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Supply Chain Analytics 2023年 2卷

作者： Vafadarnikjoo, Amin Moktadir, Md. Abdul Paul, Sanjoy Kumar Ali, Syed Mithun Sheffield University Management School The University of Sheffield Conduit Rd Sheffield S10 1FL United Kingdom Institute of Leather Engineering and Technology University of Dhaka Dhaka Bangladesh UTS Business School University of Technology Sydney Sydney Australia Department of Industrial and Production Engineering Bangladesh University of Engineering and Technology Dhaka 1000 Bangladesh

Robust and resilient agri-food supply chain management (AFSCM) is paramount to agribusinesses, given the many challenges and risks that this increased demand will bring in the coming decades. Interruptions caused by various risks to this crucial supply chain network, particularly in emerging economies, can put the lives of millions in danger, not to mention creating devastating impacts on the economy and the environment. Even so, there are only a limited number of quantitative risk management studies in the AFSCM literature. In this study, an integrated modified risk mitigation matrix (M-RMM) is developed to analyze the mitigation strategies for dealing with various risks in the context of the agri-food supply chain. The M-RMM is integrated with the grey multi-objective binary linear programming (GMOBLP) model to obtain the optimal risk mitigation strategies related to the three objective functions of risk, cost, and time minimization. The proposed model is a useful tool for formulating sustainable business policies and reducing food waste, and acquiring a context-specific (i.e., a developing economy), sector-specific (i.e., the agri-food processing sector), and multi-product (i.e., fresh and non-perishable) approach. The findings reveal that continuous training and development and vulnerability analysis of IT systems are the most effective risk mitigation strategies to lessen the impacts of lack of skilled personnel, sub-standard leadership, failure in IT systems, insufficient capacity to produce quality products, and poor customer relationships. The findings assist practitioners in managing risks in supply chains. © 2023 The Authors

关键词： Agri-food supply chain Best-worst method Grey theory multi-objective linear programming Risk mitigation Supply chain management

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Optimal irrigation planning using multi-objective fuzzy linear programming models

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ISH Journal of Hydraulic Engineering 2012年第3期18卷 232-240页

作者： Mirajkar, A.B. Patel, P.L. Department of Civil Engineering Sardar Vallabhbhai National Institute of Technology Surat Gujarat India

Crisp linear programming approach has been used to get the individual solutions of three conflicting objectives: maximisation of net benefit, maximisation of employment generation and minimisation of cost of cultivation. These crisp solutions were, then, solved together to obtain the solution under fuzzy multi-objective environment with maximum–minimum operators. Also, an effort has been made to improve the solution from maximum-minimum operator approach using two-phase as well as compromised fuzzy approaches. The applicability of the aforesaid algorithms were demonstrated through a case study of Kakrapar Right Bank Main Canal (KRBMC) under Ukai command area in India. © 2012 Taylor & Francis Group, LLC.

关键词： Compromise solution Maximum–minimum operators multi-objective linear programming Satisfaction degree Two-phase approach

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APPLICATION OF POSSIBILISTIC linear programming TO multi-objective DISTRIBUTION PLANNING DECISIONS

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JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING 2007年第2期24卷 97-109页

作者： Liang, Tien-Fu Hsiuping Inst Technol Dept Ind Engn & Management 11 Gungye Rd Taichung 412 Taiwan

In real-world distribution planning decision (DPD) problems, the decision maker (DM) must simultaneously handle conflicting objectives, and input data and related parameters are often imprecise/fuzzy owing to incomplete and/or unavailable information. This work develops an interactive possibilistic linear programming (PLP) method for solving multi-objective DPD problems involving imprecise available supply, forecast demand and unit cost/time coefficients with triangular possibility distributions. The multi-objective PLP model designed here aims to simultaneously minimize the total distribution costs and the total delivery time with reference to available supply constraint at each source, as well as forecast demand and warehouse space constraints at each destination. Additionally, the interactive PLP method provides a systematic framework that facilitates the decision-making process, enabling a DM to interactively modify the imprecise data and related parameters until a satisfactory solution is obtained. An industrial case is presented to demonstrate the feasibility of applying the interactive PLP method to real DPD problems. Consequently, the PLP method yields a set of efficient compromise solutions and overall degree of DM satisfaction with the determined objective values. Especially, several significant finding relating to the practical application of the interactive PLP method are presented.

