Operations Research Perspectives最新文献

A generalized behavioral-based goal programming approach for decision-making under imprecision 基于行为的通用目标编程方法，用于不精确条件下的决策

IF 3.7 4区管理学

Operations Research Perspectives Pub Date : 2024-09-03 DOI: 10.1016/j.orp.2024.100316

{"title":"A generalized behavioral-based goal programming approach for decision-making under imprecision","authors":"","doi":"10.1016/j.orp.2024.100316","DOIUrl":"10.1016/j.orp.2024.100316","url":null,"abstract":"<div><p>The body of literature on goal programming (GP) approaches in modeling preferences and the satisfaction philosophy in multi-objective programming (MOP) decision-making processes is extensive. However, there has been little focus on how preferences change in relation to the decision-maker's (DM) behavior within this satisfaction philosophy, particularly in situations involving risk. To address this challenge, we propose introducing a behavior-type utility function into the GP model using the concept of a behavior function. This idea offers an innovative perspective for modeling DM's behavioral preferences in the imprecise GP approach by integrating a risk-aversion parameter specific to each objective. We then formulate a generalized behavioral-based GP approach for decision-making based on this new behavior-type utility function. To validate our proposed approach, we present an illustrative example of project selection in health service institutions, followed by a sensitivity analysis and comparisons with other approaches. The results demonstrate that DM's behavioral preferences significantly impact the decision-making process, and the proposed model provides more reasonable and convenient decisions for DMs with varying degrees of risk aversion.</p></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214716024000204/pdfft?md5=b0a7089829d5bfb2f7e03c046a0abb6e&pid=1-s2.0-S2214716024000204-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142150415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

δ-perturbation of bilevel optimization problems: An error bound analysis 双层优化问题的δ扰动：误差边界分析

IF 3.7 4区管理学

Operations Research Perspectives Pub Date : 2024-08-31 DOI: 10.1016/j.orp.2024.100315

{"title":"δ-perturbation of bilevel optimization problems: An error bound analysis","authors":"","doi":"10.1016/j.orp.2024.100315","DOIUrl":"10.1016/j.orp.2024.100315","url":null,"abstract":"<div><p>In this paper, we analyze a perturbed formulation of bilevel optimization problems, which we refer to as <span><math><mi>δ</mi></math></span>-perturbed formulation. The <span><math><mi>δ</mi></math></span>-perturbed formulation allows to handle the lower level optimization problem efficiently when there are multiple lower level optimal solutions. By using an appropriate perturbation strategy for the optimistic or pessimistic formulation, one can ensure that the optimization problem at the lower level contains only a single (approximate) optimal solution for any given decision at the upper level. The optimistic or the pessimistic bilevel optimal solution can then be efficiently searched for by algorithms that rely on solving the lower level optimization problem multiple times during the solution search procedure. The <span><math><mi>δ</mi></math></span>-perturbed formulation is arrived at by adding the upper level objective function to the lower level objective function after multiplying the upper level objective by a small positive/negative <span><math><mi>δ</mi></math></span>. We provide a proof that the <span><math><mi>δ</mi></math></span>-perturbed formulation is approximately equivalent to the original optimistic or pessimistic formulation and give an error bound for the approximation. We apply this scheme to a class of algorithms that attempts to solve optimistic and pessimistic variants of bilevel optimization problems by repeatedly solving the lower level optimization problem.</p></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214716024000198/pdfft?md5=0f5d3b645bfc42cde11aa2cafd027512&pid=1-s2.0-S2214716024000198-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142137130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Strategizing emissions reduction investment for a livestock production farm amid power demand pattern: A path to sustainable growth under the carbon cap environmental regulation 电力需求模式下畜牧生产农场的减排投资战略：碳上限环境规制下的可持续增长之路

IF 3.7 4区管理学

Operations Research Perspectives Pub Date : 2024-08-14 DOI: 10.1016/j.orp.2024.100313

{"title":"Strategizing emissions reduction investment for a livestock production farm amid power demand pattern: A path to sustainable growth under the carbon cap environmental regulation","authors":"","doi":"10.1016/j.orp.2024.100313","DOIUrl":"10.1016/j.orp.2024.100313","url":null,"abstract":"<div><p>Livestock production companies come under increasing responsibility to reduce their environmental impact, and thereby, the simultaneous decision-making of inventory replenishment and emissions reduction investment has become essential for ensuring sustainable development in the livestock farming business. This study investigates, for the first time, the best investment strategy for a livestock farming business under the carbon cap (CC) environmental legislation, taking into account both the edible and non-edible parts of slaughtering mature growing items (GIs) after procuring and feeding baby GIs. By fusing economic and environmental factors, this study aims to shed light on two crucial issues: (i) figuring out the appropriate level of investment needed for the farm to adhere to the CC environmental regulation; and (ii) evaluating the effect of the investment decision on the farm's expenses and emissions levels. To deal with these insights, a thorough analytical framework integrating mathematical modeling methodology, economic evaluation, and carbon accounting approaches is employed. By analyzing the interaction between the farm's emissions reduction investments and replenishment choices, the cost-effective investment level is determined that enables the farm to satisfy the carbon cap obligation while guaranteeing maximum operational efficiency. The results of this study have important ramifications for livestock farming businesses trying to make their way through the stringent CC emission law. The results indicate that in order to keep the business feasible when the cap of the CC guideline is low, the livestock-producing farm should give priority to investing in minimizing feed emissions and using cutting-edge manure treatment methods.</p></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214716024000174/pdfft?md5=14fc8da6fbf948a57f7202bbf6ee7920&pid=1-s2.0-S2214716024000174-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142047896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Do jumps matter in discrete-time portfolio optimization? 离散时间投资组合优化中的跳跃重要吗？

