A two-stage approach to the depot shunting driver assignment problem with workload balance considerations

Abstract

Due to its environmentally sustainable and energy-saving characteristics, railway transportation nowadays plays a fundamental role in delivering passengers and goods. Emerged in the area of transportation planning, the crew (workforce) sizing problem and the crew scheduling problem have been attached great importance by the railway industry and the scientific community. In this paper, we aim to solve the two problems by proposing a novel two-stage optimization approach in the context of the electric multiple units (EMU) depot shunting driver assignment problem. Given a predefined depot shunting schedule, the first stage of the approach focuses on determining an optimal size of shunting drivers. While the second stage is formulated as a bi-objective optimization model, in which we comprehensively consider the objectives of minimizing the total walking distance and maximizing the workload balance. Then we combine the normalized normal constraint method with a modified Pareto filter algorithm to obtain Pareto solutions for the bi-objective optimization problem. Furthermore, we conduct a series of numerical experiments to demonstrate the proposed approach. Based on the computational results, the regression analysis yield a driver size predictor and the sensitivity analysis give some interesting insights that are useful for decision makers.

Reschedule the Shunting and Service Scheduling Plan After Disturbances at a Chinese High-Speed Railway Depot

“…In the SSS planning, during a planning horizon (4 h, 8 h or 12h, usually at night), a number of EMUs are scheduled to undergo different services with unfixed sequence and it should be decided how these EMUs are moved around the yards (and to and from the depot) to determine a feasible path for each EMU and in order to minimize the total operating cost. The assumptions of our study are as follows: (a) There are enough shunting drivers and service workers available in the depot, that is, the task assignment of drivers and service workers is not considered in this paper and can be solved after getting the TUSP or SSS plan [17,18]. This assumption is also proposed in most papers when solving TUSP and SSS; (b) Once a service has started, it cannot be interrupted.…”

Optimizing a Biobjective Production‐Distribution Planning Problem Using a GRASP

“…Workload balancing has been considered in decision planning problems related to vehicle routing problems [57][58][59][60] and machine scheduling problems [61]. Balancing the workload content of the supply chain is an important element when considering a vertical integrated company.…”

Bi-objective inventory allocation planning problem with supplier selection and carbon trading under uncertainty

“…Previous research provides a variety of methods to generate the Pareto frontier [23], such as weighting method, ϵ -constraint method, and normalized normal constraint method. Normalized normal constraint (NNC) method, proposed by Messac et al [24], obtains an evenly spaced Pareto solution by constructing a series of single-objective optimization problems after normalized processing of the feasible region of multi-objective optimization [25]. Among numerous heuristic and metaheuristic algorithms, the differential evolution (DE) algorithm has been proven to be a competitive contender for NP-hard optimization, such as logistics management [26] and production scheduling [27], as well as in the field of multi-level and multi-period optimization [28, 29].…”