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An efficient m-step lookahead rollout algorithm for profit-oriented selective disassembly sequence planning with operation stochastic failure

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 145卷

作者： Ren, Yaping Meng, Leilei Tian, Guangdong Li, Zhiwu Li, Yun Beijing Inst Technol Zhuhai 519088 Peoples R China Greater Bay Area Innovat Res Inst BIT Zhuhai 519088 Peoples R China Liaocheng Univ Sch Comp Sci Liaocheng 252000 Peoples R China Beijing Univ Civil Engn & Architecture Sch Mech Elect & Vehicle Engn Beijing 100044 Peoples R China Xidian Univ Sch Electromech Engn Xian 710071 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Taipa 999078 Peoples R China Univ Elect Sci & Technol China Ind Artificial Intelligence Ctr Inst Adv Study Shenzhen 518110 Peoples R China

To fully reclaim large amounts of used electro-mechanic products, an effective and high-quality disassembly sequence is crucial that separates a product into parts/components one-by-one for recovery. However, it becomes rather challenging to efficiently determine the optimal/near-optimal disassembly sequence for used products since the quality conditions of used products are uncertain and disassembly operations that constitute the disassembly sequence might fail. In this paper, the stochastic failure characteristics of disassembly operations is taken into account and a selective disassembly sequence planning is studied for maximizing the recovering profit of used products. First, we formally model a profit-oriented selective disassembly sequence planning with operation stochastic failure (PSDSP-OSF), in which the expected recovering profit of a disassembly sequence is formulated based on the stochastic failure characteristics of disassembly operations. Then, we simplify and reformulate PSDSP-OSF according to the disassembly rule. A m-step lookahead rollout algorithm is proposed to efficiently solve PSDSP-OSF, in which every disassembly operation is selected by a lookahead decision rule that looking ahead from the current decision stage to future m decision stages. Finally, three different scales of products are selected as case studies and two well-known existing methods are employed to compare with the proposed approach. The computational results demonstrate that our proposed approach is significantly superior to the existing methods in solution quality, computational efficiency, and algorithm robustness.

关键词： Selective disassembly planning Profit-oriented Operation stochastic failure Lookahead decision rule rollout algorithm

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rollout algorithm for light-weight physical-layer authentication in cognitive radio networks

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IET COMMUNICATIONS 2020年第18期14卷 3128-3134页

作者： Yan, Shengnan Wang, Xiaoding Xu, Li Fujian Normal Univ Coll Math & Informat Fuzhou 350117 Fujian Peoples R China Fujian Normal Univ Key Lab Network Secur & Cryptol Fuzhou 350117 Fujian Peoples R China

Cognitive radio networks (CRNs) are vulnerable to spoofing attacks due to their wireless and cognitive nature. Since the traditional cryptographic authentication can hardly prevent such attacks in CRNs, the physical-layer authentication has been investigated for recent years. To achieve a light-weight physical-layer authentication, a rollout partially observable Markov decision process-based algorithm, named RoPOMDP, is proposed in this study. In general, RoPOMDP formulates the physical-layer authentication as a zero-sum game, based on which a hypothesis test upon channel vectors is developed. That allows us to design the gains for both spoofers and receivers based on Bayesian risks for the game, in which the spoofing attack probability is predicted by a non-linear function approximation utilising v-support vector regression. Then, a RoPOMDP is employed to estimate the optimal threshold for the test statistic such that spoofing attacks can be detected. The theoretical analysis and simulations indicate that: (i) RoPOMDP improves the spoofing detection accuracy;(ii) as a light-weight algorithm, the complexity of RoPOMDP is lower than contemporary ones.

