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multi-stage airline scheduling problem with stochastic passenger demand and non-cruise times

TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2018年 114卷 39-67页

作者： Safak, Ozge Cavus, Ozlem Akturk, M. Selim Bilkent Univ Dept Ind Engn Ankara Turkey

We propose a three-stage stochastic programming model which determines flight timing, fleeting and routing decisions while considering the randomness of demand and non cruise times. Our model differs from the existing two-stage stochastic models by considering not only flight timing and potential passenger demand, but also expected operational expenses, such as fuel burn and carbon emission costs. We include aircraft cruise speed decisions to compensate for non-cruise time variability so as to satisfy the time requirements of the passenger connections. We handle nonlinear functions of fuel and emission costs associated with cruise speed adjustments by utilizing mixed integer second order cone programming. Because the three-stage stochastic model leads to a large decision tree and can be very time-consuming to solve optimally, we suggest a scenario group-wise decomposition algorithm to obtain lower and upper bounds for the optimal value of the proposed model. The lower and upper bounds are obtained by solving a number of group subproblems, which are similar to proposed multi-stage stochastic model defined over a reduced number of scenarios. We suggest a cutting plane algorithm, along with improvements, to efficiently solve each group subproblem. In the numerical experiments, we provide a significant cost savings over two-stage stochastic programming and deterministic approaches. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Airline scheduling Fleet assignment Aircraft routing Cruise speed control Conic integer programming multi-stage stochastic programming

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Optimal Operation of Critical Peak Pricing for an Energy Retailer Considering Balancing Costs

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ENERGIES 2019年第24期12卷

作者： Song, Hye Yoon Lee, Gyu Sub Yoon, Yong Tae Seoul Natl Univ Dept Elect & Comp Engn 1 Gwanak Ro Seoul 08826 South Korea

Recently, there have been frequent fluctuations in the wholesale prices of electricity following the increased penetration of renewable energy sources. Therefore, retailers face price risks caused by differences between wholesale prices and retail rates. As a hedging against price risk, retailers can utilize critical peak pricing (CPP) in a price-based program. This study proposes a novel multi-stage stochastic programming (MSSP) model for a retailer with self-generation photovoltaic facility to optimize both its bidding strategy and the CPP operation, in the face of several uncertainties. Using MSSP, decisions can be determined sequentially with realization of the uncertainties over time. Furthermore, to ensure a global optimum, a mixed integer non-linear programming is transformed into mixed integer linear programming through three linearization steps. In a numerical simulation, the effectiveness of the proposed MSSP model is compared with that of a mean-value deterministic model based on a rolling horizon method. We also investigate the optimal strategy of a retailer by changing various input parameters and perform a sensitivity analysis to assess the impacts of different uncertain parameters on the retailer's profit. Finally, the effect of the energy storage system on the proposed optimization problem is investigated.

关键词： critical peak pricing demand response retailer multi-stage stochastic programming rolling horizon method

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A multi-stage stochastic program for supply chain network redesign problem with price-dependent uncertain demands

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COMPUTERS & OPERATIONS RESEARCH 2018年 100卷 314-332页

作者： Fattahi, Mohammad Govindan, Kannan Keyvanshokooh, Esmaeil Shahrood Univ Technol Dept Ind Engn & Management Shahrood Iran Univ Southern Denmark Ctr Sustainable Supply Chain Engn Dept Technol & Innovat Campusvej 55 Odense Denmark Univ Michigan Dept Ind & Operat Engn Ann Arbor MI 48109 USA

In this paper, we address a multi-period supply chain network redesign problem in which customer zones have price-dependent stochastic demand for multiple products. A novel multi-stage stochastic program is proposed to simultaneously make tactical decisions including products' prices and strategic redesign decisions. Existing uncertainty in potential demands of customer zones is modeled through a finite set of scenarios, described in the form of a scenario tree. The scenarios are generated using a Latin Hypercube Sampling method and then a forward scenario construction technique is employed to create a suitable scenario tree. The multi-stage stochastic problem is formulated as a mixed-integer linear programming model and then Benders decomposition algorithm is applied for solving it. Numerical results demonstrate the significance of the stochastic model as well as the good performance of Benders algorithm. The scenario tree construction method is also evaluated in terms of out-of-sample and in-sample stability. Finally, several key managerial and practical insights in terms of pricing issues are highlighted. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Supply chain network redesign Pricing and revenue management multi-stage stochastic programming Non-anticipativity constraints Scenario reduction Benders decomposition

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

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IFAC-PapersOnLine 2019年第23期52卷 1-8页

作者： Gulten Gokayaz Selin D. Ahipasaoglu Stefano Galelli Pillar of Engineering Systems and Design Singapore University of Technology and Design 487372 Singapore

We investigate the potential use of seasonal streamflow forecasts for the realtime 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 Niño 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.

