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IEEE Global Communications Conference (GLOBECOM)

作者： Ng, Wei Chong Lim, Wei Yang Bryan Ng, Jer Shyuan Sawadsitang, Suttinee Xiong, Zehui Niyato, Dusit Alibaba Grp Hangzhou Peoples R China Alibaba NTU JRI Singapore Singapore Chiang Mai Univ Coll Arts Media & Technol Chiang Mai Thailand Singapore Univ Technol & Design Singapore Singapore Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore

ISBN: (纸本)9781728181042

Today, modern unmanned aerial vehicles (UAVs) are equipped with increasingly advanced capabilities that can run applications enabled by machine learning techniques, which require computationally intensive operations such as matrix multiplications. Due to computation constraints, the UAVs can offload their computation tasks to edge servers. To mitigate stragglers, coded distributed computing (CDC) based offloading can be adopted. In this paper, we propose an Optimal Task Allocation Scheme (OTAS) based on stochastic integer programming with the objective to minimize energy consumption during computation offloading. The simulation results show that amid uncertainty of task completion, the energy consumption in the UAV network is minimized.

关键词： Unmanned Aerial Vehicles Coded Distributed Computing stochastic integer programming Task Allocation

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A stochastic programming Model for Service Scheduling with Uncertain Demand: an Application in Open-Access Clinic Scheduling

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Operations Research Forum 2021年第3期2卷 1-32页

作者： Fu, Yu Banerjee, Amarnath Department of Industrial and Systems Engineering Texas A&M University College Station TX United States

This paper addressed a scheduling problem which handles urgent tasks along with existing schedules. The uncertainties in this problem come from random process of existing schedules and unknown upcoming urgent tasks. To deal with the uncertainties, this paper proposes a stochastic integer programming (SIP) based aggregated online scheduling method. The method is illustrated through a study case from the outpatient clinic block-wise scheduling system which is under a hybrid scheduling policy combining regular far-in-advance policy and the open-access policy. The COVID-19 pandemic brings more challenges for the healthcare system including the fluctuations of service time and increasing urgent requests which this paper is designed for. The schedule framework designed in the method is comprehensive to accommodate various uncertainties in the healthcare service system, such as: no-shows, cancellations and punctuality of patients as well as preference of patients over time slots and physicians. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Cancellation No-show Punctuality Service scheduling stochastic integer programming Urgent scheduling

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A stochastic programming model of vaccine preparation and administration for seasonal influenza interventions

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2020年第4期17卷 2984-2997页

作者： Chen, Sheng-I Wu, Chia-Yuan Natl Chiao Tung Univ Dept Ind Engn & Management 1001Univ Rd Hsinchu 30010 Taiwan

This study considers the integration of vaccine preparation and administration decisions for seasonal influenza interventions. We examine actual vaccination activities of sharing multiple vaccine products and supplementary vaccinations. A two-stage stochastic program is formulated to determine the optimal ordering and allocation of vaccines under uncertain attack rates, vaccine efficacies, and demands. We present an algorithm based on the sample average approximation and warm-start solution to solve the stochastic integer program with continuous random variables. Furthermore, the optimal solution for the deterministic model using the expected value is analyzed and obtained directly. Our analysis compares the deterministic and stochastic solutions to assess the impact of uncertainties on the immunization outcomes and costs. The result shows that the stochastic programming model provides a more robust solution than the deterministic model, and uncertain characteristics should consider when making public health decisions.

关键词： seasonal influenza vaccination activities stochastic integer programming sample-average approximation

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FACETS OF THE stochastic NETWORK FLOW PROBLEM

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SIAM JOURNAL ON OPTIMIZATION 2020年第3期30卷 2355-2378页

作者： Estes, Alexander S. Ball, Michael O. Univ Minnesota Inst Math & Its Applicat Minneapolis MN 55455 USA Univ Maryland RH Smith Sch Business College Pk MD 20742 USA Univ Maryland Inst Syst Res College Pk MD 20742 USA

We study a type of network flow problem that we call the minimum-cost F-graph flow problem. This problem generalizes the typical minimum-cost network flow problem by allowing the underlying network to be a directed hypergraph rather than a directed graph. This new problem is pertinent because it can be used to model network flow problems that occur in a dynamic, stochastic environment. We formulate this problem as an integer program, and we study specifically the case where every node has at least one outgoing edge with no capacity constraint. We show that even with this restriction, the problem of finding an integral solution is NP-hard. However, we can show that all of the inequality constraints of our formulation are either facet-defining or redundant.

