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multi-objective optimization for perishable product dispatch in a FEFO system for a food bank single warehouse

OPERATIONS RESEARCH PERSPECTIVES 2024年 12卷

作者： Suarez, Carlos Anibal Guano, Walter A. Perez, Cinthia C. Roa-Lopez, Heydi ESPOL Escuela Super Politecn Litoral Fac Nat Sci & Math Campus Gustavo GalindoKm 30-5 Via Perimetral Guayaquil 090902 Ecuador ESPOL Escuela Super Politecn Litoral Fac Mech Engn & Prod Sci Campus Gustavo GalindoKm 30-5 Via Perimetral Guayaquil 090902 Ecuador ESPOL Escuela Super Politecn Litoral Pacific Int Ctr Disaster Risk Reduct Campus Gustavo GalindoKm 30-5 Via Perimetral Guayaquil 090902 Ecuador

One of the main challenges of food bank warehouses in developing countries is to determine how to allocate perishable products to beneficiary agencies with different expiry dates while ensuring food safety, meeting nutritional requirements, and minimizing the shortage. The contribution of this research is to introduce a new multi-objective, multi-product, and multi-period perishable food allocation problem based on a single warehouse management system for a First Expired-First Out (FEFO) policy. Moreover, it incorporates the temporal aspect, guaranteeing the dispatch of only those perishable products that meet the prescribed minimum quality standards. A weighted sum approach converts the multi-objective problem of minimizing a vector of objective functions into a scalar problem by constructing a weighted sum of all the objectives. The problem can then be solved using a standard constrained optimization procedure. The proposed mixed integer linear model is solved by using the CPLEX solver. The solution obtained from the multi-objective problem allows us to identify days and products experiencing shortages. In such cases, when there is insufficient available inventory, the total quantity of product to be dispatched is redistributed among beneficiaries according to a pre-established prioritization. These redistributions are formulated as integer programming problems using a score-based criterion and solved by an exact method based on dynamic programming. Computational results demonstrate the applicability of the novel model for perishable items to a real-world study case.

关键词： Linear optimization multi-objective programming Perishable products FEFO Dynamic programming

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Optimization of above-ground environmental factors in greenhouses using a multi-objective adaptive annealing genetic algorithm

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HELIYON 2024年第12期10卷 e33036页

作者： Zhang, Ning Tan, Qinyue Song, Wancong Li, Qiuying Northwest A&F Univ Coll Water Resources & Architectural Engn Yangling 712100 Shaanxi Peoples R China China JILIANG Univ Coll Opt & Elect Technol Hangzhou 310018 Zhejiang Peoples R China

The greenhouse environment represents a dynamic, nonlinear system characterized by hysteresis and is influenced by a myriad of interacting environmental parameters, posing a complex multi- variable optimization challenge. This study proposes a multi-objective adaptive annealing genetic algorithm to optimize above-ground environmental factors in greenhouses, addressing the challenges of variable environmental conditions and extensive heating and humidity infrastructure. Initially, after analyzing the multi-objective model of greenhouse above-ground environmental factors, including temperature, relative humidity, and CO2 2 concentration, a comprehensive multi- objective, multi-constraint model was developed to encapsulate these factors in greenhouse environments. Subsequently, the model optimization incorporated multi-parameter coding of decision variables, a fitness function, and an annealing dynamic penalty factor. Validation conducted at Yangling Agricultural Demonstration Park revealed that the application of multi- objective adaptive annealing genetic algorithms (schemes 1 and 2) significantly outperformed the single-objective genetic algorithm (scheme 3) and the traditional genetic algorithm (scheme 4). Specifically, the improvements included a reduction in average temperature rise by 2.64 degrees C and 5.29 degrees C for schemes 1 and 2, respectively, equating to 20 % and 34 % decreases. Additionally, average humidification reductions of 2.39 % and 3.9 % were observed, alongside decreases in the total lengths of heating and humidification pipes by up to 2.99 km and 0.443 km, respectively, with a maximum reduction of 14 % in heating pipes. The integration of an annealing dynamic penalty factor enhanced the adaptive climbing ability of schemes 1 and 2, improving static stability and robustness. Furthermore, the number of iterations required to achieve convergence was reduced by approximately 170-240 times compared to schemes 3 and 4. This reduction in iterations

