A non-dominated sorting genetic algorithm for a bi-objective pick-up and delivery problem

Velasco, Nubia; Dejax, Pierre; Guéret, Christelle; Prins, Christian

doi:10.1080/0305215x.2011.639368

Cited by 24 publications

(12 citation statements)

References 15 publications

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“…For example, Jozefowiez et al [59] proposed a Non-dominated Sorting Genetic algorithm (NSGAII) for solving the vehicle routing problem with route balancing. In the same context, Lacomme et al [60] tested several local search procedures within the NSGAII framework for the capacitated arc routing problem, and more recently, Velasco et al [61] adapted this previous local search for solving the pick-up and delivery problem.…”

Section: Exact Linear Solver Metaheuristicmentioning

confidence: 99%

“…The principle of the Non-Dominated Sorting Genetic Algorithm (NSGAII) proposed by [64] is chosen to solve the bi-objective VRPTW-SP, since it has been successfully used for solving multi-objective combinatorial optimization problems and especially vehicle routing problems, such as in Lacomme et al [60], Velasco et al [61], and Labadie et al [65]. As far as we know, there are no works addressing the NSGAII to solve the multi-objective vehicle routing problems in the HHC context.…”

Section: Motivationmentioning

confidence: 99%

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Bicriteria Vehicle Routing Problem with Preferences and Timing Constraints in Home Health Care Services

Haddadene¹,

Labadie

Prodhon

2019

Algorithms

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Home Healthcare (HHC) is an emerging and fast-expanding service sector that gives rise to challenging vehicle routing and scheduling problems. Each day, HHC structures must schedule the visits of caregivers to patients requiring specific medical and paramedical services at home. These operations have the potential to be unsuitable if the visits are not planned correctly, leading hence to high logistics costs and/or deteriorated service level. In this article, this issue is modeled as a vehicle routing problem where a set of routes has to be built to visit patients asking for one or more specific service within a given time window and during a fixed service time. Each patient has a preference value associated with each available caregiver. The problem addressed in this paper considers two objectives to optimize simultaneously: minimize the caregivers' travel costs and maximize the patients' preferences. In this paper, different methods based on the bi-objective non-dominated sorting algorithm are proposed to solve the vehicle routing problem with time windows, preferences, and timing constraints. Numerical results are presented for instances with up to 73 clients. Metrics such as the distance measure, hyper-volume, and the number of non-dominated solutions in the Pareto front are used to assess the quality of the proposed approaches.Algorithms 2019, 12, 152 2 of 25 work (Ait Haddadene et al. [1]), the minimization of the traveling cost and the sum of non-preferences related to customers were considered as a single-objective function. The obtained results showed the conflicting nature of these criteria, which is why, we are particularly interested in solving the bi-objective version of VRPTW-SP.The problem at hand is an extension of the Vehicle Routing Problem (VRP), which is one of the most-studied combinatorial optimization problems. First introduced by Dantzig and Ramser [2], the aim of this NP-hard problem is to determine a set of minimal cost routes in which a set of customers is to be served by a fleet of vehicles based at a depot node. A detailed survey can be found in Toth and Vigo [3]. Currently, one of the most effective metaheuristics for the VRP is the hybrid genetic algorithm introduced by Vidal [4], which was latter generalized to solve more than 26 VRP variants.Among the different classes of VRP, the VRP with Time Windows (VRPTW) stands as the basis variant of our studied problem. Heuristic and metaheuristic approaches have been widely used to solve the VRPTW such as those proposed by Kindervater and Savelsbergh [5], Bräysy and Gendreau [6], Nagata [7], and Bräysy et al. [8]. In the field of healthcare, most problems are modeled as a VRPTW. In fact, the origin of vehicle routing problems in HHC systems may be linked to the door-to door-transportation of elderly or disabled persons (Dial A Ride Problems (DARP)) proposed by Bodin and Sexton [9]. A few years later, this variant was extended to the Home Healthcare (HHC) problem by Cheng and Rich [10], where caregivers are assigned to patient homes to pro...