关键词： network flow stochastic integer programming F-graphs facet-defining inequalities directed hypergraphs

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Integrated stochastic optimization of stope design and long-term underground mine production scheduling

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RESOURCES POLICY 2022年 78卷

作者： Furtado e Faria, Matheus Dimitrakopoulos, Roussos Lopes Pinto, Claudio Lucio McGill Univ Dept Min & Mat Engn COSMO Stochast Mine Planning Lab 3450 Univ StFrank Dawson Adams BldgRoom 123A Montreal PQ H3A 0E8 Canada Univ Fed Minas Gerais Dept Min Engn Belo Horizonte MG Brazil

In the commonly used underground mine planning framework, mine design is first established and is the main input for the subsequent long-term mine production scheduling optimization. This sequential optimization approach cannot, therefore, capture the synergies between the involved planning steps, generating solutions that depart substantially from a global optimum. In addition, traditional underground mine planning methods for stope design and life-of-mine production scheduling are deterministic and are based on a single estimated ore -body model. As a result, the uncertainty and variability in grades and material types are not incorporated into the optimization process, resulting in designs that misrepresent all high-, medium-and low-grade stoping volumes and production schedules with misleading forecasts. A two-stage stochastic integer program (SIP) for integrated optimization of stope and development network designs and an underground mine production scheduling are proposed for the sublevel open stoping mining method under grade uncertainty and variability, quantified by a set of geostatistical simulations of the mineral deposit considered. Assuming a mine is accessed through a shaft, the model defines a schedule of levels and stopes, which aims to maximize the discounted revenues, minimize development costs, and manage the risk of not meeting production targets, while satisfying geotechnical con-straints. The practical aspects of the proposed method are presented through an application at an underground gold mine. A comparison with the stepwise framework, where the stope design is input to a subsequent opti-mization of the production schedule, shows that the proposed approach provides a physically different design and production schedule with an 11% higher net present value (NPV) and a life-of-mine that is two years shorter, affirming the advantages of the integrated optimization process.

关键词： Underground mining Stope design optimization Underground mine production scheduling stochastic integer programming Geological uncertainty Sublevel stoping

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The stochastic critical node problem over trees

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NETWORKS 2020年第3期76卷 381-401页

作者： Hosteins, Pierre Scatamacchia, Rosario Univ Gustave Eiffel COSYS ESTAS F-59666 Lille France Politecn Torino Dipartimento Ingn Gest & Prod Turin Italy

We tackle a stochastic version of the critical node problem (CNP) where the goal is to minimize the pairwise connectivity of a graph by attacking a subset of its nodes. In the stochastic setting considered, the outcome of attacks on nodes is uncertain. In our work, we focus on trees and demonstrate that over trees the stochastic CNP actually generalizes to the stochastic critical element detection problem where the outcome of attacks on edges is also uncertain. We prove the NP-completeness of the decision version of the problem when connection costs are one, while its deterministic counterpart was proved to be polynomial. We then derive a nonlinear model for the considered CNP version over trees and provide a corresponding linearization based on the concept ofprobability chains. Moreover, given the features of the derived linear model, we devise an exact Benders decomposition (BD) approach where we solve the slave subproblems analytically. A strength of our approach is that it does not rely on any statistical approximation such as the sample average approximation, which is commonly employed in stochastic optimization. We also introduce an approximation algorithm for the problem variant with unit connection costs and unit attack costs, and a specific integer linear model for the case where all the survival probabilities of the nodes in case of an attack are equal. Our methods are capable of solving relevant instances of the problem with hundreds of nodes within 1 hour of computational time. With this work, we aim to foster research on stochastic versions of the CNP, a problem tackled mainly in deterministic contexts so far. Interestingly, we also show a successful application of the concept of probability chains for problem linearizations significantly improved by decomposition methods such as the BD.

关键词： approximation algorithm Benders decomposition critical element detection critical node problem network interdiction probability chains stochastic integer programming trees

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Optimal Nursing Home Shift Scheduling: A Two-Stage stochastic programming Approach 16

Optimal Nursing Home Shift Scheduling: A Two-Stage Stochasti...

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16th IEEE International Conference on Automation Science and Engineering (CASE)

作者： Jiang, Shujin Li, Mingyang Hyer, Kathryn Kong, Nan Purdue Univ W Lafayette IN 47907 USA Univ S Florida Tampa FL 33620 USA

ISBN: (纸本)9781728169040

In this paper, we study a nursing home staff schedule optimization problem under resident demand uncertainty. We formulate a two-stage stochastic binary program accordingly, with objective to minimize the total labor cost (linearly related to work time) incurred by both regular registered nurses (RRNs) and part-time nurses (PTNs). As a significant constraint, we balance RRNs' total amount of work time with residents' total service need for every considered shift. Besides, we restrict feasible shift schedules based on common scheduling practice. We conduct a series of computational experiments to validate the proposed model. We discuss our optimal solutions under different compositions of residents in terms of their disabilities. In addition, we compare the total labor costs and an RRN scheduling flexibility index with the given optimal solution under different combinations of RRNs and PTNs. Our analysis offers an operational approach to set the minimum number of nurses on flexible shift schedules to cover uncertain the service needs while maintaining a minimum labor cost.

