References of "Pironet, Thierry"
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See detailVehicle allocation problem with uncertain transportation requests over a multi-period rolling horizon
Crama, Yves ULiege; Pironet, Thierry ULiege

in Logistics Research (2019), 12(1), 1-22

Abstract This work investigates optimization techniques for a multi-period vehicle allocation problem with uncertain transportation requests revealed sequentially over a rolling horizon. Policies derived ... [more ▼]

Abstract This work investigates optimization techniques for a multi-period vehicle allocation problem with uncertain transportation requests revealed sequentially over a rolling horizon. Policies derived from deterministic scenarios are compared: they are generated either by simple heuristics, or by more complex approaches, such as consensus and restricted expectation algorithms, or by network flow formulations over subtrees of scenarios. Myopic and a posteriori deterministic optimization models are used to compute bounds allowing for performance evaluation and for estimating the value of information. The economic benefit of the stochastic model is highlighted: our results show that the the information about future, uncertain orders contained in the stochastic part of the horizon can be used to generate improved profits. Robustness against misspecified probability distributions is examined. Subtree formulations produce the best results, are robust and can be solved efficiently, which makes them appropriate for industrial implementations. [less ▲]

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See detailRecovery management for a dial-a-ride system with real-time disruptions
Paquay, Célia ULiege; Crama, Yves ULiege; Pironet, Thierry ULiege

E-print/Working paper (2019)

The problem considered in this work stems from a non-profit organization in charge of door-to-door passenger transportation for medical appointments. Patients are picked up at home by a driver and are ... [more ▼]

The problem considered in this work stems from a non-profit organization in charge of door-to-door passenger transportation for medical appointments. Patients are picked up at home by a driver and are then dropped at their appointment location. They may also be driven back home at the end of their appointment. Some patients have specific requirements, e.g., they may require an accompanying person or a wheelchair. Planning such activities gives rise to a so-called dial-a-ride problem. In the present work, it is assumed that the requests assigned to the drivers have been selected, and the transportation plan has been established for the next day. However, in practice, appointment durations may vary due to unforeseen circumstances, and some transportation requests may be modified, delayed or canceled during the day. The aim of this work is to propose a reactive algorithm which can adapt the initial plan in order to manage the disruptions and to take care of as many patients as possible in real-time. The plan should be modified quickly when a perturbation is observed, without resorting to major changes which may confuse the drivers and the patients. Several recourse procedures are defined for this purpose. They allow the dispatcher to temporarily delete a request, to insert a previously deleted request, or to permanently cancel a request. Simulation techniques are used to test the approach on randomly generated scenarios. Several key performance indicators are introduced in order to measure the impact of the disruptions and the quality of the solutions. [less ▲]

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See detailDial-a-ride with real-time disruptions
Paquay, Célia ULiege; Crama, Yves ULiege; Pironet, Thierry ULiege

Conference (2018)

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See detailDial-a-ride with real-time disruptions
Paquay, Célia ULiege; Crama, Yves ULiege; Pironet, Thierry ULiege

Conference (2018)

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See detailValeurs de l'information et performances algorithmiques pour des problèmes multi-périodes et stochastiques
Pironet, Thierry ULiege

Scientific conference (2016, March 30)

voir document

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See detailMulti-period vehicle assignment problem with stochastic transportation order availability
Pironet, Thierry ULiege; Crama, Yves ULiege

Conference (2015, June 01)

This work investigates optimization techniques for a vehicle-load assignment problem. A company owning a limited fleet of vehicles wants to maximize its operational profit over an infinite horizon divided ... [more ▼]

This work investigates optimization techniques for a vehicle-load assignment problem. A company owning a limited fleet of vehicles wants to maximize its operational profit over an infinite horizon divided into periods. The profit stems from revenues for transporting full truckloads and costs derived from waiting idle and moving unladen. The stochastic component of the problem arises from projections on the realization of each transportation order, i.e. load. The methodology is based on optimizing decisions for deterministic scenarios. Several policies are generated in this way, from simple heuristics to more complex approaches, such as consensus and restricted expectation algorithms, up to policies derived from network flow models formulated over subtrees of scenarios. Myopic and a-posteriori deterministic optimizations models are used to compute bounds allowing for performance evaluation. Tests are performed on various instances featuring different number of loads, graph sizes, sparsity, and probability distributions. Performances are compared statistically over paired samples. The robustness of various policies with respect to erroneous évaluations of the probability distributions is also analyzed. [less ▲]

