Decision Making: Applications in Management and Engineering最新文献

Uncovering the Hidden Insights of the Government AI Readiness Index: Application of Fuzzy LMAW and Schweizer-Sklar Weighted Framework 揭示政府人工智能就绪指数的隐藏内涵：模糊 LMAW 和 Schweizer-Sklar 加权框架的应用

Decision Making: Applications in Management and Engineering Pub Date : 2024-08-09 DOI: 10.31181/dmame7220241221

M. K. Nasution, M. Elveny, D. Pamučar, Milena Popovic, Bisera Andrić Gušavac

{"title":"Uncovering the Hidden Insights of the Government AI Readiness Index: Application of Fuzzy LMAW and Schweizer-Sklar Weighted Framework","authors":"M. K. Nasution, M. Elveny, D. Pamučar, Milena Popovic, Bisera Andrić Gušavac","doi":"10.31181/dmame7220241221","DOIUrl":"https://doi.org/10.31181/dmame7220241221","url":null,"abstract":"There is considerable promising in artificial intelligence (AI) and algorithms, with governments worldwide increasingly investing in this transformative technology. The potential benefits include improved performance, cost reduction, efficient management, and crime prediction and prevention, among others. The AI era holds the promise of revolutionizing various aspects of society. However, as countries prepare to leverage the power of artificial intelligence, questions arise about the validity of rankings published on the readiness of the governments for the application of AI. In this article, the weighting criteria that are analysed in the Oxford Insights AI Readiness Index are scrutinized, aiming to provide a more accurate assessment. Instead of conventional averaging, arithmetic and geometric non-linear functions are employed to analyse and assess the rank of the countries. Through clustering analysis, countries are categorized into three distinct groups based on observed criteria, offering a nuanced perspective on government AI readiness. This clustering approach not only facilitates a more effective categorization of countries based on their AI preparedness, but also accentuates the variations and similarities within each cluster, which enables deeper insights into regional trends and pinpoint targeted strategies for enhancement within each cluster.","PeriodicalId":504436,"journal":{"name":"Decision Making: Applications in Management and Engineering","volume":"44 29","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141924103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Risk Assessment Framework for Reverse Logistics in Waste Plastic Recycle Industry: A Hybrid Approach Incorporating FMEA Decision Model with AHP-LOPCOW- ARAS Under Trapezoidal Fuzzy Set 废塑料回收行业逆向物流风险评估框架：梯形模糊集下的 FMEA 决策模型与 AHP-LOPCOW- ARAS 的混合方法

Decision Making: Applications in Management and Engineering Pub Date : 2024-07-16 DOI: 10.31181/dmame812025984

Detcharat Sumrit, Jirawat Keeratibhubordee

{"title":"Risk Assessment Framework for Reverse Logistics in Waste Plastic Recycle Industry: A Hybrid Approach Incorporating FMEA Decision Model with AHP-LOPCOW- ARAS Under Trapezoidal Fuzzy Set","authors":"Detcharat Sumrit, Jirawat Keeratibhubordee","doi":"10.31181/dmame812025984","DOIUrl":"https://doi.org/10.31181/dmame812025984","url":null,"abstract":"In this study, a novel risk assessment framework designed for evaluating the challenges of plastic packaging waste management in the context of reverse logistics is introduced. The framework leverages Failure Mode Effect Analysis (FMEA) to address decision-making in a fuzzy environment. To augment the traditional FMEA risk criteria, encompassing severity (S), occurrence (O), and detection (D), three additional essential risk criteria are introduced: cost of failure (C), complexity of failure resolution (H), and impact on business (I). These newly incorporated criteria significantly enhance the capacity to convey the multifaceted risks inherent in reverse logistics for the plastic recycling sector. Furthermore, a comprehensive literature review and expert validation are conducted to identify ten distinct failure modes. To subjectively and objectively determine the risk criteria weightings, a combination of Analytic Hierarchy Process (AHP) and LOgarithmic Percentage Change-driven Objective Weighting (LOPCOW) is employed. Finally, the Additive Ratio Assessment (ARAS) approach is applied to prioritize such failure modes. Recognizing the inherent imprecision and uncertainty associated with human decision-making, the trapezoidal fuzzy set (TrFS) is adopted throughout all decision-making processes. To showcase the proposed framework effectiveness, the framework is applied as a case study to a waste plastic recycling manufacturer in Thailand.","PeriodicalId":504436,"journal":{"name":"Decision Making: Applications in Management and Engineering","volume":"37 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141643623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Supply Chain Safety and Security: A Novel AI-Assisted Supplier Selection Method 加强供应链安全与保障：一种新颖的人工智能辅助供应商选择方法

