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(s, S) policy model for liner shipping refueling and sailing speed optimization problem

TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2015年第Apr.期76卷 76-92页

作者： Sheng, Xiaoming Chew, Ek Peng Lee, Loo Hay Natl Univ Singapore Dept Ind & Syst Engn Singapore 119260 Singapore Natl Univ Singapore Dept Ind & Syst Engn Singapore 117576 Singapore

This work expounds on implementing an effective dynamic (s, S) policy to solve a liner shipping refueling and speed determination problem under both bunker prices and consumption uncertainties. While solving an optimization model which incorporates a continuous distribution is extremely challenging, we use sample average approximation method to solve it. However, the resulting problem is still a very large-scaled problem. Therefore, we propose two variations of the progressive hedging algorithm to tackle it. Numerical results show that our solution method is efficient and, in addition, our dynamic (s, S) policy model has significant cost reduction potential compared to stationary models. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Liner shipping (s, S) policy SAA method progressive hedging algorithm Bunker prices

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Strategic reliable supply chain network design: determining tradeoffs between cost and risk

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2023年第11期61卷 3621-3633页

作者： Meng, Lingpeng He, Junliang Hu, Shaolong Han, Chuanfeng Shanghai Maritime Univ China Inst FTZ Supply Chain Room 427Container Supply Chain Bldg Shanghai Peoples R China Southwest Jiaotong Univ Sch Econ & Management Chengdu Peoples R China Tongji Univ Sch Econ & Management Shanghai Peoples R China

The design of a supply chain network (SCN) should address two conflicting objectives, i.e. inexpensive yet reliable operation. This paper presents models to choose supply facilities and determine distribution options while considering the expected risk of not serving customers attributed to supply shortages. The goal is to design a reliable network with a lower cost under traditional cost objectives. We formulate a stochastic mixed-integer biobjective programming model considering the protection of facilities and solve this problem with a progressive hedging algorithm (PHA). We examine the tradeoff curve between the cost and risk through a case study involving Shanghai in China. The results indicate that substantial improvements in reliability can be reached with a limited increase in the total cost.

关键词： Supply chain network disruption stochastic mixed-integer programming multiobjective optimisation progressive hedging algorithm

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On proximal augmented Lagrangian based decomposition methods for dual block-angular convex composite programming problems

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COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2023年第1期86卷 117-161页

作者： Ding, Kuang-Yu Lam, Xin-Yee Toh, Kim-Chuan Natl Univ Singapore Dept Math 10 Lower Kent Ridge Rd Singapore 119076 Singapore Natl Univ Singapore Inst Operat Res & Analyt 3 Res Link Singapore 117602 Singapore

We design inexact proximal augmented Lagrangian based decomposition methods for convex composite programming problems with dual block-angular structures. Our methods are particularly well suited for convex quadratic programming problems arising from stochastic programming models. The algorithmic framework is based on the application of the abstract inexact proximal ADMM framework developed in [Chen, Sun, Toh, Math. Prog. 161:237-270] to the dual of the target problem, as well as the application of the recently developed symmetric Gauss-Seidel decomposition theorem for solving a proximal multi-block convex composite quadratic programming problem. The key issues in our algorithmic design are firstly in designing appropriate proximal terms to decompose the computation of the dual variable blocks of the target problem to make the subproblems in each iteration easier to solve, and secondly to develop novel numerical schemes to solve the decomposed subproblems efficiently. Our inexact augmented Lagrangian based decomposition methods have guaranteed convergence. We present an application of the proposed algorithms to the doubly nonnegative relaxations of uncapacitated facility location problems, as well as to two-stage stochastic optimization problems. We conduct numerous numerical experiments to evaluate the performance of our method against state-of-the-art solvers such as Gurobi and MOSEK. Moreover, our proposed algorithms also compare favourably to the well-known progressive hedging algorithm of Rockafellar and Wets.

关键词： Dual block angular structure progressive hedging algorithm Symmetric Gauss-Seidel decomposition ADMM Proximal augmented Lagrangian method Convex composite programming

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Maximizing the utilization of existing grids for renewable energy integration

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RENEWABLE ENERGY 2022年第0期189卷 618-629页

作者： Ranjbar, Hossein Kazemi, Mostafa Amjady, Nima Zareipour, Hamidreza Hosseini, Seyed Hamid Sharif Univ Technol Ctr Excellence Power Syst Control & Management Dept Elect Engn Tehran Iran Univ Isfahan Dept Elect Engn Shahreza Campus Esfahan Iran Semnan Univ Dept Elect & Comp Engn Semnan Iran Univ Calgary Schulich Sch Engn Dept Elect & Comp Engn Calgary AB Canada