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See detailMulti-period stochastic optimization problems in transportation management (Ph. D. Thesis Summary)
Pironet, Thierry ULiege

in 4OR: A Quarterly Journal of Operations Research (2015), 13(1), 113-114

Ph. D Thesis summary see other reference on ORBI

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See detailMulti-period Stochastic Optimization Problems in Transportation Management
Pironet, Thierry ULiege

Doctoral thesis (2014)

The topic of this thesis in management science is to propose a methodology to tackle multi-period decision problems including stochastic forecasts and to apply it to the field of transportation management ... [more ▼]

The topic of this thesis in management science is to propose a methodology to tackle multi-period decision problems including stochastic forecasts and to apply it to the field of transportation management. This methodology relies mostly on a sequence of numerical experimentations based on a set of algorithms to assess the value of the multi-period setting and the interest to use the stochastic information contained in the forecasts. Moreover, a statistical validation method to compare the performances of algorithms enables us to rank them meaningfully. From theory to practice, the thesis is structured into four parts. Firstly, we define the thesis subject and content. Then, based on a literature review, we present the past and present issues within the research field: "Optimization in Transportation". Mainly, we define the differences between multi-period stochastic models and classical deterministic mono-period ones. We explain how dynamism and stochasticity are taken into account within transportation problems. Secondly, our methodology, which is our main contribution, is exposed from a generic point of view in the theoretical research field: "Multi-period Stochastic Optimization Problems". On the one hand, temporal parameters and computational issues in multi-period optimization are detailed; on the other hand a summary of optimization techniques and algorithms for stochastic optimization problems is provided. Afterwards, the statistical validation of algorithmic performance is discussed. Then, part three contains two applications that lead us to set up the methodology, following an inductive method. The first problem, based on an industrial application at the start of the research, investigates a "multi-period vehicle loading problem with stochastic release dates". The second application, based on a more generic approach for the deployment of the methodology, deals with a "multi-period vehicle assignment problem with stochastic load availability". Finally, in the fourth part, we conclude on the thesis contributions and propose some perspectives. [less ▲]

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See detailMulti-period vehicle loading with stochastic release dates
Arda, Yasemin ULiege; Crama, Yves ULiege; Kronus, David ULiege et al

in EURO Journal on Transportation and Logistics (2014), 3(2), 93-119

This paper investigates a multi-period vehicle loading problem with stochastic information regarding the release dates of items to be transported. The deterministic version of the problem can be ... [more ▼]

This paper investigates a multi-period vehicle loading problem with stochastic information regarding the release dates of items to be transported. The deterministic version of the problem can be formulated as a large-scale set covering problem. Several heuristic algorithms are proposed to generate decision policies for the stochastic optimization model over a long rolling horizon. The resulting policies have been extensively tested on instances which display the main characteristics of the industrial case-study that motivated the research. The tests demonstrate the benefits of the multi-period stochastic model over simple myopic strategies. A simple and efficient heuristic is shown to deliver good policies and to be robust against errors in the estimation of the probability distribution of the release dates. [less ▲]

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See detailMulti-period vehicle assignment with stochastic load availability
Pironet, Thierry ULiege

Conference (2014, June 23)

This work investigates optimization techniques for a vehicle-load assignment problem. A company owning a limited fleet of vehicles wants to maximize its operational profit over an infinite horizon divided ... [more ▼]

