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Sustainable operations-oriented painting process optimisation in automobile maintenance service

JOURNAL OF CLEANER PRODUCTION 2021年 324卷 129191-129191页

作者： Yang, Qin Meng, Xin Zhao, Huan Cao, Cejun Liu, Yang Huisingh, Donald Sichuan Normal Univ Sch Business Chengdu 610101 Peoples R China Chongqing Technol & Business Univ Sch Management Sci & Engn Chongqing 400067 Peoples R China Linkoping Univ Dept Management & Engn SE-58183 Linkoping Sweden Univ Vaasa Dept Prod Vaasa 65200 Finland Univ Tennessee Inst Secure & Sustainable Environm Knoxville TN USA

Building a more sustainable society is an urgent requirement for today's service-oriented manufacturing enterprises, such as automobile maintenance firms. In automobile maintenance service, traditional painting process scheduling scheme usually only considers the profits of enterprises, ignoring the requirements of customers or other stakeholders. To address this gap and achieve sustainable operations of enterprises in the long term, this paper concentrated on the vehicle scheduling of painting process problem with the concern of the demands of managers, workers, customers, governments and non-government environmental protection organisations. This problem was formulated as a nonlinear 0-1 integer programming model to minimise makespan (MP), total pollutant emissions (TPE) and total customers' perceived dissatisfaction (TCPDS). A genetic algorithm was designed to solve the model, and a practical case using data from both the information system and the survey was performed to test the performance of the proposed model and algorithm. Computational results revealed that the genetic algorithm performed well in terms of validity and stability. Pareto solutions demonstrated that optimising task sequences helped increase customers' perceived satisfaction while improving the makespan of vehicle painting, decreased paint waste, and reduced worker health and safety risks. Some of the increases in the percentage of well-timed customer service reservations were catalysed by the method that combined tiered pricing, related to delivery times, the automobile painting efficiency, which improved customers' perceived satisfaction. This paper also further guides managers to incorporate sustainable development into operations in service-oriented manufacturing enterprises.

关键词： Painting scheduling Service-oriented manufacturing Sustainable operations multi-objective programming Pareto solutions

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A fuzzy compromise approach for solving multi-objective stratified sampling design

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NEURAL COMPUTING & APPLICATIONS 2021年第17期33卷 10829-10840页

作者： Ghufran, Shazia Gupta, Srikant Ahmed, Aquil Aligarh Muslim Univ Dept Stat & Operat Res Aligarh Uttar Pradesh India Jaipuria Inst Management Jaipur Rajasthan India

In this paper, we established a framework for finding out the optimal allocation in the multivariate stratified sample using the fuzzy compromise method. The problem of multivariate stratified sample is formulated as an all integer nonlinear programming problem, and a solution is obtained by using the criterion of "Minimizing the sum of the squares of coefficient of variation for different characteristics." There is also a quantitative illustration being carried out to explain the statistical nature of the approaches and solved through the LINGO program. We have also studied different techniques for comparison.

关键词： Fuzzy compromise allocation multivariate stratified sampling multi-objective programming

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Spatio-temporal evolution analysis of land use change and landscape ecological risks in rapidly urbanizing areas based on multi-Situation simulation - a case study of Chengdu Plain

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ECOLOGICAL INDICATORS 2024年 166卷

作者： Wei, Yali Zhou, Peiyun Zhang, Luoqi Zhang, Yan Sichuan Agr Univ Sch Resources Chengdu 611130 Peoples R China

