Journal: European Journal of Operational Research

Abbreviation

Eur. J. oper. res.

Publisher

Elsevier

Journal Volumes

ISSN

0377-2217
1872-6860

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Publications 1 - 10 of 50
  • Supplier default dependencies
    Item type: Journal Article
    Wagner, Stephan M.; Bode, Christoph; Koziol, Philipp (2009)
    European Journal of Operational Research
  • Solving oligopolistic equilibrium problems with convex optimization
    Item type: Journal Article
    Egging-Bratseth, Ruud; Baltensperger, Tobias; Tomasgard, Asgeir (2020)
    European Journal of Operational Research
    The approach of choice to analyze markets with oligopolistic competition has traditionally been complementarity modeling. In this paper we show that the majority of partial equilibrium models under imperfect competition in the (energy-)economic literature can in fact be cast as optimization models, not requiring the derivation and implementation of Karush–Kuhn–Tucker conditions. This is achieved by adding appropriate terms accounting for market power exertion to the well-known social welfare maximization objective. The method is applicable to both spatial Cournot oligopoly models and hybrid competition forms often implemented using conjectural variation approaches. We show how optimization and complementarity problems are equivalent, and provide a rationale for the terms accounting for market power exertion. Resulting models are solved orders of magnitude faster using off-the-shelf optimization software, compared to solving complementarity problems. Large problem instances take minutes rather than hours, and one instance solves 640 times faster. The drastically reduced solution times greatly enhance modeling capabilities as they allow increased geographical scope and represent economic, technical and other characteristics in much more detail in equilibrium problems with imperfect competition. We present practical implications for the partial and multi-level equilibrium modeling community.
  • Enhancing the elicitation of diverse decision objectives for public planning
    Item type: Journal Article
    Haag, Fridolin; Zürcher, Sara; Lienert, Judit (2019)
    European Journal of Operational Research
  • Stabilized Benders decomposition for energy planning under climate uncertainty
    Item type: Journal Article
    Göke, Leonard; Schmidt, Felix; Kendziorski, Mario (2024)
    European Journal of Operational Research
    This paper applies Benders decomposition to two-stage stochastic problems for energy planning under climate uncertainty, a key problem for the design of renewable energy systems. To improve performance, we adapt various refinements for Benders decomposition to the problem’s characteristics—a simple continuous master-problem, and few but large sub-problems. The primary focus is stabilization, specifically comparing established bundle methods to a quadratic trust-region approach for continuous problems. An extensive computational comparison shows that all stabilization methods can significantly reduce computation time. However, the quadratic trust-region and the linear box-step method are the most robust and straightforward to implement. When parallelized, the introduced algorithm outperforms the vanilla version of Benders decomposition by a factor of 100. In contrast to off-the-shelf solvers, computation time remains constant when the number of scenarios increases. In conclusion, the algorithm enables robust planning of renewable energy systems with a large number of climatic years. Beyond climate uncertainty, it can make an extensive range of other analyses in energy planning computationally tractable, for instance, endogenous learning and modeling to generate alternatives.
  • A logic-based Benders decomposition for microscopic railway timetable planning
    Item type: Journal Article
    Leutwiler, Florin; Corman, Francesco (2022)
    European Journal of Operational Research
    Railway timetable planning is one of the key factors in the successful operation of a railway network. The timetable must satisfy all operational restrictions at a microscopic representation of the railway network, while maximizing transportation capacity for passengers and freight. The microscopic planning of a railway timetable is an NP-Hard problem, difficult to solve for large-scale railway networks, such as those of entire countries. In this work, we propose a logic Benders decomposition approach to solve the problem of microscopic railway timetable planning. Our decomposition exploits the typical structure of a railway with dense networks around major hubs and sparse connections in-between hubs. A logic Benders cut is designed, which we are able to compute effectively for all decomposed problems within our considered structure, using a SAT based algorithm we developed. Moreover, an aggregation scheme for Benders cuts is proposed to speed up the iterative process. Experiments on real-world cases of the Swiss Federal Railways show a clear improvement in scalability compared to a variety of benchmarks including centralized approaches.
  • Data-Driven Dynamic Police Patrolling: An Efficient Monte Carlo Tree Search
    Item type: Journal Article
    Tschernutter, Daniel; Feuerriegel, Stefan (2024)
    European Journal of Operational Research
    Crime is responsible for major financial losses and serious harm to the well-being of individuals, and, hence, a crucial task of police operations is effective patrolling. Yet, in existing decision models aimed at police operations, microscopic routing decisions from patrolling are not considered, and, furthermore, the objective is limited to surrogate metrics (e. g., response time) instead of crime prevention. In this paper, we thus formalize the decision problem of dynamic police patrolling as a Markov decision process that models microscopic routing decisions, so that the expected number of prevented crimes are maximized. We experimentally show that standard solution approaches for our decision problem are not scalable to real-world settings. As a remedy, we present a tailored and highly efficient Monte Carlo tree search algorithm. We then demonstrate our algorithm numerically using real-world crime data from Chicago and show that the decision-making by our algorithm offers significant improvements for crime prevention over patrolling tactics from current practice. Informed by our results, we finally discuss implications for improving the patrolling tactics in police operations.
  • News-based forecasts of macroeconomic indicators: A semantic path model for interpretable predictions
    Item type: Journal Article
    Feuerriegel, Stefan; Gordon, Julius (2019)
    European Journal of Operational Research
  • Route efficiency implications of time windows and vehicle capacities in first- and last-mile logistics
    Item type: Journal Article
    Schaumann, Sarah K.; Bergmann, Felix M.; Wagner, Stephan M.; et al. (2023)
    European Journal of Operational Research
    In this paper, we analyze the route efficiency effects that emerge from combining realistically constrained first-mile pickup and last-mile delivery operations into joint vehicle routes. Specifically, we examine (i) the individual effect of discrete, non-overlapping time window constraints; (ii) the individual effect of vehicle capacity constraints; and (iii) the joint impact of both constraint types on local route efficiency gains due to the integration of pickup and delivery operations. Extending the existent literature on continuum approximation of route distances, we propose closed-form adjustment factors which accurately capture these non-trivial route efficiency effects. To derive the adjustment factors, we conduct extensive numerical experiments and apply a novel hybrid data analysis approach which combines exploratory data analysis with symbolic regression. Our analysis suggests that these real-world constraints affect the optimal stop sequence and diminish or even eliminate the expected efficiency gains from integrating first- and last-mile operations. The proposed adjustment factors are particularly relevant for the optimal strategic design and operational planning of modern, industrial-scale distribution networks in light of growing and increasingly fragmented e-commerce volumes and a high cost pressure on the logistics industry. They help researchers and practitioners to efficiently quantify the expected benefits from integrating pickup and delivery operations, and to assess under which circumstances such an integration is (not) desirable.
  • Evolutionary multi-objective optimization
    Item type: Other Journal Item
    Coello Coello, Carlos A.; Hernández Aguirre, Arturo; Zitzler, Eckart (2007)
    European Journal of Operational Research
  • Emergency response in natural disaster management: Allocation and scheduling of rescue units
    Item type: Journal Article
    Wex, Felix; Schryen, Guido; Feuerriegel, Stefan; et al. (2014)
    European Journal of Operational Research
Publications 1 - 10 of 50