arXiv subjects

Bart van Rossum

Publications and source records attributed to Bart van Rossum.

7 recordsLinked to original sources

arXiv2026

Branch and Price for Railway Crew Scheduling: Benchmark Instances and Computational Study

Railway crew scheduling consists of assigning a set of tasks to crew members in the form of feasible duties so as to minimise costs, and is one of the key planning problems faced by railway operators. While column generation is the dominant solution method, literature on exact branch-and-price approaches is scarce. Moreover, few publicly available benchmark instances exist. We present a state-of-the-art branch-and-price algorithm and conduct a systematic computational study of its components, including branching rules, primal heuristics, and reduced cost fixing techniques. To this end, we introduce a novel library of 100 benchmark instances, ranging in size from 90 to 3,016 tasks. The instances are generated by mimicking the railway planning process, validated against data from Netherlands Railways, and publicly available with best known solutions and a solution checker. We report average optimality gaps below 1% across all instance sizes, driven primarily by the strong performance of primal heuristics, whereas branching and reduced cost fixing contribute little to closing the gap. Branch and price also outperforms a multi-commodity flow formulation, which fails to find a feasible solution on many of the smallest instances. Our findings provide support for the popularity of column generation-based primal heuristics, and show that improving the dual bound remains the main challenge for exact methods in railway crew scheduling.

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arXiv2026

Line Planning at Scale: Models, Methods, and Insights

Line planning, the problem of deciding which lines to operate and at what frequency, is a fundamental step in public transport planning. To accurately model passenger routing, the problem is traditionally defined on a change-and-go network (CGN), which captures transfers between lines exactly. However, this network grows large quickly and is hard to solve at scale. We compare the CGN against three more compact models, differing with respect to how transfers are approximated, and characterize how they relate in terms of solution quality and modeling accuracy. We develop state-of-the-art solution methods tailored to each model, and evaluate all four across 972 instances based on the Dutch and Swiss railway networks. Contrary to the CGN's canonical status, we find that it is competitive only on small or easy instances and often fails to find any feasible solution on large networks. Instead, a compact direct connection model performs best overall, finding the best solution on over 83% of instances. Our results indicate that carefully designed approximations, rather than exact transfer modeling, are the more promising foundation for large-scale line planning.

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arXiv2026

Adaptive Partitioning in Column Generation for Nested Paths

We study a class of nested path problems, in which every path-based variable can be decomposed into a sequence of subpaths. Subpaths must satisfy local resources, while paths must satisfy additional global resources. This paper develops a new exact pricing algorithm in column generation for these problems that avoids the enumeration of non-dominated subpaths. The algorithm relies on adaptive partitioning of subpaths into buckets characterizing the consumption of global path resources. The algorithm represents each bucket by its subpath of minimum reduced cost, and iterates between pessimistic and optimistic pricing steps to combine subpaths into paths while maintaining upper and lower bounds on the minimum reduced cost. An adaptive refinement procedure closes the gap in a finite number of iterations. We demonstrate the effectiveness of the algorithm on two applications. For the balanced multi-period capacitated vehicle routing problem, we obtain speed-ups of up to a factor of 13 over a state-of-the-art column generation benchmark, and the resulting branch-price-and-cut algorithm solves three times as many instances to optimality as a subpath-based baseline. For the robust railway crew scheduling problem, we obtain speed-ups of up to a factor of three and produce primal solutions within 1% of optimality.

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arXiv2026

Enforcing TSP-Optimality in Fair Vehicle Routing by Cutting Planes

We study the fair capacitated vehicle routing problem, in which a fleet of vehicles must serve a set of customers such that the difference between the longest and shortest route, the range, is minimized. A key challenge is that the range objective is non-monotonic: it can be reduced by artificially lengthening routes, leading to solutions that violate TSP-optimality of individual routes. Existing exact methods struggle to handle this efficiently. We propose a branch-price-and-cut framework that enforces TSP-optimality through TSP-optimality cuts, which forbid TSP-dominated arc sequences. We strengthen the cuts through a dedicated lifting procedure. Computational experiments on benchmark instances with up to 25 customers show the method solves nearly all instances to optimality, achieving an average gap of 0.27% on the hardest configurations.

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arXiv2025

Pareto-Optimal Linear Programming

Pareto-optimality plays a central role in evaluating the efficiency of solutions to allocation problems, such as house allocation, school choice, and kidney exchange. We introduce a general linear programming problem subject to Pareto-optimality conditions, which we call Max-Pareto. Using the novel result that Pareto-optimal bipartite matchings are fractionally Pareto-optimal, we prove that Max-Pareto is $\mathcal{NP}$-complete. We propose a bilinear programming formulation of Max-Pareto, and evaluate its computational performance on the problem of finding Pareto-optimal allocations of highest welfare.

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arXiv2025

The Fair Periodic Assignment Problem

We study the periodic assignment problem, in which a set of periodically repeating tasks must be assigned to workers within a repeating schedule. The classical efficiency objective is to minimize the number of workers required to operate the schedule. We propose a O(n log n) algorithm to solve this problem. Next, we formalize a notion of fairness among workers, and impose that each worker performs the same work over time. We analyze the resulting trade-off between efficiency and fairness, showing that the price of fairness is at most one extra worker, and that such a fair solution can always be found using the Nearest Neighbor heuristic. We characterize all instances that admit a solution that is both fair and efficient, and use this result to develop a O(n log n) exact algorithm for the fair periodic assignment problem. Finally, we show that allowing aperiodic schedules never reduces the price of fairness.

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arXiv2025

Efficient Branching Rules for Optimizing Range and Order-Based Objective Functions

We consider range minimization problems featuring exponentially many variables, as frequently arising in fairness-oriented or bi-objective optimization. While branch and price is successful at solving cost-oriented problems with many variables, the performance of classical branch-and-price algorithms for range minimization is drastically impaired by weak linear programming relaxations. We propose range branching, a generic branching rule that directly tackles this issue and can be used on top of problem-specific branching schemes. We show several desirable properties of range branching and show its effectiveness on a series of instances of the fair capacitated vehicle routing problem and fair generalized assignment problem. Range branching significantly improves multiple classical branching schemes in terms of computing time, optimality gap, and size of the branch-and-bound tree, allowing us to solve many more large instances than classical methods. Moreover, we show how range branching can be successfully generalized to order-based objective functions, such as the Gini deviation.

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