Modeling The Scheduling of Loading and Unloading Operation in Railway Terminals in Order to Reduce Operating-time with An Exact Solution Approach and Metaheuristic Algorithms

Document Type : Scientific - Research

Authors
1 Assistant proffessor, Department of transportation planning, school of civil engineering, iran university of science and technology
2 Master student of the Faculty of Civil Engineering, University of Science and Technology
3 Department of Civil Engineering, Iran university of Science and Technology
Abstract
With the development of industry and the increase in the movement of goods in the world, the role of rail transportation has become more colorful than before. The main weakness of rail transportation compared to road transportation is its network delays. One of the main causes of delays in the rail network is the delay in unloading and loading. In this research, first, the factors affecting the time of train unloading and loading operations and its appropriate solutions have been investigated. Then, according to the results of the studies, the focus is on reducing the time of unloading and loading operations in the container rail terminals. To reduce operation time, a mathematical model based on mixed integer programming is proposed. In addition to the exact solution of the proposed model, the model has been solved with meta-innovative algorithms of particle swarm and colonial competition in various dimensions. Finally, by comparing the performance of the particle swarm algorithm with the colonial competition algorithm, the result was that the particle swarm algorithm performs better than the Imperialist Competitive Algorithm for larger problems. Using the model provided in container rail terminals, it is possible to schedule the sequence of tasks for cranes, trucks and other equipment in a short period of time and provide them to the terminal operators on a daily basis.

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Articles in Press, Accepted Manuscript
Available Online from 25 September 2024

  • Receive Date 14 April 2022
  • Revise Date 17 October 2022
  • Accept Date 13 November 2022