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A mathematical programming model for facility location optimization of hydrogen production from renewable energy sources

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2022年第3期44卷 6648-6659页

作者： Derse, Onur Gocmen, Elifcan Yilmaz, Ebru Erol, Rizvan Tarsus Univ Dept Management & Org Logist Mersin Turkey Munzur Univ Dept Ind Engn Fac Engn Tunceli Turkey Cukurova Univ Dept Ind Engn Fac Engn Adana Turkey

Fossil fuels are the primary energy sources and meet the global energy demands. However, environmental and health problems related with these sources boosted the demand for renewable energy sources. Hydrogen, as an energy carrier has a growing potential for solving these problems. In this article, a mathematical programming model that integrates the production, storage and transportation, safety, location, and staff assignment decisions is presented considering minimization of costs. Although most of the studies focus on location, distribution, storage decisions of hydrogen energy networks, the article also includes production, safety and staff assignment decisions to make this problem more practical. Furthermore, we also investigate the set covering constraint will ensure that each region is covered by minimum number of the hydrogen facilities. The developed model ensures a balance between location, distribution, storage, production, safety and staff decisions by installing two production facilities by assigning total 9 warehouses, 22 tank trucks, 12100 km pipeline, 35 staffs under distance constraint 2000 km in regions 1 and 5. The computational results indicate that the proposed model produces effective solutions for the coverage to all region and minimum total cost for real-case situations.

关键词： Renewable energy sources hydrogen energy hydrogen supply chain network optimization mathematical programming model

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Medical waste management during coronavirus disease 2019 (COVID-19) outbreak: A mathematical programming model

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COMPUTERS & INDUSTRIAL ENGINEERING 2021年 162卷 107668页

作者： Govindan, Kannan Nasr, Arash Khalili Mostafazadeh, Parisa Mina, Hassan Shanghai Maritime Univ China Inst FTZ Supply Chain Shanghai 201306 Peoples R China Yonsei Univ Yonsei Frontier Lab Seoul South Korea Univ Southern Denmark Ctr Sustainable Supply Chain Engn Danish Inst Adv Study Dept Technol & Innovat Campusvej 55 Odense M Denmark Sharif Univ Technol Grad Sch Management & Econ Tehran Iran Univ Tehran Coll Engn Sch Ind Engn Tehran Iran

Municipal solid waste (MSW) directly impacts community health and environmental degradation;therefore, the management of MSW is crucial. Medical waste is a specific type of MSW which is generally divided into two categories: infectious and non-infectious. Wastes generated by coronavirus disease 2019 (COVID-19) are classified among infectious medical wastes;moreover, these wastes are hazardous because they threaten the environment and living organisms if they are not appropriately managed. This paper develops a bi-objective mixedinteger linear programming model for medical waste management during the COVID-19 outbreak. The proposed model minimizes the total costs and risks, simultaneously, of the population's exposure to pollution. This paper considers some realistic assumptions for the first time, including location-routing problem, time window-based green vehicle routing problem, vehicles scheduling, vehicles failure, split delivery, population risk, and loaddependent fuel consumption to manage both infectious and non-infectious medical waste. We apply a fuzzy goal programming approach for solving the proposed bi-objective model, and the efficiency of the proposed model and solution approach is assessed using data related to 13 nodes of medical waste production in a location west of Tehran.

关键词： Municipal solid waste Medical waste management mathematical programming model Fuzzy goal programming approach Coronavirus disease 2019 (COVID-19)

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A mathematical programming model for constructing the confidence interval of process capability index C_pm in evaluating process performance: an example of five-way pipe

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JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS 2017年第2期40卷 126-133页

作者： Chen, Kuen-Suan Huang, Cheng-Fu Chang, Tsang-Chuan Natl Chin Yi Univ Technol Dept Ind Engn & Management Taichung Taiwan Feng Chia Univ Dept Business Adm Taichung Taiwan

