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Predictive/Reactive Planning and Scheduling of a Surgical Suite with Emergency Patient Arrival

JOURNAL OF MEDICAL SYSTEMS 2016年第1期40卷 30-30页

作者： Heydari, Mehdi Soudi, Asie Iran Univ Sci & Technol Tehran Iran

This paper surveys the problem of predictive / reactive scheduling of an integrated operating theatre with two types of demand for surgery: 1) elective or known demand;2) emergency or uncertain demand. The stochastic arrival of emergency patients with uncertain surgery time enforces the scheduler to react to disruption and modify scheduling plan of elective patients. We focus on this predictive / reactive scheduling problem which has not been investigated in such way before. As in hospitals, at the time of occurrence a disruption in a surgical suite, the scheduler has not enough time to make the best decision;we propose a new approach based on two-stage stochastic programming model with recourse which determines the best recourse strategy in advance of any disruption occurrence. Using the proposed approach, the primary schedule is generated in such a way that it can absorb disruption with minimum effect on planned elective surgeries. For the first time in operating theatre planning, two new significant sets of performance measures comprising "robustness" and "stability" measures are considered in generation of primary schedule which will be shown to be of great importance in efficiency of surgical suite planning. Computational experiments performed on sets of generated problem based on the data obtained from a non-profit hospital. In order to demonstrate efficiency of the proposed method, computational results of the proposed approach are compared with classic approach.

关键词： Stability Robustness Predictive / reactive scheduling Emergency patient two-stage stochastic programming

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Towards stochastic Optimization-Based Electric Vehicle Penetration in a Novel Archipelago Microgrid

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SENSORS 2016年第6期16卷 907-907页

作者： Yang, Qingyu An, Dou Yu, Wei Tan, Zhengan Yang, Xinyu Xi An Jiao Tong Univ Sch Elect & Informat Engn SKLMSE Lab Xian 710049 Peoples R China Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Peoples R China Towson Univ Dept Comp & Informat Sci Towson MD 21252 USA

Due to the advantage of avoiding upstream disturbance and voltage fluctuation from a power transmission system, Islanded Micro-Grids (IMG) have attracted much attention. In this paper, we first propose a novel self-sufficient Cyber-Physical System (CPS) supported by Internet of Things (IoT) techniques, namely "archipelago micro-grid (MG)", which integrates the power grid and sensor networks to make the grid operation effective and is comprised of multiple MGs while disconnected with the utility grid. The Electric Vehicles (EVs) are used to replace a portion of Conventional Vehicles (CVs) to reduce CO2 emission and operation cost. Nonetheless, the intermittent nature and uncertainty of Renewable Energy Sources (RESs) remain a challenging issue in managing energy resources in the system. To address these issues, we formalize the optimal EV penetration problem as a two-stage stochastic Optimal Penetration (SOP) model, which aims to minimize the emission and operation cost in the system. Uncertainties coming from RESs (e.g., wind, solar, and load demand) are considered in the stochastic model and random parameters to represent those uncertainties are captured by the Monte Carlo-based method. To enable the reasonable deployment of EVs in each MGs, we develop two scheduling schemes, namely Unlimited Coordinated Scheme (UCS) and Limited Coordinated Scheme (LCS), respectively. An extensive simulation study based on a modified 9 bus system with three MGs has been carried out to show the effectiveness of our proposed schemes. The evaluation data indicates that our proposed strategy can reduce both the environmental pollution created by CO2 emissions and operation costs in UCS and LCS.

关键词： archipelago microgrid electric vehicles (EVs) two-stage stochastic programming scheduling

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A stochastic Investment Model for Renewable Generation in Distribution Systems

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2015年第4期6卷 1466-1474页

作者： Montoya-Bueno, Sergio Ignacio Munoz, Jose Contreras, Javier Univ Castilla La Mancha ETS Ingenieros Ind E-13071 Ciudad Real Spain

A model to obtain the optimal allocation and timing of renewable distributed generation under uncertainty is proposed as part of distribution expansion planning. The problem is formulated using a stochastic two-stage multiperiod mixed-integer linear programming ( MILP) model, where investment decisions are done in the first stage and scenario-dependent operation variables are solved in the second stage. The model aims to minimize renewable distributed generation ( photovoltaic and wind) investment costs, substation expansion investment cost, operation and maintenance costs, energy losses cost, and the cost of the power purchased from the transmission system. Active and reactive power flow equations are linearized and constraints include voltage limits, substation and feeders capacities, renewable generation limits, and investment constraints. The model is tested on a 34-bus system and conclusions are duly drawn.

