Construct, Merge, Solve and Adapt Applied to a Bus Driver Scheduling Problem with Complex Break Constraints

Rosati, Roberto Maria; Kletzander, Lucas; Blum, Christian; Musliu, Nysret; Schaerf, Andrea

doi:10.1007/978-3-031-27181-6_18

Cited by 4 publications

(4 citation statements)

References 22 publications

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“…Table 2 shows the objective function values for LNS, ALNS, CMSA (Rosati et al 2023) (same time budget) and BP (Kletzander, Musliu, and Van Hentenryck 2021). For each size there are 5 instances, each value in the table is the average of the 5 results per size due to space limitations.…”

Section: Resultsmentioning

confidence: 99%

“…For instances up to size 90, BP (Kletzander, Musliu, and Van Hentenryck 2021) is the best algorithm. For larger instances, we used the values from CMSA (Rosati et al 2023).…”

Section: Discussionmentioning

confidence: 99%

“…Regarding exact methods, the BDSP is often modelled as a Set Partitioning Problem and Column Generation is often used (Smith and Wren 1988;Lin and Hsu 2016;Portugal, Lourenc ¸o, and Paixão 2008;Kletzander, Musliu, and Van Hentenryck 2021). However, due to the need to solve very large real-world problems in a reasonable time, several heuristic methods have been studied for BDSP: some examples are Greedy (Martello and Toth 1986), Tabu Search (Shen and Kwan 2001;Kletzander, Mazzoli, and Musliu 2022), Simulated Annealing (Kletzander and Musliu 2020), GRASP (De Leone, Festa, and Marchitto 2011), CMSA (Rosati et al 2023), and Genetic Algorithm (Li and Kwan 2003;Lourenc ¸o, Paixão, and Portugal 2001).…”

Section: Introductionmentioning

confidence: 99%

“…In particular, the collective agreement has stringent rules requiring the drivers to take frequent breaks, with the eventual option of splitting them into multiple parts. This problem has been introduced recently in the literature, and to the best of our knowledge, the recently introduced exact approach based on Branch and Price (Kletzander, Musliu, and Van Hentenryck 2021), and meta-heuristic and hyperheuristic based approaches (Kletzander and Musliu 2020;Kletzander, Mazzoli, and Musliu 2022;Rosati et al 2023;Kletzander and Musliu 2023) represent the current state of the art for this problem. Although these approaches give very good results, exact methods are computationally too expensive for large real-life-based instances and heuristic methods cannot obtain optimal solutions.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Investigating Large Neighbourhood Search for Bus Driver Scheduling

Mazzoli,

Kletzander,

Hentenryck

et al. 2024

ICAPS

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The Bus Driver Scheduling Problem (BDSP) is a combinatorial optimisation problem with high practical relevance. The aim is to assign bus drivers to predetermined routes while minimising a specified objective function that considers operating costs as well as employee satisfaction. Since we must satisfy several rules from a collective agreement and European regulations, the BDSP is highly constrained. Hence, using exact methods to solve large real-life-based instances is computationally too expensive, while heuristic methods still have a considerable gap to the optimum. Our paper presents a Large Neighbourhood Search (LNS) approach to solve the BDSP. We propose several novel destroy operators and an approach using column generation to repair the sub-problem. We analyse the impact of the destroy and repair operators and investigate various possibilities to select them, including adaptivity. The proposed approach improves all the upper bounds for larger instances that exact methods cannot solve, as well as for some mid-sized instances, and outperforms existing heuristic approaches for this problem on all benchmark instances.

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Section: Resultsmentioning

confidence: 99%

“…For instances up to size 90, BP (Kletzander, Musliu, and Van Hentenryck 2021) is the best algorithm. For larger instances, we used the values from CMSA (Rosati et al 2023).…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Investigating Large Neighbourhood Search for Bus Driver Scheduling

Mazzoli,

Kletzander,

Hentenryck

et al. 2024

ICAPS

View full text Add to dashboard Cite

show abstract

Introduction to CMSA

Blum

2024

Computational Intelligence Methods and Applications

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How to improve “construct, merge, solve and adapt"? Use reinforcement learning!

Reixach,

Blum

2024

Ann Oper Res

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In this work, we propose a new variant of construct, merge, solve, and adapt (CMSA), which is a recently introduced hybrid metaheuristic for combinatorial optimization. Our newly proposed variant, named reinforcement learning CMSA (RL-CMSA), makes use of a reinforcement learning (RL) mechanism trained online with data gathered during the search process. In addition to generally outperforming standard CMSA, this new variant proves to be more flexible as it does not require a greedy function for the evaluation of solution components at each solution construction step. We present RL-CMSA as a general framework for enhancing CMSA by leveraging a simple RL learning process. Moreover, we study a range of specific designs for the employed learning mechanism. The advantages of the introduced CMSA variant are demonstrated in the context of the far from most string and minimum dominating set problems, showing the improvement in performance and simplicity with respect to standard CMSA. In particular, the best performing RL-CMSA variant proposed is statistically significantly better than the standard algorithm for both problems, obtaining 1.28% and 0.69% better results on average respectively.

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Construct, Merge, Solve and Adapt Applied to a Bus Driver Scheduling Problem with Complex Break Constraints

Cited by 4 publications

References 22 publications

Investigating Large Neighbourhood Search for Bus Driver Scheduling

Investigating Large Neighbourhood Search for Bus Driver Scheduling

Introduction to CMSA

How to improve “construct, merge, solve and adapt"? Use reinforcement learning!

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