关键词： possibilisic linear programming distribution planning decisions multi-objective linear programming fuzzy set theory

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Artificial intelligence-enabled smart city management using multi-objective optimization strategies

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EXPERT SYSTEMS 2025年第1期42卷

作者： Pinki Kumar, Rakesh Vimal, S. Alghamdi, Norah Saleh Dhiman, Gaurav Pasupathi, Subbulakshmi Sood, Aarna Viriyasitavat, Wattana Sapsomboon, Assadaporn Kaur, Amandeep Lovely Profess Univ Dept Math Phagwara Punjab India Ramco Inst Technol Dept Artificial Intelligence & Data Sci Rajapalayam India Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Comp Sci Riyadh Saudi Arabia Lebanese Amer Univ Dept Elect & Comp Engn Byblos Lebanon Chitkara Univ Ctr Res Impact & Outcome Rajpura Punjab India Graph Era Deemed Univ Dept Comp Sci & Engn Dehra Dun India Lovely Profess Univ Div Res & Dev Phagwara India Middle East Univ MEU Res Unit Amman Jordan VIT Chennai Sch Comp Sci & Engn Chennai India Sanskriti Sch New Delhi India Chulalongkorn Univ Fac Commerce & Accountancy Chulalongkorn Business Sch Bangkok Thailand Chitkara Univ Chitkara Ctr Res & Dev Baddi Himachal Prades India Chandigarh Univ Dept Comp Sci & Engn Univ Ctr Res & Dev Mohali India

This article outlines an integrated strategy that combines fuzzy multi-objective programming and a multi-criteria decision-making framework to achieve a number of transportation system management-related objectives. To rank fleet cars using various criteria enhancement, the Fuzzy technique for order of preference by resemblance to optimum solution are initially integrated. We then offer a novel multi-objective Possibilistic linear programming (MOPLP) model, based on the rankings of the vehicles, to determine the number of vehicles chosen for the work while taking into consideration the constraints placed on them. The search for optimal solutions to MOPs has benefited from the decades-long development of classical optimisation techniques. As a result of its potential for use in the real world, multi-objective optimisation (MOO) under uncertainty has gained traction in recent years. Recently, fuzzy set theory has been used to solve challenges in multi-objective linear programming. In this paper, we present a method for solving MOPs that makes use of both linear and non-linear membership functions to maximize user happiness. A hypothetical case study of transportation issue is taken here. This innovative approach improves management for the betterment of transportation networks in smart cities. The method is a more robust and versatile approach to the complex difficulties of contemporary urban transportation because it incorporates the TOPSIS method for vehicle ranking and then using Distance Operator and variable Membership Functions in fuzzy goal programming operation on the selected vehicles. The results provide valuable insights into the strengths and limitations of each technique, facilitating informed decision-making in real-world optimization scenarios.

关键词： fuzzy set theory intelligent transportation system multi-objective linear programming smart city TOPSIS

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Data-driven allocation of renewables quota among regional power industries under the policy of renewable electricity standard

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EXPERT SYSTEMS 2025年第1期42卷

作者： Liu, Xiaohong Xu, Chengzhen Pan, Yinghao Li, Xingchen Zhu, Qingyuan Hefei Univ Technol Sch Humanity & Law Hefei Anhui Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Econ & Management Res Ctr Soft Energy Sci Nanjing Jiangsu Peoples R China Univ Sci & Technol China Sch Management Hefei Anhui Peoples R China Nanjing Audit Univ Sch Accounting Nanjing 211815 Jiangsu Peoples R China

China is struggling to facilitate the application of renewable portfolio standards to realize sustainable economic growth. As such, improving the current distribution mechanism is crucial. In this paper, the context-dependent data envelopment analysis and multi-objective linear programming are combined to allocate the renewables quota for each province. This integrated approach can maximize total electricity generation while minimizing the total CO2 emission with considering the disparity of production technology level. Then, the extended Gini coefficient is employed to assess the fairness of new quota mechanism. We find that (1) the eastern region is the most efficient during the power generation process. During 2016-2019, the efficiency in the western region presents an upward trend. (2) The allocation results indicate that Inner Mongolia and Qinghai have the greatest pressure to absorb renewable energy electricity, while Guangdong and Guizhou can instead reduce the most. Shandong and Inner Mongolia face the greatest burden in conserving non-renewable electricity. (3) Compared to 2020, the newly allocated scheme can mitigate inequality, with the Gini coefficient changing from 0.264 in 2020 to 0.248 after the allocation. Meanwhile, the reallocation reduces the Gini coefficient related to renewable electricity, non-renewable electricity, and CO2 emissions by 0.003, 0.028, and 0.073, respectively at the 2020 level.

关键词： context-dependent DEA Gini coefficient multi-objective linear programming power generation efficiency renewable electricity quota renewable electricity standard

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A multi-objective Method for Solving Fuzzy linear programming Based on Semi-infinite Model

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FUZZY INFORMATION AND ENGINEERING 2018年第1期10卷 91-98页

作者： Nasseri, S. H. Zavieh, H. Univ Mazandaran Dept Math Sci Babol Sar Iran

In this paper, we present a new method to solve a fuzzy linear programming problem with fuzzy coefficients in the constraints and the objective function based on solving an associated multi-objective model. In particular, we present a weighted method for linear semi-infinite programming (LSIP) model to solve the original problem. Finally, a numerical example is included to illustrate the suggested solving process.

