Computing strong lower and upper bounds for the integrated multiple-depot vehicle and crew scheduling problem with branch-and-price

Horváth, Markó; Kis, Tamás

doi:10.1007/s10100-017-0489-4

Cited by 23 publications

(8 citation statements)

References 30 publications

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“…Valid inequality & CPLEX [46] MCF & SC - [47,48] MCF & SC Decomposition approach [6] MCF & SP Heuristic [2] TSF & SP Column generation & Lagrangian relaxation [49] (Considering delays), [50] TSF & path-based formulations Lagrangian heuristic [51] TSF & SP Branch and price [52] TSF Matheuristic [53] CM Simulated annealing [54] CM Greedy randomized adaptive search [55] CM Variable neighborhood search [3] (considering the crew's reliability) CM Variable neighborhood search [56] based on MCF. Section 4 presents the EVCSP model and provides some statements.…”

Section: Mcf and Spmentioning

confidence: 99%

Minimum Cost Flow-Based Integrated Model for Electric Vehicle and Crew Scheduling

Shen,

2023

Journal of Advanced Transportation

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Vehicle and crew scheduling is vital in public transit planning. Conventionally, the issues are handled sequentially as the vehicle scheduling problem (VSP) and crew scheduling problem (CSP). However, integrating these planning steps offers additional flexibility, resulting in improved efficiency compared with sequential planning. Given the ever-growing market share of electric buses, this paper introduces a new model for integrated electric VSP and CSP, called EVCSPM. This model employs the minimum cost flow formulations for electric VSP, set partitioning for CSP, and linking constraints. Due to the nonlinear integer property of EVCSPM, we propose a method that hybrids a matching-based heuristic and integer linear programming solver, GUROBI. The numerical results demonstrate the efficiency of our methodology, and the integrated model outperforms the sequential model in real-life scenarios.

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Section: Mcf and Spmentioning

confidence: 99%

Minimum Cost Flow-Based Integrated Model for Electric Vehicle and Crew Scheduling

Shen,

2023

Journal of Advanced Transportation

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“…In their model, an integrated mathematical formulation was given in such a way that both sub-tasks were defined as set partition problems. The vehicle scheduling part is based on the model given by Ribeiro and Soumis [27], while the driver scheduling part uses the ideas of Desrochers and Soumis [28]. In the multi-depot case, first Gaffi et al [29] discussed the integrated problem, using a heuristic method.…”

Section: Literature Reviewmentioning

confidence: 99%

Combined Vehicle and Driver Scheduling with Fuel Consumption and Parking Constraints: a Case Study

Békési¹,

Nagy²

2020

ACTA POLYTECH HUNG

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Efficient operation is an important question for public transport companies, and that can most easily be achieved by reducing their operational costs. This can also be facilitated by the optimized scheduling of vehicles and the work of the drivers. Such an optimization task can be very complex. Due to the dramatically increased processing capabilities today, it can be performed using advanced optimization methods. Automation aims to reduce the time-consuming manual activities, thus increase efficiency and provide prompt opportunities of scenario planning for operational cost analysis purposes. In this paper, a case study is presented to solve the combined vehicle and driver scheduling problem. The applied mathematical model is discussed and the calculation results for practical examples are presented

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“…For example, in the CPS in [68], attendance rates were studied. A CSP integration with the vehicle scheduling problem was developed in [69]. In [70], an application of the CSP with fairness preferences was explored.…”

Section: Real-world Applicationmentioning

confidence: 99%

A Db-Scan Binarization Algorithm Applied to Matrix Covering Problems

García

Moraga

Valenzuela

et al. 2019

Computational Intelligence and Neuroscience

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The integration of machine learning techniques and metaheuristic algorithms is an area of interest due to the great potential for applications. In particular, using these hybrid techniques to solve combinatorial optimization problems (COPs) to improve the quality of the solutions and convergence times is of great interest in operations research. In this article, the db-scan unsupervised learning technique is explored with the goal of using it in the binarization process of continuous swarm intelligence metaheuristic algorithms. The contribution of the db-scan operator to the binarization process is analyzed systematically through the design of random operators. Additionally, the behavior of this algorithm is studied and compared with other binarization methods based on clusters and transfer functions (TFs). To verify the results, the well-known set covering problem is addressed, and a real-world problem is solved. The results show that the integration of the db-scan technique produces consistently better results in terms of computation time and quality of the solutions when compared with TFs and random operators. Furthermore, when it is compared with other clustering techniques, we see that it achieves significantly improved convergence times.

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Computing strong lower and upper bounds for the integrated multiple-depot vehicle and crew scheduling problem with branch-and-price

Cited by 23 publications

References 30 publications

Minimum Cost Flow-Based Integrated Model for Electric Vehicle and Crew Scheduling

Minimum Cost Flow-Based Integrated Model for Electric Vehicle and Crew Scheduling

Combined Vehicle and Driver Scheduling with Fuel Consumption and Parking Constraints: a Case Study

A Db-Scan Binarization Algorithm Applied to Matrix Covering Problems

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