IF 3.7 4区管理学

Operations Research Perspectives Pub Date : 2024-07-29 DOI: 10.1016/j.orp.2024.100312

{"title":"Do jumps matter in discrete-time portfolio optimization?","authors":"","doi":"10.1016/j.orp.2024.100312","DOIUrl":"10.1016/j.orp.2024.100312","url":null,"abstract":"<div><p>This paper studies a discrete-time portfolio optimization problem, wherein the underlying risky asset follows a Lévy GARCH model. Besides a Gaussian noise, the framework allows for various jump increments, including infinite-activity jumps. Using a dynamic programming approach and exploiting the affine nature of the model, we derive a single equation satisfied by the optimal strategy, and we show numerically that this equation leads to a unique solution in all special cases. In our numerical study, we focus on the impact of jumps and evaluate the difference to investors employing a Gaussian HN-GARCH model without jumps or a homoscedastic variant. We find that both jump-free models yield insignificant values for the wealth-equivalent loss when re-calibrated to simulated returns from the jump models. The low wealth-equivalent loss values remain consistent for modified parameters in the jump models, indicating extreme market situations. We therefore conclude, in support of practitioners’ preferences, that simpler models can successfully mimic the strategy and performance of discrete-time conditional heteroscedastic jump models.</p></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214716024000162/pdfft?md5=ce1e3a368db28fe6db0acc5879c416d5&pid=1-s2.0-S2214716024000162-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141883235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on green supply chain finance risk identification based on two-stage deep learning 基于两阶段深度学习的绿色供应链金融风险识别研究

IF 3.7 4区管理学

Operations Research Perspectives Pub Date : 2024-07-21 DOI: 10.1016/j.orp.2024.100311

{"title":"Research on green supply chain finance risk identification based on two-stage deep learning","authors":"","doi":"10.1016/j.orp.2024.100311","DOIUrl":"10.1016/j.orp.2024.100311","url":null,"abstract":"<div><p>As a resonance product between financial services and the upgrading of the green industry, green supply chain finance has garnered extensive attention in the process of ecological civilization construction. Effectively promoting the green transformation of small and medium-sized enterprises and achieving the \"dual carbon\" goals necessitate the avoidance of corporate green risks. However, the complex interdependence and information asymmetry among green supply chain finance enterprises result in data characteristics such as multi-source small samples and high-dimensional imbalance. To address these issues, this paper proposes a risk assessment model based on two-stage deep learning. In the first stage, we employ Generative Adversarial Network (GAN) to generate minority class default samples, and utilize Stacked Auto-Encoder (SAE) to extract data features with closed-form parameter calculation capability. In the second stage, the obtained features are input into a Deep Neural Network (DNN), and parameter learning and model optimization are conducted through joint training. Finally, to model low-order feature interactions, we integrate the Support Vector Machine (SVM) algorithm. The paper is grounded in the green innovation production of enterprises, collecting financial data of 176 upstream and downstream enterprises and corresponding core enterprise green indicators from 2013 to 2022. Experimental results demonstrate that GAN oversampling technique not only enhances the model's AUC metric but also significantly improves the F1 score. Compared with traditional deep learning methods, the proposed two-stage deep integration model effectively reduces training loss and exhibits superiority in identifying green supply chain finance risks.</p></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214716024000150/pdfft?md5=970d26bdb80bf86f740ed999ad2ea4a2&pid=1-s2.0-S2214716024000150-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141845423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automated machine learning methodology for optimizing production processes in small and medium-sized enterprises 优化中小型企业生产流程的自动化机器学习方法

IF 2.5 4区管理学

Operations Research Perspectives Pub Date : 2024-06-01 DOI: 10.1016/j.orp.2024.100308

Yarens J. Cruz , Alberto Villalonga , Fernando Castaño , Marcelino Rivas , Rodolfo E. Haber

