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| Mouftah, Hussein T. |
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| Dugay, Fabrice |
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| Rettenmeier, Max |
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| Tomasch, Ernst | Graz |
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| Cornaggia, Greta |
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| Palacios-Navarro, Guillermo |
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| Uspenskyi, Borys V. |
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| Khan, Baseem |
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| Fediai, Natalia |
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| Derakhshan, Shadi |
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| Somers, Bart | Eindhoven |
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| Anvari, B. |
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| Kraushaar, Sabine | Vienna |
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| Kehlbacher, Ariane |
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| Das, Raj |
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| Werbińska-Wojciechowska, Sylwia |
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| Brillinger, Markus |
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| Eskandari, Aref |
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| Gulliver, J. |
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| Loft, Shayne |
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| Kud, Bartosz |
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| Matijošius, Jonas | Vilnius |
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| Piontek, Dennis |
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| Kene, Raymond O. |
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| Barbosa, Juliana |
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Vanderbeck, François
in Cooperation with on an Cooperation-Score of 37%
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Publications (7/7 displayed)
- 2021BaPCod - a generic branch-and-price code
- 2018Solving the robust CVRP under demand uncertainty
- 2018Beyond Vehicle Routing: a general purpose branch-cut-and-price code for applications where pricing is a resource constrained shortest path (RCSP) Pricing
- 2012A Column Generation based Tactical Planning Method for Inventory Routing
- 2010A multi scalable model based on a connexity graph representationcitations
- 2010Column Generation based Primal Heuristicscitations
- 2007Column generation based heuristic for tactical planning in multi-period vehicle routingcitations
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document
Beyond Vehicle Routing: a general purpose branch-cut-and-price code for applications where pricing is a resource constrained shortest path (RCSP) Pricing
Abstract
Column generation algorithms where the pricing is solved as a resource constrained shortest path problem have been used in a variety of applications, as surveyed in [5]. Pioneering work on a generic solver using column generation based on a resource constrained shortest path subproblem was the GenCol software [13]. Our aim is to develop such a platform that includes both generic modeling tools and an highly efficient branch-cut-and-price. Our solver relies on generalizing the most advanced techniques that were recently developed for classical variants of the vehicle routing problem. It considers several resource constraints simultaneously , even allowing for continuous resources (as opposed to the discrete assumptions made by traditional dynamic programming approaches), sometimes even allowing zero or negative resource consumptions. The pricing is done by a bi-directional labeling algorithm, implemented over the so-called bucket graph (as proposed in [11]). Besides the good performance of the pricing oracle, the overall efficiency of the branch-cut-and-price relies on advanced features such as a procedure for fixing arc variables by reduced costs [4,8]; an algorithm for gradually enforcing total or partial elementarity of subproblem solution paths [10]; an self-adjusting dual price smoothing stabilization for improving the convergence of the column generation [7]; a heuristic local search separation procedure for limited-memory rank-1 Chvatal-Gomory cuts [6]; a labeling dynamic programming algorithm for enumerating elementary subproblem solution paths [1]; a multi-phase pseudo-costs based strong branching procedure [6]; and the generic diving heuristic for improving the initial primal bound of [12]. In this presentation we will focus on the scope of applications that are amenable to our branch-cut-and-price solver. The goal is to convey the ease of access to an efficient solver for the many combinatorial optimization problems that can be decomposed into resource constrained shortest path subproblems, once ...
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