Land use change in emerging nations raises landscape ecological risks (LERS), hastens the deterioration of urban and rural ecosystem services, endangers human well-being, and undermines sustainable development in the face of rapidly increasing urbanization. Here, using the Chengdu Plain as the study area and long time series data from 2000 to 2020, the optimal time span is selected for multi-context spatio-temporal simulation. The land use map of Chengdu Plain in 2025 under the four scenarios of "Natural Development" (ND), "Economic Priority Development" (END), "Ecological Priority Development" (ELD), and "Sustainable Development" (SD) was simulated, and the multi-indicator landscape ecological risk index (ERI) was generated to compare and analyze the differences between land use and landscape ecological risk under different policy preferences. Subsequently, the land use data from 2025 to 2040 were simulated, the landscape ecological risk pattern was mapped, and the spatial and temporal evolution analysis from 2010 to 2040 was conducted to explore the spatial evolution law of land use change and landscape ecological risk. Based on the results, the high ecological risk aggregation areas are prone to appear in END scenarios, whereas medium-ecological risk aggregation areas are more likely to appear in ELD scenarios, and the government should focus its policy on arable land protection. Moreover, the land use pattern of cultivated land surrounding construction land and forested land surrounding cultivated land, caused by the irrational single-core development pattern and the policy of returning farmland to forests, has exacerbated the landscape ecological risk of the Chengdu Plain, constituting a unique landscape ecological risk pattern. It's also important to remember that the Chengdu Plain's less economically developed regions need to focus on the high-quality development of ecological land use. We adopted high-precision simulation methods to simulate the complex land

关键词： Landscape ecological risk multi-contextual simulation Time-space evolution multi-objective programming PLUS model

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Self-adaptive metaheuristic-based emissions reduction in a collaborative vehicle routing problem

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SUSTAINABLE CITIES AND SOCIETY 2024年 110卷

作者： Kahalimoghadam, Masoud Thompson, Russell G. Rajabifard, Abbas Univ Melbourne Fac Engn & IT Dept Infrastruct Engn Parkville Australia

Global climate change-related initiatives such as the 2015 Paris Agreement have highlighted the necessity of sustainable transportation. Nevertheless, the rapid growth of e-commerce has notably escalated vehicle kilometres travelled (VKT) and CO2 emissions within cities, posing a direct challenge to sustainability initiatives. To address these challenges, this study formulates a collaborative multi-depot green vehicle routing problem. This model utilises micro-consolidation centres (MCCs) as shared hubs alongside a microscopic approach linking emission rates to vehicle and route characteristics, in order to assess MCCs' effectiveness in reducing CO2 emissions. Introduced here is an innovative self-adaptive metaheuristic algorithm hybridising intelligent water drops and simulated annealing. This methodology differs from established approaches by incorporating a feedback control system that actively monitors the algorithm's performance and convergence towards the global minimum solution. Through continuous adjustments to algorithm parameters via a feedback loop, this methodology strikes a balance between exploitation and exploration. The algorithm is tested in a context-specific approach, first applying it to the Cordeau benchmark and comparing it with previous state-of-the-arts, followed by a case study comparing the collaborative network to an independent one. This approach achieves 43 % and 25 % reductions in VKT and emissions, respectively, enhancing urban logistics networks' efficiency and sustainability.

关键词： MDVRP multi-objective programming Network distribution design Hybrid metaheuristics algorithm Comprehensive Modal Emission Model Knowledge-based systems

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Efficient and equitable irrigation management: A fuzzy multi-objective optimization model integrating water movement processes

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024年 371卷 123164页

作者： Chang, Hong Li, Gang Zhang, Chenglong Huo, Zailin China Agr Univ Ctr Agr Water Res China Beijing 100083 Peoples R China China Agr Univ State Key Lab Efficient Utilizat Agr Water Resourc Beijing 100083 Peoples R China

This study presents a robust multi-objective model for optimizing irrigation water allocation to balance economic benefits, water equity and irrigation efficiency in irrigation decisions under uncertainty. Daily water movement among irrigation, soil, groundwater, and drainage is modeled through water balance simulation and integrated into an optimization model considering economic, social, and resource objectives. To effectively manage conflicts arising from multiple objectives under uncertainty, interval multi-objective programming and fuzzyboundary interval programming are employed. Here, interactive algorithms are used to align constraint feasibility with objective satisfaction to generate optimal solutions for irrigation water allocation. The model's applicability is demonstrated within the Hetao Irrigation District, addressing the rising conflicts between irrigation water supply and demand. The optimization model aims to maximize net economic benefits, minimize Gini coefficient and the proportion of blue water utilization in irrigation water, incorporating daily soil water and groundwater movement processes. By setting five feasibility levels, optimal water allocation solutions are derived for five irrigation subareas and three crops across their entire growth periods. The results analysis shows that an increased feasibility level makes the uncertainty range of the objective values decrease while lower feasibility levels lead to higher satisfaction with the objective values. Moreover, it is found that the decision satisfaction peaks at a feasibility level of 0.9 after balancing feasibility and objective satisfaction, aligning more closely with the decision maker's expectations. At this level, the economic benefit is [3.62, 13.60] x 109 Yuan. Compared to a feasibility level of 1.0, the average range of the economic benefits increases by 122.50 x 106 Yuan, and the Gini coefficient decreases from [0.7973, 0.7997] to [0.7963, 0.7996]. Therefore, the above results c