关键词： Nurse scheduling stochastic integer programming demand scenario generation Minimum Data Set (MDS)

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A Learning-Based Algorithm to Quickly Compute Good Primal Solutions for stochastic integer Programs 17th

A Learning-Based Algorithm to Quickly Compute Good Primal So...

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17th International Conference on the Integration of Constraint programming, Artificial Intelligence, and Operations Research (CPAIOR)

作者： Bengio, Yoshua Frejinger, Emma Lodi, Andrea Patel, Rahul Sankaranarayanan, Sriram Polytech Montreal Canada Excellence Res Chair Montreal PQ Canada Univ Montreal Dept Comp Sci & Operat Res Montreal PQ Canada Mila Quebec Artificial Intelligence Inst Montreal PQ Canada

ISBN: (纸本)9783030589424;9783030589417

We propose a novel approach using supervised learning to obtain near-optimal primal solutions for two-stage stochastic integer programming (2SIP) problems with constraints in the first and second stages. The goal of the algorithm is to predict a representative scenario (RS) for the problem such that, deterministically solving the 2SIP with the random realization equal to the RS, gives a near-optimal solution to the original 2SIP. Predicting an RS, instead of directly predicting a solution ensures first-stage feasibility of the solution. If the problem is known to have complete recourse, second-stage feasibility is also guaranteed. For computational testing, we learn to find an RS for a two-stage stochastic facility location problem with integer variables and linear constraints in both stages and consistently provide near-optimal solutions. Our computing times are very competitive with those of general-purpose integer programming solvers to achieve a similar solution quality.

关键词： stochastic integer programming Machine learning Heuristics

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stochastic optimisation of long-term block cave scheduling with hang-up and grade uncertainty

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INTERNATIONAL JOURNAL OF MINING RECLAMATION AND ENVIRONMENT 2019年第6期33卷 371-388页

作者： Dirkx, Rein Kazakidis, Vassilios Dimitrakopoulos, Roussos McGill Univ Dept Min & Mat Engn COSMO Stochast Mine Planning Lab Montreal PQ Canada Laurentian Univ Bharti Sch Engn Sudbury ON Canada

Mass mining methods provide alternatives in developing deeper and lower-grade mineral deposits. Consequently, block cave mining has been increasingly popular mass mining method, especially for large copper deposits currently being mined by open pit methods. This study adopts similar concepts as in stochastic open pit production planning to the planning of block cave mines, to evaluate their effectiveness in a different approach to mass mining. The main contribution of this study is the incorporation of the uncertainty of delays from hang-ups and grades directly into the production scheduling process of a cave mining operation. Hang-up uncertainty relates to the uncertainty linked to the occurrence of ore that clogs the production draw points. This clogging causes time delays in the production cycle leading to tonnage losses and additional costs. Grade uncertainty is incorporated by means of stochastic orebody simulations. Both uncertainty sources are directly linked to the extraction decisions and influence the optimized schedules. The proposed stochastic integer programming model is applied to the optimization of the long-term schedule of a large-scale, low-grade copper deposit by taking into account hang-up delays in block caving. The results of the optimization maximizing net present value clearly show the capability of the formulation to mitigate the effects of both grade and hang-up uncertainty.

关键词： Mine production scheduling block caving stochastic integer programming hang-up delays stochastic schedule optimisation technical risk management

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Two-stage absolute semi-deviation mean-risk stochastic programming: An application to the supply chain replenishment problem

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COMPUTERS & OPERATIONS RESEARCH 2019年 106卷 62-75页

作者： Venkatachalam, Saravanan Narayanan, Arunachalam Wayne State Univ Dept Ind & Syst Engn 4815 Fourth St Detroit MI 48202 USA Univ Houston Dept Decis & Informat Sci 334 Melcher Hall Houston TX 77204 USA

Conventionally, two-stage stochastic programs have been devised to minimize the expectation of recourse actions. However, in the presence of high data variability, the solutions from expected value models may not be robust, and hence weighted risk measures are introduced in the objective function. In this study, we consider a two-stage stochastic program with mean absolute semi-deviation (MASD) as a risk measure for an application in supply chain planning. Models with MASD lack block-angular structures;thus, they are not suitable for traditional decomposition algorithms and pose computational challenges. We therefore propose a heuristic procedure based on the expected excess risk measure for solving the model. Specifically, we propose a heuristic for a generic replenishment problem in supply chains with MASD risk measures. We evaluate the robustness of the MASD model solutions by comparing the fill rate for replenishment plans with the optimal solutions from deterministic and expected value models, and we demonstrate the efficacy of the heuristic based on extensive computational experiments. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： stochastic integer programming Mean absolute semi-deviation Risk measure Expected excess Replenishment problem Supply chain management

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