Operations Research Perspectives最新文献

{"title":"Green retailer: A stochastic bi-level approach to support investment decisions in sustainable energy systems","authors":"Patrizia Beraldi","doi":"10.1016/j.orp.2024.100300","DOIUrl":"https://doi.org/10.1016/j.orp.2024.100300","url":null,"abstract":"<div><p>This paper presents a bi-level approach to support retailers in making investment decisions in renewable-based systems to provide clean electricity. The proposed model captures the strategic nature of the problem and combines capacity sizing decisions for installed technologies with pricing decisions regarding the electricity tariffs to offer to a reference end-user, representative of a class of residential prosumers. The interaction between retailer and end-user is modeled using the Stackelberg game framework, with the former acting as a leader and the latter as follower. The reaction of the follower to the electricity tariff affects the retailer’s profit, which is calculated as the difference between the revenue generated from selling electricity and the total investment, operation and management costs. To account for uncertainty in wholesale electricity prices, renewable resource availability and electricity request, the upper-level problem is formulated as a two-stage stochastic programming model. First-stage decisions refer to the sizing of installed technologies and electricity tariffs, whereas second-stage decisions refer to the operation and management of the designed system. The model also incorporates a safety measure to control the average profit that can be achieved in a given percentage of worst-case situations, thus providing a contingency against unforeseen changes. At the lower level, the follower reacts to the offered tariffs by defining the procurement plan in terms of energy to purchase from the retailer or potential competitors, with the final aim of minimizing the expected value of the electricity bill. A tailored approach that exploits the specific problem structure is designed to solve the proposed formulation and extensively tested on a realistic case study. The numerical results demonstrate the efficiency of the proposed approach and validate the significance of explicitly dealing with the uncertainty and the importance of incorporating a safety measure.</p></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"12 ","pages":"Article 100300"},"PeriodicalIF":2.5,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214716024000046/pdfft?md5=10af9519673ad91c7f729f13bb913696&pid=1-s2.0-S2214716024000046-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140161063","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}

{"title":"Pareto-optimal front generation for the bi-objective JIT scheduling problems with a piecewise linear trade-off between objectives","authors":"Sona Babu,&nbsp;B.S. Girish","doi":"10.1016/j.orp.2024.100299","DOIUrl":"10.1016/j.orp.2024.100299","url":null,"abstract":"<div><p>This paper proposes a novel method of Pareto front generation from a set of piecewise linear trade-off curves typically encountered in bi-objective just-in-time (JIT) scheduling problems. We have considered the simultaneous minimization of total weighted earliness and tardiness (TWET) and total flowtime (TFT) objectives in a single-machine scheduling problem (SMSP) with distinct job due dates allowing inserted idle times in the schedules. An optimal timing algorithm (OTA) is presented to generate the trade-off curve between TWET and TFT for a given sequence of jobs. The proposed method of Pareto front generation generates a Pareto-optimal front constituted of both line segments and points. Further, we employ a simple local search method to generate sequences of jobs and their respective trade-off curves, which are trimmed and merged to generate the Pareto-optimal front using the proposed method. Computational results obtained using problem instances of different sizes reveal the efficiency of the proposed OTA and the Pareto front generation method over the state-of-the-art methodologies adopted from the literature.</p></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"12 ","pages":"Article 100299"},"PeriodicalIF":2.5,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214716024000034/pdfft?md5=5b11514fe1b1cb59cc8b7fbe08ee9aed&pid=1-s2.0-S2214716024000034-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139923190","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}

{"title":"The decrease of ED patient boarding by implementing a stock management policy in hospital admissions","authors":"Sebastián Jaén","doi":"10.1016/j.orp.2024.100298","DOIUrl":"https://doi.org/10.1016/j.orp.2024.100298","url":null,"abstract":"<div><p>The presence of congestion is a common scenario in tertiary-level hospitals worldwide. Current research suggests that an increase in hospital bed capacity is not a long-term solution given that patient demand adapts to added capacity. Recent literature suggests the need for the implementation of a policy of inter-hospital transfers to divert patients to outpatient priority services or home care. This policy has proven to be effective in reducing ED boarding without compromising patient safety. However, determining the required number of patients to be admitted is key. The dynamic nature of hospital bed availability and discharges requires an admission process able to be in synchrony with those variations. A mismatch between patient demand and hospital admissions will result in either ED boarding or idle capacity. The purpose of this paper is to introduce a methodology to support the process of hospital admissions by providing as an input a threshold for the number of patients to be admitted. The methodology is tested using a system dynamics model that replicates one year of operations of a tertiary-level hospital. The simulations reveal the potential of the methodology to decrease the ED inpatient boarding rate as well as ED and hospital length of stay.</p></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"12 ","pages":"Article 100298"},"PeriodicalIF":2.5,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214716024000022/pdfft?md5=e34aaab256821953faa6b191f0fbb84f&pid=1-s2.0-S2214716024000022-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139732852","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}

