Location arc routing problem with inventory constraints

Riquelme-Rodríguez, Juan-Pablo; Gamache, Michel; Langevin, André

doi:10.1016/j.cor.2016.06.012

Cited by 24 publications

(18 citation statements)

References 24 publications

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“…Some developments set a combination of attending days to each arc [8,9,11,13,17,18], others use time intervals [7], or just frequencies [6,14] to generate solutions. Recent approaches have been discretizing the time in a way that the finishing time of one activity is the initial time for the next one [12,19,20].…”

Section: Literature Reviewmentioning

confidence: 99%

A New Ant Colony Optimization Algorithm to Solve the Periodic Capacitated Arc Routing Problem with Continuous Moves

Batista

Scarpin

Pécora

et al. 2019

Mathematical Problems in Engineering

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This paper describes a variant of the Periodic Capacitated Arc Routing Problem for inspections in a railroad network. Inspections are performed by vehicles over a time horizon on which some stretches need evaluation more frequently than others due to its use. Each car can evaluate one stretch per day without being attached to a depot; at each day, the shift may start and end at different locations. This characterizes the problem as the Periodic Capacitated Arc Routing Problem with Continuous Moves in which firstly the delays on attendances are minimized and, second, the displacement costs. We present a mathematical model and an Ant Colony Optimization algorithm to solve the problem. The use of a local search procedure and some principles of Granular Tabu Search is crucial for the algorithm’s performance. The numerical results are promising, especially for critical situations where the arcs’ needs are close to the total vehicles’ capacity.

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Section: Literature Reviewmentioning

confidence: 99%

A New Ant Colony Optimization Algorithm to Solve the Periodic Capacitated Arc Routing Problem with Continuous Moves

Batista

Scarpin

Pécora

et al. 2019

Mathematical Problems in Engineering

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“…To transfer the cash ow to the rst year, each ow is divided by (1+int) p . Constraints (2) and (3) ensure that if a customer is elected, the client should be provided with service. These constraints also ensure that, in case of serving, this action is only to be done by a vehicle and in one visit.…”

Section: Mathematical Formulationmentioning

confidence: 99%

“…Description of SA algorithm SA is a metaheuristic algorithm used to overcome largesized problems that have a large solution space and produce results close to the global optimum amount in a short amount of time. The SA algorithm was rst created by Metropolis et al in 1953 to generalize the Monte Carlo method to determine the equations of state and, also, frozen states of n-body systems in the eld of metallurgy [3][4][5]. A typical procedure of SA is illustrated in Figure 7 4.3.2.…”

Section: Mutation Operatormentioning

confidence: 99%

“…The location-routing problem is at focus in logistics administration domain where the main purpose is to determine the location and the number of facilities as well as the optimum route for the vehicles. Integrated location-routing models are used for solving the Facility Location Problems (FLP) and VRP, showing a good interaction between the above two decisions [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

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Multi Stage Investment Planning and Customer Selection in A Two Echelon Multi-Period Supply Chain

2018

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In the supply chain of Fast-Moving Consumer Goods (FMCG), logistics costs represent a major part of total expenses. At these low-level chains, one usually faces a Vehicle Routing Problem (VRP). In practice, however, due to the high cost of service in many cases, some customers are not selected to serve. Investment-related restrictions in many cases make it impossible to serve some of the potential customers. In such conditions, designing a supply chain network, including a location-allocation problem in the warehouse, Multiple Depot Vehicle Routing Problem (MDVRP) at the distribution level, and customer selection at the retail level in several periods of time, is considered. In this respect, in addition to certain methods that can be used in small sizes, metaheuristic algorithms have been used to solve large-scale models. With the aim of improving the performance, if not improving a few diversi cations, algorithms are temporarily enhanced; eventually, by using statistical approaches, it has been demonstrated that this method could have a signi cant impact on the quality of responses. Genetic Algorithm (GA) and Simulated Annealing (SA) algorithm have been used for this purpose.

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“…The problem may be called the Location Routing Problem (LRP) or the Location-Arc Routing Problem (LARP), based on whether demands are located on edges or nodes of the network. To the best of the authors' knowledge, while the LRP has been much studied (Fazayeli et al 2018;Prodhon, Prins 2014;Fazel Zarandi et al 2013;Lopes et al 2013), less attention has been paid to the LARP (Levy, Bodin 1989;Ghiani, Laporte 1999;Hashemi Doulabi, Seifi 2013;Lopes et al 2014;Riquelme-Rodríguez et al 2016). Ghiani and Laporte (2001) surveyed the most important applications, such as garbage collection, mail delivery and road maintenance, and solution approaches for the LARP.…”

Section: Introduction and Problem Definitionmentioning

confidence: 99%

A Mathematical Model for the Capacitated Location-Arc Routing Problem With Deadlines and Heterogeneous Fleet

et al. 2019

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This paper considers a Capacitated Location-Arc Routing Problem (CLARP) with Deadlines (CLARPD) and a fleet of capacitated heterogeneous vehicles. The proposed mixed integer programming model determines a subset of potential depots to be opened, the served roads within predefined deadlines, and the vehicles assigned to each open depot. In addition, efficient routing plans are determined to minimize total establishment and traveling costs. Since the CLARP is NP-hard, a Genetic Algorithm (GA) is presented to consider proposed operators, and a constructive heuristic to generate initial solutions. In addition, a Simulated Annealing (SA) algorithm is investigated to compare the performance of the GA. Computational experiments are carried out for several test instances. The computational results show that the proposed GA is promising. Finally, sensitivity analysis confirms that the developed model can meet arc routing timing requirements more precisely compared to the classical Capacitated Arc Routing Problem (CARP).

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Location arc routing problem with inventory constraints

Cited by 24 publications

References 24 publications

A New Ant Colony Optimization Algorithm to Solve the Periodic Capacitated Arc Routing Problem with Continuous Moves

A New Ant Colony Optimization Algorithm to Solve the Periodic Capacitated Arc Routing Problem with Continuous Moves

Multi Stage Investment Planning and Customer Selection in A Two Echelon Multi-Period Supply Chain

A Mathematical Model for the Capacitated Location-Arc Routing Problem With Deadlines and Heterogeneous Fleet

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