作者：
Wang, YMFuzhou Univ
Sch Publ Adm Fuzhou 350002 Fujian Peoples R China Univ Manchester
Manchester Business Sch Manchester M60 1QD Lancs England
In a recent paper by [European Journal of Operational Research 97 (1997) 53-62] the lexicographic goal programming (LGP) method was developed to generate weights from inconsistent interval comparison matrices and a nu...
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In a recent paper by [European Journal of Operational Research 97 (1997) 53-62] the lexicographic goal programming (LGP) method was developed to generate weights from inconsistent interval comparison matrices and a number of its properties and advantages as a weight determination technique were explored. It is shown in this paper that LGP is in fact defective in theory. Although the upper and lower triangular judgments of an inconsistent interval comparison matrix provide exactly the same information on the preferences of weights, in general they lead to distinctive priorities and rankings if LGP is used. (c) 2005 Elsevier Inc. All rights reserved.
Decision environments involve high degrees of uncertainty as well as multiple, conflicting objectives. Often sampling information is available as a means of describing uncertainty. This description can be utilized in ...
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Decision environments involve high degrees of uncertainty as well as multiple, conflicting objectives. Often sampling information is available as a means of describing uncertainty. This description can be utilized in the form of chance constraints. goal programming offers a means of considering multiple, conflicting objectives. A nonlinear goal programming algorithm is presented based upon the gradient method, utilizing an optimal step length for chance constrained goal programming models. The resulting algorithm requires assumptions of convex solution sets, differentiable and monotonic nonlinear constraints, and normally distributed variance of stochastic parameters. The algorithm is evaluated for generality, reliability, and precision; sensitivity to parameters and data; preparational and computational effort; and convergence. Model sensitivity to parameters is identified, as well as appropriate adjustment. The algorithm was found to require minimal preparational effort, favorable computation time, and rapid convergence to optimal solution with the exception of models containing high degrees of nonlinearity. [ABSTRACT FROM AUTHOR]
This paper provides a survey of the literature on goal programming (GP) from 1970 through 1982. Almost 300 references of methodological and applied papers on GP are categorized according to 18 areas of application and...
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This paper provides a survey of the literature on goal programming (GP) from 1970 through 1982. Almost 300 references of methodological and applied papers on GP are categorized according to 18 areas of application and according to 12 different variants of GP. [ABSTRACT FROM AUTHOR]
In group decision-making with multiplicative reciprocal paired comparison matrices (MRPCMs), existing research uses iterative procedures or optimisation models to improve consistency of individual assessments and buil...
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In group decision-making with multiplicative reciprocal paired comparison matrices (MRPCMs), existing research uses iterative procedures or optimisation models to improve consistency of individual assessments and build consensus. However, they often create numerous adjustments on original assessments and fail to achieve a comprehensive minimum adjustment cost. Furthermore, the adjustments in the resulting MRPCMs may be not within the predetermined continuous scale. To settle these issues, this article first introduces a logarithmic-distance-based consensus measurement framework. Four-stage sequential goal programming models are then developed to improve consistency of MRPCMs and build consensus with individual consistency control under a continuous or discrete scale. The first stage is to minimise the deviation between original assessments and adjusted ones. The second stage is to minimise the difference between the original priority information and the adjusted priority information. The third stage is to maximise the difference ratio between adjusted assessments and the neutral judgment characterised by ratio 1. The last stage is to minimise the number of modifications on original assessments. Afterwards, the article devises an interactive consistency improving procedure and an interactive group consensus building procedure. Three illustrative examples and comparisons with existing methods are offered to show the usability and efficiency of the developed models.
This paper proposes a goal programming approach to solve group decision-making (GDM) problems where the preference information on alternatives provided by decision makers is represented in two different formats, i.e. ...
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This paper proposes a goal programming approach to solve group decision-making (GDM) problems where the preference information on alternatives provided by decision makers is represented in two different formats, i.e. multiplicative preference relations and fuzzy preference relations. In order to narrow the gap between the collective opinion and each decision maker's opinion, a linear goal programming model is constructed to integrate the two different formats of preference relations and to compute the collective ranking values of the alternatives. Thus, the ranking of alternatives or selection of the most desirable alternative(s) is obtained directly from the computed collective ranking values. A numerical example is also used to illustrate the applicability of the proposed approach. (c) 2005 Published by Elsevier B.V.
This paper formulates fuzzy goal programming (FGP) incorporating different importance and preemptive priorities by using an additive model to maximize the sum of achievement degrees of all fuzzy goals. In contrast to ...
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This paper formulates fuzzy goal programming (FGP) incorporating different importance and preemptive priorities by using an additive model to maximize the sum of achievement degrees of all fuzzy goals. In contrast to previous works, the proposed approach allows the decision-maker to determine a desirable achievement degree for each fuzzy goal to reflect explicitly the relative importance of these goals. This approach can generate a set of achievement degrees consistent with the decision-maker's expectations, even though the relative importance of the goals may change. Furthermore, in this paper we incorporate the decision-maker's preemptive priority structure into a single formulation. The resulting solutions satisfy both the preemptive priority structure and have the maximum achievement degrees in sum. The proposed approaches' effectiveness and computational superiority over the existing approaches are demonstrated and compared with examples from the literature. (C) 2001 Published by Elsevier Science B.V.