关键词： Greenhouse Environmental factors multi-objective programming Genetic algorithm Adaptive optimization

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Joint chance-constrained multi-objective multi-commodity minimum cost network flow problem with copula theory

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COMPUTERS & OPERATIONS RESEARCH 2023年第1期156卷

作者： Khezri, Somayeh Khodayifar, Salman Inst Adv Studies Basic Sci IASBS Dept Math Zanjan *** Iran

This study focuses on a specific problem in network optimization, namely the minimum cost multi-commodity network flow (MCNF) problem. The problem is complicated by the presence of uncertain parameters, including various types of costs associated with each arc in the network. The study presents a multi-objective approach to solving this problem, where the coefficients of the capacity constraints are modelled as random variables with a normal distribution, and the dependence between them is modelled using an Archimedean copula. The capacity constraints are presented as joint chance constraints, and a multi-objective problem is formulated to deal with the uncertainty. This uncertain multi-objective problem is then converted into a certain multi-objective problem using fuzzy programming. The resulting certain multi-objective MCNF problem is converted to a certain single objective problem using second-order cone programming (SOCP), which is solved using either piecewise tangent approximation or piecewise linear approximation methods. The proposed ap- proaches are tested using numerical examples and experimental tests to demonstrate their effectiveness in solving large-scale network problems efficiently. The results show that the proposed approaches are a useful tool for solving uncertain multi-objective MCNF problems in real-world applications.

关键词： Minimum cost network flow problem multi-objective programming Joint chance-constrained optimization Copula theory

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Location selection of agricultural Machinery sheds for improved scheduling and efficiency under sustainability goals

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ECOLOGICAL INDICATORS 2023年 155卷

作者： Liu, Yang Ma, Xu Jiang, Mingjun Huang, Weilun Ren, Haisong Bengbu Univ Sch Econ & Management Bengbu Anhui Peoples R China Wenzhou Business Coll Sch Management Wenzhou Peoples R China Wenzhou Business Coll Sch Finance & Trade Wenzhou Peoples R China

The storage of agricultural machinery (AM) poses a critical operational challenge that requires attention to attain China's "Carbon Peaking and Carbon Neutrality" objectives, due to the carbon emissions and fuel consumption associated with AM operations. Nevertheless, this matter has not garnered adequate consideration. Existing studies have rarely focused on optimizing the parking locations for AM to mitigate emissions and advance sustainability. This study employs the TOPSIS-MCGP method to explore sustainable location selection for Chinese AM sheds. The principal factors influencing location selection include carbon emissions, fuel consumption, operational distance, total cost, and timeliness, which were thoroughly examined for their impact. A weighted selection approach was utilized to ascertain key factors and their optimization implications. The AM shed location within the AM dispatching system was developed through the following steps: (1) Evaluation of influential factors for sustainable AM dispatching, encompassing carbon emissions, fuel consumption rate, AM dispatching distance, cost, and timeliness. (2) Quantification of the manager's experience in the AM service center as an index weight using the Fuzzy Delphi Method (FDM). (3) Adoption of the TOPSIS-MCGP model to establish the relationship between operation points and AM shed locations during sustainable AM operations. A preliminary investigation was carried out in a company located in Harbin City, yielding an optimal solution encompassing a 13,500 mu working area, 3,300 total building area, and a 28-million RMB investment. The outcomes underscore the model's compatibility and its utility for optimizing sustainable AM service operations. In conclusion, this study underscores the significance of addressing AM storage to foster sustainability and emissions reduction. The TOPSIS-MCGP approach proves effective in selecting sustainable AM shed locations. By considering diverse factors like carbon emissions, f

关键词： Agricultural machinery TOPSIS-MCGP Sustainable dispatch Emissions multi-objective programming

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Double recovery strategy of carbon for coal-to-power based on a multi with tradable certificates

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ENERGY 2023年第1期273卷

作者： Ma, Ning Fan, Lurong Xian Univ Technol Sch Econ & Management Xian 710054 Peoples R China Sichuan Univ Business Sch Chengdu 610064 Peoples R China Sichuan Univ Inst New Energy & Low Carbon Technol Chengdu PR Brazil

