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A multi-objective production planning problem with the consideration of time and cost in clinical trials

EXPERT SYSTEMS WITH APPLICATIONS 2019年 124卷 25-38页

作者： Zhao, Hui Huang, Edward Dou, Runliang Wu, Kan Nanyang Technol Univ Sch Mech & Aerosp Engn Singapore Singapore George Mason Univ Syst Engn & Operat Res Dept Fairfax VA 22030 USA Tianjin Univ Coll Management & Econ Tianjin Peoples R China

Under increasingly challenging circumstances, pharmaceutical companies try to reduce the overproduction of clinical drugs, which is commonly seen in the pharmaceutical industry. When the overproduction is simply reduced without an efficient coordination of the inventories in the supply chain, the stock-outs at clinical sites and clinical trial delay can hardly be avoided. In this study, we propose a multi-objective model to optimize the production quantity, where the clinical trial duration and the total production and operational costs are minimized. The problem is formulated as a multi-stage stochastic programming model to capture the dynamic inventory allocation process in the supply chains. Since this problem's solving time and required memory can increase significantly with the increase of the stage and scenario numbers, the progressive hedging algorithm is applied as the solution approach in this paper. In the numerical experiments, we study this algorithm's performance and compare the solving efficiency with the direct solution approach. In addition, we identify the optimal production quantity of clinical drugs and give a discussion about the tradeoffs between the clinical trial delay and total cost. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Supply chain management Clinical trial Production and distribution planning multi-stage stochastic programming Progressive hedging algorithm

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Shelter site location under multi-hazard scenarios

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COMPUTERS & OPERATIONS RESEARCH 2019年 106卷 102-118页

作者： Ozbay, Eren Cavus, Ozlem Kara, Bahar Y. Univ Illinois Coll Business Adm Chicago IL 60607 USA Bilkent Univ Dept Ind Engn TR-06800 Ankara Turkey

Natural disasters may happen successively in close proximity of each other. This study locates shelter sites and allocates the affected population to the established set of shelters in cases of secondary disaster(s) following the main earthquake, via a three-stage stochastic mixed-integer programming model. In each stage, before the uncertainty in that stage, that is the number of victims seeking a shelter, is resolved, shelters are established, and after the uncertainty is resolved, affected population is allocated to the established set of shelters. The assumption on nearest allocation of victims to the shelter sites implies that the allocation decisions are finalized immediately after the location decisions, hence both location and allocation decisions can be considered simultaneously. And, when victims are allocated to the nearest established shelter sites, the site capacities may be exceeded. To manage the risk inherit to the demand uncertainty and capacities, conditional value-at-risk is utilized in modeling the risk involved in allocating victims to the established shelter sites. Computational results on Istanbul dataset are presented to emphasize the necessity of considering secondary disaster(s), along with a heuristic solution methodology to improve the solution qualities and times. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Shelter site location Secondary disasters multi-stage stochastic programming Conditional value-at-risk

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A multi-objective multi-stage stochastic model for project team formation under uncertainty in time requirements

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COMPUTERS & INDUSTRIAL ENGINEERING 2019年 132卷 153-165页

作者： Rahmanniyay, Fahimeh Yu, Andrew Junfang Seif, Javad Univ Tennessee Dept Ind & Syst Engn Knoxville TN 37996 USA

Team formation is one of the key stages in project management. The cost associated with the individuals who form a team and the quality of the tasks completed by the team are two of the main concerns in team formation problems. In this study, we consider simultaneous optimization of cost and quality in a team formation problem. Because these problems in a project usually arise in multi-period planning with uncertain parameters, a multi-objective multi-stage stochastic programming (MOMSP) model is developed and presented. Having sufficient scenarios to realistically represent real-world environments often leads to high computational complexity. Thus, we have adopted scenario cluster decomposition methods in our modeling and developed a heuristic algorithm. It is shown that the model can be solved for problems with practical size. The presented model and its solution methodology can be applied to different types of projects. In this study, a project that involves an overhaul of a set of aircraft is presented as a case study in which the goals are to minimize staffing costs and maximize the reliability of the aircraft by staffing workforce with high competency.

关键词： Team formation Competency multi-objective optimization multi-stage stochastic programming Overhaul projects Project management

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multi-stage stochastic framework for energy management of virtual power plants considering electric vehicles and demand response programs

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2020年 120卷

作者： Sheidaei, F. Ahmarinejad, A. Islamic Azad Univ Dept Elect Engn Yadegar E Imam Khomeini RAH Shahre Rey Branch Tehran Iran

Nowadays, the importance of the coordinated operation of power system assets has caused severe challenges that can be addressed through some recently emerged concepts, such as microgrids (MGs) and virtual power plants (VPPs). In this paper, a hierarchical model is proposed for simultaneous modeling of an MG scheduling and VPP energy management problems. Given the stochastic nature of the scheduling inputs, power production and also, load demand uncertainties are modeled using a scenario-based method. Therefore, the final model is presented as a stochastic mixed-integer linear programming (MILP) model. It is possible to cover the fluctuations at the minimum cost using demand response (DR) programs and electric vehicles (EVs). In addition, the possibility of power transaction between the adjacent MGs is analyzed in scheduling model, preventing unreal power exchanges. The simulation results indicate that the coordinated operation of MGs can improve load restoration, and reduce the load supply costs in each MG. It is also observed that the profit of the VPP would be higher in the case with the time-of-use (TOU) tariff compared to the cases with the real-time pricing (RTP) and critical peak pricing (CPP) mechanisms.