关键词： Markov processes regression analysis cognitive radio telecommunication security function approximation authorisation Bayes methods rollout algorithm light-weight physical-layer authentication cognitive radio networks CRNs wireless nature cognitive nature cryptographic authentication RoPOMDP spoofing attack probability nonlinear function approximation Markov decision process-based algorithm v-support vector regression

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A cooperative parallel rollout algorithm for the sequential ordering problem

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PARALLEL COMPUTING 2003年第5期29卷 663-677页

作者： Guerriero, F Mancini, M Univ Calabria Dipartimento Elettron Informat & Sistemist I-87030 Arcavacata Di Rende CS Italy

In this paper we deal with the solution of the sequential ordering problem (SOP) in parallel environments. In particular, we present a parallel version of the rollout algorithm, an innovative heuristic method for solving NP-Hard combinatorial optimization problems, recently proposed by Bertsekas et al. [J. Heuristics 3 (1997) 245]. The proposed parallel algorithm has been designed by considering a cooperative multi-thread parallelization strategy, where several threads visit different portions of the solution space independently and periodically exchange information about the solutions found during the search. Such an approach allows not only to speed up the convergence to the best solution, but to find also better solutions, taking roughly the same computation time. The performance of the proposed parallel RH has been evaluated on a cluster of PCs, by considering a set of test problems taken from the TSPLIB [Orsa J. Comput. 3 (1991) 376]. (C) 2003 Elsevier Science B.V. All rights reserved.

关键词： rollout algorithm metaheuristic parallelism cooperative approach sequential ordering problem

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Speeding up a rollout algorithm for complex parallel machine scheduling

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2016年第16期54卷 4993-5009页

作者： Ciavotta, Michele Meloni, Carlo Pranzo, Marco Politecn Milan Dipartimento Elettron Informaz & Bioingn Milan Italy Politecn Bari Dipartimento Ingn Elettr & Informaz Bari Italy Univ Siena Dipartimento Ingn Informaz & Sci Matemat Siena Italy CNR Ist Applicaz Calcolo M Picone Bari Italy

rollout methodology is a constructive metaheuristic algorithm and its main characteristics are its modularity, the adaptability to different objectives and constraints and the easiness of implementation. Multi-heuristic rollout extends the rollout by incorporating several constructive heuristics in the rollout framework and it is able to easily incorporate human experience inside its research patterns to fulfil complex requirements dictated by the application at hand. However, a drawback for both rollout and multi-heuristic rollout is often represented by the required computation time. This paper proposes some alternatives of the full multi-heuristic rollout algorithm aimed at improving the efficiency by reducing the computational effort while preserving the effectiveness. Namely, we propose dynamic heuristics pruning and candidates reduction strategies. As illustrative case studies, we analyse complex deterministic identical parallel machine scheduling problems showing how rollout procedures can be used to tackle several additional constraints arising in real contexts. More specifically, we considered both standard (batch production, family set-ups, release, due dates, etc.) and non-standard (machine unavailabilities, maximum campaign size) scheduling constraints. An extensive campaign of computational experiments shows the behaviour of the multi-heuristic rollout approach and the effectiveness of the different proposed speed-up methods.

关键词： rollout algorithm Pilot method job scheduling parallel machines

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A rollout algorithm framework for heuristic solutions to finite-horizon stochastic dynamic programs

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2017年第1期258卷 216-229页

作者： Goodson, Justin C. Thomas, Barrett W. Ohlmann, Jeffrey W. St Louis Univ John Cook Sch Business Dept Operat & Informat Technol Management 3674 Lindell Blvd St Louis MO 63108 USA Univ Iowa Tippie Coll Business Dept Management Sci Iowa City IA 52242 USA

rollout algorithms have enjoyed success across a variety of domains as heuristic solution procedures for stochastic dynamic programs (SDPs). However, because most rollout implementations are closely tied to specific problems, the visibility of advances in rollout methods is limited, thereby making it difficult for researchers in other fields to extract general procedures and apply them to different areas. We present a rollout algorithm framework to make recent advances in rollout methods more accessible to researchers seeking heuristic policies for large-scale, finite-horizon SDPs. We formalize rollout variants exploiting the pre- and post-decision state variables as a means of overcoming computational limitations imposed by large state and action spaces. We present a unified analytical discussion, generalizing results from the literature and introducing new results that relate the performance of the rollout variants to one another. Relative to the literature, our policy-based approach to presenting and proving results makes a closer connection to the underpinnings of dynamic programming. Finally, we illustrate our framework and analytical results via application to a dynamic and stochastic multi-compartment knapsack problem. (C) 2016 Published by Elsevier B.V.

