The management of a fishery is a complex task generally involving multiple, often conflicting, objectives. These objectives typically include economic, biological and social goals such as improving the income of fishe...
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The management of a fishery is a complex task generally involving multiple, often conflicting, objectives. These objectives typically include economic, biological and social goals such as improving the income of fishers, reducing the catch of depleted species and maintaining employment. multi-criteria decision making (MCDM) techniques appear well-suited to such a management problem, allowing compromises between conflicting objectives to be analysed in a structured framework. In comparison to other fields, such as water resource planning, forestry and agriculture, there have been few applications of MCDM to fisheries. In this paper, a goal programming model of the North Sea demersal fishery is presented. The model is used to demonstrate the potential applicability of this type of approach to the analysis and development of fisheries management plans with multiple objectives. Alternative scenarios are considered for the problem, and trade-offs between given objectives are also highlighted and discussed.
This study presents a novel means of resolving multiple objective goal programming (GP) problems with quasi-convex linear penalty functions. The proposed method initially expresses a quasi-convex function by the maxim...
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In this paper we discuss such problems: if among a group of decision making units, we increase certain inputs to a particular unit and assume that the DMU maintains its current efficiency level with respect to other u...
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In this paper we discuss such problems: if among a group of decision making units, we increase certain inputs to a particular unit and assume that the DMU maintains its current efficiency level with respect to other units, how much more outputs could the unit produce? or, if the outputs need to be increased to a certain level and the efficiency of the unit remains unchanged, how much more inputs should be provided to the unit? We treat them as inverse Data Envelopment Analysis (DEA) problems and propose a method to solve such problems. The problem is transformed into a multi-objective programming problem to solve, though in some special cases it can be answered by solving just one single-object LP problem. We use two examples to illustrate our computation method. (C) 2000 Elsevier Science B.V. All rights reserved.
The classical linear Assignment problem is considered with two objectives. The aim is to generate the set of efficient solutions. An exact method is first developed based on the two-phase approach. In the second phase...
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The classical linear Assignment problem is considered with two objectives. The aim is to generate the set of efficient solutions. An exact method is first developed based on the two-phase approach. In the second phase a new upper bound is proposed so that larger instances can be solved exactly. The so-called MOSA (multi-objective Simulated Annealing) is then recalled;its efficiency is improved by initialization with a greedy approach. Its results are compared to those obtained with the exact method. Extensive numerical experiments have been realized to measure the performance of the MOSA method.
This work presents a combination fuzzy-GA method to resolve the service restoration problem, The problem formulation proposed herein considers five different objective functions related to maximizing the amount of tot...
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This work presents a combination fuzzy-GA method to resolve the service restoration problem, The problem formulation proposed herein considers five different objective functions related to maximizing the amount of total load to he restored as well as minimizing the number of the switching operations, deviation of the bus voltage, the feeder's current and transformer's loading. Meanwhile, the operational constraints, radial structure of the network configuration and sequence of the switching operations are included in the problem formulation. These objective functions are modeled with fuzzy sets to evaluate their imprecise nature. In the interactive method, the dispatcher provides his or her anticipated value (the degree of preference) of each objective, then the optimization problem is solved by the genetic algorithm. Analyzing the results from the former interactive and updating the expected value of each objective function via the interactive procedure allow us to derive the compromised or satisfied solution efficiently, Simulation results obtained from the Tai-power system demonstrate the effectiveness of the solution algorithm.
Several successful applications of optimal control theory based on the Pontryagin's minimum principle have been recorded in literature for optimizing the operating policy of multi-reservoir systems. However, the a...
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Several successful applications of optimal control theory based on the Pontryagin's minimum principle have been recorded in literature for optimizing the operating policy of multi-reservoir systems. However, the application of optimal control theory in sizing multi-reservoir systems resulted in sub-optimal solution. In this study, an optimization model based on a new composite algorithm is introduced. This model applies optimal control theory and penalty successive linear programming as two promising techniques to optimize large and complex water supply systems. The epsilon constraint approach was implemented in the model in order to consider the two non-commensurate objectives of minimizing cost and water deficit. The application of this model to a multi-reservoir system was compared to an existing dynamic programming model. The result of this study showed that the developed model is a very promising optimization method to design multi-reservoir systems regardless of their sizes. (C) 2000 Elsevier Science Ltd. All rights reserved.
We have previously developed an adaptation of the simulated annealing for multi-objective combinatorial optimization (MOCO) problems to construct an approximation of the efficient set of such problem. In order to deal...
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We have previously developed an adaptation of the simulated annealing for multi-objective combinatorial optimization (MOCO) problems to construct an approximation of the efficient set of such problem. In order to deal with large-scale problems, we transform this approach to propose an interactive procedure. The method is tested on the multi-objective knapsack problem and the multi-objective assignment problem. Scope and purpose Meta-heuristics methods are intensively used with success to solve optimization problems and especially combinatorial problems (Pirlot. EJOR 1996;92:493-511). In the case of a single objective problem, such methods compute an approximation to the unique optimal solution. Recently, some meta-heuristics have been adapted to treat multi-objective problems. These methods construct an approximation of the set of all efficient solutions. For large-scale multi-objective combinatorial problems, the number of efficient solutions may become very large. In order to help a decision maker to make a choice between these solutions, an interactive procedure is developed in this paper. (C) 2000 Elsevier Science Ltd. All rights reserved.
We consider in this paper the solving of 0-1 knapsack problems with multiple linear objectives. We present a tabu search approach to generate a good approximation of the efficient set. The heuristic scheme is included...
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We consider in this paper the solving of 0-1 knapsack problems with multiple linear objectives. We present a tabu search approach to generate a good approximation of the efficient set. The heuristic scheme is included in a reduction decision space framework. The case of two objectives is developed in this paper. TS principles viewed into the multiobjective context are discussed. According to a prospective way, several variations of the algorithm are investigate. Numerical experiments are reported and compared with available exact efficient solutions. Intuitive justifications for the observed empirical behavior of the procedure and open questions are discussed.
Five algorithms for the simultaneous optimal design of smart structural systems are presented. These algorithms are developed based on sequential mathematical programming and guided random search techniques, being app...
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Five algorithms for the simultaneous optimal design of smart structural systems are presented. These algorithms are developed based on sequential mathematical programming and guided random search techniques, being applied to multidisciplinary optimization in which both structural layout and controller parameters are involved. A method to prevent singularities when updating the structural layout is suggested. Two adaptive trusses are optimised and comparison is made for the different optimal schemes on the basis of iteration histories and major results achieved. All of the five algorithms are shown efficient in improving the truss's performance with respect to robustness and controllability. The results show that the greatest improvement in all the performance indicators is achieved using a genetic algorithm, whilst the most efficient scheme is seen to be a novel hybrid combining sequential linear programming with simulated annealing. (C) 2000 Elsevier Science S.A. All rights reserved.
We propose a steepest descent method for unconstrained multicriteria optimization and a "feasible descent direction" method for the constrained case. In the unconstrained case, the objective functions are as...
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We propose a steepest descent method for unconstrained multicriteria optimization and a "feasible descent direction" method for the constrained case. In the unconstrained case, the objective functions are assumed to be continuously differentiable. In the constrained case, objective and constraint functions are assumed to be Lipshitz-continuously differentiable and a constraint qualification is assumed. Under these conditions, it is shown that these methods converge to a point satisfying certain first-order necessary conditions for Pareto optimality. Both methods do not scalarize the original vector optimization problem. Neither ordering information nor weighting factors for the different objective functions are assumed to be known. In the single objective case, we retrieve the Steepest descent method and Zoutendijk's method of feasible directions, respectively.
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