Solution of multi-objective transportation-p-facility location problem with effect of variable carbon emission by evolutionary algorithms

Vansia, Dhruvrajsinh O.; Dhodiya, Jayesh M.

doi:10.1007/s00500-021-05619-2

Cited by 5 publications

(4 citation statements)

References 34 publications

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“…Zheng et al [16] presented a master-slave evolutionary algorithm for a problem of integrated civilian-military emergency supply pre-positioning. Wang et al [17] presented a dual-population evolutionary algorithm to solve a facility location problem with two objectives on reliability and coverage under the uncertainty of facilities. Vansia and Dhodiya [18] utilized nondominated sorting genetic algorithm and modified selfadaptive multi-population elitism Jaya algorithm for a multi-objective transportation--facility location problem that minimizes the overall transportation time, cost of transportation, and carbon emission.…”

Section: Related Work Pmentioning

confidence: 99%

Optimization of Air Defense System Deployment Against Reconnaissance Drone Swarms

Li,

Su,

Ling

et al. 2023

Complex Syst. Model. Simul.

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Due to their advantages in flexibility, scalability, survivability, and cost-effectiveness, drone swarms have been increasingly used for reconnaissance tasks and have posed great challenges to their opponents on modern battlefields. This paper studies an optimization problem for deploying air defense systems against reconnaissance drone swarms. Given a set of available air defense systems, the problem determines the location of each air defense system in a predetermined region, such that the cost for enemy drones to pass through the region would be maximized. The cost is calculated based on a counterpart drone path planning problem. To solve this adversarial problem, we first propose an exact iterative search algorithm for small-size problem instances, and then propose an evolutionary framework that uses a specific encoding-decoding scheme for large-size problem instances. We implement the evolutionary framework with six popular evolutionary algorithms. Computational experiments on a set of different test instances validate the effectiveness of our approach for defending against reconnaissance drone swarms.

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Section: Related Work Pmentioning

confidence: 99%

Optimization of Air Defense System Deployment Against Reconnaissance Drone Swarms

Li,

Su,

Ling

et al. 2023

Complex Syst. Model. Simul.

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show abstract

“…Lim et al (2013) proposed an extension to capacitated case with correlated disruptions. Vansia and Dhodiya (2021) adapted different evolutionary algorithms (Genetic algorithm (GA), non-dominated sorting genetic algorithm (NSGA-II and NSGA-III), modified Self-Adaptive Multi-Population Elitism Jaya Algorithm (SAMPE JA)) to minimize the transportation time, transportation cost and carbon emission in a multi-objective transportation p-facility location problem. Nayeri et al (2021) addressed a responsive-resilient inventory-location problem with presence of uncertainties and considering location, allocation and inventory decisions.…”

Section: Related Workmentioning

confidence: 99%

A multi-objective optimization model for a multi-period mobile facility location problem with environmental and disruption considerations

et al. 2022

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The present work addresses a multi-period facility location decision during crisis-Pandemic-situation with highly uncertain demand. Our paper identifies failing distribution centres (DCs) over several periods of time and decides about relocation. Several scenarios arise: DCs can be opened, displaced from one location to another, closed or even reopened during a certain time, using mobile facility. We recommend a multi-objective non-linear mixed-integer mathematical model that identifies, for each period, the best possible location for DCs and their allocation to customers. Our model analyses the trade-off between economic costs and C O 2 emissions generated by facilities operations. We suggest a multiobjective approach based on a Non-dominated Sorting Genetic Algorithm II (NSGA-II). Our simulations demonstrate how mobile facility should be managed to ensure a good balance between economic and environmental criteria.

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“…Multi-objective optimization problems can be solved by different algorithms. Vansia and Dhodiya (2021) presented an evolutionary approachbased solution to solve the multi-objective transportation-pfacility location problem by using a genetic algorithm (GA), non-dominated sorting genetic algorithm (NSGA-II and NSGA-III), and modified self-adaptive multi-population elitism Jaya algorithm (SAMPE JA). Xu et al (2021) use a multi-objective learning backtracking search algorithm (MOLBSA) to solve the environmental/economic dispatch (EED) problem.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The premise of economic dispatching of power systems is to meet the safety operation of the power grid and provide highquality electric energy for users. On this premise, energy and power generation equipment can be rationally utilized, and the system operation economy is considered, that is, continuous power supply for users at the lowest power generation cost Vansia and Dhodiya, (2021). Power system economic scheduling is a multi-constrained, nonlinear, non-convex, and multi-dimensional hybrid optimization problem.…”

Section: Introductionmentioning

confidence: 99%

Optimal scheduling of power systems considering carbon markets: Based on blockchain theory and multi-objective particle swarm optimization algorithm

Wang

Lan²,

Shen³

et al. 2022

Front. Energy Res.

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In the context of double carbon, it is an inevitable requirement for the low-carbon power industry to take economic efficiency and low carbon into consideration. This article introduces the carbon emission constraint into the economic dispatching of the power system. Then, combined with the blockchain theories, the methods of particle swarm optimization and multi-objective particle swarm optimization (MOPSO) are employed to simulate the economic and environmental scheduling of a power generation system based on six thermal power units. Research shows that the constraint processing approach is practical and effective, and it can firmly adhere to equality requirements, which is superior to other algorithms’ constraint processing methods; the algorithm is stable, and the global optimal solution can be determined under different initial solutions. In the process of multi-objective optimization, the solutions of POF obtained by using the slope method are evenly distributed.

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Solution of multi-objective transportation-p-facility location problem with effect of variable carbon emission by evolutionary algorithms

Cited by 5 publications

References 34 publications

Optimization of Air Defense System Deployment Against Reconnaissance Drone Swarms

Optimization of Air Defense System Deployment Against Reconnaissance Drone Swarms

A multi-objective optimization model for a multi-period mobile facility location problem with environmental and disruption considerations

Optimal scheduling of power systems considering carbon markets: Based on blockchain theory and multi-objective particle swarm optimization algorithm

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