关键词： Dynamic programming rollout algorithm Stochastic dynamic programming Approximate dynamic programming

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The Optimization Strategy of Tests for Fault Isolation Based on rollout algorithm 16

The Optimization Strategy of Tests for Fault Isolation Based...

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16th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Xu, Xiaoyan Wang, Shiji Yang, Chunling Liu, Xuechun Harbin Inst Technol Dept Elect Engn & Automat Harbin Peoples R China Beijing Aerosp Measurement & Control Technol Co L Beijing Peoples R China

ISBN: (纸本)9781665422482

The traditional fault isolation strategy based on the rollout algorithm will increase the algorithm time complexity and use too many test points. The optimization strategy of tests for fault isolation based on rollout algorithm can solve these problems, and make the final optimization result is still better than the normal rollout algorithm. This optimization strategy not only makes the rollout algorithm effective, maximizes the system fault isolation rate, but also reduces the time complexity of the algorithm, reduces the use of unnecessary tests, simplifies the complexity of the system, and improves the compatibility of the algorithm for complex systems. The principle of this optimization strategy is to perform fault isolation operations on all tests before using the rollout algorithm, and then use the rollout algorithm to sort the tests after selecting the necessary tests. Finally, the theoretical and case analysis results prove that the optimization strategy can use the least tests under the premise of ensuring the maximum fault isolation rate, reduce the time complexity of the rollout algorithm, and is suitable for optimizing the fault diagnosis of complex systems.

关键词： rollout algorithm test optimization fault isolation binary test

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A Multi-Fidelity rollout algorithm for Dynamic Resource Allocation in Population Disease Management

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HEALTH CARE MANAGEMENT SCIENCE 2019年第4期22卷 727-755页

作者： Ho, Ting-Yu Liu, Shan Zabinsky, Zelda B. Univ Washington Dept Ind & Syst Engn Box 352650 Seattle WA 98195 USA

Dynamic resource allocation for prevention, screening, and treatment interventions in population disease management has received much attention in recent years due to excessive healthcare costs. In this paper, our goal is to design a model and an efficient algorithm to optimize sequential intervention policies under resource constraints to improve population health outcomes. We consider a discrete-time finite-horizon budget allocation problem with disease progression within a closed birth-cohort population. To address the computational challenges associated with large-state and multiple-period dynamic decision-making problems, we propose a low-fidelity approximation that preserves the population dynamics under a stationary policy. To improve the healthcare interventions in terms of population health outcomes, we then embed the low-fidelity approximation into a high-fidelity optimization model to efficiently identify a good non-stationary sequential intervention policy. Our approach is illustrated by a numerical example of screening and treatment policy implementation for chronic hepatitis C virus (HCV) infection over a budget planning period. We numerically compare our Multi-Fidelity rollout algorithm (MF-RA) to a grid search approach and demonstrate the similarity of sequential policy trends and closeness of overall health outcomes measured by quality-adjusted life-years (QALYs) and the total number of individuals that undergo screening and treatment for different annual budgets and birth-cohorts. We also show how our approach scales well to problems with high dimensionality due to many decision periods by studying time to elimination of HCV.

关键词： Screening and treatment interventions Dynamic programming Markov processes Simulation rollout algorithm Hepatitis C

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A rollout heuristic algorithm for order sequencing in robotic compact storage and retrieval systems

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 203卷

作者： Ko, Dayoung Han, Jinil Soongsil Univ Dept Ind & Informat Syst Engn 369 Sangdo Ro Seoul 06978 South Korea

Robotic compact storage and retrieval systems (RCSRSs) are robotized automated warehousing systems that are being increasingly used in several companies around the world owing to their efficient usage of space and low cost. In RCSRSs, a vertical stacking approach is used. Thus, to retrieve a target bin, all bins located above it must be reshuffled, causing the depth-wise position of bins to change repeatedly. Therefore, the amount of work required for a robot to process bins depends heavily on the sequence in which orders are processed. In this study, we address the problem of determining the processing sequence for a given set of orders in the RCSRS. According to the complexity of this problem, we propose a rollout heuristic algorithm to find a high-quality solution within a short computation time. We also introduce several strategies to improve the performance of the proposed algorithm. Through computational experiments on random instances generated based on real-world data, we demonstrate that the proposed rollout heuristic algorithm can be effectively applied to real-world instances.