关键词： multi-objective linear programming fuzzy linear programming weighted method linear semi-infinite programming fuzzy numbers

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A New Method to Solve Fuzzy Interval Flexible linear programming Using a multi-objective Approach

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FUZZY INFORMATION AND ENGINEERING 2021年第2期13卷 248-265页

作者： Nasseri, S. H. Verdegay, J. L. Mahmoudi, F. Univ Mazandaran Dept Math Babolsar Iran Univ Granada Dept Comp Sci & AI Granada Spain

Recently fuzzy interval flexible linear programs have attracted many interests. These models are an extension of the classical linear programming which deal with crisp parameters. However, in most of the real-world applications, the nature of the parameters of the decision-making problems are generally imprecise. Such uncertainties can lead to increased complexities in the related optimisation efforts. Simply ignoring these uncertainties is considered undesired as it may result in inferior or wrong decisions. Therefore, inexact linear programming methods are desired under uncertainty. In this paper, we concentrate a fuzzy flexible linear programming model with flexible constraints and the interval objective function and then propose a new solving approach based on solving an associated multi-objective model. Finally, a numerical example is included to illustrate the mentioned solving process.

关键词： multi-objective linear programming fuzzy interval flexible linear programming interval linear programming interval arithmetic flexible constraints

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SOLVING THE multi-objective FRACTIONAL SOLID TRANSPORTATION PROBLEM BY USING DIFFERENT OPERATORS

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Journal of Mathematical Sciences (United States) 2025年第1期289卷 141-153页

作者： Kara, Nurdan Tiryaki, Fatma Naval Training and Education Command Kocaeli 41900 Turkey Department of Mathematics Yildiz Technical University Istanbul 34220 Turkey

This research delves into a complex problem known as the multi-objective fractional solid transportation problem (MFSTP), which deals with various fractional ratios such as cost/profit. The problem involves three main components: origin, destination, and product type or mode of transportation. While this research offers a solution to the MFSTP, it also examines the effects of different aggregation operators. In multi-objective problems, finding Pareto optimal solutions that consider the balance between objectives can be achieved through different methods. Utilizing aggregation operators, this study has found a variety of Pareto optimal solutions, expanding the decision-maker’s options. This means that it presents the decision-maker with a set of potential solutions. This allowed them to evaluate and select the operator that best suited the problem by comparing the various solutions obtained. To illustrate the solution methodology, a numerical example is provided and the obtained solutions are compared. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Aggregation operators Fractional programming Fuzzy mathematical programming multi-objective linear programming Solid transportation problem

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A New Method to Solve Fuzzy Interval Flexible linear programming Using a multi-objective Approach

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Fuzzy Information and Engineering 2019年第2期11卷 221-238页

作者： S. H. Nasseri J. L. Verdegay F. Mahmoudi a Department of Mathematics University of Mazandaran Babolsar Iran b Department of Computer Science and A.I. Universidad de Granada Granada Spain

ABSTRACTFormulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to *** fuzzy interval flexible linear programs have attracted many interests. These models are an extension of the classical linear programming which deal with crisp parameters. However, in most of the real-world applications, the nature of the parameters of the decision-making problems are generally imprecise. Such uncertainties can lead to increased complexities in the related optimisation efforts. Simply ignoring these uncertainties is considered undesired as it may result in inferior or wrong decisions. Therefore, inexact linear programming methods are desired under uncertainty. In this paper, we concentrate a fuzzy flexible linear programming model with flexible constraints and the interval objective function and then propose a new solving approach based on solving an associated multi-objective model. Finally, a numerical example is included to illustrate the mentioned solving process.

关键词： multi-objective linear programming fuzzy interval flexible linear programming interval linear programming interval arithmetic flexible constraints

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multi-objective programming model for energy conservation and renewable energy structure of a low carbon campus

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ENERGY AND BUILDINGS 2014年第0期80卷 461-468页

作者： Ho, Yu-Feng Chang, Ching-Chih Wei, Chao-Cheng Wang, Hsiao-Lin Chaoyang Univ Technol Grad Sch Architecture & Urban Design Chaoyang Taiwan Chaoyang Univ Technol Dept Landscape & Urban Design Chaoyang Taiwan

School campus energy conservation design covers a wide range of contents;it is a complex control system and involves limited resource allocation. The purpose of this study is to formulate a decision model for energy conservation and renewable energy for campuses, by employing multi-objective linear programming (MOLP) and fuzzy two-stage algorithm to reduce CO2 emissions. An empirical study was conducted using the campus of Chaoyang University of Technology as an example. Through a fuzzy two-stage algorithm, the non-inferior solution of a set of low carbon campus energy conservation design was obtained. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Low carbon campus Renewable energy multi-objective linear programming Fuzzy two-stage algorithm

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