Application of Multiple-Population Genetic Algorithm in Optimizing the Train-Set Circulation Plan Problem

Abstract

The train-set circulation plan problem (TCPP) belongs to the rolling stock scheduling (RSS) problem and is similar to the aircraft routing problem (ARP) in airline operations and the vehicle routing problem (VRP) in the logistics field. However, TCPP involves additional complexity due to the maintenance constraint of train-sets: train-sets must conduct maintenance tasks after running for a certain time and distance. The TCPP is nondeterministic polynomial hard (NP-hard). There is no available algorithm that can obtain the optimal global solution, and many factors such as the utilization mode and the maintenance mode impact the solution of the TCPP. This paper proposes a train-set circulation optimization model to minimize the total connection time and maintenance costs and describes the design of an efficient multiple-population genetic algorithm (MPGA) to solve this model. A realistic highspeed railway (HSR) case is selected to verify our model and algorithm, and, then, a comparison of different algorithms is carried out. Furthermore, a new maintenance mode is proposed, and related implementation requirements are discussed.

Optimization of Heterogeneous Container Loading Problem with Adaptive Genetic Algorithm

“…Generate a group of initial population encoding (3) for = 1 to do (4) decode with heuristic rules (5) calculate the fitness value of (6) search the individual max with the highest fitness value, whose fitness is max (7) do the selection operator to , the result is (8) do partial mapped crossover operator to the former part coding of (9) do two point crossover operator to the latter part coding of (10) store the result as (11) do sequence reversed mutation operator to the former part of (12) do basic bit mutation operator to the latter part of (13) store the mutation result as (14) decode with heuristic rules (15) calculate the fitness value of (16) search the individual max with the highest fitness value in , whose fitness is max (17) search the individual min with the lowest fitness value in , whose fitness value is min (18) if max < max then 0 3 3 2 3 7 0 5 4 5 1 6 0 1 5 6 1 0 7 6 6 1 2 9 0 1 7 3 3 3 1 1 8 8 2 3 0 3 cannot be loaded into the container; "Rotation" indicates the direction of the corresponding cargoes placed and the value of them refers to the definition of Table 1. Rotation 1 5 2 5 0 2 2 6 2 3 0 5 3 1 3 4 0 2 4 8 0 1 7 6 5 2 3 7 0 3 6 4 2 The packing simulation results of LN02 and LN06 are shown in Figures 12 and 13.…”

“…Although it cannot guarantee the optimal solution, it can usually obtain a satisfactory feasible solution. The improved heuristic is a hybrid algorithm which combines basic heuristic with neighborhood search algorithm, such as genetic algorithm, tabu search algorithm, and greedy algorithm [12][13][14].…”

Cost-Effective, Time-Efficient Passenger Rail System for the Eastern United States

“…Zhou et al 2017 [6] used genetic algorithms to optimize the train-set circulation problem. The model optimized the number of required train-sets and their maintenance times in a high-speed rail system.…”

Robust Train Scheduling Problem with Optimized Maintenance Planning on High-Speed Railway Corridors: The China Case

“…In addition, they also explored the effects of different maintenance window widths on maintenance costs and freight traffic costs. Zhou et al [23] proposed a trainset circulation optimization model to minimize the total connection time and maintenance costs, and this model was solved by an efficient multiple-population genetic algorithm (MPGA). A realistic high-speed railway case was given to show the effectiveness of the proposed model and algorithm.…”