A hybrid particle swarm optimization algorithm for the capacitated location routing problem

Kechmane, Laila; Nsiri, Benayad; Baalal, Azeddine

doi:10.1108/ijicc-03-2017-0023

Cited by 14 publications

(9 citation statements)

References 39 publications

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“…The historical optimal position of the particle is P r = P r1 , P r2 , …, P rd T , and the optimal location of the population is G = g 1 , g 2 , …, g d T . The velocity and position of the particles are updated as follows [28].…”

Section: Hybrid Optimisation Algorithmmentioning

confidence: 99%

Parametric model for microwave filter by using multiple hidden layer output matrix extreme learning machine

Cao

2019

IET Microwaves, Antennas & Propagation

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Smart tuning of a filter depends very much on an accurate parametric model. Here, the authors develop a parametric model for a microwave filter based on an improved extreme learning machine (ELM). First, a coupling matrix is extracted from scattering parameters in undesirable states. The automatic decomposition and modular training strategy greatly reduces the network complexity. Next, by increasing the size of the output matrix of the hidden‐layer, the number of hidden layer nodes can be changed. Finally, the model parameters are optimised by combining the particle swarm optimisation (PSO) algorithm and the differential evolution (DE) algorithm. The presented parametric model benefits from the information of the fusion mechanism with a hybrid optimisation algorithm and succeeds in avoiding slow convergence and premature influence on the system. It also achieves the desired training accuracy within the target time. Compared with prediction based on back‐propagation (BP) neural networks and least‐squares support vector machines (LS‐SVMs), the proposed method can be generalised and has a better modelling accuracy.

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Section: Hybrid Optimisation Algorithmmentioning

confidence: 99%

Parametric model for microwave filter by using multiple hidden layer output matrix extreme learning machine

Cao

2019

IET Microwaves, Antennas & Propagation

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“…Some authors proposed PSO that was combined with a variable neighborhood strategy to solve the capacitated location-routing problem, the presented algorithm, improve local search and the time-consuming problem of hybrid PSO algorithms. A set of appropriate neighborhoods for the solution was used in the proposed PSO algorithm at the VNS stage (Kechmane et al , 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

Application of variable neighborhood search for solving large-scale many to many hub location routing problems

Abbasi

Mokhtari

Shahvar

et al. 2019

JAMR

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Purpose The purpose of this paper is to solve large-scale many-to-many hub location-routing problem (MMHLRP) using variable neighborhood search (VNS). The MMHLRP is a combination of a single allocation hub location and traveling salesman problems that are known as one of the new fields in routing problems. MMHLRP is considered NP-hard since the two sub-problems are NP-hard. To date, only the Benders decomposition (BD) algorithm and the variable neighborhood particle swarm optimization (VNPSO) algorithm have been applied to solve the MMHLRP model with ten nodes and more (up to 300 nodes), respectively. In this research, the VNS method is suggested to solve large-scale MMHLRP (up to 1,000 nodes). Design/methodology/approach Generated MMHLRP sample tests in the previous work were considered and were added to them. In total, 35 sample tests of MMHLRP models between 10 and 1,000 nodes were applied. Three methods (BD, VNPSO and VNS algorithms) were run by a computer to solve the generated sample tests of MMHLRP. The maximum available time for solving the sample tests was 6 h. Accuracy (value of objective function solution) and speed (CPU time consumption) were considered as two major criteria for comparing the mentioned methods. Findings Based on the results, the VNS algorithm was more efficient than VNPSO for solving the MMHLRP sample tests with 10–440 nodes. It had many similarities with the exact BD algorithm with ten nodes. In large-scale MMHLRP (sample tests with more than 440 nodes (up to 1,000 nodes)), the previously suggested methods were disabled to solve the problem and the VNS was the only method for solving samples after 6 h. Originality/value The computational results indicated that the VNS algorithm has a notable efficiency in comparison to the rival algorithm (VNPSO) in order to solve large-scale MMHLRP. According to the computational results, in the situation that the problems were solved for 6 h using both VNS and VNPSO, VNS solved the problems with more accuracy and speed. Additionally, VNS can only solve large-scale MMHLRPs with more than 440 nodes (up to 1,000 nodes) during 6 h.

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“…Constraints (13) and (14) guarantee that each customer is visited by one vehicle at most per period. Constraints (15) ensure that each vehicle performs one route per period at the most.…”

Section: Problem Description and Mathematical Formulationmentioning

confidence: 99%

“…The LRP consists in determining the optimal number of facilities, their location, allocation of customers to the open facilities, and the vehicle routing organization. This problem has been studied by many researchers such as Prins et al [8], Prins et al [9], Duhamel et al [10], Benlenger [11], Baldacci et al [12], Ting et al [13], Contardo et al [14], and Kechmane et al [15]. A recent survey on this problem is presented by Prodhon [16].…”

Section: Introductionmentioning

confidence: 99%

Optimization of a Two-Echelon Location Lot-Sizing Routing Problem with Deterministic Demand

Kechmane

Nsiri²,

Baalal

2018

Mathematical Problems in Engineering

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This paper aims to solve a multiperiod location lot-sizing routing problem with deterministic demand in a two-echelon network composed of a single factory, a set of potential depots, and a set of customers. Solving this problem involves making strategic decisions such as location of depots as well as operational and tactical decisions which include customers' assignment to the open depots, vehicle routing organization, and inventory management. A mathematical model is presented to describe the problem and a genetic algorithm combined with a local search procedure is proposed to solve it and is tested over three sets of instances.

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A hybrid particle swarm optimization algorithm for the capacitated location routing problem

Cited by 14 publications

References 39 publications

Parametric model for microwave filter by using multiple hidden layer output matrix extreme learning machine

Parametric model for microwave filter by using multiple hidden layer output matrix extreme learning machine

Application of variable neighborhood search for solving large-scale many to many hub location routing problems

Optimization of a Two-Echelon Location Lot-Sizing Routing Problem with Deterministic Demand

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