A Hybrid Genetic Algorithm, Simulated Annealing and Tabu Search Heuristic for Vehicle Routing Problems with Time Windows

Thangiah, Sam R.

doi:10.1201/9781420050080.ch9

Cited by 55 publications

(63 citation statements)

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“…During the past few years, numerous papers have been written on generating good solutions for VRPTW with GAs. Almost all these papers present hybridizations of a GA with different construction heuristics [41,42] a, local searches [16,43] and other meta-heuristics such as tabu search [44] and ant colony system [45].…”

Section: Meta-heuristicsmentioning

confidence: 99%

A Survey on the Vehicle Routing Problem and Its Variants

Kumar¹,

Panneerselvam²

2012

IIM

243

126

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In this paper, we have conducted a literature review on the recent developments and publications involving the vehicle routing problem and its variants, namely vehicle routing problem with time windows (VRPTW) and the capacitated vehicle routing problem (CVRP) and also their variants. The VRP is classified as an NP-hard problem. Hence, the use of exact optimization methods may be difficult to solve these problems in acceptable CPU times, when the problem involves real-world data sets that are very large. The vehicle routing problem comes under combinatorial problem. Hence, to get solutions in determining routes which are realistic and very close to the optimal solution, we use heuristics and meta-heuristics. In this paper we discuss the various exact methods and the heuristics and meta-heuristics used to solve the VRP and its variants.

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Section: Meta-heuristicsmentioning

confidence: 99%

A Survey on the Vehicle Routing Problem and Its Variants

Kumar¹,

Panneerselvam²

2012

IIM

243

126

View full text Add to dashboard Cite

show abstract

“…Many existing VRPTW techniques, however, are single objective-based heuristic methods that incorporate penalty functions, or combine the different criteria via a weighting function (Hasnah, 2002;Li & Lim, 2003). However, in the last five years, some hybrid algorithms have been proposed to solve the VRPTW as a MOO problem.…”

Section: Multiobjective Approaches To Vrptwmentioning

confidence: 99%

Artificial Societies and Social Simulation Using Ant Colony, Particle Swarm Optimization and Cultural Algorithms

Ochoa¹,

Cruz²,

Ponce³

et al. 2010

New Achievements in Evolutionary Computation

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“…The priority rule in Equation (34) for the selection of the hospital depends on the distance, latest time and polar coordinate angle. For details of PFIH see Solomon (Solomon, 1987), Brandão and Vasconcelos (Brandão de Oliveira & Vasconcelos, 2010) and Thangiah, Osman and Sun (Thangiah, Osman, & Sun, 1994). After the initial population is built up, these chromosomes are sorted by fitness, and then a pair of chromosomes is randomly chosen for mating using the ranking-based selection of Correa et al (Correa et al, 2001), as shown in Eq.…”

Section: Hgamentioning

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

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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.

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A Hybrid Genetic Algorithm, Simulated Annealing and Tabu Search Heuristic for Vehicle Routing Problems with Time Windows

Cited by 55 publications

References 0 publications

A Survey on the Vehicle Routing Problem and Its Variants

A Survey on the Vehicle Routing Problem and Its Variants

Artificial Societies and Social Simulation Using Ant Colony, Particle Swarm Optimization and Cultural Algorithms

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

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