The process capability index C-pm can reflect process loss as well as process yield, thus is the most frequently used index for evaluating product quality in manufacturing industries. When evaluating the process performance, confidence intervals are often used for assurance with regard to the critical value of the process capability index. Unfortunately, sampling distributions of C-pm are obtained in a very complex way, which leads to difficulty in calculating the confidence interval of C-pm. Hence, this paper develops a mathematical programming model to construct the (1 - alpha) x 100% confidence interval of C-pm. Then for verifying the effectiveness of the proposed approach, the Monte Carlo simulation is used to find the coverage percentage. The proposed mathematical programming model can obtain the (1 - alpha) x 100% confidence interval of C-pm without complex statistical computations. Besides, managers can evaluate and monitor the process performance in an easy way. We also provide a case in which a five-way pipe process is presented as an illustration of how the proposed method is implemented.

关键词： Process capability index confidence interval mathematical programming model Monte Carlo five-way pipe

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Transmission expansion planning: A deep learning approach☆

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SUSTAINABLE ENERGY GRIDS & NETWORKS 2025年 41卷

作者： Dong, Jizhe Cao, Jianshe Lu, Yu Zhang, Yuexin Li, Jiulong Xu, Chongshan Zheng, Danchen Han, Shunjie Changchun Univ Technol Sch Elect & Elect Engn Changchun 130000 Peoples R China State Grid Jilin Elect Power Co Ltd Changchun 130000 Peoples R China

This paper proposes a transmission expansion planning (TEP) method based on deep learning (DL) to address the increasing complexity and excessive reliance on mathematical formulas in current TEP models. First, we utilize a traditional mathematical programming model to obtain unit outputs and line construction decisions by varying loads, thereby generating the dataset required for DL training. Next, we build a convolutional neural network (CNN) based DL model, which includes convolutional layers, pooling layers and fully connected layers, and whose inputs consist of load data and unit output data, while output is line construction data. We use Bayesian optimization (BO) to select the best hyperparameters for the model. We conducted both single and multiple training experiments on the Garver's 6-bus, IEEE 24-bus and IEEE 118-bus systems. In the single training experiments, the R2 values achieved by our proposed method on these three systems were 0.99471, 0.99594 and 0.99676, respectively, with K-fold cross-validation showing stable results. In the multiple training experiments, we repeated the CNN training 50 times and obtained confidence intervals for each metric to further validate the model's effectiveness. Additionally, we performed significance testing on the BO results, showing that among the three comparative experiments, two had P-values less than 0.001, indicating a significant difference. The remaining one has a P-value is larger than 0.05 indicating a difference but not significant.

关键词： Bayesian optimization (BO) Convolutional neural network (CNN) Dataset generation Deep learning (DL) K -fold mathematical programming model Significance testing Transmission expansion planning (TEP)

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Sustainable Smart Parking Site Selection with Charging Stations for Electric and Hybrid Vehicles

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INTERNATIONAL JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS RESEARCH 2024年第1期23卷 76-90页

作者： Derse, Onur Tarsus Univ Fac Engn Dept Ind Engn TR-33400 Mersi?n Turkiye

In this study, in order to solve the parking problem in metropolitan cities, save time and fuel, and reduce the damage to the environment, research is conducted on the problem of smart parking site selection integrated with the charging stations of electric vehicles and hybrid vehicles. Considering the Marmara region, the most populated region of Turkey, the population, number of vehicles, the potential of renewable energy sources, and air quality index value are evaluated. The Entropy method is used to weight the evaluation criteria, and the TOPSIS method is used to rank the alternative provinces in the Marmara Region. According to the results of the MCDM methods, when the provinces are examined, it is seen that the city of Istanbul needs smart parking systems the most. When areas of Istanbul are evaluated with different scenarios and the results are examined, different results are obtained according to the results of the Center of Gravity method, while deciding to establish a smart parking lot in the Esenyurt region in all mathematical programming model results. The research, which is integrated with sustainability (use of renewable energy), environmental awareness (air quality index and use of renewable energy), and developing technology (electric vehicles and hybrid vehicles), reveals effective results. It is thought that the study will adapt to developing technology and smart transportation systems and will be effective and contribute to the decision of location selection of smart parking systems and integrated electric charging stations for the increasing number of electric vehicles.