This paper presents a new model to maximize the utilization of existing transmission system infrastructure by optimally sizing and siting the future developments of variable renewable energy sources (VRES). The model tries to maximize the integration of VRES in power systems with minimum expected energy curtailment without relying on new investments in the transmission systems. The proposed model is formulated as a linear stochastic programming optimization problem where VRES output scenarios are generated such that their spatio-temporal correlations are maintained. The progressive hedging algorithm (PHA) with bundled scenarios is utilized to solve the proposed model for large-scale cases. The proposed model is tested on the modified Garver 6-bus and IEEE 118-bus test systems, and its results are compared with the results of the conventional VRES integration model. These results and comparisons illustrate the effectiveness of the proposed approach in terms of maximizing VRES integration and enhancing computational performance.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Generation and transmission planning progressive hedging algorithm Renewable energy integration Correlated uncertainty Copula method

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A multi-stage stochastic programming approach for supply chain risk mitigation via product substitution

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COMPUTERS & INDUSTRIAL ENGINEERING 2020年 149卷 106786-106786页

作者： Khalilabadi, Seyed Mahdi Ghorashi Zegordi, Seyed Hessameddin Nikbakhsh, Ehsan Tarbiat Modares Univ Fac Ind & Syst Engn Post Box 14117-13116 Tehran Iran

Trends like globalization, shorter product life-cycles, and cost reduction strategies in the global business environment have exposed many supply chains to various risks. Disruptions are one of the supply chain risks that can interrupt product flow, delay customer deliveries, and reduce supply chain revenues considerably. Prior planning for disruptions could greatly alleviate these consequences. A method to cope with disruptions is to use product substitution in the case of a product shortage. In this research, the supply chain of a livestock-drug distribution company in Iran, facing demand disruptions, has been chosen as a case study. For this purpose, a multi-stage stochastic integer programming model is proposed and solved using a customized progressive hedging algorithm. Moreover, the effect of uncertainty on the supply chain performance is measured using the value of the stochastic solution (VSS) and the expected value of perfect information (EVPI) metrics. Based on the different instances of the problem solved, the VSS metric shows that modeling and solving the proposed stochastic model could enhance the company profit by about 3.27 percent on average. In addition, the EVPI metric demonstrates that planning and investing in proactive demand management could enhance the profit up to 9.42 percent. Finally, analyses indicate that when dealing with increased demand uncertainty levels, the importance of using the proposed method increases as the profitability of the company decreases.

关键词： Supply chain Disruption Multi-stage stochastic integer programming progressive hedging algorithm

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Stochastic Check-in Employee Scheduling Problem

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IEEE ACCESS 2020年 8卷 80305-80317页

作者： Liu, Ming Liang, Bian Zhu, Maoran Chu, Chengbin Tongji Univ Sch Econ & Management Shanghai 200092 Peoples R China Fuzhou Univ Sch Econ & Management Fuzhou 350108 Peoples R China Univ Gustave Effeil ESIEE Paris F-93162 Noisy Le Grand France

This work addresses the problem of assigning airline check-in employees to tasks related to departing flights under uncertain circumstance at an international terminal of a large airport. The uncertainty of flight departing time mainly stems from delays and traffic control. Airlines specifics the minimum and target number of staff for each flight task in each time period. Taking uncertain tasks starting time into account and introducing risk measure (e.g., conditional value-at-risk) into optimization objective function, we present a risk-averse two-stage stochastic model with the mixed-integer recourse that minimizes both staffing costs and risk measurement to respond the time uncertainty. The first-stage decision is to make personal-area allocation plan;and the particular personnel-task scheduling decisions are determined in the second stage as the actual flight departing times are realized. To solve the NP-hard problem, a progressive hedging algorithm is proposed. Numerical experiments are carried out to demonstrate the efficiency and effectiveness of the proposed approach as compared with a sample average approximation method. Finally, we utilize a real-world case to illustrate how the proposed method can be used to obtain a near-optimal solution for practical problem.

关键词： Employee scheduling stochastic programming airport ground optimization risk measure progressive hedging algorithm

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Modeling electric vehicle charging station expansion with an integration of renewable energy and Vehicle-to-Grid sources

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2019年 128卷 251-279页

作者： Quddus, Md Abdul Kabli, Mohannad Marufuzzaman, Mohammad Mississippi State Univ Dept Ind & Syst Engn Starkville MS 39759 USA

This study proposes a novel formulation for designing and managing electric vehicle charging stations, considering both long-term planning decisions and short-term operational decisions over a pre-specified planning horizon and under stochastic power demand. To solve this challenging problem, we propose a hybrid algorithm that combines Sample Average Approximation with an enhanced progressive hedging algorithm. A case study, based on the road network around Washington, D.C., is presented to visualize and validate the modeling results. Results indicate that the electric vehicle power demand are satisfied primarily via the grid and Vehicleto-Grid when energy cost is low and solar power is unavailable.

