Particle Swarm Optimization for the Vehicle Routing Problem with Stochastic Demands

Marinakis, Yannis; Iordanidou, Georgia-Roumbini; Marinaki, Magdalene

doi:10.1016/j.asoc.2013.01.007

Cited by 161 publications

(67 citation statements)

References 32 publications

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“…Particle swarm optimisation has been used by Moghaddam et al [14] and Marinakis et al [15] in cases with uncertain demand. However, the nature of uncertainty in the problem of offshore wind farm maintenance optimisation is different: a potentially large uncertainty is associated with the duration of repair.…”

Section: Calculating the Probability Of Carrying Out Successful Maintmentioning

confidence: 99%

Decision Support Tool for Offshore Wind Farm Vessel Routing under Uncertainty

2018

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This paper for the first time captures the impact of uncertain maintenance action times on vessel routing for realistic offshore wind farm problems. A novel methodology is presented to incorporate uncertainties, e.g., on the expected maintenance duration, into the decision-making process. Users specify the extent to which these unknown elements impact the suggested vessel routing strategy. If uncertainties are present, the tool outputs multiple vessel routing policies with varying likelihoods of success. To demonstrate the tool's capabilities, two case studies were presented. Firstly, simulations based on synthetic data illustrate that in a scenario with uncertainties, the cost-optimal solution is not necessarily the best choice for operators. Including uncertainties when calculating the vessel routing policy led to a 14% increase in the number of wind turbines maintained at the end of the day. Secondly, the tool was applied to a real-life scenario based on an offshore wind farm in collaboration with a United Kingdom (UK) operator. The results showed that the assignment of vessels to turbines generated by the tool matched the policy chosen by wind farm operators. By producing a range of policies for consideration, this tool provided operators with a structured and transparent method to assess trade-offs and justify decisions.

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Section: Calculating the Probability Of Carrying Out Successful Maintmentioning

confidence: 99%

Decision Support Tool for Offshore Wind Farm Vessel Routing under Uncertainty

2018

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“…In this article, the value of µ is set to be the demand amount at each demand point in the VRPTW benchmark problems, with the value of σ 2 as 2 2 ; by setting µ as the center, we pick two nearest integers, one to the left, one to the right of µ, and establish a demand interval by taking these two integers as the boundary points. For example, if the demand amount value is 10 at one demand point, we take µ as 10, and the corresponding demand interval turns out to be (9,11).…”

Section: Comparison With Other Metaheuristicsmentioning

confidence: 99%

“…In their recent study [10], they proposed a bi-objective mathematical formulation for the cross-docking with interrelated operations (vehicle routing and vehicle scheduling), and developed a cooperative coevolution approach consisting of hyper-heuristics and hybrid-heuristics for achieving continuous improvement in alternating objectives. Marinakis et al [11] introduced a new hybrid algorithmic approach based on particle swarm optimization for the vehicle routing problem with stochastic demands that are known only when the vehicle arrives to the customers. This algorithm is a combination of the particle swarm optimization algorithm with the 2-opt and 3-opt local search algorithms and with the path relinking strategy.…”

Section: Introductionmentioning

confidence: 99%

“…Following the first heuristics algorithm presented by Hochbaum and Shmoys [24], there has been a long list of work on designing heuristics algorithms for this problem over the years. Several meta-heuristic approaches were reported, including Genetic Algorithm (GA) [25][26][27], Tabu Search (TS) [28][29][30], Particle Swarm Optimization (PSO) [11,20,31], hybrid algorithm [32][33][34][35], etc., which were used to solve VRP and its variant problems. With reference to Genetic Algorithm, Ombuki et al [25] proposed an application of genetic algorithms approach for multi-depot vehicle routing problem(MDVRP) using the Pareto ranking technique.…”

Section: Introductionmentioning

confidence: 99%

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The Emergency Vehicle Routing Problem with Uncertain Demand under Sustainability Environments

Qin

Cheng

et al. 2017

Sustainability

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Abstract:The reasonable utilization of limited resources is critical to realize the sustainable developments. In the initial 72-h crucial rescue period after the disaster, emergency supplies have always been insufficient and the demands in the affected area have always been uncertain. In order to improve timeliness, utilization and sustainability of emergency service, the allocation of the emergency supplies and the emergency vehicle routes should be determined simultaneously. Assuming the uncertain demands follow normal distribution, an optimization model for the emergency vehicle routing, by considering the insufficient supplies and the uncertain demands, is developed. The objective function is applied to minimize the total costs, including the penalty costs induced by more or less supplies than the actual demands at all demand points, as well as the constraints of the time windows and vehicle load capacity taken into account. In more details, a solution method for the model, based on the genetic algorithm, is proposed, which solves the problem in two stages. A numerical example is presented to demonstrate the efficiency and validity of the proposed model and algorithm.

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“…See for example Pei et al (2014), Marinakis et al (2013 and and Vahdani et al (2012). There are few papers in this area because there are other metaheuristics more convenient for this type of problem, such as tabu search among others.…”

Section: Introductionmentioning

confidence: 99%

Application of particle swarm optimisation with backward calculation to solve a fuzzy multi-objective supply chain master planning model

Grillo

Peidro

Alemany

2015

IJBIC

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InderscienceGrillo Espinoza, H.; Peidro Payá, D.; Alemany Díaz, MDM.; Mula, J. (2015). Application of particle swarm optimisation with backward calculation to solve a fuzzy multi-objective supply chain master planning model. International Journal of Bio-Inspired Computation. 7(3): 157-169. doi:10.1504/IJBIC.2015 Copyright © 200x Inderscience Enterprises Ltd. APPLICATION OF PARTICLE SWARM OPTIMIZATION WITH BACKWARD CALCULATION (PSO-B) TO SOLVE A FUZZY MULTIOBJECTIVE SUPPLY CHAIN MASTER PLANNING MODEL Abstract:Traditionally, supply chain planning problems consider variables with uncertainty associated with uncontrolled factors. These factors have been normally modelled by complex methodologies where the seeking solution process often presents high scale of difficulty. This work presents the fuzzy set theory as a tool to model uncertainty in supply chain planning problems and proposes the particle swarm optimization (PSO) metaheuristics technique combined with a backward calculation as a solution method. The aim of this combination is to present a simple effective method to model uncertainty, while good quality solutions are obtained with metaheuristics due to its capacity to find them with satisfactory computational performance in complex problems, in a relatively short time period.

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Particle Swarm Optimization for the Vehicle Routing Problem with Stochastic Demands

Cited by 161 publications

References 32 publications

Decision Support Tool for Offshore Wind Farm Vessel Routing under Uncertainty

Decision Support Tool for Offshore Wind Farm Vessel Routing under Uncertainty

The Emergency Vehicle Routing Problem with Uncertain Demand under Sustainability Environments

Application of particle swarm optimisation with backward calculation to solve a fuzzy multi-objective supply chain master planning model

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