A population-based algorithm for the railroad blocking problem

Railroad blocking problem (RBP) is one of the problems that need an important decision in freight railroads. The objective of solving this problem is to minimize the costs of delivering all commodities by deciding which inter-terminal blocks to build and by specifying the assignment of commodities to these blocks, while observing limits on the number and cumulative volume of the blocks assembled at each terminal. RBP is an NP-hard combinatorial optimization problem with billions of decision variables. To solve the real-life RBP, developing a metaheuristic algorithm is necessary. In this paper, for the first time, a new genetic algorithm-based solution method, which is a population-based algorithm, is proposed to solve the RBP. To evaluate the efficiency and the quality of solutions of the proposed algorithm, several simulated test problems are used. The quality and computational time of the generated solutions for the test problems with the proposed genetic algorithm are compared with the solutions of the CPLEX software. The results show high efficiency and effectiveness of the proposed algorithm