关键词： Intelligent Transportation System Smart parking system Site selection MCDM Methods Center of Gravity Method mathematical programming model

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Alternative pricing for irrigation water management in the context of climate change

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JOURNAL OF WATER AND CLIMATE CHANGE 2024年第2期15卷 569-581页

作者： Jeder, Houcine Ctr Reg Rech Hort & Agr Biol Inst Reg Arides BP 57 Chott Mariem 4042 Sousse Tunisia

Irrigation water pricing is an economic regulation instrument widely used in agriculture. Constant annual pricing is always criticized by local decision-makers as well as scientific researchers because it does not take into account the seasonal availability of water in the context of climate change. This study proposes a mathematical programming model to test alternative seasonal pricing scenarios in the context of climate change. This model is applied at farm level in the Kalaa Kebira region of East-Central Tunisia. The results show that summer seasonal pricing was economically beneficial for large farms, while winter pricing was beneficial for small and average farms. Water savings were only possible for small farms using 89% of available water in summer and for average farms using 93% of available water in winter. On the other hand, the sensitivity test proved that when water demand is elastic, increasing seasonal pricing of irrigation water by a rate between 20 and 30% generates water savings for different types of farms. This seasonal water saving is also accompanied by optimal use of agricultural labor and diversity of cultivated areas.

关键词： climate change East-Central Tunisia Kalaa Kebira region irrigation water mathematical programming model seasonal pricing

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An Investigation into the Effects of Climate Change on Water-Energy-Food Nexus: A New mathematical programming Approach

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INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH 2024年第2期18卷 1-21页

作者： Ahani, Elahe Ziaee, Saman Mohammadi, Hamid Najafabadi, Mostafa Mardani Mirzaei, Abbas Univ Zabol Dept Agr Econ Fac Agr Zabol Iran Agr Sci & Nat Resources Univ Khuzestan Fac Agr Engn & Rural Dev Dept Agr Econ POB *** Mollasani Iran

Agriculture is one of the most important production sectors in the world. Water and energy are two essential inputs for food production. The agricultural sector is influenced by climate change the most. In this regard, this research aimed to present a new mathematical programming approach to studying the effects of climate change on the water-energy-food (WEF) nexus. A sustainable WEF nexus was developed for the basin of Kashfrud in Razavi Khorasan province, Iran for 2019-2020. The present approach was modeled in several climatic-hydrological-economic-environmental sectors. Analyzing the outcomes of a hydrological model in the context of climate change scenarios reveals that, given the current state of irrigated land, there will be a 45% increase in net water demand in the future, accompanied by a 13% decrease in crop yields. Consequently, by embracing a holistic approach that considers the nexus of water, food, and energy, the net water demand drops to 71%, the energy allocation to agriculture decreases to 41%, greenhouse gas emissions are reduced by 32%, and farmers' overall profits decrease by 73% in the face of climate change. This approach would also be effective in avoiding the undesirable effects of single-sectoral development policies in addition to improving resource use efficiencies. Since most non-renewable resources are consumed by the agricultural sector, the development of the nexus approach is also important from an environmental perspective in addition to the sustainability of resource use. A new multi-objective programming model based on WEF nexus management was *** application of the proposed model was used to as a case study in *** model could increase the gross profit of farmers while reducing scarce inputs.