关键词： Virtual power plant Islanded microgrid multi-stage stochastic programming Electric vehicles Demand response Renewable energy resources

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Optimal water allocation and distribution management in irrigation networks under uncertainty by multi-stage stochastic case study: Irrigation and drainage networks of Maroon*

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IRRIGATION AND DRAINAGE 2020年第4期69卷 531-545页

作者： Behbahani, Leila Amanat Moghaddasi, Mahnoosh Ebrahimi, Hossein Babazadeh, Hossein Islamic Azad Univ Dept Water Sci & Engn Sci & Res Branch Tehran Iran Arak Univ Dept Agr Arak Iran Islamic Azad Univ Dept Water Scinces & Engn Shahr Ghods Branch Tehran Iran

In the present research the aim was to prepare a spatial and temporal optimization model for allocating irrigation water and cropping pattern in the Maroon irrigation and drainage networks, which are located in the province of Khoozestan, under uncertainty. Hydrometrical data were gathered from the Maroon network station. Meteorological data were prepared from Idenak station in Behbahan City during 2006-2016. Therefore a model was designed and developed to maximize the total gross benefit of the irrigation networks of Maroon. The presented model is capable of adjusting the optimal water distribution among networks, crops and their different growing stages, determining water shortage, allocating surplus water, and the gross benefit under three scenarios of arid, normal and wet years in two sub-models of actual intra-network optimal management and optimal management from the reservoir output to the inside network by applying multi-stage stochastic programming under uncertainty. The findings show the priority of the second sub-model over the first run. In the upper and lower bounds model it was illustrated that the cropping areas were increased by respectively 33 and 19%, and of course the benefit amount had an increase of 67 and 7% in the second sub-model. (c) 2020 John Wiley & Sons, Ltd.

关键词： irrigation water allocation multi-stage stochastic programming spatial and temporal optimization uncertainty

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From Probabilistic Seasonal Streamflow Forecasts to Optimal Reservoir Operations: A stochastic programming Approach 1

From Probabilistic Seasonal Streamflow Forecasts to Optimal ...

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1st IFAC Workshop on Control Methods for Water Resource Systems (CMWRS)

作者： Gokayaz, Gulten Ahipasaoglu, Selin D. Galelli, Stefano Singapore Univ Technol & Design Pillar Engn Syst & Design Singapore 487372 Singapore

We investigate the potential use of seasonal streamflow forecasts for the real-time operation of Angat reservoir (Philippines). The system is characterized by a strong intra- and inter-annual variability in the inflow process, which is further amplified by the El Nino Southern Oscillation (ENSO). We bank on the relationship between ENSO indices and local hydro-climatological processes to issue probabilistic streamflow forecasts (with a 3-month forecast horizon) and then integrate them within a multistage stochastic programming (MSP) approach. The rolling-horizon, forecast-informed scheme is adopted for the period 1968-2014 and benchmarked against deterministic optimization solutions with perfect forecasts, climatology, and mean forecasts. We also compare its performance with the current operating rules, and the operating rules obtained by solving a stochastic Dynamic programming problem. Results show that the MSP approach can help reduce the severity of failures during prolonged droughts caused by ENSO. Copyright (C) 2019. The Authors. Published by Elsevier Ltd. All rights reserved.

关键词： multi-stage stochastic programming Water reservoir operation Probabilistic seasonal streamflow forecasts El Nino Southern Oscillation

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Business Information Systems for the Cost/Energy Management of Water Distribution Networks: A Critical Appraisal of Alternative Optimization Strategies 21st

Business Information Systems for the Cost/Energy Management ...

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21st International Conference on Business Information Systems (BIS)

作者： Candelieri, Antonio Galuzzi, Bruno G. Giordani, Ilaria Perego, Riccardo Archetti, Francesco Univ Milano Bicocca Viale Sarca 336 I-20126 Milan Italy Consorzio Milano Ric Via Roberto Cozzi 53 I-20125 Milan Italy

ISBN: (纸本)9783030048495;9783030048488

The objective of this paper is to show how smart water networks enable new strategies for the energy cost management of the network, more precisely Pump Scheduling Optimization. This problem is traditionally solved using mathematical programming and, more recently, nature inspired meta-heuristics. The schedules obtained by these methods are typically not robust both respect to random variations in the water demand and the non-linear features of the model. The authors consider three alternative optimization strategies: (i) global optimization of black-box functions, based on a Gaussian model and the use of the hydraulic simulator (EPANET) to evaluate the objective function;(ii) multi stage stochastic programming, which models the stochastic evolution of the water demand through a scenario analysis to solve an equivalent large scale linear program;and finally (iii), Approximate Dynamic programming, also known as Reinforcement Learning. With reference to real life experimentation, the last two strategies offer more modeling flexibility, are demand responsive and typically result in more robust solutions (i.e. pump schedules) than mathematical programming. More specifically, Approximate Dynamic programming works on minimal modelling assumption and can effectively leverage on line data availability into robust on-line Pump Scheduling Optimization.