Decision Making: Applications in Management and Engineering Pub Date : 2024-07-16 DOI: 10.31181/dmame8120251115

J. Pap, Csaba Makó, Adrián Horváth, Zoltán Baracskai, Tamás Zelles, Judit Bilinovics-Sipos, Sándor Remsei

{"title":"Enhancing Supply Chain Safety and Security: A Novel AI-Assisted Supplier Selection Method","authors":"J. Pap, Csaba Makó, Adrián Horváth, Zoltán Baracskai, Tamás Zelles, Judit Bilinovics-Sipos, Sándor Remsei","doi":"10.31181/dmame8120251115","DOIUrl":"https://doi.org/10.31181/dmame8120251115","url":null,"abstract":"The \"Make or Buy” decision and the supplier selection are critical steps for the efficient operation of companies' supply chains. Safety and security are paramount considerations, especially in industries like logistics, where supply chains are vulnerable to external threats and disruptions. In this scientific article, we present a novel Artificial Intelligence (AI)-assisted supplier selection method that significantly enhances the safety and security of suppliers. During our research project, we have created an expert system and a corresponding knowledge base with the relevant rules to support supply chain decision-makers in selecting logistics service providers for warehousing services. The foundation of the AI-assisted supplier selection method is advanced data analytics and the application of expert systems, enabling companies to evaluate potential suppliers in detail from a safety and security perspective. The applied expert systems can identify potential risks and make predictions about supplier performance in the future. In the turbulent environment of the global supply chain, selecting long-term partners like warehousing services providers is critical for the success of the organization. A wrong supplier selection can hardly be reversed in warehousing services, as the cost of change is usually high. The article demonstrates the practical application of the expert system-assisted supplier selection method in a real-world supply chain environment and thoroughly analyzes the achieved results and advantages. The results show that the expert system-assisted method not only increases supplier safety and security but also contributes to improving the efficiency and sustainability of the supply chain. This article provides valuable guidance and solutions for companies looking to enhance their supplier selection using expert system technologies, thereby increasing the safety and security of their supply chains.","PeriodicalId":504436,"journal":{"name":"Decision Making: Applications in Management and Engineering","volume":"13 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141643920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Environmentally Friendly Strategies for Recycling Agricultural Waste to Produce Renewable Energy: A Case Study of Durian Fruit 回收农业废弃物以生产可再生能源的环境友好型战略：榴莲果案例研究

Decision Making: Applications in Management and Engineering Pub Date : 2024-04-16 DOI: 10.31181/dmame7220241138