{"title":"Automated machine learning methodology for optimizing production processes in small and medium-sized enterprises","authors":"Yarens J. Cruz , Alberto Villalonga , Fernando Castaño , Marcelino Rivas , Rodolfo E. Haber","doi":"10.1016/j.orp.2024.100308","DOIUrl":"https://doi.org/10.1016/j.orp.2024.100308","url":null,"abstract":"<div><p>Machine learning can be effectively used to generate models capable of representing the dynamic of production processes of small and medium-sized enterprises. These models enable the estimation of key performance indicators, and are often used for optimizing production processes. However, in most industrial applications, modeling and optimization of production processes are currently carried out as separate tasks, manually in a very costly and inefficient way. Automated machine learning tools and frameworks facilitate the path for deriving models, reducing modeling time and cost. However, optimization by exploiting production models is still in infancy. This work presents a methodology for integrating a fully automated procedure that embraces automated machine learning pipelines and a multi-objective optimization algorithm for improving the production processes, with special focus on small and medium-sized enterprises. This procedure is supported on embedding the generated models as objective functions of a reference point based non-dominated sorting genetic algorithm, resulting in preference-based Pareto-optimal parametrizations of the corresponding production processes. The methodology was implemented and validated using data from a manufacturing production process of a small manufacturing enterprise, generating highly accurate machine learning-based models for the analyzed indicators. Additionally, by applying the optimization step of the proposed methodology it was possible to increase the productivity of the manufacturing process by 3.19 % and reduce its defect rate by 2.15 %, outperforming the results obtained with traditional trial and error method focused on productivity alone.</p></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214716024000125/pdfft?md5=85cdd1f40a19c1e0b701cd06bd056884&pid=1-s2.0-S2214716024000125-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141324592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ESG integration in portfolio selection: A robust preference-based multicriteria approach 将环境、社会和公司治理纳入投资组合选择：基于偏好的稳健多标准方法

IF 2.5 4区管理学

Operations Research Perspectives Pub Date : 2024-06-01 DOI: 10.1016/j.orp.2024.100305

Ana Garcia-Bernabeu , Adolfo Hilario-Caballero , Fabio Tardella , David Pla-Santamaria

{"title":"ESG integration in portfolio selection: A robust preference-based multicriteria approach","authors":"Ana Garcia-Bernabeu , Adolfo Hilario-Caballero , Fabio Tardella , David Pla-Santamaria","doi":"10.1016/j.orp.2024.100305","DOIUrl":"10.1016/j.orp.2024.100305","url":null,"abstract":"<div><p>We present a framework for multi-objective optimization where the classical mean–variance portfolio model is extended to integrate the environmental, social and governance (ESG) criteria on the same playing field as risk and return and, at the same time, to reflect the investors’ preferences in the optimal portfolio allocation. To obtain the three–dimensional Pareto front, we apply an efficient multi-objective genetic algorithm, which is based on the concept of <span><math><mi>ɛ</mi></math></span>-dominance. We next address the issue of how to incorporate investors’ preferences to express the relative importance of each objective through a robust weighting scheme in a multicriteria ranking framework. The new proposal has been applied to real data to find optimal portfolios of socially responsible investment funds, and the main conclusion from the empirical tests is that it is possible to provide the investors with a robust solution in the mean–variance–ESG surface according to their preferences.</p></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214716024000095/pdfft?md5=844c952d89a5e3cb430a5a472d124362&pid=1-s2.0-S2214716024000095-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141135964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ranking-based second stage in data envelopment analysis: An application to research efficiency in higher education 数据包络分析中基于排名的第二阶段：高等教育研究效率的应用

IF 2.5 4区管理学

Operations Research Perspectives Pub Date : 2024-06-01 DOI: 10.1016/j.orp.2024.100306

Vladimír Holý

引用次数: 0

Chance constrained directional models in stochastic data envelopment analysis 随机数据包络分析中的机会约束方向模型

IF 2.5 4区管理学

Operations Research Perspectives Pub Date : 2024-06-01 DOI: 10.1016/j.orp.2024.100307

V.J. Bolós , R. Benítez , V. Coll-Serrano

引用次数: 0

Multi-objective optimization for perishable product dispatch in a FEFO system for a food bank single warehouse 食品银行单一仓库 FEFO 系统中易腐产品调度的多目标优化

IF 2.5 4区管理学

Operations Research Perspectives Pub Date : 2024-05-07 DOI: 10.1016/j.orp.2024.100304

Carlos Aníbal Suárez , Walter A. Guaño , Cinthia C. Pérez , Heydi Roa-López

{"title":"Multi-objective optimization for perishable product dispatch in a FEFO system for a food bank single warehouse","authors":"Carlos Aníbal Suárez , Walter A. Guaño , Cinthia C. Pérez , Heydi Roa-López","doi":"10.1016/j.orp.2024.100304","DOIUrl":"https://doi.org/10.1016/j.orp.2024.100304","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214716024000083/pdfft?md5=d4934e2ec81af99eee9488876b901256&pid=1-s2.0-S2214716024000083-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140948097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0