A Mixed Integer Linear Program for Optimizing the Utilization of Locomotives with Maintenance Constraints

Frisch, Sarah; Hungerländer, Philipp; Jellen, Anna; Weinberger, Dominic

doi:10.1007/978-3-030-18500-8_14

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…There, a specialised branch-and-price-and-cut approach is used to handle such problems extending previous work [5]. In [3] as well as in [2,[6][7][8][9][10][11] Mixed Integer Programming (MIP) is applied to model and solve rolling stock and related problems -like locomotive scheduling -which seems to be the usual solution approaches for such problems. In [12] constraint propagation together with depth-first search based on backtracking was used to perform reactive scheduling for rolling stock operations.…”

Section: Related Workmentioning

confidence: 99%

Optimising Rolling Stock Planning including Maintenance with Constraint Programming and Quantum Annealing

Grozea,

Hans,

Koch

et al. 2021

Preprint

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We developed and compared Constraint Programming (CP) and Quantum Annealing (QA) approaches for rolling stock optimisation considering necessary maintenance tasks. To deal with such problems in CP we investigated specialised pruning rules and implemented them in a global constraint. For the QA approach, we developed quadratic unconstrained binary optimisation (QUBO) models. For testing, we use data sets based on real data from Deutsche Bahn and run the QA approach on real quantum computers from D-Wave. Classical computers are used to run the CP approach as well as tabu search for the QUBO models. We find that both approaches tend at the current development stage of the physical quantum annealers to produce comparable results, with the caveat that QUBO does not always guarantee that the maintenance constraints hold, which we fix by adjusting the QUBO model in preprocessing, based on how close the trains are to a maintenance threshold distance.

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Section: Related Workmentioning

confidence: 99%

Optimising Rolling Stock Planning including Maintenance with Constraint Programming and Quantum Annealing

Grozea,

Hans,

Koch

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…To remain competitive in the market, it is essential that companies develop optimization tools that improve their decision-making process [5,17,10]. In this context, managers can resort to different types of optimization problems, and one of the most common in the railroad sector is the Locomotive Scheduling Problem (LSP), also known as the Locomotive Assignment Problem (LAP), which is widely studied in the literature [15,8,1]. The LSP consists of assigning locomotives to a set of scheduled trains, based on the type of routes and the number of locomotives necessary to pull in the train.…”

Section: Introductionmentioning

confidence: 99%

“…There are a few studies that address the maintenance requirements integrated with the LSP problem, and these requirements are in general expressed by the maximum number of days between two successive stops for maintenance. For instance, in [8] and [9] the authors address the LSP with maintenance requirements to assign locomotives to the train with the maximization of locomotives utilization, proposing heuristic approaches and mixed-integer programming (MIP) models. Other papers that also consider maintenance requirements in a wide spectrum are [2] and [3].…”

Section: Introductionmentioning

confidence: 99%

Locomotive maintenance scheduling: optimization approaches and a case study

Munari,

Araujo,

Beker

et al. 2023

Preprint

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The Locomotive Maintenance Scheduling Problem arises in the planning and control operations in railroad companies. In this problem, locomotives are parked in yards adjacent to maintenance shops where various maintenance tasks are performed. These shops have limited and specific service stations for each type of maintenance. The goal is to schedule locomotives to service stations aiming at satisfying the locomotive availability targets as well as reducing the backlog for unavailable locomotives by the end of the planning horizon. Despite its practical relevance, this problem has received limited attention in the literature, particularly regarding the development of decision-making tools. To reduce this research gap, we propose mathematical models and a heuristic approach based on the real-world case of MRS Logistics, a Brazilian railroad company. Computational experiments using data provided by the company indicate the effectiveness of the proposed models and heuristic, showing potential improvements for the company's operations

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A matheuristic for tactical locomotive and driver scheduling for the Swiss national railway company SBB Cargo AG

2023

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At the scale of Switzerland, the national railway company SBB Cargo AG has to schedule its locomotives and drivers in order to be able to pull all trains. Two objective functions are considered in a two-stage lexicographic fashion: (1) the locomotive and driver costs and (2) the driver time that is spent without driving. As the problem instances tend to reach really big sizes (up to 1900 trains), we propose to schedule locomotives and drivers in a sequential way, thus having a sequence of smaller problems to solve. Moreover, for smaller instances, we also propose to schedule jointly locomotives and drivers in an integrated way, therefore increasing the search space but possibly leading to better solutions. In this paper, we present a mathematical formulation and model for the problem. We also consider the contract-related constraints of the drivers, and we propose a way to integrate some time flexibility in the schedules. Next, we propose an innovative matheuristic to solve the problem, relying on a descent local search and a rolling horizon decomposition. An important goal of this method is to explore thoroughly at which extent a general-purpose solver can be used on this problem. Finally, the benefits of each aspect of the model and of the method are analyzed in detail on the results obtained for 20 real SBB Cargo AG instances.

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A Mixed Integer Linear Program for Optimizing the Utilization of Locomotives with Maintenance Constraints

Cited by 5 publications

References 3 publications

Optimising Rolling Stock Planning including Maintenance with Constraint Programming and Quantum Annealing

Optimising Rolling Stock Planning including Maintenance with Constraint Programming and Quantum Annealing

Locomotive maintenance scheduling: optimization approaches and a case study

A matheuristic for tactical locomotive and driver scheduling for the Swiss national railway company SBB Cargo AG

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