This paper proposes a versatile and interactive route planning problem for sightseeing under various conditions based on constraints of required traveling times and total satisfaction of sightseeing activities, which ...
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This paper proposes a versatile and interactive route planning problem for sightseeing under various conditions based on constraints of required traveling times and total satisfaction of sightseeing activities, which are time-dependent. In general, traveling times among sightseeing places and the satisfactions of activities also change dependent on various conditions such as weather, climate and season. It is important to represent a satisfying route to ensure the large satisfaction even if any assumable condition happens. In order to incorporate the above-mentioned situations, a multi-objective route planning problem is formulated as a network optimization problems based on Time-Expanded Network to represent time-dependent parameters in a static network. Furthermore, an interactive approach based on the Satisficing Trade-Off Method is introduced to transform the multi-objective into the single. In addition, the proposed problem is equivalently transformed into an extended model of network optimization problems by introducing parameters, and the interactive algorithm is developed.
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 research evaluates the potential gains in benefits from using Goal programming to preserve forestland. Two- and three-dimensional Goal programming models are developed and applied to data from applicants to the U...
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This research evaluates the potential gains in benefits from using Goal programming to preserve forestland. Two- and three-dimensional Goal programming models are developed and applied to data from applicants to the U.S. Forest Service's Forest Legacy Program, the largest forest protection program in the United States. Results suggest that not only do these model yield substantial increases in benefits, but by being able to account for both environmental benefits and in-kind partner cost share, Goal programming may be flexible enough to facilitate adoption by program managers needing to account for both ecological and political factors. (C) 2012 Department of Forest Economics, Swedish University of Agricultural Sciences, Umea. Published by Elsevier GmbH. All rights reserved.
The Decision Support Systems (DSS) were widely applied to the field of multi-Criteria Decision Aid (MCDA). In fact, the most popular MCDA aggregating procedures are those supported by a DSS. Our literature review reve...
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ISBN:
(纸本)9781467358125
The Decision Support Systems (DSS) were widely applied to the field of multi-Criteria Decision Aid (MCDA). In fact, the most popular MCDA aggregating procedures are those supported by a DSS. Our literature review reveals that the multi-Criteria Decision Support Systems (MCDSS) have applied in several fields such as: Production and Operation Management, Finance, Education, Human Resources, Real Estate and multi Media. The aim of this paper is to provide a state-of-the Art and a classification by field of MCDSS applications.
The planning and management decisions often involve multiple objectives and multiple parties with conflicting interests due to the complexity of inter-basin water transfer systems. In this paper, the objectives, the g...
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The planning and management decisions often involve multiple objectives and multiple parties with conflicting interests due to the complexity of inter-basin water transfer systems. In this paper, the objectives, the groups involved and the corresponding conflicting interests that characterize water transfer decisions are analyzed. A multi-party, multi-objective decision/bargaining model based on the "satisfaction principle" is developed for inter-basin water transfer system decision-making. In order to obtain an ideal multi-party decision, bargaining is first broken down into two stages, and then decision alternatives are chosen using fuzzy pattern recognition. This model is simple, and it is more adaptable for solving practical multi-objective and multi-party decision problems. Finally, an inter-basin water transfer scheme optimization example is demonstrated by using the developed model.
In many practical applications of stochastic programming, discretization of continuous random variables in the form of a scenario tree is required. In this paper, we deal with the randomness in scenario generation and...
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In many practical applications of stochastic programming, discretization of continuous random variables in the form of a scenario tree is required. In this paper, we deal with the randomness in scenario generation and present a visual interactive method for scenario-based stochastic multi-objective problems. The method relies on multi-variate statistical analysis of solutions obtained from a multi-objective stochastic problem to construct joint confidence regions for the objective function values. The decision maker (DM) explores desirable parts of the efficient frontier using a visual representation that depicts the trajectories of the objective function values within confidence bands. In this way, we communicate the effects of randomness inherent in the problem to the DM to help her understand the trade-offs and the levels of risk associated with each objective. Journal of the Operational Research Society (2012) 63, 1773-1787. doi:10.1057/jors.2012.25 Published online 11 April 2012
In the context of transboundary issues, this paper introduces a composite water resources allocation approach that integrates both game theory and Pareto frontier concepts over the case of the Euphrates and Tigris Riv...
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In the context of transboundary issues, this paper introduces a composite water resources allocation approach that integrates both game theory and Pareto frontier concepts over the case of the Euphrates and Tigris Rivers. The proposed approach searches for an acceptable and viable solution set over the Pareto Frontier Surface via game theory based rationality constraints. For this purpose, the used base model is the Euphrates and Tigris River Basin Model, which is a linear programming model maximizing net economic benefits while optimally allocating scarce water resources in the basin. Results indicate that game theory based strategies and associated constraints provide a determinative backbone for an efficient and effective use of generated Pareto Frontier Surfaces. Additionally, estimated marginal values imply that the upstream countries have upper hand positions regarding their geographic and climatic contexts. After all the generation schemes, it appears that Turkey is the critical partner for inclusion into any form of coalition in the Euphrates and Tigris River Basin. (c) 2012 Elsevier B.V. All rights reserved.
The problem of bandwidth allocation may be simply stated, independently of the target of the allocation: an amount of bandwidth must be shared among different entities. Each entity receives a portion of the overall ba...
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The problem of bandwidth allocation may be simply stated, independently of the target of the allocation: an amount of bandwidth must be shared among different entities. Each entity receives a portion of the overall bandwidth. Bandwidth allocation may be formalized as a multi-objective programming (MOP) problem where the constraint is the maximum available bandwidth. The objectives of the allocation such as loss and power may be modelled through a group of objective functions possibly contrasting with each other. It is quite intuitive that using more bandwidth will reduce losses, but also that transmitted power will increase with the bandwidth. Which is the balance among these needs? This letter proposes an extended model for bandwidth allocation and uses a modified version of a MOP-based bandwidth allocation [1] to provide a possible solution to the mentioned balancing problems.
This paper investigates a fuzzy multi-objective vendor selection program under lean procurement based on cost minimization, delivery schedule violation minimization, and maximizing the quality level of the purchased q...
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This paper investigates a fuzzy multi-objective vendor selection program under lean procurement based on cost minimization, delivery schedule violation minimization, and maximizing the quality level of the purchased quantity. Specifically, the paper incorporates the vendor production capacity uncertainty into the model to identify an appropriate selection policy for vendors under practical operating conditions. The use of a soft time-window mechanism for the vendor selection model enables decision makers to further incorporate a time based performance metric for vendor evaluation, based on the degree of urgency or need for a part. A solution algorithm using fuzzy AHP is proposed. The results of a numerical example suggest that decision makers prefer vendors who can promise tighter delivery schedules rather than on cost or quality. A sensitivity analysis of the soft time-window on the achievement of the lean procurement objectives is also conducted. (C) 2012 Elsevier B.V. All rights reserved.
In stratified sampling, usually the cost function is taken as a linear function of sample sizes n(h). In many practical situations, the linear cost function does not approximate the actual cost incurred adequately. Fo...
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In stratified sampling, usually the cost function is taken as a linear function of sample sizes n(h). In many practical situations, the linear cost function does not approximate the actual cost incurred adequately. For example, when the cost of travelling between the units selected in the sample within a stratum is significant, instead of a linear cost function, a cost function that is quadratic in root n(h) will be a more close approximation to the actual cost. In this paper, the problem is formulated as multi-objective nonlinear integer programming problem with quadratic cost under three different situations, i.e. complete, partial or null information about the population. A numerical example is also presented to illustrate the computational details.
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