This paper presents fuzzy goal programming (FGP) algorithm for solving decentralized bi-level multi-objective programming (DBL-MOP) problems with a single decision maker at the upper level and multiple decision makers...
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This paper presents fuzzy goal programming (FGP) algorithm for solving decentralized bi-level multi-objective programming (DBL-MOP) problems with a single decision maker at the upper level and multiple decision makers at the lower level. A FGP algorithm for DBL-multi-objective linear programming (DBL-MOLP) problems is proposed. This algorithm is extended to solve bi-level multi-objective linear fractional programming (DBL-MOLFP) problems. In the proposed algorithm, the membership functions for the defined fuzzy goals of all objective functions at the two levels as well as the membership functions for vector of fuzzy goals of the decision variables controlled by ULDM are developed in the model formulation of the problem. Then FGP approach is used to achieve highest degree of each of the membership goals by minimizing their deviational variables and thereby obtaining the most satisfactory solution for all decision makers. Illustrative numerical examples are given to demonstrate the proposed algorithm. (C) 2009 Published by Elsevier B.V.
Recently, there is a growing concern about the environmental and social footprint of business operations. While most of the papers in the field of supply chain network design focus on economic performance, recently, s...
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Recently, there is a growing concern about the environmental and social footprint of business operations. While most of the papers in the field of supply chain network design focus on economic performance, recently, some studies have considered environmental dimensions. However, there still exists a gap in quantitatively modeling social impacts together with environmental and economic impacts. In this study, this gap is covered by simultaneously considering the three pillars of sustainability in the network design problem. A mixed integer programming model is developed for this multi-objective closed-loop supply chain network problem. In order to solve this NP-hard problem, three novel hybrid metaheuristic methods are developed which are based on adapted imperialist competitive algorithms and variable neighborhood search. To test the efficiency and effectiveness of these algorithms, they are compared not only with each other but also with other strong algorithms. The results indicate that the nested approach achieves better solutions compared with the others. Finally, a case study for a glass industry is used to demonstrate the applicability of the approach. (C) 2013 Elsevier B.V. All rights reserved.
This paper proposed a multi-objective optimal water resources allocation model under multiple uncertainties. The proposed model integrated the chance-constrained programming, semi-infinite programming and integer prog...
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This paper proposed a multi-objective optimal water resources allocation model under multiple uncertainties. The proposed model integrated the chance-constrained programming, semi-infinite programming and integer programming into an interval linear programming. Then, the developed model is applied to irrigation water resources optimal allocation system in Minqin's irrigation areas, Gansu Province, China. In this study, the irrigation areas' economic benefits, social benefits and ecological benefits are regarded as the optimal objective functions. As a result, the optimal irrigation water resources allocation plans of different water types (surface water and groundwater) under different hydrological years (wet year, normal year and dry year) and probabilities are obtained. The proposed multi-objective model is unique by considering water-saving measures, irrigation water quality impact factors and the dynamic changes of groundwater exploitable quantity in the irrigation water resources optimal allocation system under uncertain environment. The obtained results are valuable for supporting the adjustment of the existing irrigation patterns and identify a desired water-allocation plan for irrigation under multiple uncertainties. (C) 2014 Elsevier Inc. All rights reserved.
This paper addresses a multi-supplier, multi-affected area, multi-relief, and multi-vehicle relief allocation problem in disaster relief logistics. A multi-objective optimisation model based on disaster scenario infor...
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This paper addresses a multi-supplier, multi-affected area, multi-relief, and multi-vehicle relief allocation problem in disaster relief logistics. A multi-objective optimisation model based on disaster scenario information updates is proposed in an attempt to coordinate efficiency and equity through timely and appropriate decisions regarding issues such as vehicle routing and relief allocation. An optimal stopping rule is also proposed to determine the optimum period of delay before responding to disaster, because decision making requires accurate disaster information. The main contribution of this paper is solving relief allocation problem in a novel way by correlating operational research with statistical decision making and Bayesian sequential analysis. Finally, a case is presented based on the post-disaster rescue in Eastern China after supertyphoon Saomai to test the applicability and show the potential advantages of the proposed model.
Lack of discrimination power and poor weight dispersion remain major issues in Data Envelopment Analysis (DEA). Since the initial multiple criteria DEA (MCDEA) model developed in the late 1990s, only goal programming ...
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Lack of discrimination power and poor weight dispersion remain major issues in Data Envelopment Analysis (DEA). Since the initial multiple criteria DEA (MCDEA) model developed in the late 1990s, only goal programming approaches;that is, the GPDEA-CCR and GPDEA-BCC were introduced for solving the said problems in a multi-objective framework. We found GPDEA models to be invalid and demonstrate that our proposed bi-objectivemultiple criteria DEA (BiO-MCDEA) outperforms the GPDEA models in the aspects of discrimination power and weight dispersion, as well as requiring less computational codes. An application of energy dependency among 25 European Union member countries is further used to describe the efficacy of our approach. (C) 2013 Elsevier B.V. All rights reserved.
This paper deals with frequency control following the occurrence of a contingency. The frequency is considered by modelling the generators' governor performance during pre- and post-contingency intervals, load fre...
