A method for solving fuzzy de novo programming problem by genetic algorithms

Sato, Masato; Gen, Mitsuo; Yamashiro, Mitsuo

doi:10.1016/0360-8352(95)00125-k

Cited by 13 publications

(6 citation statements)

References 3 publications

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“…Among followup studies on de Novo programming, Li and Lee (1990) proposed a model of de Novo programming with fuzzy coefficients. Sasaki et al (1995) implemented genetic algorithm (GA) for de Novo programming with fuzzy goals and constraints, which was based on multiple criteria and multiple constraints (MC 2 ) by Seiford and Yu (1979) and fuzzy MC 2 by Shi and Liu (1993). Chen and Hsieh (2006) further built a fuzzy multi-stage method of de Novo programming with the dynamic nature.…”

Section: Methods With a Priori Informationmentioning

confidence: 99%

Application of Multiple Criteria Decision Making Methods in Construction: A Systematic Literature Review

Zhu

Meng

Zhang

2021

Journal of Civil Engineering and Management

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Decision making is a key to business or project success in any sectors, especially in construction that requires handling numerous information and knowledge. Multiple criteria decision making (MCDM) is an important tool for decision problem solving due to simultaneous consideration of multiple criteria and objectives. Various MCDM methods are continually emerging and tend to be increasingly adopted to address the real-world construction problems. Therefore, it is urged to systematically review the existing body of literature to demonstrate the evolution of the mainstream MCDM methods in general and their application status in construction. A total of 530 construction articles published from 2000 to 2019 are selected in this study and then categorized into seven major application areas using a novel systematic literature review (SLR) methodology. The bibliometric analysis is then used to describe the research trend. Subsequently, the qualitative discussion by themes is conducted to analyze the application of MCDM methods in construction. A further discussion makes it possible to identify the potential challenges (e.g. applicability, robustness, postpone effect, dynamic and prospective challenges and scale problem) to existing research. It also contributes to the recommendation of future directions for the development of MCDM methods that would benefit construction research and practice.

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Section: Methods With a Priori Informationmentioning

confidence: 99%

Application of Multiple Criteria Decision Making Methods in Construction: A Systematic Literature Review

Zhu

Meng

Zhang

2021

Journal of Civil Engineering and Management

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“…In Feng and Wu's ͑1999͒ previous study, the estimation algorithm for the basic model is fuzzy programming proposed by Zimmermann ͑1978͒ and modified by Li and Lee ͑1990͒. In many research studies ͑Bit et al 1992; Bhattacharya et al 1992;Lee and Li 1993;Sasaki et al 1995͒, fuzzy programming serves as a good method for finding compromise solutions for the multiobjective optimization problem. To follow the procedures of that method, we first need to get the ideal solution I*ϭ(W 1 * ,W 2 * ,W 3 *) and anti-ideal solution…”

Section: Estimation Algorithmmentioning

confidence: 99%

Highway Investment Planning Model for Equity Issues

Feng

2003

J. Urban Plann. Dev.

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In the present study, we revised and expanded the basic model proposed in our previous study to better deal with equity issues in highway investment planning. The issues cover horizontal ͑intraregional͒ and vertical ͑interre-gional͒ equity of the accessibility or travel cost for cities as well as the equity of budget allocation among the cities. In this study, more constraints and decision variables were added to the model to handle the exclusive and complementary properties among alternatives. In addition, adjustments for travel cost measures and objective functions are proposed to justify the horizontal and vertical equity. The revised multiobjective model is estimated by fuzzy programming for the highway system in Taiwan. Our results indicate that the model is practical and effective for acquiring reasonable solutions for the goal of efficiency and equity in highway investment planning.

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“…De Novo programming, which was effective for dealing with optimal design problems with unknown resource availability and seeking a portfolio of resource availability level to optimize multiple objective functions by allocating a budget according to the resource price, was an attractive technique in response to the above challenges (Zeleny, 1981(Zeleny, , 1986(Zeleny, , 1990. Previously, a number of research works based on the De Novo programming were applied to various system design cases Mendoza, 1988, 1990;Lee, 1990, 1993;Kim et al, 1993;Sasaki et al, 1995;Shi, 1995;Kotula, 1997;Zeleny, 2005;Chen and Hsieh, 2006;Zhang et al, 2009). For example, Bare and Mendoza (1988) employed De Novo programming to single and multi-objective forestry land management problems, where a number of constraints such as labor, picnic sites, and hiking trails were considered; the study system could be designed to perform in an ideal fashion within a constant budget level.…”

Section: Introductionmentioning

confidence: 99%

“…Kim et al (1993) formulated a De Novo 0-1 bicriteria linear programming with interval coefficients under generalized upper bounding structure, where interval coefficients were trans-formed into a fuzzy state by the fuzzy transformation based on the degree of satisfying inequality relationship and order relationship between intervals; however, the main limitation of this transformation method was to generate a generalized De Novo model. Sasaki et al (1995) proposed an implementtation of the genetic algorithm for solving De Novo programming problems with fuzzy goal and constraints, which possessed the flexibility to obtain better solutions compared to crisp constraints. Shi (1995) introduced several optimum-path ratios for enforcing different budget levels of resources to identify alternative optimal system designs for solving multicriteria De Novo programming problems.…”

Section: Introductionmentioning

confidence: 99%

Planning Water Resources Systems under Uncertainty Using an Interval-Fuzzy De Novo Programming Method

Miao

Huang

et al. 2014

J ENV INFORM

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This study presents an interval-fuzzy De Novo programming (IFDNP) method for planning water-resourcesmanagement systems under uncertainty. IFDNP is derived by incorporating the concepts of interval parameters and fuzzy sets within a De Novo programming framework. IFDNP has the advantages in constructing optimal system design through introducing the flexibility into the available resources in the model's constraints. Moreover, IFDNP allows the decision makers to achieve a metaoptimal system performance and improve the performance of compromise solutions, and it is effective for dealing with the system design problems involving multiple objectives and multiple uncertainties. The IFDNP is then applied to a case study of designing an inexact optimal system with budget limit for water resources management and planning. Various scenarios that are associated with different levels of economic implication consequences and water allocation patterns under uncertainty are analyzed. Results can help decision makers to evaluate alternatives of system designs and to determine which of these designs can most efficiently achieve the desired economic objective constrained by limited resources.

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A method for solving fuzzy de novo programming problem by genetic algorithms

Cited by 13 publications

References 3 publications

Application of Multiple Criteria Decision Making Methods in Construction: A Systematic Literature Review

Application of Multiple Criteria Decision Making Methods in Construction: A Systematic Literature Review

Highway Investment Planning Model for Equity Issues

Planning Water Resources Systems under Uncertainty Using an Interval-Fuzzy De Novo Programming Method

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