New mathematical model for the bi-objective inventory routing problem with a step cost function: A multi-objective particle swarm optimization solution approach

Dabiri, Nooraddin; Tarokh, Mohammad Jafar; Alinaghian, Mahdi

doi:10.1016/j.apm.2017.03.022

Cited by 35 publications

(16 citation statements)

References 59 publications

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“…Incentives offered to each participant are: G 1 = 298, G 2 = 341, G 3 = 268, G 4 = 314. The parameters used for AGPSO are as follows [39,40,63,64]: nPOP = 150, nREP = 20, W = 0.5,…”

Section: Optimization Resultsmentioning

confidence: 99%

“…the effectiveness of AGPSO. The selection process of related parameters is important and necessary, and there are several methods to select related parameters, including comparative experiment and selection [4,39,40,63,64], orthogonal experimental design [65], etc.…”

Section: Plos Onementioning

confidence: 99%

“…The total operational cost, the number of vehicles used for delivery, and the computation time are compared to assess the effectiveness of AGPSO. The selection process of related parameters is important and necessary, and there are several methods to select related parameters, including comparative experiment and selection [ 4 , 39 , 40 , 63 , 64 ], orthogonal experimental design [ 65 ], etc. A parameter sensitivity analysis is performed to determine the parameter sensitivity and select parameter values.…”

Section: Case Studymentioning

confidence: 99%

“…Incentives offered to each participant are: G 1 = 298, G 2 = 341, G 3 = 268, G 4 = 314. The parameters used for AGPSO are as follows [ 39 , 40 , 63 , 64 ]: nPOP = 150, nREP = 20, W = 0.5, R 1 = 1, R 2 = 2, nGrid = 9, α = 0.1, β = 2, λ = 2, mut = 0.1, TS = 40, L k = 1500, L v = 200, M k = 1500, M v = 500, θ 1 = 0.05, θ 2 = 0.1. We consider two collaborative forms in this study, and 52 working periods are included in a year.…”

Section: Case Studymentioning

confidence: 99%

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Collaborative multicenter logistics delivery network optimization with resource sharing

et al. 2020

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Collaboration among logistics facilities in a multicenter logistics delivery network can significantly improve the utilization of logistics resources through resource sharing including logistics facilities, vehicles, and customer services. This study proposes and tests different resource sharing schemes to solve the optimization problem of a collaborative multicenter logistics delivery network based on resource sharing (CMCLDN-RS). The CMCLDN-RS problem aims to establish a collaborative mechanism of allocating logistics resources in a manner that improves the operational efficiency of a logistics network. A bi-objective optimization model is proposed with consideration of various resource sharing schemes in multiple service periods to minimize the total cost and number of vehicles. An adaptive grid particle swarm optimization (AGPSO) algorithm based on customer clustering is devised to solve the CMCLDN-RS problem and find Pareto optimal solutions. An effective elite iteration and selective endowment mechanism is designed for the algorithm to combine global and local search to improve search capabilities. The solution of CMCLDN-RS guarantees that cost savings are fairly allocated to the collaborative participants through a suitable profit allocation model. Compared with the computation performance of the existing nondominated sorting genetic algorithm-II and multi-objective evolutionary algorithm, AGPSO is more computationally efficient. An empirical case study in Chengdu, China suggests that the proposed collaborative mechanism with resource sharing can effectively reduce total operational costs and number of vehicles, thereby enhancing the operational efficiency of the logistics network.

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“…Incentives offered to each participant are: G 1 = 298, G 2 = 341, G 3 = 268, G 4 = 314. The parameters used for AGPSO are as follows [39,40,63,64]: nPOP = 150, nREP = 20, W = 0.5,…”

Section: Optimization Resultsmentioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

See 2 more Smart Citations

Collaborative multicenter logistics delivery network optimization with resource sharing

et al. 2020

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“…Among these numerous types of meta-heuristic algorithms, PSO has shown great advantage of solving complex problems due to its simple structure and less parameters. Up to now, PSO has been successfully applied in several areas, such as pattern clustering [10], crew scheduling problems [11], multi-robot path planning [12], quality control [13], network reliability [14], inventory routing problems [15], time series forecasting [16], constrained shortest path problems [17], layer-packing problems [18], cost-sensitive attribute reduction [19].…”

Section: Introductionmentioning

confidence: 99%

An Advanced Particle Swarm Optimization Method based on T-Distribution Random Process

Zhang¹,

Yong-sheng²

2019

Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)

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There are many real-life engineering problems that entail appropriate optimization methods. Although almost all the problems can be modeled into simple forms described by mathematical formula, it is hard to solve all the decisive problems by a single optimization method. Researchers have developed many effective optimization techniques to solve assorted problems. Among these particle swarm optimization (PSO) has played an important role in optimization of complex and high-dimensional problems. However, PSO suffers from premature convergence and low precision. For this purpose, the paper proposed a TPSO which adapts a stochastic process based on t-distribution and a mechanism of reference set. Subsequently simulations tested on 13 classical benchmark functions demonstrated that the TPSO can achieve faster convergence speed and higher accuracy. Finally, the application on the path planning problem of UAV evaluated the efficiency of the proposed algorithm.

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Inventory system with generalized triangular neutrosophic cost pattern incorporating maximum life-time-based deterioration and novel demand through PSO

Bhavani¹,

Mahapatra²

2022

Soft Comput

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At the manufacturing plant or while the products are being transferred from one supply layer to another, there is a considerable possibility of receiving damaged or faulty items mixed in with non-defective commodities. This research focuses on the non-defective and defective products that are shipped to retailers by their suppliers. The retailer reworks faulty items to make them non-defective, and the retailer receives a discount on the cost of purchasing defective items. The presented inventory system addresses the uncertainty in inventory costs and also considers the deterioration of items with prioritized maximum product life. In this study, our aim is to minimize the total inventory cost when demand rate as a function of quality and power pattern of time under crisp and generalized triangular neutrosophic environments.Based on the payment deal, interest charges are imposed only when the payment delay has passed a particular allowable time limit. The neutrosophic number, which provides three different types of membership functions representing truth, hesitation, and falseness, is used in the inventory model to handle the cost pattern's uncertainty. A particle swarm optimization approach is used to analyze the proposed inventory model, and the results are validated using a numerical example and sensitivity analysis for various parameters.

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New mathematical model for the bi-objective inventory routing problem with a step cost function: A multi-objective particle swarm optimization solution approach

Cited by 35 publications

References 59 publications

Collaborative multicenter logistics delivery network optimization with resource sharing

Collaborative multicenter logistics delivery network optimization with resource sharing

An Advanced Particle Swarm Optimization Method based on T-Distribution Random Process

Inventory system with generalized triangular neutrosophic cost pattern incorporating maximum life-time-based deterioration and novel demand through PSO

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