing problem

Authors:
Hamid Reza Sayarshad;Keivan Ghoseiri
Affiliations:
Department of Industrial Engineering, Mazandaran University of Science and Technology, Tehran 16388-95114, Iran;School of Railway Engineering, Iran University of Science and Technology, Iran
Venue:
Computers and Operations Research
Year:
2009

Citing 7
Cited 5

Fleet sizing and empty equipment redistribution for center-terminal transportation networks

Management Science
Static and dynamic time-space strategic models and algorithms for multilevel rail-car fleet management

Management Science
Determining Rail Fleet Sizes for Shipping Automobiles

Interfaces
Network Design for Express Shipment Delivery

Computational Optimization and Applications
Dynamic Control of Logistics Queueing Networks for Large-Scale Fleet Management

Transportation Science
A Multiplier Adjustment Method for Dynamic Resource Allocation Problems

Transportation Science
A network algorithm for empty freight car allocation

IBM Systems Journal

On determining optimal fleet size and vehicle transfer policy for a car rental company

Computers and Operations Research
Optimization of wire electrical discharge machining for pure tungsten using a neural network integrated simulated annealing approach

Expert Systems with Applications: An International Journal
Using simulated annealing for determination of the capacity of yard stations in a railway industry

Applied Soft Computing
Optimization of mechanical characteristics of short glass fiber and polytetrafluoroethylene reinforced polycarbonate composites using the neural network approach

Expert Systems with Applications: An International Journal
Quantifying the impact of inland transport times on container fleet sizing in liner shipping services with uncertainties

OR Spectrum

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper, we proposed a new formulation and a solution procedure for optimizing the fleet size and freight car allocation wherein car demands and travel times are assumed to be deterministic and unmet demands are backordered. We assume that unmet demands become zero at the end of the planning horizon, i.e., the car demands are totally satisfied through the horizon. There are important interactions between decisions on sizing a rail car fleet and utilizing that fleet. Consequently, the optimum use of rail-cars for demands response in the length of the time periods is one of the main advantages of the proposed model. The model also provides rail network information such as yard capacity, unmet demands, and number of loaded and empty rail-car at any given time and location. Computational tests showed that small-size instances can be solved by the exact approach in a fair amount of CPU time, but it is not feasible for medium and large-size instances. To tackle this problem, a Simulated Annealing (SA) algorithm is proposed to solve the model. The algorithm works efficiently on a neighborhood search within solution space, acceptance probability, and inferior solutions to escape from trap (i.e., local optimal solution). Numerical examples are solved to check for the efficiency and validity of the SA algorithm.