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Integrated consultation and chemotherapy scheduling with stochastic treatment times

JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2023年第9期74卷 2012-2027页

作者： Haghi, Maryam Doulabi, Hossein Hashemi Contreras, Ivan Bhuiyan, Nadia Concordia Univ Dept Mech Ind & Aerosp Engn MIAE Montreal PQ Canada Interuniv Res Ctr Enterprise Networks Logist & Tr Montreal PQ Canada

This paper studies the integrated scheduling of consultation and treatment appointments for chemotherapy patients, while taking into account the stochastic duration of injection. Patients may require one or both types of consultation and treatment appointments. The objective is to minimize the clinic's overtime and the waiting time of patients in the clinic. To formulate the problem, we develop two two-stage stochastic programming models. We also propose a sample average approximation algorithm as the solution method. To improve the efficiency of our solution approach, we devise a specialized algorithm that quickly evaluates a given first-stage solution for a large set of scenarios, without solving the second-stage models. Several computational experiments are carried out to evaluate the performance of proposed models and algorithms.

关键词： Chemotherapy scheduling stochastic programming sample average approximation

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Optimization of OmniChannel Distribution Network Using Micro Fulfillment Center Under Demand Uncertainty

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IEEE ACCESS 2023年 11卷 107496-107510页

作者： Lee, Taehoon Han, So Rim Song, Byung Duk Kyung Hee Univ Dept Big Data Analyt Gyeonggi Do 17104 South Korea Korea Univ Sch Ind & Management Engn Seoul 02841 South Korea

Quick commerce has recently become the most important issue in the logistics industry due to intensifying competition for a faster delivery. A fulfillment service that integrates various logistics processes has emerged to conduct delivery service as quickly as possible. With a need for facilities that can serve a fulfillment service near the customer, a Micro Fulfillment Center (MFC) is suggested as a solution. MFC refers to a small fulfillment center located in the city center and functions as an advanced base for quick commerce services. However, quick commerce services, including the operation of MFC, are suffering from high costs and operational inefficiency. In this situation, the integration of online and offline distribution networks in the omnichannel logistics system is showing a new possibility for Quick commerce service. for instance, the waste cost resulting from the failures of demand forecasting can be alleviated by the integration of both networks, especially for perishable goods. Accordingly, this study aims to determine the optimal MFC location for Quick commerce service to serve an online order in an omnichannel system that deals with perishable goods, and to identify operational benefits that can be led by the connection between MFCs and Retail stores. This study has introduced a two-stage stochastic optimization model to deal with demand uncertainty. we developed two optimization models to explain the integration between MFCs and offline stores. In addition, we conduct a numerical analysis based on real-world data including actual retail stores and modifications of online demand to verify the models we introduce. The case study is executed in Gangdong-gu, Songpa-gu in Seoul, Korea. Rep of. The results say that the connection of the different channels can be significantly beneficial in operating costs.

关键词： Quick commerce omnichannel stochastic programming demand uncertainty MFC micro fulfillment center urban logistics

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Short-Term nurse schedule adjustments under dynamic patient demand

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2023年第1期74卷 310-329页

作者： Aydas, Osman T. Ross, Anthony D. Scanlon, Matthew C. Aydas, Buket Oakland Univ Decis & Informat Sci Rochester MI 48309 USA Univ Missouri Dept Management Columbia MO USA Med Coll Wisconsin Milwaukee WI 53226 USA Blue Cross Blue Shield Michigan Southfield MI USA

We study two-stage short-term staffing adjustments for the upcoming nursing shift. Our proposed adjustments are first used at the beginning of each 4-hour nursing shift, shift t, for the upcoming shift, shift t + 1. Then, after observing actual patient demand for nursing at the start of shift t + 1, we make our final staffing adjustments to meet the patient demand. We model six different adjustment options for the two-stage stochastic programming model, five options available as first-stage decisions and one option available as the second-stage decision. We develop a two-stage stochastic integer programming model, which minimizes total nurse staffing costs and the cost of adjustments to the original schedules, while ensuring the coverage of nursing demand. Our experimental results, using the data from an urban Children's Hospital, indicate that the developed stochastic nurse schedule adjustment model can deliver cost savings up to 18% for the medical units, compared to alternative no short-term adjustment scheduling models. The proposed stochastic adjustments model successfully keeps average understaffing percentages under 2% throughout the staffing horizon.

