This paper deals with the modeling and optimization of a bi-level multi-objective production planning problem, where some of the coefficients of objective functions and parameters of constraints are multi-choice. A ge...
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This paper deals with the modeling and optimization of a bi-level multi-objective production planning problem, where some of the coefficients of objective functions and parameters of constraints are multi-choice. A general transformation technique based on a binary variable has been used to transform the multi-choices parameters of the problem into their equivalent deterministic form. Finally, two different types of secularization technique have been used to achieve the maximum degree of individually membership goals by minimizing their deviational variables and obtained the most satisfactory solution of the formulated problem. An illustrative real case study of production planning has been discussed and, also compared to validate the efficiency and usefulness of the proposed work.
In this study, we develop an extended multi-objective mixed integer programming (EMOMIP) approach for water resources management under uncertainty, in which the parameters are fuzzy random variables while the decision...
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In this study, we develop an extended multi-objective mixed integer programming (EMOMIP) approach for water resources management under uncertainty, in which the parameters are fuzzy random variables while the decision variables are interval variables. Furthermore, some alternatives are considered to retrieve the difference between the quantities of promised water-allocation targets and the actual allocated water. Then, the proposed EMOMIP for the problem is solved by a new method using fuzzy random chance-constrained programming based on the idea of possibility theory. This method can satisfy both optimistic and pessimistic decision makers simultaneously. Finally, a real example is given to explain the proposed method.
In this paper, we consider a multi-objective Stochastic Interval-Valued Linear Fractional Integer programming problem (MOSIVLFIP). We especially deal with a multi-objective stochastic fractional problem involving an i...
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In this paper, we consider a multi-objective Stochastic Interval-Valued Linear Fractional Integer programming problem (MOSIVLFIP). We especially deal with a multi-objective stochastic fractional problem involving an inequality type of constraints, where all quantities on the right side are log-normal random variables, and the objective functions coefficients are fractional intervals. The proposed solving procedure is divided in three steps. In the first one, the probabilistic constraints are converted into deterministic ones by using the chance constrained programming technique. Then, the second step consists of transforming the studied problem objectives on an optimization problem with an interval-valued objective functions. Finally, by introducing the concept of weighted sum method, the equivalent converted problem obtained from the two first steps is transformed into a single objective deterministic fractional problem. The effectiveness of the proposed procedure is illustrated through a numerical example.
This paper presents a new approach to Differential Evolution algorithm for solving stochastic programming problems, named DESP. The proposed algorithm introduces a new triangular mutation rule based on the convex comb...
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This paper presents a new approach to Differential Evolution algorithm for solving stochastic programming problems, named DESP. The proposed algorithm introduces a new triangular mutation rule based on the convex combination vector of the triangle and the difference vector between the best and the worst individuals among the three randomly selected vectors. The proposed novel approach to mutation operator is shown to enhance the global and local search capabilities and to increase the convergence speed of the new algorithm compared with conventional DE. DESP uses Deb's constraint handling technique based on feasibility and the sum of constraint violations without any additional parameters. Besides, a new dynamic tolerance technique to handle equality constraints is also adopted. Two models of stochastic programming (SP) problems are considered: Linear Stochastic Fractional programming Problems and multi-objective Stochastic Linear programming Problems. The comparison results between the DESP and basic DE, basic particle swarm optimization (PSO), Genetic Algorithm (GA) and the available results from where it is indicated that the proposed DESP algorithm is competitive with, and in some cases superior to, other algorithms in terms of final solution quality, efficiency and robustness of the considered problems in comparison with the quoted results in the literature. (C) 2016 Production and hosting by Elsevier B.V. on behalf of Faculty of Computers and Information, Cairo University.
In this paper, an uncertain multi-objectivemulti-item Solid Transportation Problem (MMSTP) based on uncertainty theory is presented. In the model, transportation costs, supplies, demands and conveyances parameters ar...
