Dynamic And Stochastic Vehicle Routing In Practice

Flatberg, Truls; Hasle, Geir; Kloster, Oddvar; Nilssen, Eivind Jodaa; Riise, Atle

doi:10.1007/978-0-387-71722-7_3

Cited by 43 publications

(34 citation statements)

References 22 publications

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“…Computational experiments on instances adapted from the Solomon benchmark [133] showed that MSA outperforms MPA both in terms of serviced customers and traveled distances, especially for instances with high degrees of dynamism [9]. Flatberg et al [47] adapted the SPIDER commercial solver to use multiple scenarios and a consensus algorithm to tackle the D-VRP, while Pillac et al [107] implemented an event-driven optimization framework based on MSA and showed significant improvements over state-of-the-art algorithms for the D-VRPSD. An important component of scenario based-approaches such as MSA is the decision process, which defines how the information from the scenario pool is used to reach upon a decision regarding the next customer to visit.…”

Section: Samplingmentioning

confidence: 99%

A review of dynamic vehicle routing problems

Pillac

Gendreau

Guéret

et al. 2013

European Journal of Operational Research

965

496

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A number of technological advances have led to a renewed interest on dynamic vehicle routing problems. This survey classifies routing problems from the perspective of information quality and evolution. After presenting a general description of dynamic routing, we introduce the notion of degree of dynamism, and present a comprehensive review of applications and solution methods for dynamic vehicle routing problems.

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Section: Samplingmentioning

confidence: 99%

A review of dynamic vehicle routing problems

Pillac

Gendreau

Guéret

et al. 2013

European Journal of Operational Research

965

496

View full text Add to dashboard Cite

show abstract

“…Surveys of dynamic vehicle routing problems may be found in Psaraftis [18], Bianchi [3], and Flatberg et al [9]. Li et al [16] propose a DVRPTW model, in the context of a package delivery service that seeks to minimize the number of initially-scheduled customers that are no longer served as a result of vehicle breakdowns.…”

Section: Classical Routing Problemsmentioning

confidence: 99%

An extensible modeling framework for dynamic reassignment and rerouting in cooperative airborne operations

Murray

Karwan

2010

Naval Research Logistics

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Unmanned aerial vehicles (UAVs), increasingly vital to the success of military operations, operate in a complex and dynamic environment, sometimes in concert with manned aircraft. We present an extensible modeling framework for the solution to the dynamic resource management (DRM) problem, where airborne resources must be reassigned to time-sensitive tasks in response to changes in battlespace conditions. The DRM problem is characterized by diverse tasks with time windows, heterogeneous resources with fuel-and payload-capacity limitations, and multiple competing objectives. We propose an integer linear programing formulation for this problem, where mathematical feasibility is guaranteed. Although motivated by airborne military operations, the proposed general modeling framework is applicable to a wide array of settings, such as disaster relief operations. Additionally, land-or water-based operations may be modeled within this framework, as well as any combination of manned and unmanned vehicles.

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“…To deal with stochastic information, solution methods are either based on sampling approaches where possible future scenarios are included [6], [7] or considering stochastic information explicitly [26], [76].…”

Section: Information Availabilitymentioning

confidence: 99%

Hybrid Metaheuristics for Dynamic and Stochastic Vehicle Routing

Ritzinger

Puchinger

2013

Hybrid Metaheuristics

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Abstract. Recent developments in telematics, such as the wide spread use of positioning services and mobile communication technologies, allow the exact monitoring of vehicles. These advances build the basis for automatic real-time fleet management systems. To be successful such systems have to rely on optimization algorithms for solving dynamic and stochastic vehicle routing problems based on ingredients such as historical data, stochastic modeling, machine learning, fast shortest-path calculation, fast construction heuristics, and exact and (meta)heuristic optimization methods. This book documents the growing interest in and success of hybrid metaheuristics. They are often used to solve complex and large real-world optimization problems, combining advantages from various fields of computer science and mathematical optimization. Within this chapter the application of such methods for the dynamic and stochastic vehicle routing problem is studied. After a general introduction in this field, the main commonalities of dynamic and stochastic vehicle routing problems are described and a short overview of classical algorithms for these problems is given. Then, in the third part hybrid metaheuristics for dynamic problems vehicle routing problems are be described. The third part focusses on stochastic problems. The fourth part examines the combination of dynamic and stochastic problems. The chapter is concluded with an outlook towards future developments in the field as well as promising open research areas. IntroductionIn todays globalized economy fast, reliable but also flexible supply chains are among the main factors for successful enterprises. In real-world applications it is most often the case that some information about the future is available (stochastic information) and that known information is revealed over time (dynamic information). For example, an estimation for the customer demand is given at the beginning, whereas

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Dynamic And Stochastic Vehicle Routing In Practice

Cited by 43 publications

References 22 publications

A review of dynamic vehicle routing problems

A review of dynamic vehicle routing problems

An extensible modeling framework for dynamic reassignment and rerouting in cooperative airborne operations

Hybrid Metaheuristics for Dynamic and Stochastic Vehicle Routing

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