关键词： Different CMIP6-based scenarios mathematical programming model Multi-objective optimization Resource allocation Sustainable development

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Optimal design of culling compensation policy under the African swine fever——Based on simulations of typical pig farms in China

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Journal of Integrative Agriculture 2023年第2期22卷 611-622页

作者： GUO Shi-juan Lü Xin-ye HU Xiang-dong Institute of Agricultural Economics and Development Chinese Academy of Agricultural SciencesBeijing 100081P.R.China Institute of Agricultural Information and Economy Shandong Academy of Agricultural SciencesJinan 250000P.R.China

This paper aims to optimize the culling compensation policy from a micro perspective through scenario *** on an investigation of 273 pig farms in eight regions,four typical pig farms were constructed according to farm size and breeding mode,representing the swine producers in ***,a decision objective function of pig farms facing suspected African swine fever(ASF)outbreaks was *** study used a mathematical programming model to design and simulate scenarios based on compensation standards and local implementation levels,aiming to incentivize pig farms to report *** results show that the optimal decisions on epidemic reports differed among typical farms and by herd daily *** results suggest the following adjustments for optimizing culling compensation policies:(1)to set culling compensation standards based on the market value and(2)to maintain a high level of epidemic surveillance capability in the animal husbandry and veterinary sector.

关键词： culling compensation African swine fever epidemic report mathematical programming model

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Multi-criteria group decision-making method based on total distance and BWM with spatial information in Hesitant Pythagorean fuzzy environment

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第6期45卷 12279-12299页

作者： Wang, Jia-Li Jiang, Wen-Qi Tao, Xi-Wen Yang, Shan-Shan Nanjing Univ Sci & Technol Sch Econ & Management Nanjing Peoples R China Nanjing Inst Technol Sch Econ & Management Nanjing Peoples R China

The processing method of fuzzy information is a critical element in multi-criteria group decision-making (MCGDM). The hesitant Pythagorean fuzzy set (HPFS) has a higher capacity in express the uncertainty of human inherent preference. A composite weighted mathematical programming model with prospect theory and best-worst method (BWM) is proposed to solve the uncertainty of criterion weight acquisition and decision-makers (DMs) psychological behavior under the HPF environment. The decision-making process is as follows: Firstly, a novel spatial distance measurement method is designed which considers the extension space of HPFSs space by five parameters under the HPF environment. Secondly, the optimal criteria weights model minimizes the total distance between the alternatives and the HPF positive ideal solution (HPFPIS), as well as minimizes the consistency ratio of BWM. Thirdly, we propose the prospect decision matrix by the prospect theory and optimal weights, then use the ordered weighted average operator under the normal distribution to calculate the weight of DMs and rank the decision alternatives. Finally, an example is illustrated here, sensitivity and reliability, and comparative analysis are conducted to verify the effectiveness of the proposed method.

关键词： Multiple-criteria group decision-making BWM prospect theory mathematical programming model combination weights spatial distance measure

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A mathematical programming approach for assessing the impact of climate change on Mediterranean farming systems: A Tunisian case study

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SCIENTIFIC AFRICAN 2025年 27卷

作者： Ben Mimoun, Anouar Mazhoud, Houda Chemak, Fraj Natl Agron Inst Tunisia INAT Dept Econ Agr & Agrifood Management Tunis Tunisia Univ Carthage Natl Inst Agr Res Tunisia INRAT Rural Econ Lab LR16INRAT07 1004 El Menzah 1 Tunis 1001 Tunisia

The paper aims to assess the short-term effects of climate change on economic and social performance of selected farming systems in Tunisia. The purpose is to analyze the evolution of land use and water allocation under different climate change scenarios at regional and local levels. In order to achieve the objective, a regional mathematical programming model was developed considering 2 regions and 7 farming production systems. Results showed that farming systems are affected differently by climate change. Large farming whose profitability is confirmed at the current state, their sustainability is not strongly threatened in the case of the climate change scenario. The subsistence and the small farming systems which are in difficulty because of their modest productive resources, their technological backwardness, and their structural constraints. Water availability will have modest welfare impacts, with an average decrease of 16 %. Despite the small aggregated effects, it is expected that climate change will have uneven consequences across regional income. For instance, even though the Medium farming system in Tell inferior showed the smallest income changes 1 % (average), the impacts within the others systems (large farm system, subsistence system and small farm system) range from 2 % to 16 % decrease in agricultural income. This situation suggests large distributional consequences of climate change for the Tunisian agricultural sector.

关键词： Climate change Farming systems mathematical programming model Tunisia

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