The present paper considers the problem of scheduling a set of jobs where some jobs may be rejected. The objective function consists of minimizing two criteria simultaneously: the sum of the weighted completion times ...
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The present paper considers the problem of scheduling a set of jobs where some jobs may be rejected. The objective function consists of minimizing two criteria simultaneously: the sum of the weighted completion times of the accepted jobs and the sum of the rejection costs. Although the characteristics of this problem have been discussed in the literature, no solution algorithm has yet been proposed. Herein, solution algorithms are developed using two meta-heuristic methods: Pareto simulated annealing (PSA), a multiple-objective optimization approach;and colonial competitive algorithm (CCA), a novel method which is adopted for the first time in a discrete multiple-objective optimization problem. Computational testing illustrates the practicality of both algorithms to find a good estimation of the Pareto optimal set. The quality of the proposed algorithms are evaluated and compared by some available performance measures and a new measure introduced in the paper. The comparative results show that CCA offers better estimation of the Pareto optimal set than PSA.
This paper proposes two parallel algorithms which are improved by heuristics for a bi-objective flowshop scheduling problem with sequence-dependent setup times in a just-in-time environment. In the proposed algorithms...
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This paper proposes two parallel algorithms which are improved by heuristics for a bi-objective flowshop scheduling problem with sequence-dependent setup times in a just-in-time environment. In the proposed algorithms, the population will be decomposed into the several sub-populations in parallel. Multiple objectives are combined with min-max method then each sub-population evolves separately in order to obtain a good approximation of the Pareto-front. After unifying the obtained results, we propose a variable neighborhood algorithm and a hybrid variable neighborhood search/tabu search algorithm to improve the Pareto-front. The non-dominated sets obtained from our proposed algorithms, a genetic local search and restarted iterated Pareto greedy algorithm are compared. It is found that most of the solutions in the net non-dominated front are yielded by our proposed algorithms. (C) 2013 Elsevier B.V. All rights reserved.
In a recent work, Alizadeh et al. (2013) studied a capacitated multi-facility location-allocation problem in which customers had stochastic demands based on the Bernoulli distribution function. Authors considered capa...
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In a recent work, Alizadeh et al. (2013) studied a capacitated multi-facility location-allocation problem in which customers had stochastic demands based on the Bernoulli distribution function. Authors considered capacitated sub-sources of facilities to satisfy customer demands. In this discrete stochastic problem, the goal was to find optimal locations of facilities among candidate locations and optimal allocations of existing customers to operating facilities so that the total sum of fixed costs of operating facilities, allocation costs of customers and expected values of servicing and outsourcing costs was minimized. The model was formulated as a mixed-integer nonlinear programming problem. Since finding an optimal solution may require an excessive amount of time depending on the nonlinear constraints, here we transform the nonlinear constraints of the problem to linear ones to obtain a simple formulation of the model. An empirical study of an automobile manufacturer, namely Geelran Motor and three sets of test problems of small, medium and large sizes were considered to show the applicability of the presented model and efficiency of the proposed meta-heuristic algorithms. Numerical results show that the LINGO 9.0 software package is capable of solving the empirical study and small problems. For medium and large problems, we propose two meta-heuristic algorithms, a genetic algorithm (GA) and a discrete version of the colonial competitive algorithm (CCA). Computational investigations illustrate the efficiency of the proposed algorithms in obtaining effective solutions. (C) 2015 Elsevier B.V. All rights reserved.
A microgrid (MG) comprises a low-voltage network with several microsources, critical and noncritical loads, and energy storage systems (ESSs). It can operate in the grid-connected or islanded modes. In islanded mode, ...
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A microgrid (MG) comprises a low-voltage network with several microsources, critical and noncritical loads, and energy storage systems (ESSs). It can operate in the grid-connected or islanded modes. In islanded mode, the voltage and frequency of the MG should be controlled by different distributed energy resources (DERs). This paper focuses on the analysis of frequency stability in an autonomous MG with renewable energy sources. In this paper, colonial competitive algorithms are used to design the DERs controllers in a cooperative manner that these controllers can keep the MG stable. Artificial neural network tool trained by Levenberg-Marquardt algorithm is used to generate controlling signal for every controller to keep the frequency and voltage in a desired range. This paper investigates a new optimal control solution for maintaining the frequency stability in the MG, by using a combination of an ESS and load-shedding procedure. The cooperative game theory is used in this paper to model the interaction among different DERs, ESSs, and loads.
This paper proposes a colonial competitive algorithm which is improved by variable neighborhood search algorithm for the simultaneous effects of learning and deterioration on hybrid flowshop scheduling with sequence-d...
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This paper proposes a colonial competitive algorithm which is improved by variable neighborhood search algorithm for the simultaneous effects of learning and deterioration on hybrid flowshop scheduling with sequence-dependent setup times. By the effects of learning and deterioration, the processing time of a job is determined by position in the sequence and its execution start time. In addition, it is assumed that the processing time of any job depends on the number of workers assigned to the job on a particular stage, and the more workers assigned to a stage, the shorter the job processing time. These additional traits that are added to the scheduling problem coexist in many realistic scheduling situations. This problem consists of two basic questions of job scheduling and worker assignment. Minimization of the earliness, tardiness, makespan, and total worker employing costs is considered as the objective function. To evaluate the performance of the hybrid colonial competitive algorithm, the random key genetic algorithm, immune algorithm, variable neighborhood search, and hybrid simulated annealing metaheuristic presented previously are investigated for comparison purposes, and computational experiments are performed on standard test problems. Results show that our proposed algorithm performs better than the other algorithms for various test problems.
In this paper a novel evolutionary global search strategy called colonial competitive algorithm (CCA) is utilized to determine an optimal decoupling matrix and also to tune a decentralized PID controller for the multi...
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In this paper a novel evolutionary global search strategy called colonial competitive algorithm (CCA) is utilized to determine an optimal decoupling matrix and also to tune a decentralized PID controller for the multi-input multi-output evaporator plant. Recently, introduced CCA has proven its excellent capabilities, such as faster convergence and better global minimum achievement. First, parameters of the evaporator plant are identified based on prediction error model algorithm. The decoupling matrix for the identified plant transfer matrix is obtained through shrinking the Gershgorin bands by minimization of an appropriate cost function in frequency domain, to make the compensated plant diagonally dominant. Then a decentralized PID controller for the MIMO evaporator is designed based on CCA. The simulation results verify the superiority of the proposed method to the conventional approaches and also to the centralized MIMO PID controller designed with CCA, along With its less complexity.
This paper is intended to present a method for the localization and evaluation of damage in plates based on the changes in natural frequencies and mode shapes of the damaged plate using an optimization approach. The c...
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This paper is intended to present a method for the localization and evaluation of damage in plates based on the changes in natural frequencies and mode shapes of the damaged plate using an optimization approach. The colonial competitive algorithm is employed to detect damage (or damages) in plates by optimizing a damage function. The performance of the proposed method is demonstrated by implementing the technique to two examples;a shear wall and a four-fixed supported plate with and without modal data noise including one or a large number of damages. The results confirm the applicability and efficiency of the presented method in detecting damage localization and quantification in the shear walls. Furthermore, the proposed method is implemented to the four-fixed supported plate aimed at demonstrating the high sensitivity of the proposed method in quantitative estimation of damaged plate structures. Finally, the reliability of the presented method is explored through the comparison of the obtained results and those of the other methods. It is concluded that the proposed method can be viewed as a powerful and robust method for structural damage detection in plate structures.
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