Vehicle Routing with Stochastic Demands: Properties and Solution Frameworks

Dror, Moshe; Laporte, Gilbert; Trudeau, Pierre

doi:10.1287/trsc.23.3.166

Cited by 240 publications

(126 citation statements)

References 22 publications

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“…Bertsimas and Simchi-Levi [9] surveyed the development in the VRPSD with an emphasis on the insights gained and on the algorithms proposed. Besides the conventional stochastic programming framework, a Markov decision process for single stage and multistage stochastic models were introduced to investigate the VRPSD in [17,16]. More recently, a re-optimization type routing policy for the VRPSD was introduced by [41].…”

Section: Svrps With Stochastic Customers and Demandsmentioning

confidence: 99%

The Stochastic Vehicle Routing Problem for Minimum Unmet Demand

Shen

Ordóñez

Dessouky

2009

Springer Optimization and Its Applications

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Summary. In this paper, we are interested in routing vehicles to minimize unmet demand with uncertain demand and travel time parameters. Such a problem arises in situations with large demand or tight deadlines, so that routes that satisfy all demand points are difficult or impossible to obtain. An important application is the distribution of medical supplies to respond to large-scale emergencies, such as natural disasters or terrorist attacks. We present a chance constrained formulation of the problem that is equivalent to a deterministic problem with modified demand and travel time parameters, under mild assumptions on the distribution of stochastic parameters; and relate it with a robust optimization approach. A tabu heuristic is proposed to solve this MIP and simulations are conducted to evaluate the quality of routes generated from both deterministic and chance constrained formulations. We observe that chance constrained routes can reduce the unmet demand by around 2%-6% for moderately tight deadline and total supply constraints.

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Section: Svrps With Stochastic Customers and Demandsmentioning

confidence: 99%

The Stochastic Vehicle Routing Problem for Minimum Unmet Demand

Shen

Ordóñez

Dessouky

2009

Springer Optimization and Its Applications

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“…This approach was adopted here, modelling average typical loads, as it was outside the scope of this study to consider how rounds would have to be adjusted in practice to handle seasonal variation. The reader is referred to Dror et al (1989) for a discussion of stochastic vehicle routing problems. The waste data were aggregated to postcode level, with the number of houses in each postcode area and the average round weight being used to calculate the weight of waste to be collected from each postcode.…”

Section: Data Collection and Preparationmentioning

confidence: 99%

Quantifying the transport impacts of domestic waste collection strategies

McLeod

Cherrett

2008

Waste Management

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This paper models the effects of three different options for domestic waste collection using data from three Hampshire authorities: (i) joint working between neighbouring waste collection authorities; (ii) basing vehicles at waste disposal sites; (iii) alternate weekly collection of residual waste and dry recyclables. A vehicle mileage saving of 3% was modelled for joint working, where existing vehicle allocations to depots were maintained, which increased to 5.9% when vehicles were re-allocated to depots optimally. Vehicle mileage was reduced by 13.5% when the collection rounds were based out of the two waste disposal sites rather than out of the existing depots, suggesting that the former could be the most effective place to keep vehicles providing that travel arrangements for the crews could be made. Alternate weekly collection was modelled to reduce vehicle mileage by around 8% and time taken by 14%, when compared with a typical scenario of weekly collection of residual and fortnightly collection of recyclable waste. These results were based on an assumption that 20% of the residual waste would be directly diverted into the dry recyclables waste stream.

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“…In this study, we consider a particular SVRP that is VRP with Stochastic Demands, denoted by the abbreviation VRPSD, where the demand is stochastic. Dror et al [8] mentioned that there are two solution frameworks for Stochastic VRP (including VRPSD), namely stochastic programming and Markov decision process. Further in this study, we focus the discussion on the stochastic programming.…”

Section: Introductionmentioning

confidence: 99%

“…Dror et al [8] introduce theoretically a new recourse defined by a return to the depot whenever the vehicle is unable to satisfy the demand (the capacity becomes exceeded) and resume the service at the customer on the planned route where route failure had occurred. Savelsbergh and Goetschalckx [15] simplified the recourse to achieve computational efficiency assuming at most one failure in a route.…”

Section: Introductionmentioning

confidence: 99%

“…Savelsbergh and Goetschalckx [15] simplified the recourse to achieve computational efficiency assuming at most one failure in a route. All subsequent publications (e.g., by [10,13] ) have maintained the simple recourse policy defined in [8] with the exception of [3,4,19] that using preventive restocking and the recourse of [5] that is to terminate the route and impose a penalty such as lost revenue and/or emergency deliveries.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Solving the Vehicle Routing Problem with Stochastic Demands via Hybrid Genetic Algorithm-Tabu Search

Ismail¹,

Irhamah²

2008

J. of Mathematics and Statistics

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This study considers a version of the stochastic vehicle routing problem where customer demands are random variables with known probability distribution. A new scheme based on a hybrid GA and Tabu Search heuristic is proposed for this problem under a priori approach with preventive restocking. The relative performance of the proposed HGATS is compared to each GA and TS alone, on a set of randomly generated problems following some discrete probability distributions. The problem data are inspired by real case of VRPSD in waste collection. Results from the experiment show the advantages of the proposed algorithm that are its robustness and better solution qualities resulted.

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Vehicle Routing with Stochastic Demands: Properties and Solution Frameworks

Cited by 240 publications

References 22 publications

The Stochastic Vehicle Routing Problem for Minimum Unmet Demand

The Stochastic Vehicle Routing Problem for Minimum Unmet Demand

Quantifying the transport impacts of domestic waste collection strategies

Solving the Vehicle Routing Problem with Stochastic Demands via Hybrid Genetic Algorithm-Tabu Search

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