Linear programming with fuzzy parameters: An interactive method resolution

Jiménez, Mariano; Arenas, Mar; Bilbao, Amelia; ́guez, M. Victoria Rodrı

doi:10.1016/j.ejor.2005.10.002

Cited by 530 publications

(381 citation statements)

References 19 publications

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“…Although the results obtained from three fuzzy mathematical programming models are different, as the viewpoint of these fuzzy methods are different and variability in values of outcomes are seen, but the trend of outcome in all of the fuzzy mathematical approaches (i.e., Jiménez et al (2007), Werners (1987), and Tan and Cao (2005)) support a single truth about performance of DDM approach in comparison with CDM approach. This truth is plotted in Figure 4.…”

Section: Computational Resultsmentioning

confidence: 99%

“…In order to make a better sense of steps of proposed coordination mechanism between manufacturer and retailers in the supply chain, the results of Jiménez (2007) approach for data set1are presented in Table 4.…”

Section: Computational Resultsmentioning

confidence: 99%

“…The Jiménez et al (2007) approach used the expected interval and expected value of a fuzzy number to change the fuzzy mathematical programming into a crisp mathematical programming. The Werners (1987) method calculated the membership function of objective function and constraints of fuzzy mathematical programming.…”

Section: Computational Resultsmentioning

confidence: 99%

“…For triangular functions, Jiménez et al (2007) showed that the expected interval of fuzzy coefficient ã = (a 1 , a 2 , a 3 ), can be calculated using (34). …”

Section: Jiménez Approachmentioning

confidence: 99%

“…(Jiménez et al, 2007) This approach was first used to change the deterministic model into a fuzzy model. It was presented to combine fuzzy coefficients with trapezoidal membership functions into linear programming models.…”

Section: Solution Proceduresmentioning

confidence: 99%

See 4 more Smart Citations

Uncertain Centralized/Decentralized Production-Distribution Planning Problem in Multi-Product Supply Chains: Fuzzy Mathematical Optimization Approaches

Khalili‐Damghani¹,

Ghasemi²

2016

Industrial Engineering and Management Systems

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Complex and uncertain issues in supply chain result in integrated decision making processes in supply chains. So decentralized (distributed) decision making (DDM) approach is considered as a crucial stage in supply chain planning. In this paper, an uncertain DDM through coordination mechanism is addressed for a multi-product supply chain planning problem. The main concern of this study is comparison of DDM approach with centralized decision making (CDM) approach while some parameters of decision making are assumed to be uncertain. The uncertain DDM problem is modeled through fuzzy mathematical programming in which products' demands are assumed to be uncertain and modeled using fuzzy sets. Moreover, a CDM approach is customized and developed in presence of fuzzy parameters. Both approaches are solved using three fuzzy mathematical optimization methods. Hence, the contribution of this paper can be summarized as follows: 1) proposing a DDM approach for a multi-product supply chain planning problem; 2) Introducing a coordination mechanism in the proposed DDM approach in order to utilize the benefits of a CDM approach while using DDM approach; 3) Modeling the aforementioned problem through fuzzy mathematical programming; 4) Comparing the performance of proposed DDM and a customized uncertain CDM approach on multiproduct supply chain planning; 5) Applying three fuzzy mathematical optimization methods in order to address and compare the performance of both DDM and CDM approaches. The results of these fuzzy optimization methods are compared. Computational results illustrate that the proposed DDM approach closely approximates the optimal solutions generated by the CDM approach while the manufacturer's and retailers' decisions are optimized through a coordination mechanism making lasting relationship. .

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Section: Computational Resultsmentioning

confidence: 99%

Section: Computational Resultsmentioning

confidence: 99%

Section: Computational Resultsmentioning

confidence: 99%

“…For triangular functions, Jiménez et al (2007) showed that the expected interval of fuzzy coefficient ã = (a 1 , a 2 , a 3 ), can be calculated using (34). …”

Section: Jiménez Approachmentioning

confidence: 99%

Section: Solution Proceduresmentioning

confidence: 99%

See 3 more Smart Citations

Uncertain Centralized/Decentralized Production-Distribution Planning Problem in Multi-Product Supply Chains: Fuzzy Mathematical Optimization Approaches

Khalili‐Damghani¹,

Ghasemi²

2016

Industrial Engineering and Management Systems

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An interactive optimization model for energy systems planning associated with clean-energy development under uncertainty

Jin

Huang

2016

Int. J. Energy Res.

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Summary In this study, an interactive fuzzy chance‐constrained resolution (IFCR) method is developed for supporting energy systems planning (ESP) under uncertainty. IFCR can not only tackle multiple uncertainties presented as fuzzy membership functions and probability distributions using an interactive resolution method, but also enable decision makers to seek optimal solutions between satisfaction degree of objective and feasibility of constraints. Then, an IFCR‐ESP model is formulated for energy systems of Bayingolin Mongol Autonomous Prefecture (abbreviated as Bazhou). Results provide decision makers with a complete view of the relationship between uncertain inputs and solutions. Policy‐based solutions concerning energy consumption, electricity generation, capacity expansion, pollutant emission, and system cost are analyzed, which can help decision makers to identify desired strategies for ESP. Results indicate that transitioning from the conventional energy system to a sustainable one is associated to policy support, resources availability, energy supply security, and capital investment; clean energy policy (e.g. shift from coal to gas) is an effective way to facilitate the local energy system development in a sustainable manner; the solutions under different feasibility degrees can help decision makers to conduct in‐depth analyses of tradeoffs between system cost and constraint‐violation risk in an interactive way. Copyright © 2016 John Wiley & Sons, Ltd.

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An inexact optimization model for distributed multi‐energy systems management in sustainable airports

Jin

2021

Int J Energy Res

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This paper proposes a fuzzy chance-constrained fractional programming (FCFP) method for planning distributed multi-energy systems (DMES). FCFP can deal with uncertainties expressed as fuzzy information, probability distributions, and multiple objectives. The FCFP-DMES model was applied to a real airport in a case study, and a series of scenarios were selected to examine the effects of the uncertainty on the energy supply and technology selection. Additionally, a comparison related to conventional energy system (CES) and DMES are discussed from energy consumption, economic, and environmental aspects. The results revealed the following: the combined cooling, heat, and power would serve as a primary distributed energy resource providing heating, cooling, and electricity in different seasons, accounting for approximately 40% of the total; among different alternative technologies, heating supplied by gas-fired boiler and thermal storage would serve as auxiliary heaters to cover 6.6% and 15.2% of the heating load, respectively, under high-level demand; although the DMES cannot bring cost-cutting, it has better environmental performance and a peak shaving function. Compared with the DMES, the CES would almost double the electricity purchasing cost (reaching $9.56 million), and an additional 136.24 MW of electricity would be needed, which would result in 127.5 tons/year of pollutant emissions. The findings of this study indicate that the FCFP-DMES model can provide a comprehensive and systematic strategy considering the multi-energy, multi-technology, and multi-uncertainty within the DMES.

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Linear programming with fuzzy parameters: An interactive method resolution

Cited by 530 publications

References 19 publications

Uncertain Centralized/Decentralized Production-Distribution Planning Problem in Multi-Product Supply Chains: Fuzzy Mathematical Optimization Approaches

Uncertain Centralized/Decentralized Production-Distribution Planning Problem in Multi-Product Supply Chains: Fuzzy Mathematical Optimization Approaches

An interactive optimization model for energy systems planning associated with clean-energy development under uncertainty

An inexact optimization model for distributed multi‐energy systems management in sustainable airports

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