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rollout strategies for sequential fault diagnosis

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS 2003年第1期33卷 86-99页

作者： Tu, F Pattipati, KR Univ Connecticut Dept Elect & Comp Engn Storrs CT 06269 USA

Test sequencing is a binary identification problem wherein one needs to develop a minimal expected cost testing procedure to determine which one of A finite number of possible failure sources, if any, is present. The problem can be solved optimally using dynamic programming or AND/OR graph search methods (AO*, CF, and HS). However, for large systems, the associated computation with dynamic programming or AND/OR graph search methods is substantial, due to the rapidly increasing number of OR nodes (denoting ambiguity states) and AND nodes (denoting tests) in the search, graph. In order to overcome the computational explosion, the one-step or multistep lookahead heuristic algorithms have been developed to solve the test sequencing problem. In this, paper, we propose to apply rollout strategies, which can be combined with the one-step or multistep lookahead heuristic algorithms, in a computationally more efficient manner than the optimal strategies, to obtain solutions superior to those using the one-step or multistep lookahead heuristic algorithms. The rollout strategies are illustrated and tested using a range of real-world systems. We show computational results, which suggest that the information-heuristic based rollout policies are significantly better than other rollout policies based on Huffman coding and entropy.

关键词： ANDIOR graph search AO entropy huffman coding information heuristic rollout algorithm sequential fault diagnosis test sequencing problem

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Optimization of Joint Replacement Policies for Multipart Systems by a rollout Framework

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2008年第4期5卷 609-619页

作者： Sun, Tao Zhao, Qianchuan Luh, Peter B. Tomastik, Robert N. Tsinghua Univ TNLIST Ctr Intelligent & Networked Syst Beijing 100084 Peoples R China Univ Connecticut Dept Elect & Comp Engn Storrs CT 06269 USA Pratt & Whitney E Hartford CT 06108 USA

Maintaining an asset with life-limited parts, e.g., a jet engine or an electric generator, may be costly. Certain costs, e.g., setup cost, can be shared if some parts of the asset are replaced jointly. Reducing the maintenance cost by good joint replacement policies is difficult in view of complicate asset dynamics, large problem sizes and the irregular optimal policy structures. This paper addresses these difficulties by using a rollout optimization framework. Based on a novel application of time-aggregated Markov decision processes, the "One-Stage Analysis" method is first developed. The policies obtained from the method are investigated and their effectiveness is demonstrated by examples. This method and the existing threshold method are then improved by the "rollout algorithm" for the total cost case and the average cost case. Based on ordinal optimization, it is shown that excessive simulations are not necessary for the rollout algorithm. Numerical testing demonstrates that the policies obtained by the rollout algorithms with either the "One-Stage Analysis" or the threshold method significantly outperform traditional threshold policies.

关键词： Joint replacement Markov decision processes multipart maintenance rollout algorithm time aggregation

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Novel models and algorithms for location assignment for outbound containers in container terminals

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第2期308卷 722-737页

作者： Zhang, Canrong Wang, Qi Yuan, Guoping Tsinghua Univ Res Ctr Modern Logist Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China

In this paper, we investigate the stacking problem of outbound containers. It focuses on developing an optimal stacking strategy to allocate the arriving containers in a preassigned yard bay, with the aim of reducing extra rehandles during later retrieval. Some studies employ dynamic programming models to tackle the problem, which can offer exact solutions. Following this line of research, we propose an alter-native dynamic programming model by redefining the bay representation and adjusting the formulations accordingly. The results show that the new model strikes a good balance between stacking quality and computational time. Considering that the dynamic programming model encounters difficulty in solving large-scale instances, we design two neural network algorithms that adopt a self-attention mechanism to enhance the fitting ability of the network. The neural network algorithms learn the decision patterns from the optimal results for small-scale instances obtained by dynamic programming and then are applied to solve large-scale instances. In addition, we propose a data-driven rollout algorithm by integrating the branching strategy and the aforementioned neural network algorithms to prevent the myopic drawback of using solely the neural network. To the best of our knowledge, this is the first paper that integrates the optimization method and machine learning techniques to tackle the container stacking problem. Finally, extensive computational experiments are performed to verify the effectiveness of the proposed models and algorithms.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Logistics Container stacking Dynamic programming Self-attention-based neural network rollout algorithm

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Scheduling Dispensing and Counting in Secondary Pharmaceutical Manufacturing

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AICHE JOURNAL 2009年第5期55卷 1161-1170页

作者： Ciavotta, Michele Meloni, Carlo Pranzo, Marco Univ Siena Dipartimento Ingn Informaz I-53100 Siena Italy Univ Politecn Valencia Inst Tecnol Informat E-46022 Valencia Spain Politecn Bari Dipartimento Elettrotecn & Elettron I-70125 Bari Italy