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

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Strategic Flexibility Analysis in Emergency Medical Service Systems using Decision Rules 15

Strategic Flexibility Analysis in Emergency Medical Service ...

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15th IEEE International Conference on Networking, Sensing and Control (ICNSC)

作者： Zhou, Yuan Cardin, Michel-Alexandre Zhang, Sizhe China Guangfa Bank Postdoctoral Res Stn Guangzhou Guangdong Peoples R China Natl Univ Singapore Dept Ind Syst Engn & Management Singapore Singapore Grab Dept Data Sci Singapore Singapore

ISBN: (纸本)9781538650530

The capacity deployment of emergency medical service systems has been studied over decades. The design problem is still challenging due to the long-term uncertainty like the change of demographics. A novel approach in the form of multi-stage stochastic programming that considers flexibility in engineering design could be a valuable alternative to EMS infrastructure systems design. This paper proposes a hybrid algorithm to the stochastic model to improve its computational efficiency. The new algorithm outperforms other alternative methods in the numerical analysis by 3-6% in terms of quality of the solution, and is at least four times faster than the default methods.

关键词： flexibility in engineering design emergency medical services multi-stage stochastic programming experimental algorithm metaheuristics

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Deadline-constrained stochastic Optimization of Resource Provisioning, for Cloud Users 8

Deadline-constrained Stochastic Optimization of Resource Pro...

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8th International Conference on Cloud Computing and Services Science (CLOSER)

作者： Tajvidi, Masoumeh Essam, Daryl Maher, Michael J. UNSW Sch Engn & Informat Technol Canberra ACT Australia Reasoning Res Inst Canberra ACT Australia

ISBN: (纸本)9789897582950

Acquiring computational resources dynamically, in response to demand, and only paying for the resources used, is the main benefit cloud computing may bring for cloud customers. However, this benefit can only be realized when customers can determine the right size of the resources required and allocate such resources in a cost-effective way. While resource over-provisioning can cost users more than necessary, resource under provisioning hurts application performance. To leverage the potential of clouds, a major concern, hence is optimizing the monetary cost spent in using cloud resources while ensuring the quality of service (QoS) and meeting deadlines. Unfortunately, there is still a lack of a good understanding of such cost optimization. The resource provisioning, from the cloud-user perspective, is a complicated optimization problem that consists of much uncertainty, as well as heterogeneity in its parameters. The variety of pricing plans further complicates this problem. There has been little work on solving this problem as it is in the real world and from the end users view. Most works relax the problem by not considering the dynamicity or heterogeneity of the environment. The aim of this paper, however, is optimizing the operational cost whilst guaranteeing performance and meeting deadline constraints, by taking into account parameters' uncertainty and heterogeneity, as well as considering all three available pricing plans, i.e. on-demand, reservation, and spot pricing. The experimental implementation using a real cloud workload shows that, however the proposed model has not the perfect foresight of future;results are very close or in many cases similar to, the full knowledge model. We also analyse the results of various users with different workload pattern, based on a k-means clustering.

关键词： Cloud Computing stochastic Optimization and Scheduling multi-stage stochastic programming Deadline Google Trace Data

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A Sample Average Approximation-Based Parallel Algorithm for Application Placement in Edge Computing Systems

A Sample Average Approximation-Based Parallel Algorithm for ...

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IEEE International Conference on Cloud Engineering (IEEE IC2E)

作者： Badri, Hossein Bahreini, Tayebeh Grosu, Daniel Yang, Kai Wayne State Univ Dept Ind & Syst Engn Detroit MI 48202 USA Wayne State Univ Dept Comp Sci Detroit MI USA

ISBN: (纸本)9781538650080

Mobile Edge Computing (MEC) is a new paradigm that aims at decreasing the response time of running mobile applications by offloading the component of the applications on the servers located at the edge of the network instead of on the cloud servers. In this paper, we address a very important problem in the management of MEC systems, that is, the problem of finding an efficient application placement on the edge servers such that the cost of execution is minimized. We develop a multi-stage stochastic programming model for the application placement problem in edge computing systems and design a novel parallel greedy algorithm based on the Sample Average Approximation method to solve it. We evaluate the performance of the proposed algorithm by conducting extensive experimental analysis using data extracted from a real-world dataset. The experimental results show that the proposed algorithm can solve the problem efficiently.

