arXiv subjects

Han Hoogeveen

Publications and source records attributed to Han Hoogeveen.

5 recordsLinked to original sources

arXiv2026

Simulation Strategies for an Efficient Local Search to solve Stochastic Scheduling Problems

In scheduling problems, deterministic task durations are often assumed. This usually does not capture reality and may lead to schedules that are not robust to (small) changes to these task lengths. The use of stochastic task durations therefore seems preferable. Including these in local search, which is the way to find good solutions for difficult scheduling problems, is not straightforward, though. The objective value becomes stochastic then too, and computing the expected value is often not possible. One way out of this it to approximate this value by using simulation. This is quite easy to implement in a local search algorithm, but it may require many simulations each iteration to get a reliable estimate. Hence such an approach comes with a performance penalty. In this paper, we study techniques to limit the number of simulations. Besides comparing known techniques, we propose our own method for this, which is based on $t$-tests. We evaluate these techniques on the Stochastic Parallel Machine Scheduling Problem and the Stochastic Electric Vehicle Scheduling Problem. In these case studies, we show the effectiveness of using such methods to reduce runtime while retaining solution quality. Our method using $t$-tests turns out to be most effective in both problems.

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arXiv2026

Solution of a bilevel optimistic scheduling problem on parallel machines

We consider the uniform parallel machines scheduling problem in the context of optimistic bilevel optimization, where two speed options are considered. In this scenario, the leader aims to minimize the weighted number of tardy jobs, while the follower seeks to minimize the total completion time on a set of uniform machines. This problem has practical applications in Industry 4.0. We show that this problem is NP-hard in the strong sense by providing a reduction from the Numerical 3-Dimensional Matching problem and we provide a moderately exponential-time dynamic programming algorithm. The problem is solved by means of a concise MIP formulation and a branch-and-bound algorithm that embeds a column generation approach for the lower bound computation. Computational experiments are presented for instances with up to 80 jobs and 4 machines while larger problems are out of reach for the proposed approaches.

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arXiv2025

Robustness Measures for Stochastic Parallel Machine Scheduling and Train Unit Shunting

In many real world scheduling problems, the processing times of tasks are subject to uncertainty. This makes it essential to design schedules that are robust and able to handle potential disruptions. Therefore, we investigate measures that give us information about the robustness of a schedule. Although many measures can be found in literature, there is no consensus on which measures are the best. We identify 14 robustness measures from the literature, as well as introduce 4 new ones. To find out which of these measures are best used for generating robust schedules, we perform an elaborate simulation study to investigate how well these robustness measures correlate with the stability of the objective function under disturbances (quality robustness) and with the stability of the schedule itself (solution robustness). We first consider the Stochastic Parallel Machine Scheduling Problem (SPMSP) with precedence constraints, which is a very general setting that is relevant for many practical situations. We then perform a second simulation study by taking the best performing measures from the first experiment, and using them for the Train Unit Shunting Problem with Service Scheduling (TUSPwSS). After establishing the correlation with quality and solution robustness, we included the measures as objective in a local search algorithm. We make a comparison between the theoretical setting of the SPMSP and the TUSPwSS, and identify a set of robustness measures that can be applied in many different settings. We show that we can achieve up to 90% decreases in delays compared to using no robustness measures. Lastly, we also identify properties that can be used to predict the effectiveness of such a robustness measure.

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arXiv2024

A hybrid optimization framework for the General Continuous Energy-Constrained Scheduling Problem

We present a hybrid optimization framework for a class of problems, formalized as a generalization of the Continuous Energy-Con\-strained Scheduling Problem (CECSP), introduced by Nattaf et al. (2014). This class is obtained from challenges concerning demand response in energy networks. Our framework extends a previously developed approach. A set of jobs has to be processed on a continuous, shared resource. Consequently, a schedule for a job does not only contain a start and completion time, but also a resource consumption profile, where we have to respect lower and upper bounds on resource consumption during processing. In this work, we develop a hybrid approach for the case where the objective is a step-wise increasing function of completion time, using local search, linear programming and O(n) lower bounds. We exploit that the costs are known in the local search and use bounds to assess feasibility more efficiently than by LP. We compare its performance to a mixed-integer linear program. After that, we extend this to a hybrid optimization framework for the General CECSP. This uses an event-based model, and applies a decomposition in two parts: 1) determining the order of events and 2) finding the event times, and hence the start and completion times of jobs, together with the resource consumption profiles. We argue the broad applicability of this framework.

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arXiv2023

A hybrid local search algorithm for the Continuous Energy-Constrained Scheduling Problem

We consider the Continuous Energy-Constrained Scheduling Problem (CECSP). A set of jobs has to be processed on a continuous, shared resource. A schedule for a job consists of a start time, completion time, and a resource consumption profile. We want to find a schedule such that: each job does not start before its release time, is completed before its deadline, satisfies its full resource requirement, and respects its lower and upper bounds on resource consumption during processing. Our objective is to minimize the total weighted completion time. We present a hybrid local search approach, using simulated annealing and linear programming, and compare it to a mixed-integer linear programming (MILP) formulation. We show that the hybrid local search approach matches the MILP formulation in solution quality for small instances, and is able to find a feasible solution for larger instances in reasonable time.

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