A tabu search heuristic for the heterogeneous vehicle routing problem on a multigraph

Lai, David S.W.; Demirag, Ozgun Caliskan; Leung, Janny

doi:10.1016/j.tre.2015.12.001

Cited by 107 publications

(49 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They pursue two main objectives: evaluating the tractability of this approach and demonstrating its benefits against using a traditional customer‐based graph. Several authors have built on these results and addressed the same issues since, for different problems: Lai et al for a time‐constrained mixed‐fleet VRP, Ben Ticha et al for the Vehicle Routing Problem with Time‐Windows (VRPTW), Huang et al for a time‐dependent time‐constrained VRP. We review below the contributions of these papers, with the section organized according to the two aforementioned issues: tractability and benefits.…”

Section: Vehicle Routing Problems With Several Attributes On Road Segmentioning

confidence: 99%

“…Then, efficient paths, with different compromises between the different attributes, are missed when constructing the customer‐based graph. Good solutions, perhaps optimal, can be discarded from the solution space . Another difficulty might come from the computation of shortest paths for a given attribute. In some cases, like when fuel consumption minimization is searched for, the value of an attribute (the cost here) can be a complex function of several parameters as starting time or load.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Vehicle routing problems with road‐network information: State of the art

et al. 2018

View full text Add to dashboard Cite

Vehicle routing problems have drawn researchers’ attention for more than 50 years. Most approaches found in the literature address these problems using the so‐called customer‐based graph, a complete graph representing the road network, where a node is introduced for every point of interest (eg, customers, depot…) and an arc represents the best path between two points. In many situations, this representation induces negative effects on the solution quality or efficiency. A growing number of works in the literature investigate these issues and propose modeling taking account of more detailed information from the road‐network. In this article, we review these works and classify them with respect to the type of negative effects provoked by the customer‐based graph.

show abstract

Section: Vehicle Routing Problems With Several Attributes On Road Segmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vehicle routing problems with road‐network information: State of the art

et al. 2018

View full text Add to dashboard Cite

show abstract

“…For the past two decades, meta-heuristics have been developed to solve VRPs instead of traditional heuristics in order to find good solutions quickly. Later, many researchers have frequently used meta-heuristics (Birim, 2016;Brandão, 2009Brandão, , 2011Kalayci & Kaya, 2016;Lai et al, 2016;Tang et al, 2013;Tavakkoli-Moghaddam et al, 2007) such as Tabu search (TS), Ant colony optimization (ACO), Simulated annealing (SA) and Genetic algorithm (GA) to solve various VRPs in some real world problems. Nowadays there are many new meta-heuristics that occur in the literature such as differential evolution (DE) and Bee colony (BC).…”

Section: Related Literaturementioning

confidence: 99%

Solving a multi-objective location routing problem for infectious waste disposal using hybrid goal programming and hybrid genetic algorithm

Wichapa¹,

Khokhajaikiat²

2018

10.5267/j.ijiec

View full text Add to dashboard Cite

Infectious waste disposal remains one of the most serious problems in the medical, social and environmental domains of almost every country. Selection of new suitable locations and finding the optimal set of transport routes for a fleet of vehicles to transport infectious waste material, location routing problem for infectious waste disposal, is one of the major problems in hazardous waste management. Determining locations for infectious waste disposal is a difficult and complex process, because it requires combining both intangible and tangible factors. Additionally, it depends on several criteria and various regulations. This facility location problem for infectious waste disposal is complicated, and it cannot be addressed using any standalone technique. Based on a case study, 107 hospitals and 6 candidate municipalities in UpperNortheastern Thailand, we considered criteria such as infrastructure, geology and social & environmental criteria, evaluating global priority weights using the fuzzy analytical hierarchy process (Fuzzy AHP). After that, a new multi-objective facility location problem model which hybridizes fuzzy AHP and goal programming (GP), namely the HGP model, was tested. Finally, the vehicle routing problem (VRP) for a case study was formulated, and it was tested using a hybrid genetic algorithm (HGA) which hybridizes the push forward insertion heuristic (PFIH), genetic algorithm (GA) and three local searches including 2-opt, insertion-move and interexchange-move. The results show that both the HGP and HGA can lead to select new suitable locations and to find the optimal set of transport routes for vehicles delivering infectious waste material. The novelty of the proposed methodologies, HGP, is the simultaneous combination of relevant factors that are difficult to interpret and cost factors in order to determine new suitable locations, and HGA can be applied to determine the transport routes which provide a minimum number of vehicles and minimum transportation cost under the actual situation efficiently in this case.

show abstract

“…In this way it is possible to overcome trapping in local minima. TS algorithm has been successfully used to lots of optimization problems [59][60][61].…”

Section: Tabu Search (Ts) Algorithm and Quantum Tabu Search (Qts) Algmentioning

confidence: 99%

Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR) for Load Forecasting

Lee

Lin

2016

Energies

View full text Add to dashboard Cite

Abstract:Hybridizing chaotic evolutionary algorithms with support vector regression (SVR) to improve forecasting accuracy is a hot topic in electricity load forecasting. Trapping at local optima and premature convergence are critical shortcomings of the tabu search (TS) algorithm. This paper investigates potential improvements of the TS algorithm by applying quantum computing mechanics to enhance the search information sharing mechanism (tabu memory) to improve the forecasting accuracy. This article presents an SVR-based load forecasting model that integrates quantum behaviors and the TS algorithm with the support vector regression model (namely SVRQTS) to obtain a more satisfactory forecasting accuracy. Numerical examples demonstrate that the proposed model outperforms the alternatives.

show abstract

A tabu search heuristic for the heterogeneous vehicle routing problem on a multigraph

Cited by 107 publications

References 64 publications

Vehicle routing problems with road‐network information: State of the art

Vehicle routing problems with road‐network information: State of the art

Solving a multi-objective location routing problem for infectious waste disposal using hybrid goal programming and hybrid genetic algorithm

Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR) for Load Forecasting

Contact Info

Product

Resources

About