关键词： Mobile edge computing multi-stage stochastic programming sample average approximation parallel algorithm

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stochastic SHELTER SITE LOCATION UNDER multi-HAZARD SCENARIOS

STOCHASTIC SHELTER SITE LOCATION UNDER MULTI-HAZARD SCENARIO...

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作者： Eren Ozbay Bilkent University

学位级别：硕士

In some cases, natural disasters happen successively (e. g. a tsunami following an earthquake) in close proximity of each other, even if they are not correlated. This study locates shelter sites and allocates the affected population to the established set of shelters by considering the aftershock(s) following the initial earthquake, via a three-stage stochastic mixed-integer programming model. In each stage, before the uncertainty, which is the number of affected people, in the corresponding stage is resolved, shelters are established, and after the uncertainty is resolved, affected population is allocated to the established set of shelters. To manage the inherent risk related to the uncertainty, conditional value-at-risk is utilized as a risk measure in allocation of victims to the established set of shelters. Computational results on the Istanbul dataset are presented to emphasize the necessity of considering secondary disaster(s), along with a heuristic method to improve the solution times and qualities. During these computational analyses, it is observed that the original single-objective model poses some obstacles in parameter selection. As in humanitarian operations, choosing parameters may cause conflict of interests and hence may be criticized, a multi-objective framework is developed with various formulations. Some generalizations regarding the performance and applicability of the developed formulations are discussed and finally, another heuristic for the multi-objective formulation is presented to tackle the curse of dimensionality and improve the solution times.

关键词： Shelter Site Location Secondary Disasters multi-stage stochastic programming Conditional Value-at-Risk multi Objective programming

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Application of Scenario Tree Reduction Via Quadratic Process to Medium-Term Hydrothermal Scheduling Problem

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IEEE TRANSACTIONS ON POWER SYSTEMS 2017年第6期32卷 4351-4361页

作者： Beltran, Felipe de Oliveira, Welington Finardi, Erlon Cristian Univ Fed Santa Catarina BR-88040900 Florianopolis SC Brazil Univ Estado Rio de Janeiro BR-20550900 Rio De Janeiro Brazil Univ Fed Santa Catarina Dept Elect Engn BR-88040900 Florianopolis SC Brazil

In systems with hydraulic predominance, the medium-term hydrothermal scheduling problem (MTHS) is usually modeled as a stochastic programming model where the objective is to obtain an optimal operation policy over a planning horizon. Inflows follow a stochastic process that is approximated by a multistage scenario tree and a multistage stochastic programming model is employed to compute the generation policy. The difficulty in solving the MTHS depends on the size of the scenario tree. To obtain a small but representative scenario tree, we employ the scenario reduction algorithm of Kovacevic and Pichler with quadratic process distances defined by appropriate metrics exploiting relevant features of the problem. Numerical assessments of the MTHS problem, using a reduced hydrothermal configuration extracted from the Brazilian system, show that reduced trees obtained by eliminating 80% scenarios provide approximate solutions to the problem with less than 1% accuracy errors and CPU time reduction of around 90%.

关键词： Hydrothermal scheduling problem multi-stage stochastic programming scenario tree reduction

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Responsive and resilient supply chain network design under operational and disruption risks with delivery lead-time sensitive customers

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2017年 101卷 176-200页

作者： Fattahi, Mohammad Govindan, Kannan Keyvanshokooh, Esmaeil Univ Southern Denmark Dept Technol & Innovat Ctr Engn Operat Management Campusvej 55 Odense Denmark Shahrood Univ Technol Sch Ind Engn & Management Shahrood Iran Univ Michigan Dept Ind & Operat Engn Ann Arbor MI 48109 USA

We address a multi-period supply chain (SC) network design where demands of customers depend on facilities serving them based on their delivery lead-times. Potential customer demands are stochastic, and facilities' capacity varies randomly because of possible disruptions. Accordingly, we develop a multi-stage stochastic program, and model disruptions' effect on facilities' capacity. The SC responsiveness risk is limited and, to obtain a resilient network, both mitigation and contingency strategies are exploited. Computational results on a real-life case study and randomly generated problem instances demonstrate the model's applicability, risk-measurement policies' performance, and the influence of mitigation and contingency strategies on SC's resiliency. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Supply chain network design Risk management multi-stage stochastic programming Scenario reduction Mitigation and contingency strategies Disruption risk

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