Coal production and consumption cause methane and carbon dioxide emissions. The high emission cost and the limited renewable energy in some areas restrict the low-carbon energy system development. This paper proposes a double recovery system based on a multi-energy system to promote energy efficiency and emission reduction, considering the life cycle carbon emissions of coal and carbon trading. A nonlinear multi-objective two-stage stochastic programming model and an approximate solution algorithm based on NSGA-II are built to find the economic-ecological balanced strategy for the system investment and operation under uncertainty. Finally, a case study about the collieries in Xianyang City is adopted to test the effectiveness of the proposed model and algorithm. The results show the multi-energy system with a double recovery system can obtain 5%-40% more profits compared with the single energy system when the GHG emission rate requirement is lower than 0.95kgCO2e/kwh. And this paper finds that carbon capture and storage has more universal utility in obtaining tradable green certificates, and the efficiency of P2G technology depends on the abundance of renewable energy. The tradable green certificates are effective incentives for coal mine methane utilization.

关键词： multi-energy production system Coal mine methane utilization Tradable green certificates Double recovery of carbon multi-objective programming Economic-ecological benefit balance

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An integrated multi-Criteria Decision Making and multi-Choice Conic Goal programming approach for customer evaluation and manager assignment

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Decision Analytics Journal 2023年 8卷

作者： Dagistanli, Hakan Ayhan Üstün, Özden Department of Industrial and Systems Engineering Turkish Military Academy National Defence University Ankara Turkey Department of Industrial Engineering Aksaray University Aksaray Turkey

Companies adopt a customer-centric approach to maintain their competitive position or dominate the market. In this context, managers are pivotal in navigating the competitive markets in accomplishing these goals. Setting targets and conducting performance evaluations are vital for managers’ long-term success. This study aims to optimise customer evaluation and manager assignment in the defence industry. An integrated approach utilising the Analytical Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is employed to conduct a comprehensive market analysis and conduct this evaluation. A bi-objective integer programming model is formulated to maximise total integrated scores and potential returns. The efficient number of visits is determined by combining multi-Criteria Decision Making (MCDM) and multi-Choice Conic Goal programming (MCCGP). This study offers an objective alternative to traditional survey-based evaluations by proposing a novel decision support system for Goal-Based Performance Appraisals (GBPA) of managers. The proposed approach yields performance values that are considerably more reliable than questionnaire results. Moreover, the proposed decision support system can effectively benefit employees across diverse sectors, extending beyond the confines of the defence industry. © 2023 The Author(s)

关键词： Analytical Hierarchy Process Defence industry multi-Choice Conic Goal programming multi-Criteria Decision Making multi-objective programming TOPSIS

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Fair efficiency decomposition in network DEA: A compromise programming approach

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SOCIO-ECONOMIC PLANNING SCIENCES 2022年 79卷

作者： Koronakos, Gregory Sotiros, Dimitris Despotis, Dimitris K. Kritikos, Manolis N. Univ Piraeus Dept Informat 80 Karaoli & Dimitriou Piraeus 18534 Greece Wroclaw Univ Sci & Technol Dept Operat Res & Business Intelligence PL-50370 Wroclaw Poland Athens Univ Econ & Business Dept Management Sci & Technol Management Sci Lab Athens 11362 Greece

We introduce in this paper a novel network DEA approach to determine a single and fair efficiency decomposition for series multi-stage processes. We provide direct comparisons with the bargaining approach of Zhou et al. (2013) [1]. First, we prove the equivalence of the proposed approach with the bargaining approach for series two-stage processes in which only the intermediate products are the outputs of the first stage and the inputs to the second one. Next, we reveal the inadequacy of the bargaining approach to be applied in series processes with more than two stages, as it yields multiple non-extreme efficiency decompositions instead of one. On the contrary, we show that our approach provides a single non-extreme efficiency decomposition. In addition to these contributions, our approach is novel in the sense that the obtained efficiency decomposition represents a fair compromise between the stages, as it is as close as possible to the maximum efficiency score and as far away as possible to the minimum efficiency score attained by each stage. To reach such a fair compromise among the stages we employ the weighted min-max method in a multi-objective programming framework, and we guide the search direction on the line that connects the ideal point with the nadir point. We provide graphical illustrations and comparisons based on data drawn from the literature as well as on synthetic data.

