Operations Research Perspectives最新文献

{"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":"2024-06-01","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":"Chance constrained directional models in stochastic data envelopment analysis","authors":"V.J. Bolós ,&nbsp;R. Benítez ,&nbsp;V. Coll-Serrano","doi":"10.1016/j.orp.2024.100307","DOIUrl":"10.1016/j.orp.2024.100307","url":null,"abstract":"<div><p>We construct a new family of chance constrained directional models in stochastic data envelopment analysis, generalizing the deterministic directional models and the chance constrained radial models. We prove that chance constrained directional models define the same concept of stochastic efficiency as the one given by chance constrained radial models and, as a particular case, we obtain a stochastic version of the generalized Farrell measure. Finally, we give some examples of application of chance constrained directional models with stochastic and deterministic directions, showing that inefficiency scores obtained with stochastic directions are less or equal than those obtained considering deterministic directions whose values are the means of the stochastic ones.</p></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"12 ","pages":"Article 100307"},"PeriodicalIF":2.5,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214716024000113/pdfft?md5=b203f1d3524e063c3af56ce9551bd228&pid=1-s2.0-S2214716024000113-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141407217","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":"2024-06-01","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":"A recent review of solution approaches for green vehicle routing problem and its variants","authors":"Annisa Kesy Garside ,&nbsp;Robiah Ahmad ,&nbsp;Mohd Nabil Bin Muhtazaruddin","doi":"10.1016/j.orp.2024.100303","DOIUrl":"https://doi.org/10.1016/j.orp.2024.100303","url":null,"abstract":"<div><p>The green vehicle routing problem (GVRP) has been a prominent topic in the literature on logistics and transportation, leading to extensive research and previous review studies covering various aspects. Operations research has seen the development of various exact and approximation approaches for different extensions of the GVRP. This paper presents an up-to-date and thorough review of GVRP literature spanning from 2016 to 2023, encompassing 458 papers. significant contribution lies in the updated solution approaches and algorithms applied to both single-objective and multi-objective GVRP. Notably, 92.58 % of the papers introduced a mathematical model for GVRP, with many researchers adopting mixed integer linear programming as the preferred modeling approach. The findings indicate that both metaheuristics and hybrid are the most employed solution approaches for addressing single-objective GVRP. Among hybrid approaches, the combination of metaheuristics-metaheuristics is particularly favored by GVRP researchers. Furthermore, large neighborhood search (LNS) and its variants (especially adaptive large neighborhood search) emerges as the most widely adopted algorithm in single-objective GVRP. These algorithms are proposed within both metaheuristic and hybrid approaches, where A-/LNS is often combined with other algorithms. Conversely, metaheuristics are predominant in addressing multi-objective GVRP, with NSGA-II being the most frequently proposed algorithm. Researchers frequently utilize GAMS and CPLEX as optimization modeling software and solvers. Furthermore, MATLAB is a commonly employed programming language for implementing proposed algorithms.</p></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"12 ","pages":"Article 100303"},"PeriodicalIF":2.5,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214716024000071/pdfft?md5=c229fa600d5a6f4ce847c43d2270f761&pid=1-s2.0-S2214716024000071-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140823518","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":"2023-12-01","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}

{"title":"Interventions in demand and supply sides for vaccine supply chain: An analysis on monkeypox vaccine","authors":"Hamid R. Sayarshad","doi":"10.1016/j.orp.2023.100285","DOIUrl":"https://doi.org/10.1016/j.orp.2023.100285","url":null,"abstract":"<div><p>After a pandemic, all countries experience a shortage in vaccine supply due to limited vaccine stocks and production capacity globally. One particular problem is that it is hard to predict demands for vaccines during the global crisis. On the other hand, vaccines are usually made and packaged in different places, raising logistical issues and concerns that can further delay distribution. In this paper, we propose an optimization formulation model to link infectious disease dynamics and supply chain networks considering a one-to-one relationship between demand and supply for vaccines. We focus on designing a vaccine coordination system using government subsidy that considers the equilibrium behaviors of manufacturers under an actual demand for the vaccine. This study evaluates vaccine manufacturers and government behaviors that help the vaccine market to reach the socially optimal. Different decisions, such as vaccine demands and vaccine production and distribution are investigated. A study of the monkeypox pandemic in the U.S. is performed to validate our model and its results. The obtained results from testing the proposed system problem revealed that the vaccine coverage increased by up to 35%, while the unmet demand reduced by up to 60%, in comparison to when vaccine manufacturers act individually.</p></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"11 ","pages":"Article 100285"},"PeriodicalIF":2.5,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49906134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The effect of an uncertain commission rate on the decisions of a capital-constrained developer","authors":"Tal Avinadav,&nbsp;Priel Levy","doi":"10.1016/j.orp.2023.100288","DOIUrl":"https://doi.org/10.1016/j.orp.2023.100288","url":null,"abstract":"<div><p>This study investigates a green supply chain consisting of a capital-constrained developer who sells a product via a platform. The parties interact via an agency contract, in which the platform charges a fixed proportion of the revenue gained from each sold unit and the developer receives the remaining sum. Since the development process is relatively protracted, at the early stages of this process, the commission rate to be charged by the platform is random from the developer’s perspective. Upon receiving information about the amount of capital the developer has committed to investing in greenness from his own resources, an external investor offers the developer a loan at a certain interest rate (to further enhance the developer’s investment in greenness), based on which the developer sets the product’s greenness level and selling price. The study provides a game-theoretic analysis of this model and compares its equilibrium solution with the optimal solution of a fully self-financing developer. The innovative feature of the study lies in its comparison between the case of a developer who might not be able to repay the loan, because his revenue from selling the product might be lower than the amount he is required to repay the investor (the loan plus interest), and the case in which it is certain that the developer will be able to repay any debt to the investor. Our study shows that, in the case where the investor takes on the financing risk, the customers benefit from a higher greenness level (albeit at a higher price), resulting in greater demand for the product.</p></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"11 ","pages":"Article 100288"},"PeriodicalIF":2.5,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214716023000234/pdfft?md5=1458938b4de7ea39854d91dc6bbbdcb8&pid=1-s2.0-S2214716023000234-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134832827","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":"2023-12-01","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":"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":"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}