关键词： Nurse scheduling workforce planning healthcare services stochastic programming

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A study of data-driven distributionally robust optimization with incomplete joint data under finite support

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第2期305卷 754-765页

作者： Ren, Ke Bidkhori, Hoda Univ Pittsburgh Dept Ind Engn Pittsburgh PA 15261 USA

Missing data is a common issue for many practical data-driven stochastic programming problems. The state-of-the-art approaches first estimate the missing data values and then separately solve the corre-sponding stochastic programming. Accurate estimation of missing values is typically inaccessible as it requires enormous data and sophisticated statistical methods. Therefore, this paper proposes an inte-grated approach, a distributionally robust optimization (DRO) framework, that simultaneously tackles the missing data problem and data-driven stochastic optimization by hedging against the uncertainties of the missing values. This paper adds to the DRO literature by considering the practical scenario where the data can be incomplete and partially observable;it particularly focuses on data distributions with finite sup-port. We construct several classes of ambiguity sets for our DRO model utilizing the incomplete data sets, maximum likelihood estimation method, and different metrics. We prove the statistical consistency and finite sample guarantees of the corresponding models and provide tractable reformulations of our model for different scenarios. We perform computational studies on the multi-item inventory control problem and portfolio optimization using synthetic and real-world data. We validate that our method outperforms the traditional estimate-then-optimized approaches.(c) 2022 Elsevier B.V. All rights reserved.

关键词： stochastic programming Distributionally robust optimization Missing data Uncertainty modeling Data-driven decision-making

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Budget allocation of food procurement for natural disaster response

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第2期311卷 754-768页

作者： Dang, Duc-Cuong Currie, Christine S. M. Onggo, Bhakti Stephan Chaerani, Diah Achmad, Audi Luqmanul Hakim Univ Southampton Southampton Business Sch Southampton SO171BJ England Univ Southampton Math Sci Southampton SO171BJ England Univ Padjadjaran Fac Math & Nat Sci Dept Math Sumedang 45363 Indonesia Univ Passau Chair Algorithms Intelligent Syst D-94032 Passau Germany

This paper studies a variant of the lot sizing problem that arises in the context of disaster management. In this problem, a fixed budget has to be allocated efficiently over multiple time periods to procure large quantities of a staple food that will be stored and later delivered to people affected by disaster strikes whose numbers are unknown in advance. Starting from the deterministic model where perfect infor-mation is assumed, different formulations to address the uncertainties are constructed: classical robust optimisation, risk-minimisation stochastic programming, and adjustable robust optimisation. Experiments conducted using data from West Java, Indonesia allow us to discuss the advantages and drawbacks of each method. Our methods constitute a toolbox to support decision makers with making procurement decisions and answering managerial questions such as which annual budget is fair and safe, or when storage peaks are likely to occur.& COPY;2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license ( http://***/licenses/by/4.0/ )

关键词： Humanitarian logistics Disaster management Procurement lot sizing Robust optimisation stochastic programming

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stochastic scheduling of chemotherapy appointments considering patient acuity levels

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第2期305卷 902-916页

作者： Karakaya, Sirma Gul, Serhat Celik, Melih TED Univ Dept Ind Engn Ankara Turkiye Univ Bath Sch Management Bath Avon England

The uncertainty in infusion durations and non-homogeneous care level needs of patients are the critical factors that lead to difficulties in chemotherapy scheduling. We study the problem of scheduling patient appointments and assigning patients to nurses under uncertainty in infusion durations for a given day. We consider instantaneous nurse workload, represented in terms of total patient acuity levels, and chair availability while scheduling patients. We formulate a two-stage stochastic mixed-integer programming model with the objective of minimizing expected weighted sum of excess patient acuity, waiting time and nurse overtime. We propose a scenario bundling-based decomposition algorithm to find near-optimal schedules. We use data of a major university hospital to generate managerial insights related to the impact of acuity consideration, and number of nurses and chairs on the performance measures. We compare the schedules obtained by the algorithm with the baseline schedules and those found by applying several relevant scheduling heuristics. Finally, we assess the value of stochastic solution.(c) 2022 Elsevier B.V. All rights reserved.

关键词： OR in health services Chemotherapy scheduling Patient acuity stochastic programming

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DATA-DRIVEN APPROXIMATION OF CONTEXTUAL CHANCE-CONSTRAINED stochastic PROGRAMS

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SIAM JOURNAL ON OPTIMIZATION 2023年第3期33卷 2248-2274页

作者： Rahimian, Hamed Pagnoncelli, Bernardo Clemson Univ Dept Ind Engn Clemson SC 29634 USA Univ Cote dAzur SKEMA Business Sch Lille France

Uncertainty in classical stochastic programming models is often described solely by independent random parameters, ignoring their dependence on multidimensional features. We describe a novel contextual chance-constrained programming formulation that incorporates features, and argue that solutions that do not take them into account may not be implementable. Our formulation cannot be solved exactly in most cases, and we propose a tractable and fully data-driven approximate model that relies on weighted sums of random variables. We obtain a stochastic lower bound for the optimal value and feasibility results that include convergence to the true feasible set as the number of data points increases, as well as the minimal number of data points needed to obtain a feasible solution with high probability. We illustrate our findings in a vaccine allocation problem and compare the results with a naive sample average approximation approach.