This work investigates optimization techniques for a vehicle-load assignment problem. A company owning a limited fleet of vehicles wants to maximize its operational profit over an infinite horizon divided into periods. The profit stems from revenues for transporting full truckloads and costs derived from waiting idle and moving unladen. The stochastic component of the problem arises from projections on the realization of each transportation order, i.e. load. The methodology is based on optimizing decisions for deterministic scenarios. Several policies are generated in this way, from simple heuristics to more complex approaches, such as consensus and restricted expectation algorithms, up to policies derived from network flow models formulated over subtrees of scenarios. Myopic and a-posteriori deterministic optimizations models are used to compute bounds allowing for performance evaluation. Tests are performed on various instances featuring different number of loads, graph sizes, sparsity, and probability distributions. Performances are compared statistically over paired samples. The robustness of various policies with respect to erroneous evaluations of the probability distributions is also analyzed. [less ▲]

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See detailMulti-period vehicle assignment with stochastic load availability
Pironet, Thierry ULiege

Conference (2014, February 27)

This work investigates optimization techniques for a vehicle-load assignment problem. A company owning a limited fleet of vehicles wants to maximize its operational profit over an infinite horizon divided ... [more ▼]

This work investigates optimization techniques for a vehicle-load assignment problem. A company owning a limited fleet of vehicles wants to maximize its operational profit over an infinite horizon divided into periods. The profit stems from revenues for transporting full truckloads and costs derived from waiting idle and moving unladen. The stochastic component of the problem arises from projections on the realization of each transportation order, i.e. load. The methodology is based on optimizing decisions for deterministic scenarios. Several policies are generated in this way, from simple heuristics to more complex approaches, such as consensus and restricted expectation algorithms, up to policies derived from network flow models formulated over subtrees of scenarios. Myopic and a-posteriori deterministic optimizations models are used to compute bounds allowing for performance evaluation. Tests are performed on various instances featuring different number of loads, graph sizes, sparsity, and probability distributions. Performances are compared statistically over paired samples. The robustness of various policies with respect to erroneous evaluations of the probability distributions is also analyzed. [less ▲]

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See detailMulti-period vehicle assignment with stochastic load availability
Pironet, Thierry ULiege

Conference (2014, January 31)

We investigate the following problem, which is faced by major forwarding companies active in road transportation (see [2]). A company owning a limited fl eet of vehicles wants to maximize its operational ... [more ▼]

We investigate the following problem, which is faced by major forwarding companies active in road transportation (see [2]). A company owning a limited fl eet of vehicles wants to maximize its operational pro fit over an infi nite horizon divided into equal periods (days). The pro fit stems from revenues for transporting full truckloads and from costs derived from waiting idle and moving empty. A decision leading to a set of actions is made at every period and is based on the dispatcher's information over a restricted horizon, called rolling horizon, which subsequently rolls over period per period. The data provided by the customers concern their prospective loads, or requirements for transportation: locations of departure and destination cities, and a unique pick-up period for each load. Moreover, the dispatcher has data regarding travel times between cities, current location and status (empty or loaded) of trucks. These data are known with certainty and represent the deterministic component of the problem. The stochastic component of the problem arises from the uncertainty on the eff ective materialization of each transportation order. More precisely, the availability of each order can be either con rmed, or denied, a few periods ahead of the loading period (meaning that clients con firm their order, which the transporter may still decide to ful ll, or not). For prospective orders in the remote part of the rolling horizon, the dispatcher only knows the order con firmation probability which represents the stochastic load availability. In this setting, trucking orders are provided by the dispatching center to the drivers and to the customers on the eve of the pick-up period at the latest. Typically, the loading decisions are made when all orders are con firmed for the next day. The decision problem faced by the dispatcher is to select or to reject loads, and to assign the selected loads to trucks, taking into account con firmed and expected loads as well as the availability and current location of trucks. The main objective of this research is to provide e fficient algorithmic strategies to tackle this multi-period vehicle-load assignment problem over a rolling horizon including prospective transportation orders. This problem is computationally di fficult owing to the large number of possible realizations of the random variables, and to the combinatorial nature of the decision space. The methodology is based on optimizing decisions for deterministic scenarios. By solving the assignment problem for a sample of scenarios, by mixing solutions and by evaluating them at each period, we aim at finding actions per decision period leading to pro table policies in the long run. Several policies are generated in this way, from simple myopic heuristics to more complex approaches, such as consensus and restricted expectation algorithms [3], up to policies derived from network flow models formulated over subtrees of scenarios. Similar approaches have proved eff ective for other problems; see, e.g., [1]. Myopic and a-posteriori deterministic optimization models are used to compute bounds allowing for performance evaluation. Test are performed on various instances featuring di fferent numbers of loads, graph sizes, sparsity, and probability distributions. Performances are compared statistically over paired samples to assess the signi ficance of the observed differences among algorithmic policies. The robustness of various policies with respect to erroneous evaluations of the probability distributions is also analyzed. Numerical experiments show that the best algorithms close a signi ficant fraction of the gap between the worst (myopic) and best (a posteriori) bounds for a broad range of datasets and for several probability distributions. Furthermore, the subtree algorithm remains quite robust against a variety of probability distributions when it is calibrated with a distribution re flecting maximum uncertainty . Acknowledgements. The project leading to these results was partially funded by the Interuniversity Attraction Poles Programme initiated by the Belgian Science Policy O ffice (grant P7/36). References [1] Arda, Y., Crama, Y., Kronus, D., Pironet, Th., and Van Hentenryck, P. (2013), Multi-period vehicle loading with stochastic release dates, EURO Journal on Transportation and Logistics, pp. 1-27, available on-line http://dx.doi.org/10.1007/s13676-013-0035-z. [2] Powell, W. B. (1996), A stochastic formulation of the dynamic assignment problem, with an application to truckload motor carriers, Transportation Science, Vol. 30, pp. 195-219. [3] Van Hentenryck, P., and Bent,R. W. (2006), Online Stochastic Combinatorial Optimization, MIT Press, Cambridge, Massachussetts. [less ▲]