关键词： multi-objective programming Irrigation planning Physical processes Interactive fuzzy programming Uncertainty Decision-making

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Synergistic CO₂ and Air Pollution Reduction Based on multi-objective programming

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2nd International Conference on Energy, Environment and Sustainable Development (EESD 2012)

作者： Chang, Wentao Wen, Juan Bao, Jingling Li, Ran Tianjin Acad Environm Planning Tianjin Peoples R China Hebei Univ Technol Tianjin Peoples R China Nankai Univ Tianjin Peoples R China Tianjin Environm Protect Bur Tianjin Peoples R China

ISBN: (纸本)9783037855522

With increasing frequency of hazards caused by global climate change, the importance of regulating carbon dioxide emission and coping with climate change has been recognized by the entire society. This paper utilized an Energy Flow Diagram to incorporate the energy supplies, conversion and final consumption for analyses. A multi-objective programming model was established to reduce the economic investment on the reduction of air pollution and carbon emission, and applied in a case study of Tianjin, one of the economic hubs of North China. This study can be potentially useful for making decisions on the synergistic CO2 and air pollution reduction and solving conflicts between economic development and environmental protection in China.

关键词： multi-objective programming Air Pollutants CO2 Synergistic Carbon Emission Reduction

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On solving multi-objective fully quadratic fractional optimisation model with interactive FGP approach

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INTERNATIONAL JOURNAL OF QUALITY & RELIABILITY MANAGEMENT 2022年第10期39卷 2286-2301页

作者： Rani, Namrata Goyal, Vandana Gupta, Deepak Maharishi Markandeshwar Deemed Univ Dept Math Ambala India

Purpose - The main motive behind framing this paper is to provide a compromised solution for trapezoidal fuzzy number-multi-objective fully quadratic fractional optimisation model (TrFN-MOFQFOM) by avoiding ambiguities and confusion of decision-makers (DMs). Many researchers have used Taylor's series and parametric approach to transform fractional objective function into non-fractional ones, but Taylor's series expansion is valid only up to a neighbourhood. To avoid these extra efforts, this article suggests a methodology in which numerator of objective function is optimised under the condition of optimising denominator. Design/methodology/approach - This paper suggests an efficient procedure to search for compromised solution of MOFQFOM with fuzzy coefficients using alpha-level set and FGP approach. Incomplete data in model is dealt with alpha-level set. Then after defuzzification, non-fractional models are built from fractional model to get optimal solution of every objective. Finally, the linear weighted sum of negative deviational variables is minimised to satisfy all objective functions up to maximum possible extent. Findings - On applying suggested approach to the example given in end, the authors arrived at compromised solution having mu(O1) (O-1(x)) =1 and mu(O2) (O-2(x)) = 0.71. The applied procedure requires less computational efforts and provides the preferred compromised solution. Originality/value - This work has not been done previously by anyone. The idea being developed here of constructing non-fractional model by dealing numerators and denominators separately is completely new. 10;In the end, an algorithm, flowchart and numerical are also given to clarify the applicability of the suggested approach.