关键词： Robotic compact storage and retrieval systems Automated warehouse Order sequencing rollout algorithm Optimization Scheduling

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rollout algorithms for resource allocation in humanitarian logistics

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IISE TRANSACTIONS 2019年第8期51卷 887-909页

作者： Yu, Lina Yang, Huasheng Miao, Lixin Zhang, Canrong Tsinghua Univ Dept Ind Engn Beijing Peoples R China Tsinghua Univ Grad Sch Shenzen Shenzhen Peoples R China

Large-scale disasters and catastrophic events typically result in a significant shortage of critical resources, posing a great challenge to allocating limited resources among different affected areas to improve the quality of emergency logistics operations. This article pays attention to the performance of resource allocation, which includes three metrics: efficiency, effectiveness, and equity, respectively corresponding to economic cost, service quality, and fairness. In particular, the effectiveness metric considers human suffering by depicting it as deprivation cost, an economic valuation measurement that has been recently proposed and the equity metric concerns about the service equality at the end of planning horizon. A nonlinear integer model is first proposed and then an equivalent dynamic programming model is developed to avoid the nonlinear terms created by the introduction of the deprivation cost. The dynamic programming method can solve small-scale problems to optimality but meets difficulty when solving medium- and large-scale problems, due to the curse of dimensionality. Therefore, an approximate dynamic programming algorithm, called the rollout algorithm, is proposed to overcome this computational difficulty. The computational complexity of the proposed algorithm is theoretically analyzed. Furthermore, a modified version of the rollout algorithm is presented, with its computational complexity analyzed. Extensive numerical experiments are conducted to test the performance of the proposed algorithms, and the experimental results demonstrate that the initially proposed rollout algorithm yields optimal or near-optimal solutions within a reasonable amount of time. In addition, the impacts of some important parameters are investigated and managerial insights are drawn.

关键词： Humanitarian logistics resource allocation human suffering approximate dynamic programming rollout algorithm

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E2RLIXT: An end-to-end framework for robust index tuning based on reinforcement learning

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COMPUTERS & ELECTRICAL ENGINEERING 2025年 122卷

作者： Lai, Sichao Wu, Xiaoying Peng, Zhiyong Wuhan Univ Sch Comp Sci Wuhan 430072 Hubei Peoples R China Wuhan Univ Big Data Inst Wuhan 430072 Hubei Peoples R China

Index selection is crucial for improving database query performance, and yet it remains a challenging problem. Recent work has explored using Reinforcement Learning (RL) to address this problem by formulating it as a decision problem where an agent learns to recommend indexes. However, existing approaches have not thoroughly investigated the formulation and representation of this index selection problem (ISP) in the context of RL, nor have they addressed the adaptation to highly recurrent workloads common in real-world systems. We propose E2RLIXT, an End-to-End RL-based robust IndeX Tuning framework, to address these gaps. Within this framework, we design a unified strategy for representing both single- and multi-column indexes, explore two state representation strategies, and employ a reward design that considers index interactions without biasing the agent's learning. We employ Proximal Policy Optimization with data augmentation for stable training and design a rollout algorithm to enhance the agent's ability to adapt to varied workloads sharing common query templates. To the best of our knowledge, we are the first to design and integrate rollout algorithms into RL-based ISP solutions. Experimental results show that our solutions outperform comparative approaches and provide robust performance across diverse workloads.

关键词： Index tuning Reinforcement learning rollout algorithm

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