关键词： chance constraints data-driven optimization stochastic programming large deviations

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A Decision-Dependent stochastic Approach for Joint Operation and Maintenance of Overhead Transmission Lines After Sandstorms

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IEEE SYSTEMS JOURNAL 2023年第1期17卷 1489-1500页

作者： Yin, Wenqian Feng, Shuanglei Hou, Jiazuo Qian, Chengchen Hou, Yunhe Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China HKU Shenzhen Inst Res & Innovat Shenzhen 518057 Peoples R China China Elect Power Res Inst Renewable Energy Res Ctr Beijing 200223 Peoples R China State Grid Shanghai Municipal Elect Power Co Shanghai 200122 Peoples R China

A sandstorm is one of the extreme weather events causing extensive damage to overhead transmission lines (OTLs). Due to potential postsandstorm insulator flashover, OTLs face high failure probabilities, and implementing in-time maintenance on OTLs is crucial to mitigate postsandstorm losses. Aiming at determining an optimal maintenance sequence of targeted OTLs, this article proposes a decision-dependent stochastic approach for the joint operation and maintenance of OTLs. First, considering the inherent dependency of the uncertain availability of OTLs on maintenance decisions, the multiperiod maintenance process of OTLs is modeled as a stochastic process with decision-dependent uncertainty (DDU). Second, a two-stage stochastic model with DDU is formulated, where the maintenance sequence and unit commitment decisions are made in the first stage and the second stage comprises scenariowise operation. Then, to tackle the coupling relation between decisions and DDU, a unique modeling transformation technique is adopted to convert the established decision-dependent stochastic model into a computationally efficient form. Case studies verify the effectiveness of the proposed method for postsandstorm maintenance scheduling.

关键词： Maintenance engineering Power transmission lines Insulators stochastic processes Wind power generation Generators Schedules Decision-dependent uncertainty joint operation and maintenance sandstorm stochastic programming wind power generation

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Operation and Planning of Energy Hubs Under Uncertainty-A Review of Mathematical Optimization Approaches

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IEEE ACCESS 2023年 11卷 7208-7228页

作者： Jasinski, Michal Najafi, Arsalan Homaee, Omid Kermani, Mostafa Tsaousoglou, Georgios Leonowicz, Zbigniew Novak, Tomas Wroclaw Univ Sci & Technol Fac Elect Engn Dept Elect Engn Fundamentals PL-50370 Wroclaw Poland VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci Dept Elect Power Engn Ostrava 70800 Czech Republic Chalmers Univ Technol Dept Elect Engn S-41296 Gothenburg Sweden Tech Univ Denmark Dept Appl Math & Comp Sci DK-2800 Lyngby Denmark

Co-designing energy systems across multiple energy carriers is increasingly attracting attention of researchers and policy makers, since it is a prominent means of increasing the overall efficiency of the energy sector. Special attention is attributed to the so-called energy hubs, i.e., clusters of energy communities featuring electricity, gas, heat, hydrogen, and also water generation and consumption facilities. Managing an energy hub entails dealing with multiple sources of uncertainty, such as renewable generation, energy demands, wholesale market prices, etc. Such uncertainties call for sophisticated decision-making techniques, with mathematical optimization being the predominant family of decision-making methods proposed in the literature of recent years. In this paper, we summarize, review, and categorize research studies that have applied mathematical optimization approaches towards making operational and planning decisions for energy hubs. Relevant methods include robust optimization, information gap decision theory, stochastic programming, and chance-constrained optimization. The results of the review indicate the increasing adoption of robust and, more recently, hybrid methods to deal with the multi-dimensional uncertainties of energy hubs.

关键词： Optimization Energy management Uncertainty Resistance heating Trigeneration programming Heat pumps stochastic processes Energy hub multi-carrier energy systems mathematical optimization robust optimization IGDT stochastic programming chance constrained uncertainty

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On the sample complexity of actor-critic method for reinforcement learning with function approximation

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MACHINE LEARNING 2023年第7期112卷 2433-2467页

作者： Kumar, Harshat Koppel, Alec Ribeiro, Alejandro Univ Penn Dept Elect & Syst Engn Philadelphia PA 19104 USA JPMorgan AI Res New York NY USA

Reinforcement learning, mathematically described by Markov Decision Problems, may be approached either through dynamic programming or policy search. Actor-critic algorithms combine the merits of both approaches by alternating between steps to estimate the value function and policy gradient updates. Due to the fact that the updates exhibit correlated noise and biased gradient updates, only the asymptotic behavior of actor-critic is known by connecting its behavior to dynamical systems. This work puts forth a new variant of actor-critic that employs Monte Carlo rollouts during the policy search updates, which results in controllable bias that depends on the number of critic evaluations. As a result, we are able to provide for the first time the convergence rate of actor-critic algorithms when the policy search step employs policy gradient, agnostic to the choice of policy evaluation technique. In particular, we establish conditions under which the sample complexity is comparable to stochastic gradient method for non-convex problems or slower as a result of the critic estimation error, which is the main complexity bottleneck. These results hold in continuous state and action spaces with linear function approximation for the value function. We then specialize these conceptual results to the case where the critic is estimated by Temporal Difference, Gradient Temporal Difference, and Accelerated Gradient Temporal Difference. These learning rates are then corroborated on a navigation problem involving an obstacle and the pendulum problem which provide insight into the interplay between optimization and generalization in reinforcement learning.

关键词： Actor-critic Reinforcement learning Markov decision process Non-convex optimization stochastic programming

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