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See detailOptimization of Stochastic Multi-Period Problems in Transportation
Pironet, Thierry ULiege

Scientific conference (2013, July 09)

Présentation générale de la méthodologie et des travaux réalisés dans le domaine de l'optimisation de problèmes multi-périodes en transport dans le cadre de la thèse de Th. Pironet

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See detailStochastic Optimization in Multi-periods problems in Transportation
Pironet, Thierry ULiege

Scientific conference (2013, June 13)

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See detailMultiperiod vehicle loading with stochastic release dates
Pironet, Thierry ULiege

Conference (2013, February 08)

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See detailMultiperiod vehicle loading with stochastic release dates
Arda, Yasemin ULiege; Crama, Yves ULiege; Kronus, David ULiege et al

Conference (2012, May 24)

Production scheduling and vehicle routing problems are well-known topics in operations management. Although these tasks are consecutive in the supply chain, few optimization models tackle the associated ... [more ▼]

Production scheduling and vehicle routing problems are well-known topics in operations management. Although these tasks are consecutive in the supply chain, few optimization models tackle the associated issues. A most common situation, in practice, is actually that transportation management is disconnected from production planning: when production items or batches have been completely processed by the manufacturing plant, they become available for shipping, and they are consequently handled by the transportation managers. From a global managerial perspective, and with a view towards coordination of the product flows and customer satisfaction, this is not an ideal process. It is by far preferable, indeed, to set up an integrated production-transportation plan taking into account, among other constraints, the capacity of the plants and the customer due-dates. The present research proposes a methodology to investigate a multi-period vehicle loading problem with deterministic or stochastic information concerning items arrivals from production. Results from related optimization techniques are statistically compared and the benefits of the multi-period and stochastic modeling is demonstrated. Finally, an efficient heuristic is highlighted and is shown to be robust to the deviation from item arrival forecasts. [less ▲]

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See detailARCELOR
Pironet, Thierry ULiege

Conference given outside the academic context (2011)

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See detailMultiperiod vehicle loading optimization with stochastic supply
Amand, Guillaume ULiege; Arda, Yasemin ULiege; Crama, Yves ULiege et al

Scientific conference (2011, April 07)

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See detailVehicle loading optimization with stochastic supply
Amand, Guillaume ULiege; Arda, Yasemin ULiege; Crama, Yves ULiege et al

Conference (2010, January 29)

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