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Sequencing mixed-model assembly lines with risk-averse stochastic mixed-integer programming

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2022年第12期60卷 3774-3791页

作者： Guo, Ge Ryan, Sarah M. Univ Baltimore Dept Informat Syst & Decis Sci 1420 N Charles St Baltimore MD 21201 USA Iowa State Univ Dept Ind & Mfg Syst Engn Ames IA USA

Sequencing decisions in mixed-model assembly lines are complicated by various uncertainty factors. This paper addresses a real-life uncertainty factor identified in a manufacturer of large vehicles, by modelling unreliable part delivery and quality. Stochastic optimisation is applied to find sequencing policies that improve the on-time performance of its mixed-model assembly lines. As schedulers have different levels of risk aversion, a risk-averse programme is further presented to protect against the decision maker's chosen fraction of worst scenarios. Computational studies with progressive hedging as the solution method, and its lower bounding approach, demonstrate the high quality of resulting sequencing decisions and the time efficiency of the solution method.

关键词： Mixed-model assembly line sequencing part unavailability stochastic mixed-integer programming risk-averse optimisation progressive hedging algorithm

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

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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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Scenario cluster Lagrangean decomposition for risk averse in multistage stochastic optimization

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COMPUTERS & OPERATIONS RESEARCH 2017年第Sep.期85卷 154-171页

作者： Escudero, Laureano F. Araceli Garin, Maria Unzueta, Aitziber Univ Rey Juan Carlos Dpto Estadist & Invest Operat Madrid Spain Univ Basque Country UPV EHU Dpto Econ Aplicada 3 Bilbao Bizkaia Spain Univ Basque Country UPV EHU Dpto Matemat Aplicada Bilbao Bizkaia Spain

In this work we present a decomposition approach as a mixture of dualization and Lagrangean Relaxation for obtaining strong lower bounds on large-sized multistage stochastic mixed 0-1 programs with a time stochastic dominance risk averse measure. The objective function to minimize is a composite function of the expected cost along the time horizon over the scenarios and the penalization of the expected cost excess on reaching the set of thresholds under consideration, subject to a bound on the expected cost excess for each threshold and a bound on the failure probability of reaching it. The main differences with some other risk averse strategies are presented. The problem is represented by a mixture of the splitting representation up to a given stage, so-called break stage, and the compact representation for the other stages along the time horizon. The dualization of the nonanticipativity constraints for the node-based and risk averse variables up to the break stage and the Lagrangean Relaxation of the cross node constraints of the risk averse strategy result in a model that can be decomposed into a set of independent scenario cluster submodels. Three Lagrangean multipliers updating schemes as the Subgradient method, the Lagrangean progressive hedging algorithm and the Dynamic Constrained Cutting Plane are computationally compared. We have observed in the randomly generated instances we have experimented with that the smaller the number of clusters, the stronger the lower bound provided for the original problem (even, frequently, it is the solution value) obtained with an affordable computing time. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Multistage stochastic mixed 0-1 optimization Time stochastic dominance risk averse measure Cluster Lagrangean problem Subgradient method progressive hedging algorithm Dynamic Constrained Cutting Plane algorithm

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On Multistage Pseudomonotone Stochastic Variational Inequalities

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JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS 2023年第1期199卷 363-391页

作者： Cui, Xingbang Sun, Jie Zhang, Liping Tsinghua Univ Dept Math Sci Beijing 100084 Peoples R China Natl Univ Singapore Singapore 119245 Singapore Curtin Univ Perth WA 6845 Australia

This paper focuses on the solvability of multistage pseudomonotone stochastic variational inequalities (SVIs). On the one hand, some known solvability results of pseudomonotone deterministic variational inequalities cannot be directly extended to multistage pseudomonotone SVIs, so we construct the isomorphism between both and then establish theoretical results on the existence, convexity, boundedness and compactness of the solution set for multistage pseudomonotone SVIs via such an isomorphism. On the other hand, there does not exist a special algorithm for solving the multistage pseudomonotone SVIs so far, so we propose some sufficient conditions on the elicitability of multistage pseudomonotone SVIs, which opens the door for applying Rockafellar's elicited progressive hedging algorithm to solve such SVIs. Numerical results on solving a two-stage stochastic market optimization problem and randomly generated two-stage pseudomonotone linear complementarity problems are presented.

