A Framework For Probabilistic Vehicle Routing

Golden, Bruce L.; Yee, J.R.

doi:10.1080/05695557908974449

Cited by 44 publications

(16 citation statements)

References 4 publications

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“…Golden and Stewart 1978 tackle problems with Poisson-distributed loads. Golden and Yee 1979 consider other load distributions and give theoretical explanations for the relations found empirically by Stewart. Stewart 1981 andStewart and formulate SVRP as a stochastic programming problem with recourse.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Multivehicle Truckload Pickup and Delivery Problems

Yang

Jaillet

Mahmassani

2004

Transportation Science

246

163

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In this paper we formally introduce a generic real-time multi-vehicle truckload pick-up and delivery problem. The problem includes the consideration of various costs associated with trucks' empty travel distances, jobs' delayed completion times, and job rejections. Although very simple, the problem captures most features of the operational problem of a real-world trucking fleet that dynamically moves truckloads between different sites according to customer requests that arrive continuously over time.We propose a mixed integer programming formulation for the off-line version of the problem. We then consider and compare five rolling horizon strategies for the real-time version. Two of the policies are based on a repeated reoptimization of various instances of the off-line problem, while the others use simpler local (heuristic) rules. One of the re-optimization strategies is new while the other strategies have recently been tested for similar real-time fleet management problems.The comparison of the policies is done under a general simulation framework. The analysis is systematic and consider varying traffic intensities, varying degrees of advance information, and varying degrees of flexibility for job rejection decisions. The new re-optimization policy is shown to systematically outperform the others under all these conditions.

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Section: Introductionmentioning

confidence: 99%

Real-Time Multivehicle Truckload Pickup and Delivery Problems

Yang

Jaillet

Mahmassani

2004

Transportation Science

246

163

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“…This algorithm has been implemented at a large chemical company and produced significant economic benefits. Golden and Yee (1979) have also suggested a general framework for this problem.…”

Section: Vehicle Routing/schedulingmentioning

confidence: 99%

Analytical Evaluation of Hierarchical Planning Systems

Dempster¹,

Fisher

Jansen³

et al. 1981

Operations Research

110

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Hierarchical planning systems have become popular for multilevel decision problems. After reviewing the concept of hierarchical planning and citing some examples, we describe a method for analytic evaluation of a hierarchical planning system. We show that multilevel decision problems can be nicely modeled as multistage stochastic programs. Then any hierarchical planning system can be measured against the yardstick of optimality in this stochastic program. We demonstrate this approach on a hierarchical system that can be shown to be asymptotically optimal for a job shop design/scheduling problem.

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“…Golden and Yee (1979) introduced a chance constrained programming model for VRPs with stochastic demands and obtained some analytic results. Stewart and Golden (1983) extended the work of Golden and Yee (1979) and presented some computational results. Gendreau et al (1995) used an exact algorithm to solve the SVRP.…”

Section: Introductionmentioning

confidence: 99%

Vehicle routing problem with uncertain demands: An advanced particle swarm algorithm

Moghaddam

Ruíz

Sadjadi

2012

Computers & Industrial Engineering

104

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The Vehicle Routing Problem (VRP) has been thoroughly studied in the last decades. However, the main focus has been on the deterministic version where customer demands are fixed and known in advance. Uncertainty in demand has not received enough consideration. When demands are uncertain, several problems arise in the VRP. For example, there might be unmet customers' demands, which eventually lead to profit loss. A reliable plan and set of routes, after solving the VRP, can significantly reduce the unmet demand costs, helping in obtaining customer satisfaction. This paper investigates a variant of an uncertain VRP in which the customers' demands are supposed to be uncertain with unknown distributions. An advanced Particle Swarm Optimization (PSO) algorithm has been proposed to solve such a VRP. A novel decoding scheme has also been developed to increase the PSO efficiency. Comprehensive computational experiments, along with comparisons with other existing algorithms, have been provided to validate the proposed algorithms.

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A Framework For Probabilistic Vehicle Routing

Cited by 44 publications

References 4 publications

Real-Time Multivehicle Truckload Pickup and Delivery Problems

Real-Time Multivehicle Truckload Pickup and Delivery Problems

Analytical Evaluation of Hierarchical Planning Systems

Vehicle routing problem with uncertain demands: An advanced particle swarm algorithm

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