Solution of Multi-Crew Depots Railway Crew Scheduling Problems: The Chinese High-Speed Railway Case

This paper presents a novel mathematical formulation in crew scheduling, considering real challenges most railway companies face such as roundtrip policy for crew members joining from different crew depots and stricter working time standards under a sustainable development strategy. In China, the crew scheduling is manually compiled by railway companies respectively, and the plan quality varies from person to person. An improved genetic algorithm is proposed to solve this large-scale combinatorial optimization problem. It repairs the infeasible gene fragments to optimize the search scope of the solution space and enhance the efficiency of GA. To investigate the algorithm’s efficiency, a real case study was employed. Results show that the proposed model and algorithm lead to considerable improvement compared to the original planning: (i) Compared with the classical metaheuristic algorithms (GA, PSO, TS), the improved genetic algorithm can reduce the objective value by 4.47%; and (ii) the optimized crew scheduling plan reduces three crew units and increases the average utilization of crew unit working time by 6.20% compared with the original plan.

A Crew Scheduling Model to Incrementally Optimize Workforce Assignments for Offshore Wind Farm Constructions

In the literature, different authors attribute between 15% to 30% of a wind farm’s costs to logistics during the installation, e.g., for vessels or personnel. Currently, there exist only a few approaches for crew scheduling in the offshore area. However, current approaches only satisfy subsets of the offshore construction area’s specific terms and conditions. This article first presents a literature review to identify different constraints imposed on crew scheduling for offshore installations. Afterward, it presents a new Mixed-Integer Linear Model that satisfies these crew scheduling constraints and couples it with a scheduling approach using a Model Predictive Control scheme to include weather dynamics. The evaluation of this model shows reliable scheduling of persons/teams given weather-dependent operations. Compared to a conventionally assumed full staffing of vessels and the port, the model decreases the required crews by approximately 50%. Moreover, the proposed model shows good runtime behavior, obtaining optimal solutions for realistic scenarios in under an hour.

Robust Tactical Crew Scheduling Under Uncertain Demand

We analyze a tactical freight railway crew scheduling problem, when train drivers must be informed several weeks before operations about the start and end times and locations of their duties. Between informing the train drivers and start of operations, trip demand changes due to cancellations, new bookings, and reroutings of trains, which might result in mismatches between train driver capacity at a location and demand. We analyze an approach that incorporates uncertain trip demand as scenarios, such that the start and end times and locations of the duties of a crew schedule are recoverable robust against deviations in trip demand. We develop a column generation solution method that dynamically aggregates trips to duties and decomposes the subproblems into smaller, computationally tractable instances. Our model determines duty frames that cover duties in many scenarios, creating recoverable robust crew schedules. We test our model on three real data sets of a major European freight railway operator. Our results show that our schedules are considerably more recoverable robust than those of the nominal solution, resulting in smaller mismatches between train driver capacity and demand.

CONTROL MODEL FOR GROUND CREW SCHEDULING PROBLEM AT SMALL AIRPORTS: CASE OF SERBIA

Present-day airline industry is quite a competitive field and crew scheduling represents one of the crucial problems due to significant impact on the airline’s cost. The crew scheduling problem is based on the assignment of crew members to operate different tasks of route. The main goal of this paper is to provide an analysis and a solution to one of the biggest problems detected on a small airport in the Serbia - the problem of ground crew scheduling. The paper presents the main characteristics, goals and limitations of a real-life problem identified at this small airport. In order to solve the problem, we developed a dynamic discrete simulation model. The model is developed in a spreadsheet environment of Microsoft Excel. Some of the main limitations found in the development of the model are strong constraints and multiple goals. The model presented in the paper is designed as a useful management tool for smaller airports and is aimed at the improvement of operative processes.

A flexible mathematical model for crew pairing optimization to generate n-day pairings considering the risk of COVID-19: a real case study

PurposeOne of the main advantages of the proposed model is that it is flexible to generate n-day pairings simultaneously. It means that, despite previous researches, one-day to n-day pairings can be generated in a single model. The flexibility in generating parings causes that the proposed model leads to better solutions compared to existing models. Another advantage of the model is minimizing the risk of COVID-19 by limitation of daily flights as well as elapsed time minimization. As airports are among high risk places in COVID-19 pandemic, minimization of infection risk is considered in this model for the first time. Genetic algorithm is used as the solution approach, and its efficiency is compared to GAMS in small and medium-size problems.Design/methodology/approachOne of the most complex issues in airlines is crew scheduling problem which is divided into two subproblems: crew pairing problem (CPP) and crew rostering problem (CRP). Generating crew pairings is a tremendous and exhausting task as millions of pairings may be generated for an airline. Moreover, crew cost has the largest share in total cost of airlines after fuel cost. As a result, crew scheduling with the aim of cost minimization is one of the most important issues in airlines. In this paper, a new bi-objective mixed integer programming model is proposed to generate pairings in such a way that deadhead cost, crew cost and the risk of COVID-19 are minimized.FindingsThe proposed model is applied for domestic flights of Iran Air airline. The results of the study indicate that genetic algorithm solutions have only 0.414 and 0.380 gap on average to optimum values of the first and the second objective functions, respectively. Due to the flexibility of the proposed model, it improves solutions resulted from existing models with fixed-duty pairings. Crew cost is decreased by 12.82, 24.72, 4.05 and 14.86% compared to one-duty to four-duty models. In detail, crew salary is improved by 12.85, 24.64, 4.07 and 14.91% and deadhead cost is decreased by 11.87, 26.98, 3.27, and 13.35% compared to one-duty to four-duty models, respectively.Originality/valueThe authors confirm that it is an original paper, has not been published elsewhere and is not currently under consideration of any other journal.

Applying Constraint Programming and Integer Programming to Solve the Crew Scheduling Problem for Railroad Systems: Model Formulation and a Case Study

Crew scheduling is one of the crucial processes in railroad operation planning. Based on current regulations and working and break time requirements, as well as the operational rules, this process aims to find a duty arrangement with minimal cost that covers all trips. Most past studies considered this subject for railroad systems as an optimization problem and solved it with mathematical programming-based methods or heuristic algorithms, despite numerous logical constraints embedded in this problem. Few studies have applied constraint programming (CP) approaches to tackle the railroad crew scheduling problem. This paper proposes a hybrid approach to solve the problem with a CP model for duty generation, and an integer programming model for duty optimization. These models have been applied to the Kaohsiung depot of Taiwan Railways Administration, the largest railroad operator in Taiwan. The encouraging results show that the proposed approach is more efficient than the manual process and can achieve 30% savings of driver cost. Moreover, the approach is robust and provides flexibility to easily accommodate related operational concerns such as minimizing the number of overnight duties. Thus, this hybrid two-phase approach seems to have the potential for applications to the railroad crew scheduling problems outside Taiwan.