{"title":"Sustainability inventory management model with warm-up process and shortage","authors":"Erfan Nobil ,&nbsp;Leopoldo Eduardo Cárdenas-Barrón ,&nbsp;Dagoberto Garza-Núñez ,&nbsp;Gerardo Treviño-Garza ,&nbsp;Armando Céspedes-Mota ,&nbsp;Imelda de Jesús Loera-Hernández ,&nbsp;Neale R. Smith ,&nbsp;Amir Hossein Nobil","doi":"10.1016/j.orp.2024.100297","DOIUrl":"https://doi.org/10.1016/j.orp.2024.100297","url":null,"abstract":"<div><p>Fast-paced markets require complex interactions from all supply-chain agents to satisfy customer demands and needs. The manufacturing industries face some difficulties in terms of production amounts and smooth delivery rates. Technical experts found that a warm-up period before a production run helps address those challenges and improves the workability of machine tools in the manufacturing process. The use of a warm-up process causes a reduction of faulty products (an adverse production outcome) and improves operational efficiency. Also, a shortage in the supply of commodities creates difficult conditions for inventory management decisions, posing the same production problems as mentioned above. Consideration of the warm-up process has recently been included in the scope of operations research, but it is necessary to study its interaction with the presence of shortage. This study presents a system where a manufacturing environment utilizes the warm-up process in its initial phase and shortages are allowed during the production period, in addition, the study takes into account carbon emissions during manufacturing to integrate environmental concerns. We assume that the company has the capability to trade the surplus carbon capacity it hasn't produced. This study offers a comprehensive framework that incorporates former research that addresses warm-up process, carbon emissions, shortages, and defective items. To solve the proposed non-linear programming problem with inequality constraints, we employ the Karush-Kuhn-Tucker (KKT) conditions method to determine the optimal solutions. Managerial insights are derived, and sensitivity analysis highlights the effects of the system parameters on the decision variables. The sensitivity analysis results indicate that the carbon trading cost has a significant impact on the overall cost, and subsequently, the company's profit.</p></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"12 ","pages":"Article 100297"},"PeriodicalIF":2.5,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214716024000010/pdfft?md5=7033e5efa447c56295bda49d96a018da&pid=1-s2.0-S2214716024000010-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139738664","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}

{"title":"Introduction to the SI “Advances in operations research and machine learning focused on pandemic dynamics”","authors":"Massimiliano Ferrara ,&nbsp;Ali Ahmadian ,&nbsp;Soheil Salashour ,&nbsp;Bruno Antonio Pansera","doi":"10.1016/j.orp.2023.100287","DOIUrl":"10.1016/j.orp.2023.100287","url":null,"abstract":"","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"11 ","pages":"Article 100287"},"PeriodicalIF":2.5,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214716023000222/pdfft?md5=5b57aed72114ce233d955462a23c4e54&pid=1-s2.0-S2214716023000222-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48033841","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}

