A soft-computing Pareto-based meta-heuristic algorithm for a multi-objective multi-server facility location problem

Rahmati, Seyed Habib A.; Hajipour, Vahid; Niaki, Seyed Taghi Akhavan

doi:10.1016/j.asoc.2012.12.016

Cited by 101 publications

(37 citation statements)

References 34 publications

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“…After improvising a new solution, the HSM is updated by replacing the worse solution with the new solution. Interested readers should refer to Geem et al [24], Geem [28], and Rahmati et al [29] for additional information. Figure 4 presents a schematic view of the relationship between di erent HSA probabilities.…”

Section: Improvising Processmentioning

confidence: 99%

“…In the cases where the number of agents and their levels is high, Taguchi method is more e cient than complete factorial method. For orthogonal array, L27 equaling 27 is much less than the number required for complete factorial method [33,29]. In order to tune the parameters, the Mean Ideal Distance (MID) is selected as the main response in Taguchi analysis.…”

Section: Parameter Tuningmentioning

confidence: 99%

“…To do so, the pitch adjusting operator of MOHSA is designed similar to the mutation operator of the GA as a swap strategy [27,29]. We also represent the Pseudocode of HSA as well in Figure 5.…”

Section: Improvising Processmentioning

confidence: 99%

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Optimizing Reliability and Cost of system for Aggregate Production Planning in Supply Chain

2017

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Abstract. In this paper, the researchers present a multi-objective model for multiproduct, multi-site aggregate production planning model in a supply chain. The goals are to minimize the total cost of the supply chain, including inventory costs, manufacturing costs, work force costs, hiring and ring costs, and also to maximize the minimum of suppliers' reliability by considering probabilistic lead times to simultaneously improve the system performance. Since the problem is NP-Hard, a Pareto-based multi-objective harmony search algorithm is proposed. To demonstrate the performance of the presented algorithm, a Non-dominated Sorting Genetic Algorithm (NSGA-II) and a Non-dominated Ranking Genetic Algorithm (NRGA) are applied. The results demonstrate the robustness of the proposed algorithm to probe the Pareto solutions.

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Section: Improvising Processmentioning

confidence: 99%

Section: Parameter Tuningmentioning

confidence: 99%

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Optimizing Reliability and Cost of system for Aggregate Production Planning in Supply Chain

2017

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“…Thus, we expect to obtain precise outputs. This metric is called the multi-objective coefficient of variation (MOCV) [42]:…”

Section: Algorithm Parameter Tuningmentioning

confidence: 99%

“…Pasandideh et al [39] presented a multi-objective facility location model in which batch demands arrive on the system; they solved the problem by a multi-objective GA and SA (simulated annealing) algorithm. Rahmati et al [42] presented multi-objective FLPs considering multiple servers at each facility. They solved the model with multi-objective Pareto-based metaheuristic algorithms.…”

Section: Introductionmentioning

confidence: 99%

Bi-objective vibration damping optimization for congested location–pricing problem

Hajipour

Farahani

2016

Computers & Operations Research

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This paper presents a bi-objective mathematical programming model for the restricted facility location problem, under a congestion and pricing policy. Motivated by various applications such as locating server on internet mirror sites and communication networks, this research investigates congested systems with immobile servers and stochastic demand as M/M/m/k queues. For this problem, we consider two simultaneous perspectives; (1) customers who desire to limit waiting time for service and (2) service providers who intend to increase profits. We formulate a bi-objective facility location problem with two objective functions: (i) maximizing total profit of the whole system and (ii) minimizing the sum of waiting time in queues; the model type is mixed-integer nonlinear. Then, a multi-objective optimization algorithm based on vibration theory (so-called multi-objective vibration damping optimization (MOVDO)), is developed to solve the model. Moreover, the Taguchi method is also implemented, using a response metric to tune the parameters.The results are analyzed and compared with a non-dominated sorting genetic algorithm (NSGA-II) as a well-developed multi-objective evolutionary optimization algorithm. Computational results demonstrate the efficiency of the proposed MOVDO to solve large-scale problems.

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Modelling a chemical plant using grey‐box models employing the support vector regression and artificial neural network

Ghasemi,

Jazayeri‐Rad,

Behbahani

2024

Can J Chem Eng

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In this work, the performances of a nonlinear dynamic industrial process are examined using grey‐box (GB) models. To understand the dynamics of the system, the transient state is targeted. A white‐box (WB) model holds the prevailing knowledge using some assumptions. The performance of this model is limited. Artificial neural network (ANN) and support vector regression (SVR), which are techniques employed in numerous chemical engineering applications, are employed to construct the associated black‐box (BB) models. GA is used to optimize the SVR parameters. Dimensional and range extrapolations of different manipulated inputs, feed concentrations, feed temperatures, and cooling temperatures of the GB model and BB model are discussed. The different inputs extrapolation has different results because each input's effectiveness in the system is different. The results are compared, and the best model is suggested among the models, ANN, SVR, first principle (FP)‐ANN serial structure, FP‐ANN parallel structure, FP‐SVR serial structure, and FP‐SVR parallel structure.

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A soft-computing Pareto-based meta-heuristic algorithm for a multi-objective multi-server facility location problem

Cited by 101 publications

References 34 publications

Optimizing Reliability and Cost of system for Aggregate Production Planning in Supply Chain

Optimizing Reliability and Cost of system for Aggregate Production Planning in Supply Chain

Bi-objective vibration damping optimization for congested location–pricing problem

Modelling a chemical plant using grey‐box models employing the support vector regression and artificial neural network

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