Exact Solution of the Vehicle Routing Problem

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.

show abstract

“…Consequently, a multitude of 'non-exact', heuristic solution methods have been proposed in the literature (Toth and Vigo, 2001).…”

Section: Modelling Domestic Waste Collectionsmentioning

confidence: 46%

Quantifying the transport impacts of domestic waste collection strategies

McLeod

Cherrett

2008

Waste Management

View full text Add to dashboard Cite

show abstract

“…This problem was first introduced by Dantzig and Ramser in 1959 to solve a real-world application concerning the delivery of gasoline to service stations. A comprehensive overview of the Vehicle Routing Problem can be found in [48] which discusses problem formulations, solution techniques, important variants and applications. Other general surveys on the deterministic VRP also can be found in [34].…”

Section: Stochastic Vrpsmentioning

confidence: 46%

The Stochastic Vehicle Routing Problem for Minimum Unmet Demand

Shen

Ordóñez

Dessouky

2009

Springer Optimization and Its Applications

View full text Add to dashboard Cite

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.

show abstract

“…The deterministic version of many routing optimization problems (e.g., shortest path problems, traveling salesman problems, vehicle routing problems) has been studied extensively over many decades (see the literature reviews of Toth and Vigo, 2002;Öncan et al, 2009;and Laporte, 2010, to name a few). Due to the recognized practical importance of incorporating uncertainty, the uncertain version of routing problems has also attracted increasing attention.…”

Section: Related Workmentioning

confidence: 47%

Routing Optimization Under Uncertainty

Jaillet

Sim

2016

Operations Research

117

View full text Add to dashboard Cite

We consider a class of routing optimization problems under uncertainty in which all decisions are made before the uncertainty is realized. The objective is to obtain optimal routing solutions that would, as much as possible, adhere to a set of specified requirements after the uncertainty is realized. These problems include finding an optimal routing solution to meet the soft time window requirements at a subset of nodes when the travel time is uncertain, and sending multiple capacitated vehicles to different nodes to meet the customers' uncertain demands. We introduce a precise mathematical framework for defining and solving such routing problems. In particular, we propose a new decision criterion, called the Requirements Violation (RV) Index, which quantifies the risk associated with the violation of requirements taking into account both the frequency of violations and their magnitudes whenever they occur. The criterion can handle instances when probability distributions are known, and ambiguity, when distributions are partially characterized through descriptive statistics such as moments information. We develop practically efficient algorithms involving Benders decomposition to find the exact optimal routing solution in which the RV Index criterion is minimized, and give numerical results from several computational studies that show the attractive performance of the solutions.

show abstract

Exact Solution of the Vehicle Routing Problem

Cited by 169 publications

References 39 publications

Quantifying the transport impacts of domestic waste collection strategies

Quantifying the transport impacts of domestic waste collection strategies

The Stochastic Vehicle Routing Problem for Minimum Unmet Demand

Routing Optimization Under Uncertainty

Contact Info

Product

Resources

About