A unified tabu search algorithm for vehicle routing problems with soft time windows

Zhuo, Fu; Eglese, Richard W.; Li, Leon Y. O.

doi:10.1057/palgrave.jors.2602371

Cited by 62 publications

(55 citation statements)

References 22 publications

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“…The authors in (Fu et al, 2007) classified the possible types of problems with linear penalty functions into 6 types. So far Types 1-5 have been considered in the literature.…”

Section: Related Workmentioning

confidence: 99%

“…In terms of number of routes and total distances the Tabu Search outperforms the heuristics on all instances, this at the cost of a higher percentage of violated time windows. The unified tabu search from (Fu et al, 2007) is tested on problems of Type 1, 2 & 3.…”

Section: Related Workmentioning

confidence: 99%

“…An evaluation is done on the Type 3 problems. The authors are able to improve the number of vehicles of (Fu et al, 2007) in more than half of the instances. A problem generator-solver heuristic for VRPSTWs is proposed in (Ioannou et al, 2003).…”

Section: Related Workmentioning

confidence: 99%

“…In (Fu et al, 2007) a classification of VRPSTWs into 6 problem Types has been proposed. We consider all Types in our experimental section.…”

Section: Problem Types and Benchmark Instancesmentioning

confidence: 99%

“…Moreover, each stage uses a variable neighborhood descent (VND) (Mladenović and Hansen, 1997). Experimental results indicate that our multi-stage VLSN algorithm improves bestknown solutions on 37%, 57% and 100% of the Type 1,2 and 3 instances respectively, which are standard benchmarks for the VRPSTW (see (Fu et al, 2007)). Equally interesting is the fact that the multi-stage algorithm decreases the number of routes in 27% of the instances (up to 4 less routes) and the soft time window violations in 35% (up to 27% less violated time windows) of the remaining instances.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Multistage Very Large-Scale Neighborhood Search for the Vehicle Routing Problem with Soft Time Windows

Mouthuy

Massen

Deville

et al. 2015

Transportation Science

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This paper considers the Vehicle Routing Problem with Soft Time Windows, a challenging routing problem, where customer's time windows may be violated at a certain cost. The Vehicle Routing Problem with Soft Time Windows has a lexicographic objective function, aiming at minimizing first the number of routes, then the number of violated time windows and finally the total routing distance. We present a multi-stage Very Large-Scale Neighborhood search for this problem. Each stage corresponds to a Variable Neighborhood Descent over a parametrizable Very Large-Scale Neighborhood. These neighborhoods contain an exponential number of neighbors, as opposed to classical local search neighborhoods. Often, searching Very Large-Scale Neighborhoods can produce local optima of a higher quality than polynomial-sized neighborhoods. Furthermore we use a sophisticated heuristic to determine service start times allowing to minimize the number of violated time windows. We test our approach on number of different problem types, and compare the results to the relevant state-of-the-art. The experimental results show that our algorithm improves best-known solutions on 53% of the most studied instances. Many of these improvements stem from a reduction of the number of vehicles, a critical objective in Vehicle Routing Problems.

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“…The authors in (Fu et al, 2007) classified the possible types of problems with linear penalty functions into 6 types. So far Types 1-5 have been considered in the literature.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%