Vehicle routing problem with uncertain demands: An advanced particle swarm algorithm

Moghaddam, Babak Farhang; Ruíz, Rubén; Sadjadi, Seyed Jafar

doi:10.1016/j.cie.2011.10.001

Cited by 104 publications

(53 citation statements)

References 40 publications

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“…In contrast, ROOT has been used only with PSO algorithms [46]. Robust optimization was found to be useful when dealing with problem uncertainties with PSO [47] and ACO [48,49], e.g., reducing computational efforts.…”

Section: Measurementsmentioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

490

213

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Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given.

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Section: Measurementsmentioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

490

213

View full text Add to dashboard Cite

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“…They concluded the developed PSO algorithm got the optimal solutions much faster than the genetic algorithm (GA) algorithm due to it is a swarm-based algorithm. Similarly Moghaddam et al [31] used PSO to solve the model to overcome uncertainly in vehicle routing planning. Nan et al [32] used PSO to solve reverse logistics location of remanufacturing factory, and the distribution of goods is solved by the PSO algorithm to minimize cost.…”

Section: A Particle Swarm Optimizationmentioning

confidence: 99%

A Review of Nature-Based Algorithms Applications in Green Supply Chain Problems

Sadrnia¹,

Soltani²,

Zulkifli³

et al. 2014

IJET

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Abstract-In the recent years, there is a significant attention among researchers and practitioners to environmental issues and green supply chain (GrSC) because of legislations and profit oriented motivations.Because of considering environmental issues GrSC besides the economic variables, the models are very difficult to find optimal. Hence, complicated green problems call for development of modern optimization methods and algorithms to solve optimization models by efficient techniques. In recent decades, meta-heuristic algorithms have been developed to overcome the problem that most of them are inspired from nature. Some of the algorithms have been inspired from natural generation, some of them inspired from swarm behavior, and others simulate natural processes. In this research we summarize the recent advance evolutionary optimization algorithms and swarm intelligence algorithms which are applied to GrSC and green logistics. Literature reviewed in this paper shows the current state of the art and discusses the potential future research trends.Index Terms-Evolutionary algorithms, green supply chain, meta-heuristic, swarm intelligence algorithms.

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“…Since the model is an NP-hard model, a particle swarm optimization method (PSO) is used to solve the problem. PSO is a good way to solve vehicle routing problems such as discussed by [23], [24], [25], and [26]. This paper is divided into five sections.…”

Section: This Issue Is Called As An Inventory Routing Problem (Irp)mentioning

confidence: 99%

Modelling Multi Tour Inventory Routing Problem for Deteriorating Items with Time Windows

Widyadana

Irohara

2018

Scientia Iranica

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In recent decades, there are intensive researches on deteriorating inventory. However, only a few researchers focus on the inventory routing problem for deteriorating item. There are many items such as foods, electronic products that deteriorate with time, and many other products in the market also have perishable characteristic. The items not only decay during the stockpiling period but they also deteriorate throughout transportation time. Since deteriorated rate and time is necessary, in this paper, an inventory routing problem with time windows for deteriorating items is developed. Particle Swarm Optimization (PSO) is used to solve the problem since PSO can solve problems in a reasonable period with near optimal solutions. We use two examples to illustrate the model. In a sensitivity analysis, way parameters that impact costs are demonstrated. Our results show that the deteriorating rate in inventory has bigger effects than deteriorating rate in the vehicle, so this research has a significant contribution and managers can give more effort to reduce deteriorating in inventory than the deteriorating rate in vehicles.

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Vehicle routing problem with uncertain demands: An advanced particle swarm algorithm

Cited by 104 publications

References 40 publications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

A Review of Nature-Based Algorithms Applications in Green Supply Chain Problems

Modelling Multi Tour Inventory Routing Problem for Deteriorating Items with Time Windows

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