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Section: Exact Linear Solver Metaheuristicmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

Bicriteria Vehicle Routing Problem with Preferences and Timing Constraints in Home Health Care Services

Haddadene¹,

Labadie

Prodhon

2019

Algorithms

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“…non Delivery Problems with Time Windows and Multi-Vehicles -158 -dominated solutions) are kept either in the population itself or in a separate archive. In NSGA-II algorithm, the best solutions kept participate in the reproduction process which guides the exploration of the search space towards interesting areas [30,31]. But, Strength Pareto Evolutionary Algorithm 2 (SPEA2) [32], preserves elitism by using an archive of non-dominated solutions, which does not necessarily take part in reproduction.…”

Section: Multi-criteria Optimization Problemmentioning

confidence: 99%

“…Individual example of Pcouple/depot After applying the regrouping step we have: 7 couples are assigned to depot 1 indexed 37 { C2(3,4), C4 (5,6), C5(18,8),C6 (1,12), C10(15,16), C11(25,36), C18(28,30) }; 6 couples are assigned to depot 2 indexed 38 { C3(13,23), C7(11,19), C9(31,14) C12(20,35), C14(27,10), C15(17,24)} 5 couples are assigned to depot 2 indexed 39 {C1 (7, 34), C8(26,33), C13 (2,22) C16…”

mentioning

confidence: 99%

Genetic Algorithm for Multi-Criteria Optimization of Multi-Depots Pick-up and Delivery Problems with Time Windows and Multi-Vehicles

Alaïa¹,

Dridi

Bouchriha³

et al. 2015

APH

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In this paper, multi-vehicles, multi-depots pick-up and delivery problems with time windows (m-MDPDPTW) is presented as a multi-criteria optimization problem. The m-MDPDPTW is a variant of pick-up and delivery problem (PDP) and a challenging problem in the field of vehicle routing problem (VRP). The aim is to discover a set of satisfying solutions (routes) minimizing total travel distance, total tardiness time and the total number of vehicles. These routes satisfy transportation requests without contravening any of the instance specific constraints (precedence, capacity and time window constraints). In our problem each request is transported by one of the vehicles between paired pick-up and delivery locations. Such that, the depot does not retain the goods. In this paper, we assume that all vehicles have the same capacity and depart from and return to the same depot. The new encoding and structure algorithm on which this contribution is based uses a genetic algorithm, a selection process using ranking with several Pareto fronts and an elitist selection strategy for replacement. An improved encoding chromosome path representation is given to simulate the process of evolution using several types of populations in different sizes. The performance of the new algorithm is tested on data sets instances of Li & Lim's PDPTW benchmark problems. The results indicate that the proposed algorithm gives good results.

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“…Aktar et al (2009) employed linear and nonlinear programming to obtain the optimal design attribute settings for affective design. An alternative approach to design optimization is based on heuristic algorithms, such as GAs and simulated annealing (Velasco, et al, 2012;Kaplan and Rabadi, 2013), which are stochastic and effective optimization techniques to search for near-optimal solutions for various problems of engineering design (Saridakis and Dentsoras, 2008;Chakraborty et al, 2003). GAs in particular have been applied in various areas of product design, such as product planning (Jiao et al, 2007;D'Souza, 2003), interactive generative design (Kim and Cho, 2000;Yanagisawa and Fukuda, 2005), and optimization of affective design (Hsiao and Liu, 2004;Hsiao and Tsai, 2005;Jiao et al, 2008;Yang and Shieh, 2010).…”

mentioning

confidence: 99%

A guided search genetic algorithm using mined rules for optimal affective product design

Fung

Kwong

Chan

et al. 2013

Engineering Optimization

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Affective design is an important aspect of new product development, especially for consumer products, to achieve a competitive edge in the marketplace. It can help companies develop new products that can better satisfy the emotional needs of customers. However, product designers usually encounter difficulties in determining the optimal settings of the design attributes for affective design. In this paper, a novel guided search genetic algorithm (GA) approach is proposed to determine the optimal design attribute settings for affective design. The optimisation model formulated based on the proposed approach applied constraints and guided search operators, which were formulated based on mined rules, to guide the GA search and to achieve desirable solutions. A case study on the affective design of mobile phones was conducted to illustrate the proposed approach and validate its effectiveness. Validation tests were conducted, and the results show that the guided search GA approach outperforms the GA approach without the guided search strategy in terms of GA convergence and computational time. In addition, the guided search optimization model is capable of improving GA to generate good solutions for affective design.

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A non-dominated sorting genetic algorithm for a bi-objective pick-up and delivery problem

Cited by 24 publications

References 15 publications

Bicriteria Vehicle Routing Problem with Preferences and Timing Constraints in Home Health Care Services

Bicriteria Vehicle Routing Problem with Preferences and Timing Constraints in Home Health Care Services

Genetic Algorithm for Multi-Criteria Optimization of Multi-Depots Pick-up and Delivery Problems with Time Windows and Multi-Vehicles

A guided search genetic algorithm using mined rules for optimal affective product design

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