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In this paper, an uncertain multi-objectivemulti-item Solid Transportation Problem (MMSTP) based on uncertainty theory is presented. In the model, transportation costs, supplies, demands and conveyances parameters are taken to be uncertain parameters. There are restrictions on some items and conveyances of the model. Therefore, some particular items cannot be transported by some exceptional conveyances. Using the advantage of uncertainty theory, the MMSTP is first converted into an equivalent deterministic MMSTP. By applying convex combination method and minimizing distance function method, the deterministic MMSTP is reduced into single objectiveprogramming problems. Thus, both single objectiveprogramming problems are solved using Maple 18.02 optimization toolbox. Finally, a numerical example is given to illustrate the performance of the models.
Under the current situation, distributed power generation becomes key solutions to the problems of environmental pollution and resources shortage that the electricity industry is facing. Rational power generation prog...
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Under the current situation, distributed power generation becomes key solutions to the problems of environmental pollution and resources shortage that the electricity industry is facing. Rational power generation programming schemes will reduce energy consumption, and optimize energy structure on the premise of meeting the increasing requirements for electric energy. The multi-objective programming model was established to optimize economical and technical objectives of distributed generation system (DGS) containing waste incineration generation (WIG) and hybrid energy storage equipment (HESE), and then memorized-firefly algorithm (M-FA) is introduced to determine the model and installed capacity of various power generation units in distributed generation system. The case study demonstrated that the proposed programming model can obtain rational solutions with taking various objectives and constraints into consideration, and the M-FA shows the abilities of global search and better convergence in solving power generation programming problems, which will provide theoretic and practical references for the multi-objective programming problems of distributed generation system.
Based upon Ben-Tal's generalized algebraic operations, new classes of functions, namely (h,phi)-type-I, quasi (h,phi)-type-I, and pseudo (h,phi)-type-I, are defined for a multi-objective programming problem. Suffi...
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Based upon Ben-Tal's generalized algebraic operations, new classes of functions, namely (h,phi)-type-I, quasi (h,phi)-type-I, and pseudo (h,phi)-type-I, are defined for a multi-objective programming problem. Sufficient optimality conditions are obtained for a feasible solution to be a Pareto efficient solution for this problem. Some duality results are established by utilizing the above defined classes of functions, considering the concept of a Pareto efficient solution.
The multi-choice goal programming allows the decision maker to set multi-choice aspiration levels for each goal to avoid underestimation of the decision. In this paper, we propose an alternative multi-choice goal prog...
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The multi-choice goal programming allows the decision maker to set multi-choice aspiration levels for each goal to avoid underestimation of the decision. In this paper, we propose an alternative multi-choice goal programming formulation based on the conic scalarizing function with three contributions: (1) the alternative formulation allows the decision maker to set multi-choice aspiration levels for each goal to obtain an efficient solution in the global region, (2) the proposed formulation reduces auxiliary constraints and additional variables, and (3) the proposed model guarantees to obtain a properly efficient (in the sense of Benson) point. Finally, to demonstrate the usefulness of the proposed formulation, illustrative examples and test problems are included. (C) 2011 Elsevier Inc. All rights reserved.
This paper focuses on the design, development and implementation of new Pareto efficiency detection and restoration techniques for integer goal programming. The design of the algorithms and their implementation issues...
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This paper focuses on the design, development and implementation of new Pareto efficiency detection and restoration techniques for integer goal programming. The design of the algorithms and their implementation issues within (an otherwise continuous) goal programming system are detailed. The differences between continuous and integer goal programming regarding Pareto efficiency detection and restoration analysis are described. The integer Pareto efficiency techniques have been applied to a selection of problems from different industrial contexts in order to assess their computational performance. Finally, Pareto restoration and detection techniques are applied to an integer goal programming problem to illustrate the methodology. (C) 1999 Elsevier Science Ltd. All rights reserved.
This paper develops the goal programming technique to solve the multiple objective assignment problem. The required model is formulated and an appropriate solution method is presented. The proposed method, which is a ...
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This paper develops the goal programming technique to solve the multiple objective assignment problem. The required model is formulated and an appropriate solution method is presented. The proposed method, which is a decomposition method, exploits the total unimodularity feature of the assignment problem and effectively reduces the computational efforts. Some issues related to the efficiency of a GP solution are stated and some specialized techniques for detecting and restoring efficiency are proposed. (C) 2007 Elsevier Inc. All rights reserved.
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