Solving large scale crew scheduling problems

1997 ◽  
Vol 97 (2) ◽  
pp. 260-268 ◽  
Author(s):  
Hai D. Chu ◽  
Eric Gelman ◽  
Ellis L. Johnson
Keyword(s):  
Large Scale ◽  
Crew Scheduling ◽  
Scheduling Problems

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

2022 ◽  
Vol 14 (1) ◽  
pp. 491
Author(s):  
Chunxiao Zhao ◽  
Junhua Chen ◽  
Xingchen Zhang ◽  
Zanyang Cui
Keyword(s):  
Genetic Algorithm ◽  
Development Strategy ◽  
High Speed ◽  
Large Scale ◽  
Mathematical Formulation ◽  
Solution Space ◽  
Working Time ◽  
Crew Scheduling ◽  
Scheduling Problems ◽  
Improved Genetic Algorithm

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.

scholarly journals Solving large scale crew scheduling problems in practice

2011 ◽  
Vol 3 (2) ◽  
pp. 149-164 ◽  
Author(s):  
E. J. W. Abbink ◽  
L. Albino ◽  
T. Dollevoet ◽  
D. Huisman ◽  
J. Roussado ◽  
...  
Keyword(s):  
Large Scale ◽  
Crew Scheduling ◽  
Scheduling Problems

A graph partitioning strategy for solving large-scale crew scheduling problems

OR Spectrum ◽  
2014 ◽  
Vol 37 (1) ◽  
pp. 137-170 ◽  
Author(s):  
Silke Jütte ◽  
Ulrich W. Thonemann
Keyword(s):  
Graph Partitioning ◽  
Large Scale ◽  
Crew Scheduling ◽  
Scheduling Problems ◽  
Partitioning Strategy

scholarly journals Clustering technique for large-scale home care crew scheduling problems

2017 ◽  
Vol 47 (2) ◽  
pp. 443-455 ◽  
Author(s):  
David Quintana ◽  
Alejandro Cervantes ◽  
Yago Saez ◽  
Pedro Isasi
Keyword(s):  
Home Care ◽  
Large Scale ◽  
Crew Scheduling ◽  
Scheduling Problems ◽  
Clustering Technique

scholarly journals Tabu Search-Based Algorithm for Large Scale Crew Scheduling Problems

2005 ◽  
Author(s):  
Marco Caserta
Keyword(s):  
Tabu Search ◽  
Large Scale ◽  
Optimal Solution ◽  
Crew Scheduling ◽  
Set Covering ◽  
Benchmark Problems ◽  
Scheduling Problems ◽  
Dual Algorithm ◽  
Airline Crew ◽  
Primal Dual

<p>In this paper, the problem of finding a work schedule for airline crew members in a given time horizon is tackled. This problem is known in the literature as airline crew scheduling. The objective is to define the minimum cost schedules where each crew, associated to a combination of commercial flights or "legs" called "pairing", is assigned to one or more flights ensuring that the whole set of flights is covered by crew members. The crew scheduling problem can be modeled by using the set covering formulation. This paper presents a new algorithm whose centerpiece is a primal-to-dual scheme aimed at linking any primal solution to the dual feasible vector that best reflects the quality of the primal solution. This new mechanism is used to intertwine a tabu search based, primal intensive, scheme with a lagrangian based, dual intensive, scheme to design a primal-dual algorithm that progressively reduces the gap between upper and lower bound. The algorithm has been tested on benchmark problems from the literature. In this paper, results on real-world airline instances are presented: out of six well-known problems, the algorithm is able to match the optimal solution for four of them while for the last two, whose optimal solution is not known, a new best known solution is found.</p>

Sign in / Sign up

Export Citation Format

Share Document