Dino Rimantho, Dita Ariyanti, R. Maryana

{"title":"Environmentally Friendly Strategies for Recycling Agricultural Waste to Produce Renewable Energy: A Case Study of Durian Fruit","authors":"Dino Rimantho, Dita Ariyanti, R. Maryana","doi":"10.31181/dmame7220241138","DOIUrl":"https://doi.org/10.31181/dmame7220241138","url":null,"abstract":"The increasing need for harvests, resources, and facilities is associated with significant biowaste production from agricultural processes. Although the waste is nutrient-rich, it may become refinement lands for ailment-triggering bacteria when not managed correctly. The waste can be transformed into raw materials for valuable crops or sources of environmentally friendly energy. Therefore, this study examined agricultural waste management strategies as Indonesia's sustainable renewable energy source. SWOT and TOPSIS methods were used to identify the optimal approach for advancing renewable energy in Riau Province, while multiple respondents participated in identifying critical criteria and evaluating each option. The results showed that based on SWOT analysis, the Strength–Opportunity (SO) factor favored using agricultural waste for renewable energy in Indonesia. Furthermore, TOPSIS analysis indicated that Alternative A2 (Bioethanol) had the most significant distance among the alternatives, with a weight of 0.825. Future studies are needed to provide more accurate results and improve the current understanding regarding the evolution of renewable energy in Indonesia. Additionally, in-depth investigations should prioritize increased consumer awareness of renewable consumption, higher producer productivity, and strengthened policies.","PeriodicalId":504436,"journal":{"name":"Decision Making: Applications in Management and Engineering","volume":"9 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140697927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Objective Optimization Technology for Building Energy-Saving Renovation Strategy Based on Genetic Algorithm 基于遗传算法的建筑节能改造策略多目标优化技术

Decision Making: Applications in Management and Engineering Pub Date : 2024-04-08 DOI: 10.31181/dmame7220241073

Shuibo Deng, Lei Lv

{"title":"Multi-Objective Optimization Technology for Building Energy-Saving Renovation Strategy Based on Genetic Algorithm","authors":"Shuibo Deng, Lei Lv","doi":"10.31181/dmame7220241073","DOIUrl":"https://doi.org/10.31181/dmame7220241073","url":null,"abstract":"Building energy-saving design is significant for the industry to achieve carbon reduction and sustainable development. Firstly, a multi-objective model for energy consumption, cost, and carbon emissions is established based on the three-dimensional perspectives of society, nature, and economy. Then, a polynomial operator is used to improve the non dominated sorting genetic algorithm to calculate the optimal solution set. The low computational efficiency caused by direct coupling of algorithms in traditional optimization processes is expected to be addressed. According to the results, for the Square1 dataset and Iris dataset, the algorithm proposed in this study improved the reverse distance and convergence metrics by more than 70% compared to support vector machine-genetic algorithm and multi-objective clustering algorithm, with values closer to 0. The solution solved by this algorithm had lower building costs, energy consumption, and carbon emissions, with values of 345200 yuan, 2374 KWh/year, and 26 tons, respectively. This validates the effectiveness of the multi-objective model and solving algorithm established in the study, which helps to obtain the optimal energy-saving design scheme and provides reference for low-carbon optimization of building.","PeriodicalId":504436,"journal":{"name":"Decision Making: Applications in Management and Engineering","volume":"35 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140732371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improved Multi-objective Particle Swarm Optimization in Software Engineering Supervision 软件工程监理中的改进型多目标粒子群优化技术

Decision Making: Applications in Management and Engineering Pub Date : 2024-03-12 DOI: 10.31181/dmame7220241074

Ping Yue, Zhiguo Wang

{"title":"Improved Multi-objective Particle Swarm Optimization in Software Engineering Supervision","authors":"Ping Yue, Zhiguo Wang","doi":"10.31181/dmame7220241074","DOIUrl":"https://doi.org/10.31181/dmame7220241074","url":null,"abstract":"In the 21st century, the software industry has achieved great development. The development complexity and volume of software projects are also continuously increasing. The design of software engineering supervision network plans is becoming increasingly important. In response to the poor optimization performance and poor convergence and distribution of optimal solutions in existing network planning algorithms, the Pareto optimal solution set construction method, global extremum selection method, and fitness value determination method of multi-objective particle swarm optimization algorithm are improved to enhance the convergence and distribution of the algorithm. Traditional methods only optimize one or two objectives of network planning, resulting in inconsistency with actual engineering. A multi-objective model based on resources, duration, cost, and quality is established for comprehensive optimization. Based on the results, the Pareto optimal solution curves obtained by the proposed algorithm on three classic test functions are consistent with the actual theoretical Pareto frontier curves. The proposed method is applied to engineering project examples. 10 solutions that meet the schedule requirements are obtained. Most engineering projects have a quality of over 80%, which verifies the practicality of the algorithm. The algorithm has achieved good results in optimizing engineering quality. Therefore, this model has the ability to consider various indicators such as resources and costs to obtain software engineering quality improvement plans. It has certain application potential.","PeriodicalId":504436,"journal":{"name":"Decision Making: Applications in Management and Engineering","volume":"26 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140248779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluation of Node Importance in Collaborative Network of Traditional Manufacturing Enterprises Based on Multiple Attribute Decision Making 基于多属性决策的传统制造企业协作网络节点重要性评估