{"title":"Deep reinforcement learning based medical supplies dispatching model for major infectious diseases: Case study of COVID-19","authors":"Jia-Ying Zeng ,&nbsp;Ping Lu ,&nbsp;Ying Wei ,&nbsp;Xin Chen ,&nbsp;Kai-Biao Lin","doi":"10.1016/j.orp.2023.100293","DOIUrl":"https://doi.org/10.1016/j.orp.2023.100293","url":null,"abstract":"&lt;div&gt;&lt;p&gt;Stockpiling and scheduling plans for medical supplies represent essential preventive and control measures in major public health events. In the face of major infectious diseases, such as the novel coronavirus disease (COVID-19), the outbreak trend and variability of disease strains are often unpredictable. Hence, it is necessary to optimally adjust the prevention and control dispatching strategy according to the circumstances and outbreak locations to maintain economic development while ensuring the human health survival, however, many models in this scenario seldom consider the dynamic material prediction and the measurement of multiple costs at the same time. Taking the COVID-19 scenario as a case study, we establish a deep reinforcement learning (DRL)-based medical supplies dispatching (MSD) model for major infectious diseases, considering the volatility of the COVID-19 situation and the discrepancy between medical material demand and supply due to the high infectiousness of the Omicron series strains. The present model has three main components: 1) First, for the dynamic medical material prediction problem in complex infectious disease scenarios, taking the lifted COVID-19 lockdown scenario as an example, the modified susceptible-exposed-infected-recovered (SEIR) model was utilized to analyze the spread of the COVID-19, understand its characteristics, and map out the related medical supplies demand; 2) Second, to break away from the previous premise of only considering supply-demand, this study adds scheduling rules and cost function that weighs health and economic costs. An epidemic dispatching optimization model (Epi_DispatchOptim) was established using the OpenAI Gym toolkit to form an environment structure with virus transmission space, and emergency MSD while considering both human health and economic costs. This architecture interprets the balance between the supply-demand of medical supplies and reflects the importance of MSD in the balanced development of health and economy under the spread of infectious diseases; 3) Finally, the MSD strategy under the balance of health and economic cost is explored in Epi_DispatchOptim using reinforcement learning (RL) and the evolutionary algorithm (EA). Experiments conducted on two datasets indicate that the RL and EA reduce economic as well as health costs compared to the original environmental strategies. The above study illustrates how to use epidemiological models to predict the demand for healthcare supplies as the premise of scheduling models, and use Epi_DispatchOptim to explore the dynamic MSD decisions under mortality and economic equilibrium. In Shanghai, China, the economic cost of the exploration strategy is reduced by 27.36–27.07B compared to static scheduling, and deaths are reduced by 126–150 in 150 day compared to the no-intervention scenario. By integrating knowledge of epidemiology, optimal decision making, and economics, Epi_DispatchOptim further constructs epidemiologica","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"11 ","pages":"Article 100293"},"PeriodicalIF":2.5,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214716023000283/pdfft?md5=ea4f042b5fe351d77ed253105f2650f7&pid=1-s2.0-S2214716023000283-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138471749","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}

{"title":"Early detection of students’ failure using Machine Learning techniques","authors":"Aarón López-García ,&nbsp;Olga Blasco-Blasco ,&nbsp;Marina Liern-García ,&nbsp;Sandra E. Parada-Rico","doi":"10.1016/j.orp.2023.100292","DOIUrl":"https://doi.org/10.1016/j.orp.2023.100292","url":null,"abstract":"<div><p>The educational system determines one of the significant strengths of an advanced society. A country with a lack of culture is less competitive due to the inequality suffered by its people. Institutions and organizations are putting their efforts into tackling that problem. Nevertheless, it is not an easy task to ascertain why their students have failed or what are the conditions that affect such situations. In this work, an intelligent system is proposed to predict academic failure by using student information stored by the Industrial University of Santander (Colombia). The prediction model is powered by the XGBoost algorithm, where a TOPSIS-based feature extraction and ADASYN oversampling have been conducted. Hyperparameters of the classifier were tuned by a cross-validated grid-search algorithm. We have compared our results with other decision-tree classifiers and displayed the feature importance of our intelligent system as an explainability phase. In conclusion, our intelligent system has shown a superior performance of our prediction model and has indicated to us that economic, health and social factors are decisive for the academic performance of the students.</p></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"11 ","pages":"Article 100292"},"PeriodicalIF":2.5,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214716023000271/pdfft?md5=d89e17319a9f617588eb399039619fc0&pid=1-s2.0-S2214716023000271-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138437032","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}

{"title":"Research on the scheduling method of ground resource under uncertain arrival time","authors":"Guoning Xu,&nbsp;Yupeng Lin,&nbsp;Zhiying Wu,&nbsp;Qingxin Chen,&nbsp;Ning Mao","doi":"10.1016/j.orp.2023.100291","DOIUrl":"https://doi.org/10.1016/j.orp.2023.100291","url":null,"abstract":"<div><p>We present a two-stage scheduling approach including proactive and reactive scheduling to solve the ground resource scheduling problem with uncertain arrival time. In the first stage, an integer programming model is constructed to minimize the delay and transfer costs. After solving this model, we obtain a baseline scheduling plan that considers the service arrival time uncertainty. In the second stage, the feasibility of the subsequent benchmark plan is evaluated based on the current state of the services and resources. The reactive scheduling model is enabled when trigger conditions are met. Moreover, an improved adaptive large neighborhood search is designed to solve the proactive scheduling model effectively. Real data from an international airport in South China is used as a test case to compare different scheduling strategies. The results show that it is difficult to handle the uncertainty of the problem with the benchmark plan that simply considered buffer time. Compared with rolling time-domain scheduling, the average transfer cost of the scheduling strategy proposed in this paper increased slightly, but the average service delay cost can be reduced significantly. Algorithm-wise, instances of different scales are designed to verify the effectiveness of the improved adaptive large neighborhood search algorithm. The efficiency of the algorithm scheme is better than that of the Gurobi solver scheme in medium to large-scale problems. Therefore, the forward and reactive strategies can better handle the uncertainty of airport ground protection services as they can simultaneously guide the allocation and utilization of airport ground protection resources.</p></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"11 ","pages":"Article 100291"},"PeriodicalIF":2.5,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S221471602300026X/pdfft?md5=3628e72a2def27b5ee8146cd369ce7f4&pid=1-s2.0-S221471602300026X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136697100","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}