关键词： Data envelopment analysis Network DEA Non-unique efficiency decomposition multi-objective programming Compromise efficiency decomposition

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Addressing a sustainable supplier selection and order allocation problem by an integrated approach: a case of automobile manufacturing

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JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING 2021年第4期38卷 239-253页

作者： Beiki, Hossein Mohammad Seyedhosseini, Seyed V. Ponkratov, Vadim Olegovna Zekiy, Angelina Ivanov, Sergei Anatolyevich Islamic Azad Univ Naragh Branch Sch Ind Engn Naragh Iran Iran Univ Sci & Technol Dept Ind Engn Tehran Iran Sechenov First Moscow State Med Univ Dept Prosthet Dent Moscow Russia First Cossack Univ KG Razumovsky Moscow State Univ Technol & Managem RAZUMOVSKY MSUTM FCU Moscow Russia Financial Univ Govt Russian Federat Moscow Russia

This study introduces a novel integration of language entropy weight method (LEWM) and multi-objective programming (MOP) method to address a sustainable supplier selection and order allocation problem. To show the applicability of the proposed method, a real case study of automobile manufacturing is considered. First of all, the indicators of the proposed problem are defined. Then, the LEWM is considered to select the suppliers. To meet the standards of the sustainable development goals and the global supply chains for a case of automobile manufacturing system, an optimization model is established. The proposed model has three objectives including the total cost and the carbon emissions to be minimized while the procurement value to be maximized. These objectives are based on the economic, environmental, and social impacts simultaneously to address the triple bottom lines of sustainability. In addition, the proposed method improves the relationship between the supply chain practitioners and the potential suppliers to achieve the product development capabilities and qualities to achieve the sustainable development goals and the global supply chain trend.

关键词： Sustainable development goals global supply chain supplier selection order allocation language entropy weight method multi-objective programming

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On solving matrix games with payoffs presented by dual hesitant fuzzy sets

ITALIAN JOURNAL OF PURE AND APPLIED MATHEMATICS

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ITALIAN JOURNAL OF PURE AND APPLIED MATHEMATICS 2021年第46期 34-52页

作者： Song, Yang Yang, Zhihui East China Univ Technol Sch Sci Nanchang 330013 Jiangxi Peoples R China

The solving methodology of matrix games, with payoffs presented by dual hesitant fuzzy sets, is investigated in this paper. Firstly, the notion of dual hesitant fuzzy sets is given. Next, the concept of solutions for the matrix games is defined on the basis of dual hesitant fuzzy sets, which thereby prove the solutions of the matrix games can be obtained through solving a pair of linear programming models. Lastly, a numerical example is given to illustrate the effectiveness of the proposed method.

关键词： matrix games dual hesitant fuzzy set multi-objective programming linear programming

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Predictive multi-period multi-objective portfolio optimization based on higher order moments: Deep learning approach

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COMPUTERS & INDUSTRIAL ENGINEERING 2023年第1期183卷

作者： Abolmakarem, Shaghayegh Abdi, Farshid Khalili-Damghani, Kaveh Didehkhani, Hosein Islamic Azad Univ Dept Ind Engn South Tehran Branch Tehran Iran Islamic Azad Univ Dept Ind Engn Aliabad Katoul Branch Aliabad Iran

We propose a multi-Period multi-objective Portfolio Optimization model (MPMOPO). We used deep-learning approach to predict future behavior of stock returns. We consider four objectives, i.e., wealth, variance, skewness, and kurtosis and several constraints such as cardinality, budget, upper and lower limits of purchase, and diversification to address real-world situations. The investor can rebalance the portfolio through daily trade by buying or selling subject to transaction costs. We applied the proposed method in a daily closing price prediction of six stocks from FTSE 100. Goal programming method was used to solve the models. The results of statistical analysis show the applicability and efficacy of the proposed method in comparison with those methods which used historical data to form the portfolio.

关键词： Long short-term memory Convolutional neural network Stock prediction multi-period portfolio optimization multi-objective programming Goal programming

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