Decision Making: Applications in Management and Engineering Pub Date : 2024-03-04 DOI: 10.31181/dmame7220241018

Tao Yang, Yihuan Ding, Fang Jiang

{"title":"Evaluation of Node Importance in Collaborative Network of Traditional Manufacturing Enterprises Based on Multiple Attribute Decision Making","authors":"Tao Yang, Yihuan Ding, Fang Jiang","doi":"10.31181/dmame7220241018","DOIUrl":"https://doi.org/10.31181/dmame7220241018","url":null,"abstract":"The construction and operation of collaborative production networks based on multi-subject collaboration is an important path and means for enterprises to adapt to personalized, diversified, and differentiated market demand. It is of great practical significance to identify the key collaborative subjects in the collaborative network and protect and maintain them to ensure its normal operation. To identify the key collaborative subjects in the collaborative network of traditional manufacturing enterprises, this paper proposes a method for identifying and evaluating the importance of nodes in traditional manufacturing enterprise collaborative networks. Firstly, the method uses four parameters, degree centrality, betweenness centrality, closeness centrality, and subgraph centrality, as node importance evaluation indexes, based on complex network theory. Secondly, the coefficient of variation method (CVM) is used to calculate the weights of evaluation indexes. The Multiple Attribute Decision Making (MADM) based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is then used to comprehensively evaluate node importance and identify key nodes (key collaborative subjects) in the network. Finally, the proposed method's effectiveness, rationality, and scientific nature are verified by using the measurement index of network connectivity in combination with specific enterprise cases. The results show that the failure of key nodes has a more significant impact on network connectivity. Therefore, the node importance evaluation method based on Multiple Attribute Decision Making has better performance. It helps traditional manufacturing enterprises to focus on the protection and maintenance of the key collaborative subjects when coping with the competitive environment of the external market and provides a valuable reference for the normal operation of collaborative network organizations.","PeriodicalId":504436,"journal":{"name":"Decision Making: Applications in Management and Engineering","volume":"22 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140265797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Logistics Distribution Route Optimization in Artificial Intelligence and Internet of Things Environment 人工智能和物联网环境下的物流配送路线优化

Decision Making: Applications in Management and Engineering Pub Date : 2024-02-23 DOI: 10.31181/dmame7220241072

Qianfei Liu

{"title":"Logistics Distribution Route Optimization in Artificial Intelligence and Internet of Things Environment","authors":"Qianfei Liu","doi":"10.31181/dmame7220241072","DOIUrl":"https://doi.org/10.31181/dmame7220241072","url":null,"abstract":"With the increasing challenges facing the logistics industry, especially in meeting the growing demand for distribution efficiency and accuracy, the use of modern technology to optimize logistics distribution routes has become a key issue. This study explores the application of artificial intelligence (AI) and Internet of Things (IoT) technologies in the optimization of logistics distribution routes. The research first focused on collecting and processing logistics-related data, including historical delivery records, real-time traffic data, and cargo tracking information. Then, by building optimization models based on AI and IoT technologies, the study explores the potential of these technologies to improve logistics distribution efficiency and reduce costs. In addition, through cost-benefit analysis and discussion of challenge-coping strategies, this study not only verified the effectiveness of the proposed scheme in theory but also demonstrated its feasibility in practical application. Finally, the study presents implications for industry practice and recommendations for future research, emphasizing the importance of continuous technology evaluation and adaptation to market changes.","PeriodicalId":504436,"journal":{"name":"Decision Making: Applications in Management and Engineering","volume":"26 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140435694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Framework for Improving Patient Satisfaction by Reducing the Length of Stay in The Operation Suite Using the Combined DEMATEL-ANP Model 使用 DEMATEL-ANP 组合模型缩短手术室逗留时间以提高患者满意度的框架