关键词： alpha-Cut method Non-fractional models multi-objective programming Trapezoidal fuzzy number (TrFN) Fuzzy goal programming (FGP)

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Towards multi-objective Optimization of Sustainable Insect Production Chains 22

Towards Multi-Objective Optimization of Sustainable Insect P...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Mouhrim, Nisrine Smetana, Sergiy Bhatia, Anita Mathys, Alexander Green, Ashley Peguero, Daniela Tonda, Alberto INRAE MIA Paris UMR 518 Paris France German Inst Food Technol Quakenbruck Quakenbruck Germany Swiss Fed Inst Technol Sustainable Food Proc Lab Zurich Switzerland

ISBN: (纸本)9781450392686

Due to the relatively recent history of industrial insect farming, there are still no extensive studies on the optimization of insect production chains. In this context, the present work aims to be a first step in filling this gap. A tentative set of mathematical models is proposed to take into account three different, conflicting objectives: maximizing economic viability, minimizing environmental impacts, and maximizing societal benefits. The state-of-the-art multi-objective algorithm NSGA-II is used to obtain an approximate Pareto fronts of solutions, that are later analyzed to identify suitable trade-offs. While preliminary, the results are encouraging enough for the computer-assisted design and development of sustainable insect production chains. Future works will take into account more extensive models, able to simulate scenarios in different European countries, and include parts of the chain such as transportation of goods to and from the production facilities.

关键词： life cycle assessment sustainability insects decision making genetic algorithm multi-objective programming NSGA-II

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Portfolio optimization using higher moments in an uncertain random environment

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INFORMATION SCIENCES 2021年 567卷 348-374页

作者： Mehlawat, Mukesh Kumar Gupta, Pankaj Khan, Ahmad Zaman Univ Delhi Dept Operat Res Delhi 110007 India

In this paper, a multi-objective portfolio optimization problem is studied in an uncertain random environment using higher moments. We consider a scenario involving an asset universe wherein some assets have sufficient historical return data for modelling as random variables, and others, listed relatively recently, lack historical data. The asset returns of such assets are modelled as uncertain variables. Thus, a hybrid environment involving both uncertain and random variables is considered. We use mean absolute semi deviation as a risk measure and employ skewness (i.e., third moment) in the portfolio optimization model. The expressions for the mean absolute semi-deviation and skewness of an uncertain random variable have been derived. We show that the derivation of mean absolute semi-deviation is not based on any stipulation and thus is an accurate measure of risk (unlike the variance of an uncertain random variable, which uses stipulation). We propose a hybrid genetic algorithm as a solution methodology and provide empirical proofs to illustrate its advantages. The proposed methodology has been applied to a case study involving 100 assets listed in the NASDAQ-100 index of the U.S. stock market. We do an ex-post analysis to track the performance of our model out-of-sample and illustrate its advantages. (c) 2021 Elsevier Inc. All rights reserved.

关键词： Uncertain random variable Chance distribution multi-objective programming Uncertain measure Portfolio optimization Real-coded genetic algorithm

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ORDER ALLOCATION MODEL IN LOGISTICS SERVICE SUPPLY CHAIN WITH DEMAND UPDATING AND INEQUITY AVERSION: A PERSPECTIVE OF TWO OPTION CONTRACTS COMPARISON

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JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION 2021年第6期17卷 3269-3295页

作者： Liu, Weihua Shen, Xinran Wang, Di Wang, Jingkun Tianjin Univ Coll Management & Econ 92 Weijin Rd Tianjin 300072 Peoples R China

This paper considers an logistics service supply chain consisting of a logistics service integrator (LSI) and a number of functional logistics ser-vice providers (FLSPs). In the environment of demand updating, we focus on the inequity aversion among the FLSPs and introduce two option contracts (the reservation option contract and the option guarantee contract), build the multi-objective programming models, to explore effects of the inequity aver-sion behavior on the order allocation, and whether the two option contracts can mitigate the impact of inequity aversion on order allocation. Three im-portant conclusions are obtained after two option contracts comparisons: first, there is an optimal update time, at which point, the order allocation results reach the optimal value and tend to be stable. Second, two option contracts both can not only increase the total performance of the supply chain, but also mitigate the impact of inequity aversion on the allocation under certain condi-tions. Third, when demand decreases, the reservation option contract is better than option guarantee contract, in contrast, when demand increases, option guarantee contract is better.

关键词： Order allocation model demand updating option contract inequity aversion multi-objective programming

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