{"title":"Effects of variable prepayment installments on pricing and inventory decisions with power demand pattern and non-linear holding cost under carbon cap-and-price regulation","authors":"Md. Al-Amin Khan ,&nbsp;Leopoldo Eduardo Cárdenas-Barrón ,&nbsp;Gerardo Treviño-Garza ,&nbsp;Armando Céspedes-Mota ,&nbsp;Imelda de Jesús Loera-Hernández ,&nbsp;Neale R. Smith","doi":"10.1016/j.orp.2023.100289","DOIUrl":"10.1016/j.orp.2023.100289","url":null,"abstract":"<div><p>Regulators’ increasingly stringent carbon rules to protect the environment are encouraging practitioners to modify their operational activities that are accountable for releasing emissions into the atmosphere. Thereby, practitioners dealing with product inventory planning are seeking proper management strategies not only to increase profits but also to reduce released carbons from operations. In addition, increasing uncertainty in supply operations has motivated suppliers to impose prepayment mechanisms in recent decades. This study examines the best prepayment installment policy for a practitioner for the first time, where the consumption behavior of consumers changes as a result of the combined effects of unit selling price and storage time. Moreover, to make the present inventory planning more realistic, the unit holding cost function is adopted as a power function of the inventory unit's storage period. The goal of this study is to provide the best combined installment for advance payment, price, and replenishment strategies for a practitioner under cap-and-price, cap-and-trade, and carbon tax environmental guidelines by ensuring maximum profit. For this purpose, an algorithm is created by combining all derived theoretical results from the analytical study, whereas the efficacy of the algorithm is assessed through the examination of five illustrative numerical instances. A plethora of noteworthy management insights for the practitioner are obtained by investigating the dynamic shifts in optimal strategies resulting from fluctuations in system parameters. The results reveal that if the demand is low in the nascent phases of the business cycle, then the prudent approach for the practitioner entails procuring a comparatively smaller lot-size using a modest number of payment frequencies and then setting a relatively small unit selling price to increase profits.</p></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"12 ","pages":"Article 100289"},"PeriodicalIF":2.5,"publicationDate":"2023-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214716023000246/pdfft?md5=8854a838a12dde2cb691c4ab51bc822e&pid=1-s2.0-S2214716023000246-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135671433","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}

{"title":"Prescriptive price optimization using optimal regression trees","authors":"Shunnosuke Ikeda ,&nbsp;Naoki Nishimura ,&nbsp;Noriyoshi Sukegawa ,&nbsp;Yuichi Takano","doi":"10.1016/j.orp.2023.100290","DOIUrl":"10.1016/j.orp.2023.100290","url":null,"abstract":"<div><p>This paper is concerned with prescriptive price optimization, which integrates machine learning models into price optimization to maximize future revenues or profits of multiple items. The prescriptive price optimization requires accurate demand forecasting models because the prediction accuracy of these models has a direct impact on price optimization aimed at increasing revenues and profits. The goal of this paper is to establish a novel framework of prescriptive price optimization using optimal regression trees, which can achieve high prediction accuracy without losing interpretability by means of mixed-integer optimization (MIO) techniques. We use the optimal regression trees for demand forecasting and then formulate the associated price optimization problem as a mixed-integer linear optimization (MILO) problem. We also develop a scalable heuristic algorithm based on the randomized coordinate ascent for efficient price optimization. Simulation results demonstrate the effectiveness of our method for price optimization and the computational efficiency of the heuristic algorithm.</p></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"11 ","pages":"Article 100290"},"PeriodicalIF":2.5,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214716023000258/pdfft?md5=4e424d41dfd20c9c705fe65d9b931e91&pid=1-s2.0-S2214716023000258-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135566026","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}