Stochastic Vehicle Routing Problem: A Literature Survey

Berhan, Eshetie; Beshah, Birhanu; Kitaw, Daniel; Abraham, Ajith

doi:10.1142/s0219649214500221

Cited by 44 publications

(19 citation statements)

References 65 publications

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“…In this section we hence do not consider dynamic VRPs and rather focus on existing studies that have been carried on static and stochastic VRPs. Specific literature reviews on the SS-VRP may be found in Gendreau et al (1996b), Bertsimas and Simchi-Levi (1996), Cordeau et al (2007), Campbell and Thomas (2008a) and recently in Toth and Vigo (2014), Berhan et al (2014), Kovacs et al (2014) and Gendreau et al (2016). According to Pillac et al (2013), the most studied cases in SS-VRPs are:…”

Section: Related Workmentioning

confidence: 99%

The Static and Stochastic VRP with Time Windows and both Random Customers and Reveal Times

Saint-Guillain

Solnon

Deville

2017

Applications of Evolutionary Computation

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Unlike its deterministic counterpart, static and stochastic vehicle routing problems (SS-VRP) aim at modeling and solving real-life operational problems by considering uncertainty on data. We consider the SS-VRPTW-CR introduced in Saint- Guillain et al. (2017). Like the SS-VRP introduced by Bertsimas (1992), we search for optimal first stage routes for a fleet of vehicles to handle a set of stochastic customer demands, i.e., demands are uncertain and we only know their probabilities. In addition to capacity constraints, customer demands are also constrained by time windows. Unlike all SS-VRP variants, the SS-VRPTW-CR does not make any assumption on the time at which a stochastic demand is revealed, i.e., the reveal time is stochastic as well. To handle this new problem, we introduce waiting locations: Each vehicle is assigned a sequence of waiting locations from which it may serve some associated demands, and the objective is to minimize the expected number of demands that cannot be satisfied in time. In this paper, we propose two new recourse strategies for the SS-VRPTW-CR, together with their closed-form expressions for efficiently computing their expectations: The first one allows us to take vehicle capacities into account; The second one allows us to optimize routes by avoiding some useless trips. We propose two algorithms for searching for routes with optimal expected costs: The first one is an extended branchand-cut algorithm, based on a stochastic integer formulation, and the second one is a local search based heuristic method. We also introduce a new public benchmark for the SS-VRPTW-CR, based on real-world data coming from the city of Lyon. We evaluate our two algorithms on this benchmark and empirically demonstrate the expected superiority of the SS-VRPTW-CR anticipative actions over a basic "wait-and-serve" policy.

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Section: Related Workmentioning

confidence: 99%

The Static and Stochastic VRP with Time Windows and both Random Customers and Reveal Times

Saint-Guillain

Solnon

Deville

2017

Applications of Evolutionary Computation

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“…Vehicle routing problem have received increasing attention in recent years. However, in practical situations, the deterministic and single VRP cannot cover real-life contexts, the objectives can be multiple [20] and some components of the VRP are random [25]. In this work, we are interested an important variant of VRP, the so called multiobjective stochastic VRP with two stochastic objectives.…”

Section: Case Study:stochastic Multi-objective Vrpmentioning

confidence: 99%

Solving stochastic multiobjective vehicle routing problem using probabilistic metaheuristic

2017

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In this paper we propose a new metaheuristic algorithm for solving stochastic multiobjective combinatorial optimization(SMOCO) problems. Indeed, we find that the various initiatives that have been launched recently on this subject, they propose the classical metaheuristics to solve a stochastic multi-objective problems, but when the stochasticity effects is not taken into account, the choice of an arbitrary value leading to a particular configuration and a high loss of information. To conserve the stochastic nature of SMOCO problems, the pareto ranking should be defined on the random objectives functions directly rather than converted deterministic objectives. From these considerations, the scope of this research should consist of: (i)Proposed novel methodology of stochastic optimality for ranking objective functions characterized by non-continuous and no closed form expression. This novel approach is based on combinatorial probability and can be incorporated in a multiobjective evolutionary algorithm. (ii)Provide probabilistic approaches to elitism and diversification in multiobjective evolutionary algorithms. Finally, The behavior of the resulting Probabilistic Multi-objective Evolutionary Algorithms (PrMOEAs) is empirically investigated on the multi-objective stochastic VRP problem.

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“…Three recent surveys on dynamic vehicle routing problems were presented by Pillac et al (2013), Bektas et al (2014) and Psaraftis et al (2015). A review of stochastic vehicle routing problems was written by Berhan et al (2014), and nally, a survey of dynamic and stochastic vehicle routing problems was presented by Ritzinger et al (2015).…”

Section: Literature Reviewmentioning

confidence: 99%

Determining departure times in dynamic and stochastic maritime routing and scheduling problems

Tirado

Hvattum

2016

Flex Serv Manuf J

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In maritime transportation, decisions are made in a dynamic setting where many aspects of the future are uncertain. However, most academic literature on maritime transportation considers static and deterministic routing and scheduling problems. This work addresses a gap in the literature on dynamic and stochastic maritime routing and scheduling problems, by focusing on the scheduling of departure times. Five simple strategies for setting departure times are considered, as well as a more advanced strategy which involves solving a mixed integer mathematical programming problem. The latter strategy is significantly better than the other methods, while adding only a small computational eort.

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Stochastic Vehicle Routing Problem: A Literature Survey

Cited by 44 publications

References 65 publications

The Static and Stochastic VRP with Time Windows and both Random Customers and Reveal Times

The Static and Stochastic VRP with Time Windows and both Random Customers and Reveal Times

Solving stochastic multiobjective vehicle routing problem using probabilistic metaheuristic

Determining departure times in dynamic and stochastic maritime routing and scheduling problems

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