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Walter Rei

Bio: Walter Rei is an academic researcher from Université du Québec à Montréal. The author has contributed to research in topics: Vehicle routing problem & Stochastic programming. The author has an hindex of 25, co-authored 54 publications receiving 2201 citations. Previous affiliations of Walter Rei include Université de Montréal & Université du Québec.


Papers
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Journal ArticleDOI
TL;DR: The metaheuristic combines the exploration breadth of population-based evolutionary search, the aggressive-improvement capabilities of neighborhood-based metaheuristics, and advanced population-diversity management schemes and proves extremely competitive for the capacitated VRP.
Abstract: We propose an algorithmic framework that successfully addresses three vehicle routing problems: the multidepot VRP, the periodic VRP, and the multidepot periodic VRP with capacitated vehicles and constrained route duration. The metaheuristic combines the exploration breadth of population-based evolutionary search, the aggressive-improvement capabilities of neighborhood-based metaheuristics, and advanced population-diversity management schemes. Extensive computational experiments show that the method performs impressively in terms of computational efficiency and solution quality, identifying either the best known solutions, including the optimal ones, or new best solutions for all currently available benchmark instances for the three problem classes. The proposed method also proves extremely competitive for the capacitated VRP.

545 citations

Journal ArticleDOI
TL;DR: A state-of-the-art survey of the Benders Decomposition algorithm, emphasizing its use in combinatorial optimization and introducing a taxonomy of algorithmic enhancements and acceleration strategies based on the main components of the algorithm.

506 citations

Journal ArticleDOI
TL;DR: This paper shows how local branching can be used to accelerate the classical Benders decomposition algorithm by applying local branching throughout the solution process, and shows how Benders feasibility cuts can be strengthened or replaced with local branching constraints.
Abstract: This paper shows how local branching can be used to accelerate the classical Benders decomposition algorithm. By applying local branching throughout the solution process, one can simultaneously improve both the lower and upper bounds. We also show how Benders feasibility cuts can be strengthened or replaced with local branching constraints. To assess the performance of the different algorithmic ideas presented in this hybrid solution approach, extensive computational experiments were performed on two families of network design problems. Numerical results clearly illustrate their benefits.

158 citations

Journal ArticleDOI
01 Sep 2011-Networks
TL;DR: A two‐stage stochastic programming formulation, where design decisions make up the first stage, while recourse decisions are made in the second stage to distribute the commodities according to observed demands, which is numerically shown to be computationally efficient and to yield high‐quality solutions under various problem characteristics and demand correlations.
Abstract: We consider the stochastic fixed-charge capacitated multicommodity network design (S-CMND) problem with uncertain demand. We propose a two-stage stochastic programming formulation, where design decisions make up the first stage, while recourse decisions are made in the second stage to distribute the commodities according to observed demands. The overall objective is to optimize the cost of the first-stage design decisions plus the total expected distribution cost incurred in the second stage. To solve this formulation, we propose a metaheuristic framework inspired by the progressive hedging algorithm of Rockafellar and Wets. Following this strategy, scenario decomposition is used to separate the stochastic problem following the possible outcomes, scenarios, of the random event. Each scenario subproblem then becomes a deterministic CMND problem to be solved, which may be addressed by efficient specialized methods. We also propose and compare different strategies to gradually guide scenario subproblems to agree on the status of design arcs and aim for a good global design. These strategies are embedded into a parallel solution method, which is numerically shown to be computationally efficient and to yield high-quality solutions under various problem characteristics and demand correlations. © 2011 Wiley Periodicals, Inc. NETWORKS, 2011. © 2011 Wiley Periodicals, Inc.

128 citations

Journal ArticleDOI
TL;DR: The state-of-the-art in stochastic vehicle routing is examined by examining the main classes of stoChastic VRPs, the modeling paradigms used to formulate them, and existing exact and approximate solution methods that have been proposed to tackle them.
Abstract: Stochastic vehicle routing, which deals with routing problems in which some of the key problem parameters are not known with certainty, has been an active, but fairly small research area for almost 50 years. However, over the past 15 years we have witnessed a steady increase in the number of papers targeting stochastic versions of the vehicle routing problem (VRP). This increase may be explained by the larger amount of data available to better analyze and understand various stochastic phenomena at hand, coupled with methodological advances that have yielded solution tools capable of handling some of the computational challenges involved in such problems. In this paper, we first briefly sketch the state-of-the-art in stochastic vehicle routing by examining the main classes of stochastic VRPs (problems with stochastic demands, with stochastic customers, and with stochastic travel or service times), the modeling paradigms that have been used to formulate them, and existing exact and approximate solution meth...

120 citations


Cited by
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01 Dec 1971

979 citations

Journal ArticleDOI
TL;DR: This classification is the first to categorize the articles of the VRP literature to this level of detail and is based on an adapted version of an existing comprehensive taxonomy.

800 citations

Journal ArticleDOI
TL;DR: The purpose is to review the most up-to-date state-of-the-art of GVRP, discuss how the traditional VRP variants can interact with G VRP and offer an insight into the next wave of research into GVRp.
Abstract: Green Logistics has emerged as the new agenda item in supply chain management. The traditional objective of distribution management has been upgraded to minimizing system-wide costs related to economic and environmental issues. Reflecting the environmental sensitivity of vehicle routing problems (VRP), an extensive literature review of Green Vehicle Routing Problems (GVRP) is presented. We provide a classification of GVRP that categorizes GVRP into Green-VRP, Pollution Routing Problem, VRP in Reverse Logistics, and suggest research gaps between its state and richer models describing the complexity in real-world cases. The purpose is to review the most up-to-date state-of-the-art of GVRP, discuss how the traditional VRP variants can interact with GVRP and offer an insight into the next wave of research into GVRP. It is hoped that OR/MS researchers together with logistics practitioners can be inspired and cooperate to contribute to a sustainable industry.

741 citations

Book
15 Feb 2012
TL;DR: This book provides a complete and comprehensive reference/guide to Pyomo (Python Optimization Modeling Objects) for both beginning and advanced modelers, including students at the undergraduate and graduate levels, academic researchers, and practitioners.
Abstract: This book provides a complete and comprehensive reference/guide to Pyomo (Python Optimization Modeling Objects) for both beginning and advanced modelers, including students at the undergraduate and graduate levels, academic researchers, and practitioners. The text illustrates the breadth of the modeling and analysis capabilities that are supported by the software and support of complex real-world applications. Pyomo is an open source software package for formulating and solving large-scale optimization and operations research problems. The text begins with a tutorial on simple linear and integer programming models. A detailed reference of Pyomo's modeling components is illustrated with extensive examples, including a discussion of how to load data from data sources like spreadsheets and databases. Chapters describing advanced modeling capabilities for nonlinear and stochastic optimization are also included. The Pyomo software provides familiar modeling features within Python, a powerful dynamic programming language that has a very clear, readable syntax and intuitive object orientation. Pyomo includes Python classes for defining sparse sets, parameters, and variables, which can be used to formulate algebraic expressions that define objectives and constraints. Moreover, Pyomo can be used from a command-line interface and within Python's interactive command environment, which makes it easy to create Pyomo models, apply a variety of optimizers, and examine solutions. The software supports a different modeling approach than commercial AML (Algebraic Modeling Languages) tools, and is designed for flexibility, extensibility, portability, and maintainability but also maintains the central ideas in modern AMLs.

683 citations