In this article, we describe a general methodology for operations scheduling in dispensing and counting departments of pharmaceutical manufacturing plants. The departments are modeled as a multiobjective parallel machines scheduling problem under a number of both standard and realistic constraints, such as release times, due dates and deadlines, particular sequence-dependent setup times, machine unavailabilities, and maximum campaign size. Main characteristics of the methodology are the modularity of the solution algorithms, the adaptability to different objectives and constraints to fulfill production requirements, the easiness of implementation, and the ability of incorporating human experience in the scheduling algorithms. Computational experience carried out on two case studies from a real pharmaceutical plant shows the effectiveness of this approach. (C) 2009 American Institute of Chemical Engineers AIChE J, 55:1161-1170, 2009

关键词： scheduling operations management pharmaceutical industry rollout algorithm pilot method

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On a multimode test sequencing problem

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 2004年第3期34卷 1490-1499页

作者： Ruan, S Tu, F Pattipati, KR Patterson-Hine, A Univ Connecticut Elect & Comp Engn Dept Storrs CT 06269 USA Qualtech Syst Inc Wethersfield CT 06109 USA NASA Ames Res Ctr Moffett Field CA 94035 USA

Test sequencing is a binary identification problem wherein one needs to develop a minimal expected cost test procedure to determine which one of a finite number of possible failure states, if any, is present. In this paper, we consider a multimode test sequencing (MMTS) problem, in which tests are distributed among multiple modes and additional transition costs will be incurred if a test sequence involves mode changes. The multimode test sequencing problem can be solved optimally via dynamic programming or AND/OR graph search methods. However, for large systems, the associated computation with dynamic programming or AND/OR graph search methods is substantial due to the rapidly increasing number of OR nodes (denoting ambiguity states and current modes) and AND nodes (denoting next modes and tests) in the search graph. In order to overcome the computational explosion, we propose to apply three heuristic algorithms based on information gain: information gain heuristic (IG), mode capability evaluation (MC), and mode capability evaluation With limited exploration of depth and degree of mode Isolation (MCLEI). We also propose to apply rollout strategies, which are guaranteed to improve the performance of heuristics, as long as the heuristics are sequentially improving. We show computational results, which suggest that the information-heuristic based rollout policies are significantly better than traditional information gain heuristic. We also show that among the three information heuristics proposed, MCLEI achieves the best tradeoff between optimality and computational complexity.

关键词： AND/OR graph search AO entropy huffman coding information heuristic rollout algorithm sequential fault diagnosis test sequencing problem

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Patient scheduling in hemodialysis service

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JOURNAL OF COMBINATORIAL OPTIMIZATION 2019年第1期37卷 337-362页

作者： Liu, Zhenyuan Lu, Jiongbing Liu, Zaisheng Liao, Guangrui Zhang, Hao Howard Dong, Junwu Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Hubei Peoples R China Educ Minist Image Proc & Intelligent Control Key Lab Wuhan 430074 Hubei Peoples R China Univ Maryland Sch Med Dept Radiat Oncol Baltimore MD 21201 USA Pu Ai Hosp Wuhan 430074 Hubei Peoples R China

The patient scheduling presents a number of operations management challenges in hemodialysis service center. The homogeneity of the break time between treatments, satisfying the patients preferences on time, space and equipment and the multi-function dialysis devices make for an interesting and complex scheduling problem that could benefit from computerized decision support. In this paper, patient scheduling problem in hemodialysis service is formulated as a synthetic-objective optimization model combined with several criteria on minimizing the gross utilization cost of devices, the number of night treatment, satisfying the patients preferences and the equilibrium of the devices. A basic heuristics and a rollout algorithm based on the heuristics are developed for solving the problem where three levels of treatment schedule sets are constructed one by one. The performances of the rollout algorithm and the basic heuristics are compared on the real cases. Computational results show that significant improvement of patients degree of satisfaction can be achieved with the rollout algorithm while simultaneously considering to reduce the number of night shifts.

关键词： Patient scheduling Hemodialysis service Synthetic-objective optimization Heuristics rollout algorithm

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Long-Term Scheduling for Cascaded Hydro Energy Systems With Annual Water Consumption and Release Constraints

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2010年第4期7卷 969-976页

作者： Zhao, Yanjia Chen, Xi Jia, Qing-Shan Guan, Xiaohong Zhang, Shuanghu Jiang, Yunzhong Tsinghua Univ Dept Automat TNList Ctr Intelligent & Networked Syst Beijing 100084 Peoples R China Xi An Jiao Tong Univ SKLMS Lab Xian 710049 Peoples R China Xi An Jiao Tong Univ MOE KLINNS Lab Xian 710049 Peoples R China Chinese Inst Water Resources & Hydropower Res Beijing 100038 Peoples R China

Long-term scheduling for cascaded hydro energy systems is very important for low carbon energy production. It aims at determining the water release over a planning horizon to meet water resource requirements and the system demands for electric power. The problem is challenging in view of the complicated and stochastic system dynamics, non-linear marginal cost, coupled hydraulic constraints, and the large problem size. In this paper we formulate the long-term scheduling problem of cascaded hydro energy systems with annual consumption and release constraints as a finite horizon constrained Markov decision process (CMDP), and develop a new rollout algorithm to optimize the policies. Numerical results demonstrate the effectiveness and the efficiency of the formulation and the new algorithm. Note to Practitioners-Long-term scheduling of cascaded hydro energy systems is an important planning activity in the reservoir management and electric industry. This paper formulates a long-term scheduling problem for cascaded hydro energy systems with annual water consumption and release constraints as a finite horizon CMDP, and develops a new rollout algorithm to deal with the large problem size. Motivated by a practical project on Yellow River in north China, this work has extended applicability since the new algorithm can start with any feasible policies currently used in practical systems and improve their performance. Numerical results demonstrate the effectiveness and efficiency of the formulation and the new algorithm.