关键词： Pseudomonotonicity Multistage stochastic optimization Stochastic variational inequalities progressive hedging algorithm Elicited monotonicity

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Stochastic medical tourism problem with variable residence time considering gravity function

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RAIRO-OPERATIONS RESEARCH 2022年第3期56卷 1685-1716页

作者： Hasannia Kolaee, Mansoureh Mirzapour Al-e-Hashem, Seyed Mohammad Javad Amirkabir Univ Technol Tehran Polytech Dept Ind Engn & Management Syst Tehran Iran Rennes Sch Business 2 Rue Robert Arbrissel F-35065 Rennes France

Medical tourism is a recent term in healthcare logistics referring to travel of patients to receive health services and spending leisure time in a destination country. This transferring of patients leads to access high-quality health services which are cheaper than the original country of patients. During this travel, passengers who are the patients from another country, have this opportunity for complimentary entertainment packages (e.g., pleasure tours) in the aftercare period. As far as we know, the term of medical tourism is rarely studied in healthcare logistics and such services are highly important for developing countries. Such facts motivate us to develop a practical optimization model for the Medical Tour Centers (MTCs) for allocation of patients to hospitals in proper time and creation of memorable aftercare time for them. In this regard, the main aim of the proposed model is to maximize the total profit of MTCs through optimal allocation of patients to hospitals while considering an aftercare tour for the passengers. To make the proposed model more realistic, the optimal residence time in attractive places is simulated by a time-dependent gravity function. To address the uncertainty of medical tourism problem, a scenario-based two-stage stochastic optimization approach is extended to encounter different sources of uncertainty existing in surgical success, medical time, restoration restrictions, and the attraction of tourist places. Another novelty of this work is to propose an innovative hybrid meta-heuristic for large-scale instances, which is a combination of progressive hedging algorithm (PHA) and Genetic algorithm (GA). The model is analyzed by different test problems for small, medium, and large-scale instances where the hybrid meta-heuristic algorithm could solve them with an average gap of 3.4% in comparison with the commercial solver. The results revealed the importance of tourist opinion and public preferences in medical and pleasure tours, resp

关键词： Medical tourism orienteering problem two-stage stochastic optimization variable residence time progressive hedging algorithm

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A Stochastic Sequential Service Restoration Model for Distribution Systems Considering Microgrid Interconnection

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IEEE TRANSACTIONS ON SMART GRID 2024年第3期15卷 2396-2409页

作者： Cai, Sheng Xie, Yunyun Zhang, Menglin Jin, Xiaolong Wu, Qiuwei Guo, Jian Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Tianjin Univ Key Lab Smart Grid Minist Educ Tianjin 30072 Peoples R China Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Tsinghua Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China

Sequential service restoration (SSR) is essential to restore critical loads after the distribution system outage through gradually forming microgrids (MGs). However, high penetration of renewables induces low-inertia issues, especially in small-scale MGs, which could impede a large-size load restoration for ensuring the MG dynamic stability. To expedite the restoration process, we propose the interconnection of MGs as part of SSR. A developed SSR model is proposed to generate restoration sequence and time of MG interconnection operation. An auxiliary variable is introduced to indicate the system topology before and after MG interconnection, and then the maximum permissive step load in each MG is quantified. By this means, isolated MGs are formed gradually, and adjacent MGs can be reconnected to improve the primary response when picking up a large load. Moreover, to address uncertainties of renewables, the stochastic approach is used. And an improved progressive hedging algorithm (PHA) is utilized to solve the stochastic model. The original model is decomposed by bundles of scenarios, and penalty factors are adaptively updated to accelerate the PHA convergence. Simulation results show the effectiveness of the proposed SSR method in improving the restoration efficiency of distribution systems and reducing computational burdens.

关键词： Load modeling Renewable energy sources Topology Stochastic processes Uncertainty Switches Stability analysis Microgrid interconnection sequential service restoration renewables stochastic programming progressive hedging algorithm

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A multiobjective model for the green capacitated location-routing problem considering drivers' satisfaction and time window with uncertain demand

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2022年第4期29卷 5052-5071页

作者： Alamatsaz, Kayhan Ahmadi, Abbas Al-E-hashem, Seyed Mohammad Javad Mirzapour Amirkabir Univ Technol Dept Ind Engn & Management Syst 424 Hafez Ave Tehran Iran Concordia Univ Dept Bldg Civil & Environm Engn Montreal PQ Canada

Location-routing problem is a combination of facility location problem and vehicle routing problem. Numerous logistics problems have been extended to investigate greenhouse issues and costs related to the environmental impact of transportation activities. The green capacitated locating-routing problem (LRP) seeks to find the best places to establish facilities and simultaneously design routes to satisfy customers' stochastic demand with minimum total operating costs and total emitted carbon dioxide. In this paper, features that make the problem more practical are: considering time windows for customers and drivers, assuming city traffic congestion to calculate travel time along the edges, and dealing with capacitated warehouses and vehicles. The main novelty of this study is to combine the mentioned features and consider the problem closer to the real-world case uses. A mixed-integer programming model has been developed and scenario production method is used to solve this stochastic model. Since the problem belongs to the class of NP-hard problems, a combination of the progressive hedging algorithm (PHA) and genetic algorithm (GA) is considered to solve large-scale problems. It is the first time, as per our knowledge, that this combination is implemented on a green capacitated location routing problem (G-CLPR) and resulted in satisfactory solutions. Nondominating sorting genetic algorithm II (NSGA-II) and epsilon constraints methods are used to face with the bi-objective problem. Finally, sensitivity analysis is performed on the problem's input parameters and the efficiency of the proposed method is measured. Comparing the results of the proposed solution approach with those of the exact method indicates that the solution approach is computationally efficient in finding promising solutions.