关键词： Electric vehicles Charging station Renewable energy progressive hedging algorithm Rolling horizon heuristics

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A multi-stage stochastic programming model for relief distribution considering the state of road network

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TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2019年 123卷 64-87页

作者： Hu, Shaolong Han, Chuanfeng Dong, Zhijie Sasha Meng, Lingpeng Tongji Univ Sch Econ & Management 1239 Siping Rd Shanghai 200092 Peoples R China Texas State Univ 601 Univ Dr San Marcos TX 78666 USA Shanghai Maritime Univ China Inst FTZ Supply Chain 1550 Haigang Ave Shanghai 201306 Peoples R China

As an important aspect in disaster operations management, relief distribution has been challenged by lots of factors, such as unpredictable occurrence time, intensity and location of secondary disasters (e.g. aftershocks and landslides, which usually occur after an earthquake), and availability of vehicles. A multi-stage stochastic programming model is developed for disaster relief distribution with consideration of multiple types of vehicles, fluctuation of rental, and the state of road network. The state of road network is characterized using uncertain and dynamic road capacity. The scenario tree is employed to represent the uncertain and dynamic road capacity, and demonstrate the decision process of relief distribution. A progressive hedging algorithm (PHA) is proposed for solving the proposed model in large-scale size. Based on a real-world case of Yaan earthquake in China, numerical experiments are presented to study the applicability of the proposed model and demonstrate the effectiveness of the proposed PHA. Useful managerial insights are provided by conducting numerical analysis. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Emergency logistics Transportation Uncertain and dynamic road capacity Multi-stage stochastic programming progressive hedging algorithm

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A matheuristic algorithm for stochastic home health care planning

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第3期288卷 753-774页

作者： Nikzad, Erfaneh Bashiri, Mahdi Abbasi, Babak Shahed Univ Fac Engn Ind Engn Dept Tehran Iran Coventry Univ Fac Business & Law Sch Strategy & Leadership Coventry W Midlands England RMIT Univ Dept Informat Syst & Business Analyt Melbourne Vic 3000 Australia

Efficient human resource planning is the cornerstone of designing an effective home health care system. Human resource planning in home health care system consists of decisions on districting/zoning, staff dimensioning, resource assignment, scheduling, and routing. In this study, a two-stage stochastic mixed integer model is proposed that considers these decisions simultaneously. In the planning phase of a home health care system, the main uncertain parameters are travel and service times. Hence, the proposed model takes into account the uncertainty in travel and service times. Districting and staff dimensioning are defined as the first stage decisions, and assignment, scheduling, and routing are considered as the second stage decisions. A novel algorithm is developed for solving the proposed model. The algorithm consists of four phases and relies on a matheuristic-based method that calls on various mixed integer models. In addition, an algorithm based on the progressive hedging and Frank and Wolf algorithms is developed to reduce the computational time of the second phase of the proposed matheuristic algorithm. The efficiency and accuracy of the proposed algorithm are tested through several numerical experiments. The results prove the ability of the algorithm to solve large instances. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Home health care Matheuristic algorithm Districting Staff dimensioning progressive hedging algorithm Fix and optimize method

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Stochastic Look-Ahead Economic Dispatch With Variable Generation Resources

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IEEE TRANSACTIONS ON POWER SYSTEMS 2017年第1期32卷 17-29页

作者： Gu, Yingzhong Xie, Le GE Energy Consulting Schenectady NY 12345 USA Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA

With the continuing growth of renewable penetration in power systems, it becomes increasingly challenging to manage the operational uncertainty at near-real-time stage via deterministic scheduling approaches. This paper explores the necessity, benefits and implementability of applying stochastic programming to security constrained economic dispatch (SCED). We formulate a stochastic look-ahead economic dispatch (LAED-S) model for near-real-time power system operation. A concept of uncertainty responses is introduced to assess the power system economic risk with respect to net load uncertainties. This concept offers the system operator a simple yet effective gauge to decide whether a stochastic approach is more desirable than a deterministic one. For an efficient stochastic dispatch algorithm, an innovative hybrid computing architecture is proposed. It leverages the progressive hedging algorithm and the L-shaped method. Numerical experiments are conducted on a practical 5889-bus system to illustrate the effectiveness of the proposed approach.

关键词： L-shaped method optimal size reduction parallel computing power system economic risk progressive hedging algorithm renewable integration stochastic look-ahead dispatch uncertainty response

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