Metaheuristic algorithms for solving two interconnected vehicle routing problems in a hospital complex

Kergosien, Yannick; Lenté, Christophe; Billaut, Jean‐Charles; Perrin, Sean

doi:10.1016/j.cor.2013.01.009

Cited by 36 publications

(15 citation statements)

References 22 publications

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“…Other classical scheduling problems are encountered in computer systems, project management, timetabling, etc. More recent application domains appear, particularly for treating requests in big data environment [4,5], in hospital environments [14], in rail companies [11], etc.…”

Section: Scheduling Problemsmentioning

confidence: 99%

Robust Machine Scheduling Based on Group of Permutable Jobs

Artigues

Billaut

Cheref

et al. 2016

International Series in Operations Research &Amp; Management Science

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This chapter presents the "group of permutable jobs" structure to represent set of solutions to disjunctive scheduling problems. Traditionally, solutions to disjunctive scheduling problems are represented by assigning sequence of jobs to each machine. The group of permutable jobs structure assigns an ordered partition of jobs to each machine, i.e. a group sequence. The permutation of jobs inside a group must be all feasible with respect to the problem constraints. Such a structure provides more flexibility to the end user and, in particular, allows a better reaction to unexpected events. The chapter considers the robust scheduling framework where uncertainty is modeled via a discrete set of scenarios, each scenario specifying the problem parameters values. The chapter reviews the models and algorithms that have been proposed in the literature for evaluating a group sequence with respect to scheduling objectives for a fixed scenario as well as the recoverable robust optimization methods that have been proposed for generating robust group sequence5) M 2 (4) 7 0 2 4 Fig. 9.9: Gantt representation of a group sequence for the single machine problem Example 2: Job Shop EnvironmentIn Fig. 9.10, two groups of permutable operations are proposed. The first one is composed by the operations of J 1 and J 3 performed on the first machine. The second is composed of the operations of the same jobs on the third machine. One can see that whatever the order of the operations inside each group, the sequence remains feasible. Of course, this flexibility has a price since the makespan is now equal to 32.

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Section: Scheduling Problemsmentioning

confidence: 99%

Robust Machine Scheduling Based on Group of Permutable Jobs

Artigues

Billaut

Cheref

et al. 2016

International Series in Operations Research &Amp; Management Science

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“…In the theory of computational complexity, the VRP belongs to a class of NP-hard problems [1]. Equations (1)- (10) shows that the WVRP is more complex than the VRP and consequently, is also an NP-hard problem.…”

Section: Max-min Ant System With Beam Search (Beam-mmas)mentioning

confidence: 99%

“…T HE VEHICLE ROUTING PROBLEM (VRP) [1]- [7] is a well-known operations research model for a class of transportation and logistics management problems in industrial systems. Large-scale problems arise in practical production systems, e.g., charge planning, casting planning, and rolling planning in an iron-steel making process, which can be formulated as variants of the VRP model.…”

Section: Introductionmentioning

confidence: 99%

“…The aim in a typical vehicle routing problem (VRP) is to find the optimal tours for several homogeneous vehicles that travel from a depot to many customers with known demands and return to the depot. All of the vehicles must visit all of the customers exactly once and the vehicle capacity constraints cannot be exceeded [1]. In the classical VRP model and its variants, a constant weight load in the vehicle is assumed during the visiting route, which results in a minimum cost measure that is equivalent to the minimum distance measure.…”

Section: Introductionmentioning

confidence: 99%

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Beam Search Combined With MAX-MIN Ant Systems and Benchmarking Data Tests for Weighted Vehicle Routing Problem

Tang

Guan

et al. 2014

IEEE Trans. Automat. Sci. Eng.

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In real-world cargo transportation, there are charges associated with both the traveling distance and the loading quantity. Cargo trucks must comply with a mandatory lower carbon emissions policy: the emissions of large-volume cargo truck/containers depend greatly on the cargo loading and the traveling distance. To address this issue, instead of assuming a constant vehicle loading from one customer to another, a variable vehicle loading should be used in optimizing the vehicle routine, which is known as a weighted vehicle routing problem (WVRP) model. The WVRP is an NP-hard problem; thus, the purpose of this paper is to develop a BEAM-MMAS algorithm that combines a MAX-MIN ant system with beam search to show that the WVRP is more effective than the VRP and to determine the types of VRP instances for which the WVRP has more cost-savings than the VRP. To this end, computational experiments are carried out on benchmark problems of the capacitated VRP for seven types of distributions, and the effectiveness of the BEAM-MMAS algorithm is compared with that of general ACO and MMAS algorithms for large-size benchmarking instances. The benchmarking tests show that lower operation costs are produced using the WVRP than using the optimal or best known paths of the CVRP and that the WVRP can increase cost savings for the instances with a dispersed customer distribution and a large weight.Note to Practitioners-The VRP is a well-known operations research model for a class of transportation and logistics management problems in industrial systems. Large-scale problems arise in practice in production systems, e.g., charge planning, casting planning, and rolling planning in an iron-steel making process, which can be formulated as variants of the VRP model. This paper is motivated by practical needs for clean production and cost-savings, lower carbon emission policies and the observation that most VRP models neglect the effect of the cargo weight on the total costs. The WVRP model incorporates the cargo weight into the optimization objective function, resulting in an exact formulation. This model can be applied to logistics and transportation management problems in industrial systems and is particularly useful given the mandatory lower carbon emissions policy. The WVRP model results in higher cost savings than traditional VRP models for instances with a dispersed customer distribution and a large weight. The BEAM-MMAS algorithm combines beam search and MAX-MIN ant systems and has been shown to be an effective algorithm for solving such problems; this algorithm can be used to ensure high quality solutions for medium-scale problems. For largescale problems, the MMAS algorithm can produce a satisfactory solution in less time than the BEAM-MMAS algorithm.Index Terms-Ant colony algorithm, beam search, benchmarking data testing, meta-heuristics, weighted vehicle routing problem. 1545-5955 His research interest includes informationprocessing, decision-making, and operational management.

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“…The MDVRP uses multiple depots instead of one depot to construct the routes. Delivery of food and drugs, chemical and oil products is some applications of the MDVRP (Wasner and Zäpfel 2004;Kergosien et al 2013;Lalla-Ruiz et al 2016). For further studies, readers can refer to Toth and Vigo (2001) and Golden et al (2008).…”

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confidence: 99%

Time-constrained maximal covering routing problem

Amiri

Salari

2018

OR Spectrum

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We introduce the time-constrained maximal covering routing problem (TCMCRP), as a generalization of the covering salesman problem. In this problem, we are given a central depot, a set of facilities and several customers which are located within a pre-determined coverage distance of available facilities. Each facility can supply the demand of some customers which are within its coverage radius. Starting from the depot, the goal is to maximize the total number of covered customers, by constructing a set of p length constraint Hamiltonian cycles. We have proposed a mixed integer linear programming model and three heuristic algorithms, namely iterated local search (ILS), tabu search (TS) and variable neighborhood search (VNS), to solve the problem. Extensive computational tests on this problem and some of its variants clearly indicate the effectiveness of the developed solution methods.

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Metaheuristic algorithms for solving two interconnected vehicle routing problems in a hospital complex

Cited by 36 publications

References 22 publications

Robust Machine Scheduling Based on Group of Permutable Jobs

Robust Machine Scheduling Based on Group of Permutable Jobs

Beam Search Combined With MAX-MIN Ant Systems and Benchmarking Data Tests for Weighted Vehicle Routing Problem

Time-constrained maximal covering routing problem

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