关键词： Green location routing problem Time window Bi-objective optimization Genetic algorithm progressive hedging algorithm NSGA-II

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Expansion planning under uncertainty for hydrothermal systems with variable resources

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2018年第Dec.期103卷 644-651页

作者： Maluenda, Benjamin Negrete-Pincetic, Matias Olivares, Daniel E. Lorca, Alvaro Pontificia Univ Catolica Chile Dept Elect Engn OCM Lab Santiago 7820436 Chile Pontificia Univ Catolica Chile Dept Ind & Syst Engn Santiago 7820436 Chile Pontificia Univ Catolica Chile UC Energy Res Ctr Santiago 7820436 Chile

The significant integration of variable energy resources in power systems requires the consideration of greater operational details in capacity expansion planning processes. In hydrothermal systems, this motivates a more thorough assessment of the flexibility that hydroelectric reservoirs may provide to cope with variability. This work proposes a stochastic programming model for capacity expansion planning that considers representative days with hourly resolution and uncertainty in yearly water inflows. This allows capturing high resolution operational details, such as load and renewable profile chronologies, ramping constraints, and optimal reservoir management. In addition, long-term scenarios in the multi-year scale are included to obtain investment plans that yield reliable operations under extreme conditions, such as water inflow reduction due to climate change. The progressive hedging algorithm is applied to decompose the problem on a long-term scenario basis. Computational experiments on an actual power system show that the use of representative days significantly outperforms traditional load blocks to assess the flexibility that reservoir hydroelectric plants provide to the system, enabling an economic and reliable integration of variable resources. The results also illustrate the impacts of considering extreme long-term scenarios in the obtained investment plans.

关键词： Hydrothermal power systems Power system expansion planning progressive hedging algorithm Stochastic programming Long-term uncertainty

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Collaborative stochastic expansion planning of cyber-physical system considering extreme scenarios

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IET GENERATION TRANSMISSION & DISTRIBUTION 2023年第10期17卷 2419-2434页

作者： Zhang, Yiwei Li, Chengze Wan, Haiyang Shi, Qingxin Liu, Wenxia Xu, Yanhui North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewable Beijing Peoples R China North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewable Beijing 102206 Peoples R China

In this paper, a collaborative stochastic expansion planning model of cyber-physical system (CPS) with resilience constraints is proposed. The model can reduce the coupling risk and enhance the resilience under extreme scenarios, from the perspective of the structural/functional coupling between the physical and cyber systems. The model is to collaboratively optimize expansion transmission line siting, expansion communication fibre siting, and service routing distribution to minimize total investment cost. Constraints are divided into conventional constraints and resilience constraints. In the resilience constraints, the impact of cyber failures on the dispatch strategy is analyzed, and the load shedding in stochastic scenarios is limited to guarantee the resilience of the planning scheme. Particularly, to reach a stable solution for the stochastic planning, a mixed scenario set is proposed based on the probabilistic typhoon model and the complex network theory. Finally, the model is solved by the progressive hedging (PH) algorithm. The case studies of the IEEE RTS-79 test system demonstrate that, compared with the traditional independent planning model, the model proposed in this paper performs better in limiting load loss and enhancing resilience under extreme scenarios.

关键词： collaborative planning cyber physical system progressive hedging algorithm resilience stochastic planning cyber-physical systems power system planning stochastic programming

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A hybrid decomposition algorithm for designing a multi-modal transportation network under biomass supply uncertainty

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2016年第0期94卷 1-25页

作者： Poudel, Sushil Raj Marufuzzaman, Mohammad Bian, Linkan Mississippi State Univ Dept Ind & Syst Engn Starkville MS 39759 USA

This study presents a two-stage stochastic programming model for the design and management of a biomass co-firing supply chain network under feedstock supply uncertainty. To represent a more realistic case, we generate scenarios from prediction errors of the historical and forecasted biomass supply availabilities. We solve the model using a hybrid decomposition algorithm that combines Sample average approximation with an enhanced progressive hedging algorithm. The proposed algorithm is validated via a real-world case study using data from Mississippi and Alabama. Computational results indicate that the proposed algorithm is capable of producing high quality solutions in a reasonable amount of time. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Coal supply chain network Multi-modal facilities Sample average approximation progressive hedging algorithm Rolling horizon heuristics

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