关键词： Distributed generation planning (DGP) load levels mixed-integer linear programming (MILP) renewable energy sources (RES) two-stage stochastic programming

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A humanitarian logistics model for disaster relief operation considering network failure and standard relief time: A case study on San Francisco district

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2015年第Mar.期75卷 145-163页

作者： Ahmadi, Morteza Seifi, Abbas Tootooni, Behnam Amirkabir Univ Technol Dept Ind Engn & Management Syst Tehran Iran

We propose a multi-depot location-routing model considering network failure, multiple uses of vehicles, and standard relief time. The model determines the locations of local depots and routing for last mile distribution after an earthquake. The model is extended to a two-stage stochastic program with random travel time to ascertain the locations of distribution centers. Small instances have been solved to optimality in GAMS. A variable neighborhood search algorithm is devised to solve the deterministic model. Computational results of our case study show that the unsatisfied demands can be significantly reduced at the cost of higher number of local depots and vehicles. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Humanitarian logistics Transportation network failure two-stage stochastic programming Multi-depot location-routing problem GIS data

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A stochastic model for operating room planning under capacity constraints

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2015年第24期53卷 7252-7270页

作者： Jebali, Aida Diabat, Ali Masdar Inst Sci Technol Dept Engn Syst & Management Abu Dhabi U Arab Emirates

The present paper describes a two-stage stochastic programme for operating room planning that takes into account capacity constraints of three hospital resources: operating rooms, beds in the intensive care unit (ICU) and beds in the ward (or medium care unit). Operating room planning consists of deciding on the elective surgeries to perform over each period of the planning horizon, while considering uncertainties related to surgery duration as well as patient length of stay in the ICU and the ward. Sample average approximation is then used to solve the planning problem, aiming to minimise the sum of patient-related costs and expected resource utilisation costs. Computational experiments are conducted to evaluate the performance of the proposed solution method. The obtained results highlight the robustness of operating room plans obtained by a stochastic approach, in comparison to those generated by a deterministic approach, and the importance of considering both ICU and ward beds in operating room planning.

关键词： operating room planning capacity constraints two-stage stochastic programming sample average approximation

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Resilient supplier selection and order allocation under operational and disruption risks

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2015年 79卷 22-48页

作者： Torabi, S. A. Baghersad, M. Mansouri, S. A. Univ Tehran Sch Ind Engn Coll Engn Tehran Iran Virginia Polytech Inst & State Univ Dept Business Informat Technol Blacksburg VA 24061 USA Brunel Univ London Brunel Business Sch London England

This study proposes a bi-objective mixed possibilistic, two-stage stochastic programming model to address supplier selection and order allocation problem to build the resilient supply base under operational and disruption risks. The model accounts for epistemic uncertainty of critical data and applies several proactive strategies such as suppliers' business continuity plans, fortification of suppliers and contracting with backup suppliers to enhance the resilience level of the selected supply base. A five-step method is designed to solve the problem efficiently. The computational results demonstrate the significant impact of considering disruptive events on the selected supply base. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Supply chain disruption/risk management Resilient supplier base Business continuity management Possibilistic programming two-stage stochastic programming Differential evolution

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Planning operations before market launch for balancing time-to-market and risks in pharmaceutical supply chains

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2015年 161卷 129-139页

作者： Hansen, Klaus Reinholdt Nyhuus Grunow, Martin Tech Univ Denmark Dept Engn Management DK-2800 Lyngby Denmark Tech Univ Munich TUM Sch Management DE-80333 Munich Germany

Shorter product life cycles and the resulting increase in new product introductions boost the importance of product launch operations. In the pharmaceutical sector, product launch operations are of particular importance, as companies seek to reduce time-to-market to better exploit patent protection. Large volumes of product need to be ready to fill the downstream supply chain immediately at market launch. Building up the required inventory is, however, connected to several risks. In addition to the risk associated with the lack of demand information for a new product, there are several risks unique to the pharmaceutical sector. After approval by central authorities such as the FDA or EMA, a new drug still needs to receive market authorization, which is in most cases granted by some local authorities - in Europe, for example, by more than 30 national and regional bodies. The duration of these different market authorization processes as well as their outcomes (e.g. price and reimbursement levels, requirements of label or leaflet changes) are highly uncertain. We develop a two-stage stochastic model to support market launch preparation decisions. It trades off the costs of accepting these risks, for example by risk packaging before authorization, against the lost revenue caused by risk-averse operations. The model is applied to a case based on an empirical study. Our approach results in significant savings compared to current practices. We hereby provide an example of how quantitative methodology can provide valuable decision support for product launch operations, even when complex regulatory affairs need to be considered. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Market launch planning New product introduction two-stage stochastic programming Regulatory affairs

