Customer satisfaction in dynamic vehicle routing problem with time windows

Barkaoui, Mohamed; Berger, Jean; Boukhtouta, Abdeslem

doi:10.1016/j.asoc.2015.06.035

Cited by 42 publications

(12 citation statements)

References 25 publications

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“…Aghezzaf (2005), Romauch and Hartl (2005), Gabor and Van Ommeren (2006), Manzini and Gebennini (2008), Acar et al (2009) and Wang (2014) studied the effect of demand parameter uncertainty in SDFLPs. Also Marufuzzaman and Eksioglu (2014) and Barkaoui and Boukhtouta (2015) developed probability of disruption and visit customer.…”

Section: Stochastic and Probabilistic Dynamic Facility Location Problmentioning

confidence: 99%

Models, solution, methods and their applicability of dynamic location problems (DLPs) (a gap analysis for further research)

et al. 2016

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Determining the best location to be profitable for the facility's lifetime is the important decision of public and private firms, so this is why discussion about dynamic location problems (DLPs) is a critical significance. This paper presented a comprehensive review from 1968 up to most recent on published researches about DLPs and classified them into two parts. First, mathematical models developed based on different characteristics: type of parameters (deterministic, probabilistic or stochastic), number and type of objective function, numbers of commodity and modes, relocation time, number of relocation and relocating facilities, time horizon, budget and capacity constraints and their applicability. In second part, It have been also presented solution algorithms, main specification, applications and some real-world case studies of DLPs. At the ends, we concluded that in the current literature of DLPs, distribution systems and production-distribution systems with simple assumption of the tackle to the complexity of these models studied more than any other fields, as well as the concept of variety of services (hierarchical network), reliability, sustainability, relief management, waiting time for services (queuing theory) and risk of facility disruption need for further investigation. All of the available categories based on different criteria, solution methods and applicability of them, gaps and analysis which have been done in this paper suggest the ways for future research.

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Section: Stochastic and Probabilistic Dynamic Facility Location Problmentioning

confidence: 99%

Models, solution, methods and their applicability of dynamic location problems (DLPs) (a gap analysis for further research)

et al. 2016

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“…In such models, vehicles must visit customers in a limited period of time and early or tardy visits are not permitted (Desaulniers et al 2014). Some of the VRP papers have been published in recent years considered various characteristics for the problem (Afshar-Bakeshloo et al 2016;Barkaoui et al 2015;Cheng and Wang 2009;Desaulniers et al 2014;Hashimoto et al 2006;Lahyani et al 2015;Lalla-Ruiz et al 2016;Lin et al 2009;Mirabi et al 2010;Respen et al 2014;Semet et al 2014;Zibaei et al 2016). The pickup and delivery as a VRP extension has been considered by Mosheiov (1998) and was solved by two heuristic algorithms to minimize transportation cost and maximize vehicles efficiency.…”

Section: Literature Reviewmentioning

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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“…Ant Colony Optimization (ACO) algorithm, a metaheuristic method that converges to better quality solutions in Static VRP was also used to solve Dynamic VRP [1,11]. ACO achieved the capability to deal with dynamism by incorporating immigrant schemes [24].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Vehicle Routing Problem: Solution by Ant Colony Optimization with Hybrid Immigrant Schemes

K.M.¹,

Kanmani²

2017

IJISA

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Abstract-During past decades, several Meta-Heuristics were considered by researchers to solve Dynamic Vehicle Routing Problem.In this paper, Ant Colony Optimization integrated with Hybrid Immigrant Schemes methods are proposed for solving Dynamic Vehicle Routing Problem. Ant Colony Optimization with hybrid immigrant schemes methods namely HIACO-I, HIACO-II and HIACO-III focused on establishing the proper balance between intensification and diversification. The performance evaluation of the algorithms in which Random Immigrants and Elitism based Immigrants were hybridized in different proportions and added to Ant Colony Optimization algorithm showed that they had produced better results in many dynamic test cases generated from three Vehicle Routing Problem instances.

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Customer satisfaction in dynamic vehicle routing problem with time windows

Cited by 42 publications

References 25 publications

Models, solution, methods and their applicability of dynamic location problems (DLPs) (a gap analysis for further research)

Models, solution, methods and their applicability of dynamic location problems (DLPs) (a gap analysis for further research)

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

Dynamic Vehicle Routing Problem: Solution by Ant Colony Optimization with Hybrid Immigrant Schemes

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