A goal programming model was used to analyse optimum fertilizer combinations. Under this approach, the fertilizer requirements, instead of being fixed values as in traditional linear programming, are considered target...
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A goal programming model was used to analyse optimum fertilizer combinations. Under this approach, the fertilizer requirements, instead of being fixed values as in traditional linear programming, are considered targets which may or may not be achieved. A penalty system coupled to the goal programming model makes the specified lower and upper levels of nutrients more flexible and realistic. A simple example is used to expound the model, and then applied to real data to give optimum combinations of fertilizers for sugar beet in Western Andalusia (Spain).
This paper presents a goal programming model to optimise the deployment of pyrolysis plants in Punjab, India. Punjab has an abundance of waste straw and pyrolysis can convert this waste into alternative biofuels, whic...
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This paper presents a goal programming model to optimise the deployment of pyrolysis plants in Punjab, India. Punjab has an abundance of waste straw and pyrolysis can convert this waste into alternative biofuels, which will facilitate the provision of valuable energy services and reduce open field burning. A goal programming model is outlined and demonstrated in two case study applications: small scale operations in villages and large scale deployment across Punjab's districts. To design the supply chain, optimal decisions for location, size and number of plants, downstream energy applications and feedstocks processed are simultaneously made based on stakeholder requirements for capital cost, payback period and production cost of bin-oil and electricity. The model comprises quantitative data obtained from primary research and qualitative data gathered from farmers and potential investors. The Punjab district of. Fatehgarh Sahib is found to be the ideal location to initially utilise pyrolysis technology. We conclude that goal programming is an improved method over more conventional methods used in the literature for project planning in the field of bio-energy. The model and findings developed from this study will be particularly valuable to investors, plant developers and municipalities interested in waste to energy in India and elsewhere. (c) 2014 Elsevier Ltd. All rights reserved.
Chang [C.-T. Chang, Multi-choice goal programming, Omega, The Inter. J. Manage. Sci. 35 (2007) 389-396] has recently proposed a new method namely multi-choice goal programming (MCGP) for multi-objective decision probl...
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Chang [C.-T. Chang, Multi-choice goal programming, Omega, The Inter. J. Manage. Sci. 35 (2007) 389-396] has recently proposed a new method namely multi-choice goal programming (MCGP) for multi-objective decision problems. The multi-choice goal programming allows the decision maker to set multi-choice aspiration levels for each goal to avoid underestimation of the decision. However, to express the multi-choice aspiration levels, multiplicative terms of binary variables are involved in their model. This leads to difficult implementation and it is not easily understood by industrial participants. In this paper, we propose an alternative method to formulate the multi-choice aspiration levels with two contributions: (1) the alternative approach does not involve multiplicative terms of binary variables, this leads to more efficient use of MCGP and is easily understood by industrial participants, and (2) the alternative approach represents a linear form of MCGP which can easily be solved by common linear programming packages, not requiring the use of integer programming packages. In addition, a new concept of constrained MCGP is introduced for constructing the relationships between goals in this paper. Finally, to demonstrate the usefulness of the proposed method, an illustrate example is included. (C) 2007 Elsevier Inc. All rights reserved.
An application of goal programming (GP) methodology with its three approaches namely Min-Max goal programming (MMGP), Weighted goal programming (WGP) and Preemptive goal programming (PGP) to a system of reservoir for ...
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An application of goal programming (GP) methodology with its three approaches namely Min-Max goal programming (MMGP), Weighted goal programming (WGP) and Preemptive goal programming (PGP) to a system of reservoir for optimal monthly operation has been presented in this paper. The objective of the present work is to find out an improved optimal operation model for Mahanadi Reservoir Project (MRP) system and to study the results in light of implicit fundamental philosophy associated with the models. The goal programming approach possesses significant advantage due to the fact that it may be based on physical operating criteria. The system goals and constraints are expressed deterministically. A constraint must be strictly satisfied, while for a goal it is desired to achieve the solution as close as possible to the specified target. The MMGP model is based on the philosophy of minimization of maximum range of deviation of all decision variables from their target value uniformly. WGP focus on the weights assigned to decision variables according to their relative importance. Whereas, PGP model deals sequentially with each goal according to their order of priorities. All the three GP models have been developed and applied to the MRP Complex which comprises of six multipurpose reservoirs with two inter basin linkage canals in the state of Chhattisgarh, India. The system goals and constraints are expressed deterministically for the application of model in perfect mode. The input data set was kept same to facilitate a justifiable comparison of performance of GP models. The strengths and limitations of all the three GP models have been analysed and the basic salient features of models in light of results obtained are discussed and presented. The best operating policy has been resulted from PGP model as compared to other two models.
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