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AN ADAPTIVE PARTITION-BASED APPROACH FOR SOLVING two-stage stochastic PROGRAMS WITH FIXED RECOURSE

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SIAM JOURNAL ON OPTIMIZATION 2015年第3期25卷 1344-1367页

作者： Song, Yongjia Luedtke, James Virginia Commonwealth Univ Dept Stat Sci & Operat Res Richmond VA 23284 USA Univ Wisconsin Dept Ind & Syst Engn Madison WI 53706 USA

We study an adaptive partition-based approach for solving two-stage stochastic programs with fixed recourse. A partition-based formulation is a relaxation of the original stochastic program, and we study a finitely converging algorithm in which the partition is adaptively adjusted until it yields an optimal solution. A solution guided refinement strategy is developed to refine the partition by exploiting the relaxation solution obtained from a partition. In addition to refinement, we show that in the case of stochastic linear programs, it is possible to merge some components in a partition, without weakening the corresponding relaxation bound, thus allowing the partition size to be kept small. We also show that for stochastic linear programs with simple recourse, there exists a small partition that yields an optimal solution. The size of this partition is independent of the number of scenarios used in the model. Our computational results show that the proposed adaptive partition-based approach converges very fast to a small partition for our test instances. In particular, on our test instances the proposed approach outperforms basic versions of Benders decomposition and is competitive with the state-of-art methods such as the level method for stochastic linear programs with fixed recourse.

关键词： two-stage stochastic programming scenario partitions simple recourse

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Supply chain network optimization considering assembly line balancing and demand uncertainty

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2015年第10期53卷 2970-2994页

作者： Hamta, Nima Shirazi, M. Akbarpour Ghomi, S. M. T. Fatemi Behdad, Sara Amirkabir Univ Technol Dept Ind Engn Tehran Iran SUNY Buffalo Dept Ind & Syst Engn Buffalo NY 14260 USA

In supply chain optimisation problems, determining the location, number and capacity of facilities is concerned as strategic decisions, while mid-term and short-term decisions such as assembly policy, inventory levels and scheduling are considered as the tactical and operational decision levels. This paper addresses the optimisation of strategic and tactical decisions in the supply chain network design (SCND) under demand uncertainty. In this respect, a two-stage stochastic programming model is developed in which strategic location decisions are made in the first-stage, while the second-stage contains SCND problem and the assembly line balancing as a tactical decision. In the solution scheme, the combination of sample average approximation and Latin hypercube sampling methods is utilised to solve the developed two-stage mixed-integer stochastic programming model. Finally, computational experiments on randomly generated problem instances are presented to demonstrate the performance and power of developed model in handling uncertainty. Computational experiments showed that stochastic model yields better results compared with deterministic model in terms of objective function value, i.e. the sum of the first-stage costs and the expected second-stage costs. This issue proved that uncertainty would be a significant and fundamental element of developed model and improve the quality of solutions.

关键词： sample average approximation two-stage stochastic programming supply chain network design assembly line balancing

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Chance-constrained two-stage fractional optimization for planning regional energy systems in British Columbia, Canada

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APPLIED ENERGY 2015年 154卷 663-677页

作者： Zhou, Xiong Huang, Guohe Zhu, Hua Chen, Jiapei Xu, Jinliang Univ Regina Inst Energy Environm & Sustainable Communities Regina SK S4S 0A2 Canada North China Elect Power Univ Inst Energy Environm & Sustainabil Res UR NCEPU Beijing 102206 Peoples R China North China Elect Power Univ MOE Key Lab Reg Energy & Environm Syst Optimizat SC Resources & Environm Res Acad Beijing 102206 Peoples R China North China Elect Power Univ Sch Energy & Power Engn Beijing 102206 Peoples R China

In this study, a chance-constrained two-stage fractional optimization (CTFO) method is proposed for planning regional energy systems in the province of British Columbia, Canada. Through simultaneously integrating two-stage stochastic programming (TSP), chance-constrained programming (CCP), and mixed-integer linear programming (MILP) techniques into a linear fractional programming (LFP) framework, CTFO can effectively tackle multiobjective and capacity-expansion issues, as well as uncertainties described as probability distributions in the constraints and objectives. Based on the developed CTFO method, a chance-constrained two-stage fractional regional energy model (CTFO-REM) is developed in this study for supporting energy management in the province of British Columbia. Conflicts between environmental protection that maximizes the renewable energy resource utilization and economic development that minimizes the system cost can be effectively addressed through the CTFO-REM model without setting a factor for each objective. The results also indicate that the CTFO-REM model can facilitate dynamic analysis of the interactions among efficiency, policy scenarios, economic cost, and system reliability. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： two-stage stochastic programming Linear fractional programming Energy systems Multiple objectives Uncertainty

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