A survey of genetic algorithms for solving multi depot vehicle routing problem

Karakatič, Sašo; Podgorelec, Vili

doi:10.1016/j.asoc.2014.11.005

Cited by 250 publications

(122 citation statements)

References 52 publications

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“…The idea was derived from the nature of biological genetics "survival of the fittest", opened and enlightened by biological evolutionism. The detailed GA steps can be found in References [25][26][27]. Here, we focus on the designing of the key parameters.…”

Section: The Improved Genetic Algorithm Of the Proposed Problemmentioning

confidence: 99%

“…Following the first heuristics algorithm presented by Hochbaum and Shmoys [24], there has been a long list of work on designing heuristics algorithms for this problem over the years. Several meta-heuristic approaches were reported, including Genetic Algorithm (GA) [25][26][27], Tabu Search (TS) [28][29][30], Particle Swarm Optimization (PSO) [11,20,31], hybrid algorithm [32][33][34][35], etc., which were used to solve VRP and its variant problems. With reference to Genetic Algorithm, Ombuki et al [25] proposed an application of genetic algorithms approach for multi-depot vehicle routing problem(MDVRP) using the Pareto ranking technique.…”

Section: Introductionmentioning

confidence: 99%

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The Emergency Vehicle Routing Problem with Uncertain Demand under Sustainability Environments

Qin

Cheng

et al. 2017

Sustainability

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Abstract:The reasonable utilization of limited resources is critical to realize the sustainable developments. In the initial 72-h crucial rescue period after the disaster, emergency supplies have always been insufficient and the demands in the affected area have always been uncertain. In order to improve timeliness, utilization and sustainability of emergency service, the allocation of the emergency supplies and the emergency vehicle routes should be determined simultaneously. Assuming the uncertain demands follow normal distribution, an optimization model for the emergency vehicle routing, by considering the insufficient supplies and the uncertain demands, is developed. The objective function is applied to minimize the total costs, including the penalty costs induced by more or less supplies than the actual demands at all demand points, as well as the constraints of the time windows and vehicle load capacity taken into account. In more details, a solution method for the model, based on the genetic algorithm, is proposed, which solves the problem in two stages. A numerical example is presented to demonstrate the efficiency and validity of the proposed model and algorithm.

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Section: The Improved Genetic Algorithm Of the Proposed Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Emergency Vehicle Routing Problem with Uncertain Demand under Sustainability Environments

Qin

Cheng

et al. 2017

Sustainability

View full text Add to dashboard Cite

show abstract

“…Genetic algorithm as an evolutionary algorithm has been successfully used for solving NP-hard problems, such as different supply chain management problems (Izadi and Kimiagari 2014;Kannan et al 2010;Kuo and Han 2011;Raj and Rajendran 2012) and different VRP problems (Elhassania et al 2014;Karakatič and Podgorelec 2015). In a GA, we have an initial population including individuals that evolves during the algorithm by genetic operators, i.e., selection, cross over and mutation.…”

Section: Proposed Genetic Algorithmmentioning

confidence: 99%

Two phase genetic algorithm for vehicle routing and scheduling problem with cross-docking and time windows considering customer satisfaction

2017

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Cross-docking is a new warehousing policy in logistics which is widely used all over the world and attracts many researchers attention to study about in last decade. In the literature, economic aspects has been often studied, while one of the most significant factors for being successful in the competitive global market is improving quality of customer servicing and focusing on customer satisfaction. In this paper, we introduce a vehicle routing and scheduling problem with cross-docking and time windows in a three-echelon supply chain that considers customer satisfaction. A set of homogeneous vehicles collect products from suppliers and after consolidation process in the cross-dock, immediately deliver them to customers. A mixed integer linear programming model is presented for this problem to minimize transportation cost and early/tardy deliveries with scheduling of inbound and outbound vehicles to increase customer satisfaction. A two phase genetic algorithm (GA) is developed for the problem. For investigating the performance of the algorithm, it was compared with exact and lower bound solutions in small and large-size instances, respectively. Results show that there are at least 86.6% customer satisfaction by the proposed method, whereas customer satisfaction in the classical model is at most 33.3%. Numerical examples results show that the proposed two phase algorithm could achieve optimal solutions in small-size instances. Also in large-size instances, the proposed two phase algorithm could achieve better solutions with less gap from the lower bound in less computational time in comparison with the classic GA.

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“…Therefore, heuristic techniques will be applied to solve VRP in this case. Due to the combinatorial nature of the VRP and the Genetic Algorithm's efficiency in solving combinatorial problems, the Genetic Algorithm (GA), the most famous meta-heuristic algorithm in the world (Golmohammadi et al, 2016), is one of the meta-heuristics which is often used to solve the VRPs in the literature (Ahmadizar et al, 2015;Karakatič & Podgorelec, 2015;Razali, 2015), and the success of this is mainly due to its simplicity, easy operation, and great flexibility . However, this traditional algorithm has been developed in many ways by integrating the GA with other tools to handle the various problems efficiently.…”

Section: Introductionmentioning

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 survey of genetic algorithms for solving multi depot vehicle routing problem

Cited by 250 publications

References 52 publications

The Emergency Vehicle Routing Problem with Uncertain Demand under Sustainability Environments

The Emergency Vehicle Routing Problem with Uncertain Demand under Sustainability Environments

Two phase genetic algorithm for vehicle routing and scheduling problem with cross-docking and time windows considering customer satisfaction

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

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