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This paper deals with frequency control following the occurrence of a contingency. The frequency is considered by modelling the generators' governor performance during pre- and post-contingency intervals, load frequency dependency, rate of change of frequency (Rocof) of generators and Rocof of the system and frequency-based reserve scheduling. The time intervals following the occurrence of a contingency are formulated in detail to analyze the influential factors in static frequency. A novel index besides the frequency dependent social welfare function and frequency excursion index has been proposed to control the frequency and the Rocof during post-contingency intervals. The proposed stochastic multi-objective model incorporates the precise scheduling of reference power setting of generators based on participants' bids for energy and reserve services. The eventual goal of the proposed approach is to help the ISOs to make a trade-off concurrently between system frequency profile, Rocof and total operating cost to operate the power system securely in an economically efficient manner. This multi-objective programming formulation is simulated through two case studies;a three-bus system scheduled over 1 h and the IEEE Reliability Test System over 24 h, solved by means of lexicographic optimization and epsilon-constraint method. (C) 2013 Elsevier Ltd. All rights reserved.
In forest harvest scheduling problems, one must decide which stands to harvest in each period during a planning horizon. A typical requirement in these problems is a steady flow of harvested timber, mainly to ensure t...
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In forest harvest scheduling problems, one must decide which stands to harvest in each period during a planning horizon. A typical requirement in these problems is a steady flow of harvested timber, mainly to ensure that the industry is able to continue operating with similar levels of machine and labor utilizations. The integer programming approaches described use the so-called volume constraints to impose such a steady yield. These constraints do not directly impose a limit on the global deviation of the volume harvested over the planning horizon or use pre-defined target harvest levels. Addressing volume constraints generally increases the difficulty of solving the integer programming formulations, in particular those proposed for the area restriction model approach. In this paper, we present a new type of volume constraint as well as a multi-objective programming approach to achieve an even flow of timber. We compare the main basic approaches from a computational perspective. The new volume constraints seem to more explicitly control the global deviation of the harvested volume, while the multi-objective approach tends to provide the best profits for a given dispersion of the timber flow. Neither approach substantially changed the computational times involved.
Purpose: The aim of this paper is to deal with the supply chain management (SCM) with quantity discount policy under the complex fuzzy environment, which is characterized as the bifuzzy variables. By taking into accou...
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Purpose: The aim of this paper is to deal with the supply chain management (SCM) with quantity discount policy under the complex fuzzy environment, which is characterized as the bifuzzy variables. By taking into account the strategy and the process of decision making, a bifuzzy nonlinear multiple objective decision making (MODM) model is presented to solve the proposed problem. Design/methodology/approach: The bi-fuzzy variables in the MODM model are transformed into the trapezoidal fuzzy variables by the DMs's degree of optimism a 1 and a 2, which are de-fuzzified by the expected value index subsequently. For solving the complex nonlinear model, a multi-objective adaptive particle swarm optimization algorithm (MO-APSO) is designed as the solution method. Findings: The proposed model and algorithm are applied to a typical example of SCM problem to illustrate the effectiveness. Based on the sensitivity analysis of the results, the bifuzzy nonlinear MODM SCM model is proved to be sensitive to the possibility level alpha(1). Practical implications: The study focuses on the SCM under complex fuzzy environment in SCM, which has a great practical significance. Therefore, the bi-fuzzy MODM model and MO-APSO can be further applied in SCM problem with quantity discount policy. Originality/value: The bi-fuzzy variable is employed in the nonlinear MODM model of SCM to characterize the hybrid uncertain environment, and this work is original. In addition, the hybrid crisp approach is proposed to transferred to model to an equivalent crisp one by the DMs's degree of optimism and the expected value index. Since the MODM model consider the bi-fuzzy environment and quantity discount policy, so this paper has a great practical significance.
Uncertain random variables are used to describe the phenomenon of simultaneous appearance of both uncertainty and randomness in a complex system. For modeling multi-objective decision-making problems with uncertain ra...
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Uncertain random variables are used to describe the phenomenon of simultaneous appearance of both uncertainty and randomness in a complex system. For modeling multi-objective decision-making problems with uncertain random parameters, a class of uncertain random optimization is suggested for decision systems in this paper, called the uncertain random multi-objective programming. For solving the uncertain random programming, some notions of the Pareto solutions and the compromise solutions as well as two compromise models are defined. Subsequently, some properties of these models are investigated, and then two equivalent deterministic mathematical programming models under some particular conditions are presented. Some numerical examples are also given for illustration.
Convergence speed and diversity of nondominated solutions are two important performance indicators for multi-objective Evolutionary Algorithms (MOEAs). In this paper, we propose a Resource Allocation (RA) model based ...
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Convergence speed and diversity of nondominated solutions are two important performance indicators for multi-objective Evolutionary Algorithms (MOEAs). In this paper, we propose a Resource Allocation (RA) model based on Game Theory to accelerate the convergence speed of MOEAs, and a novel Double-Sphere Crowding Distance (DSCD) measure to improve the diversity of nondominated solutions. The mechanism of RA model is that the individuals in each group cooperate with each other to get maximum benefits for their group, and then individuals in the same group compete for private interests. The DSCD measure uses hyper-spheres consisting of nearest neighbors to estimate the crowding degree. Experimental results on convergence speed and diversity of nondominated solutions for benchmark problems and a real-world problem show the efficiency of these two proposed techniques. (C) 2013 Elsevier B.V. All rights reserved.
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