关键词： Cascaded hydro energy systems constrained Markov decision process (CMDP) long-term scheduling rollout algorithm

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Delay Sensitive Communications over Cognitive Radio Networks

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2012年第4期11卷 1402-1411页

作者： Wang, Feng Huang, Jianwei Zhao, Yuping Chinese Univ Hong Kong Dept Informat Engn Shatin Hong Kong Peoples R China Peking Univ State Key Lab Adv Opt Commun Syst & Networks Beijing 100871 Peoples R China China Acad Space Technol Beijing Space Technol Dev & Test Ctr Beijing 100094 Peoples R China

Supporting the quality of service of unlicensed users in cognitive radio networks is very challenging, mainly due to the dynamic resource availability induced by the licensed users' activities. In this paper, we derive the optimal admission control and channel allocation decisions in cognitive overlay networks to support delay sensitive communications of unlicensed users. We formulate it as a Markov decision process problem, and solve it by transforming the original formulation into a stochastic shortest path problem. We then propose a simple heuristic control policy, which includes a threshold-based admission control scheme and and a largest-delay-first channel allocation scheme, and prove the optimality of the largest-delay-first channel allocation scheme. We further propose an improved policy using the rollout algorithm. By comparing the performance of both proposed policies with the upper-bound of the maximum revenue, we show that our policies achieve close-to-optimal performances with low complexities.

关键词： Admission control Markov decision process Bellman's equation rollout algorithm cognitive radio networks spectrum overlay

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Solving the Dynamic Vehicle Routing Problem Under Traffic Congestion

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2016年第8期17卷 2367-2380页

作者： Kim, Gitae Ong, Yew Soon Cheong, Taesu Tan, Puay Siew Hanbat Natl Univ Dept Ind Management Engn Daejeon 305719 South Korea Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore Nanyang Technol Univ Sch Comp Engn Computat Intelligence Res Lab Singapore 639798 Singapore Nanyang Technol Univ Joint Lab Collaborat Res Programme Complex Syst SIMTech Singapore 639798 Singapore Korea Univ Sch Ind Management Engn Seoul 136713 South Korea ASTAR Singapore Inst Mfg Technol Singapore 138632 Singapore

This paper proposes a dynamic vehicle routing problem (DVRP) model with nonstationary stochastic travel times under traffic congestion. Depending on the traffic conditions, the travel time between two nodes, particularly in a city, may not be proportional to distance and changes both dynamically and stochastically over time. Considering this environment, we propose a Markov decision process model to solve this problem and adopt a rollout-based approach to the solution, using approximate dynamic programming to avoid the curse of dimensionality. We also investigate how to estimate the probability distribution of travel times of arcs which, reflecting reality, are considered to consist of multiple road segments. Experiments are conducted using a real-world problem faced by Singapore logistics/delivery company and authentic road traffic information.

关键词： Dynamic vehicle routing problem approximate dynamic programming uncertain travel times rollout algorithm

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Near-optimal planning using approximate dynamic programming to enhance post-hazard community resilience management

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2019年 181卷 116-126页

作者： Nozhati, Saeed Sarkale, Yugandhar Ellingwood, Bruce Chong, Edwin K. P. Mahmoud, Hussam Colorado State Univ Dept Civil & Environm Engn Ft Collins CO 80523 USA Colorado State Univ Dept Elect & Comp Engn Ft Collins CO 80523 USA Colorado State Univ Dept Math Ft Collins CO 80523 USA

The lack of a comprehensive decision-making approach at the community level is an important problem that warrants immediate attention. Network-level decision-making algorithms need to solve large-scale optimization problems that pose computational challenges. The complexity of the optimization problems increases when various sources of uncertainty are considered. This research introduces a sequential discrete optimization approach, as a decision-making framework at the community level for recovery management. The proposed mathematical approach leverages approximate dynamic programming along with heuristics for the determination of recovery actions. Our methodology overcomes the curse of dimensionality and manages multi-state, large-scale infrastructure systems following disasters. We also provide computational results showing that our methodology not only incorporates recovery policies of responsible public and private entities within the community but also substantially enhances the performance of their underlying strategies with limited resources. The methodology can be implemented efficiently to identify near-optimal recovery decisions following a severe earthquake based on multiple objectives for an electrical power network of a testbed community coarsely modeled after Gilroy, California, United States. The proposed optimization method supports risk-informed community decision makers within chaotic post-hazard circumstances.

关键词： Approximate dynamic programming Combinatorial optimization Community resilience Electrical power network rollout algorithm

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