关键词： Pump Scheduling Optimization Bayesian Optimization multi-stage stochastic programming Reinforcement Learning

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multi-stage stochastic Planning of Wind Generation Considering Decision-Dependent Uncertainty in Wind Power Curve

Multi-Stage Stochastic Planning of Wind Generation Consideri...

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IEEE PES Innovative Smart Grid Technologies Europe (IEEE ISGT-Europe)

作者： Yin, Wenqian Xue, Yan Lei, Shunbo Hou, Yunhe Univ Hong Kong Hong Kong Peoples R China HKU Shenzhen Inst Res & Innovat Shenzhen Peoples R China State Grid Wuhan Power Supply Co Wuhan Peoples R China Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48109 USA

ISBN: (纸本)9781538682180

Variability and uncertainty in wind generation brings new challenges to power system planning. In addition to inaccurate prediction of wind speed, recent studies have revealed that the actual wind turbine power curve (WTPC) is also uncertain before installation and operation of wind turbine (WT), which has unneglected impacts on future operations and should also be taken into consideration when make wind generation expansion plan. The uncertainty in actual WTPC is decision-dependent since its resolve time depends on previous investment decisions to acquire more accurate information of WTPC. Under this background, in this paper, we propose a multi-stage stochastic planning model for wind generation, considering both the exogenous uncertainty in load and wind speed prediction and the decision-dependent uncertainty in WTPC. First, the uncertainty modeling is presented and the decision-dependency of uncertainty in actual WTPC is illustrated. Then the multi-stage wind generation planning model is established with objectives of minimizing total expected cost in planning horizon, followed by physical constraints of power system and non-anticipativity constraints among scenarios. Case study demonstrated advantages of the proposed multi-stage stochastic planning of wind generation.

关键词： wind generation decision-dependent uncertainty generation expansion planning multi-stage stochastic programming stochastic wind power curve

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A multi-stage stochastic program for the sustainable design of biofuel supply chain networks under biomass supply uncertainty and disruption risk: A real-life case study

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2018年 118卷 534-567页

作者： Fattahi, Mohammad Govindan, Kannan Shahrood Univ Technol Dept Ind Engn & Management Shahrood Iran Univ Southern Denmark Dept Technol & Innovat Ctr Sustainable Supply Chain Engn Campusvej 55 Odense Denmark

We focus on design and planning of a biofuel supply chain (SC) network from biomass to demand centers where biomass supply is stochastic and seasonal, and facilities' capacity varies randomly because of possible disruptions. We propose a cost-efficient multi-stage stochastic program in which the greenhouse gas emissions are mitigated and the social impact of the SC is considered. A rolling horizon procedure is presented to implement and evaluate the stochastic model solution. Computational results on a real-life case study demonstrate the proposed optimization tools' applicability as well as the effect of disruption risk and sustainability dimensions on biofuel SC planning.

关键词： Biofuel supply chain Sustainable development multi-stage stochastic programming Disruption risk Scenario tree construction Rolling horizon simulation

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A stochastic multi-stage fixed charge transportation problem: Worst-case analysis of the rolling horizon approach

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2018年第2期267卷 555-569页

作者： Bertazzi, Luca Maggioni, Francesca Univ Brescia Dept Econ & Management Contrada S Chiara 50 I-25122 Brescia Italy Univ Bergamo Dept Management Econ & Quantitat Methods Via Caniana 2 I-24127 Bergamo Italy

We introduce a stochastic multi-stage fixed charge transportation problem, in which a producer has to satisfy an uncertain demand within a deadline. At each time period, a fixed transportation cost can be paid to buy a transportation capacity. If the transportation capacity is used, the supplier also pays an uncertain unit transportation cost. A unit inventory cost is charged for the unsatisfied demand. The aim is to determine the transportation capacities to buy and the quantity to send at each time period in order to minimize the expected total cost. We prove that this problem is NP-hard, we propose a multi-stage stochastic optimization model formulation, and we determine optimal policies for particular cases, with deterministic unit transportation costs or demand and zero fixed costs. Furthermore, we provide the worst-case analysis of the rolling horizon approach, a classical heuristic approach for solving multi-stage stochastic programming models, applied to this NP-hard problem and to polynomially solvable particular cases. Worst-case results show that the rolling horizon approach can be very suboptimal. We also provide experimental results. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Logistics Fixed charge transportation problem multi-stage stochastic programming Rolling horizon Worst-case analysis

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