Decision Making: Applications in Management and Engineering Pub Date : 2024-02-22 DOI: 10.31181/dmame722024882

Seyedeh Raahil Mousavi, Mehdi Sepehri, Seyed Esmaeil Najafi

{"title":"A Framework for Improving Patient Satisfaction by Reducing the Length of Stay in The Operation Suite Using the Combined DEMATEL-ANP Model","authors":"Seyedeh Raahil Mousavi, Mehdi Sepehri, Seyed Esmaeil Najafi","doi":"10.31181/dmame722024882","DOIUrl":"https://doi.org/10.31181/dmame722024882","url":null,"abstract":"Operating room (OR) planning has gradually become an important element of Hospital administration in recent years. The OR performance plays a significant role in enhancing the quality of the care provided to the patients and reducing patients' length of stay (LOS), which is the function of the OR performance, decreasing costs. Following a survey of the existing research and conducted interviews with clinical and non-clinical OR staff, there are several factors influencing patients’ length of stay (LOS) in the OR and, consequently, the costs of the OR. In this work, first, we implemented the DEMATEL to define the relationship between OR factors, next, employed ANP to identify the most influential factors on the LOS in the OR. The results show the most influential factors are operative time, operative setup, patient waiting time, anesthesia, and patient preparation. Running two rooms concurrently and minimizing turnover time (TOT) reduce patients' LOS by 10% and 5.6%, respectively, and improve the patients' and clinical personnel's satisfaction. Reducing the LOS not only leads to reduced OR costs but also helps increase the number of operations and, thereby, hospitals' revenue.","PeriodicalId":504436,"journal":{"name":"Decision Making: Applications in Management and Engineering","volume":"71 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140439159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Designing a Fuzzy Mathematical Model for a Two-Echelon Allocation-Routing Problem by Applying Route Conditions: A New Interactive Fuzzy Approach 通过应用路由条件为双梯队分配-路由问题设计模糊数学模型：一种新的交互式模糊方法

Decision Making: Applications in Management and Engineering Pub Date : 2024-02-20 DOI: 10.31181/dmame7220241029

Zahra Yadegari, S. M. H. Molana, A. H. Kashan, Seyyed Esmaeil Najafi

{"title":"Designing a Fuzzy Mathematical Model for a Two-Echelon Allocation-Routing Problem by Applying Route Conditions: A New Interactive Fuzzy Approach","authors":"Zahra Yadegari, S. M. H. Molana, A. H. Kashan, Seyyed Esmaeil Najafi","doi":"10.31181/dmame7220241029","DOIUrl":"https://doi.org/10.31181/dmame7220241029","url":null,"abstract":"In vehicle routing problems (VRP), the optimal allocation of transportation by considering factors such as route hardness, driver experience and vehicle worn-out has a significant effect on costs reduction and approaching real-world conditions. In this paper, a novel fuzzy mixed integer non-linear mathematical model to address the two-echelon allocation-routing problem under uncertainty is proposed by applying route and fleet conditions. The cost of allocating drivers to diverse vehicles is computed at the first echelon of the problem, considering factors such as vehicle type, vehicle wear-out, and driver experience. Additionally, different routes are defused with varying levels of hardness. The goal of the second echelon of the model is to improve reliability by defining the reliability of routes within each section. To solve the model, the Torabi and Hessini (TH), the Selimi and Ozkarahan (SO) methods, and a newly proposed approach (PIA) were utilized to transform the multi-objective model into a single-objective one. Numerical tests and performance indicators were used to validate the effectiveness of both the multi-objective mathematical model and the proposed solution method. The validation computation results indicate that the proposed solution approach outperforms both the TH and SO approaches.","PeriodicalId":504436,"journal":{"name":"Decision Making: Applications in Management and Engineering","volume":"114 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140448871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2