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A Q-Learning Based Hybrid meta-heuristic for Integrated Scheduling of Disassembly and Reprocessing Processes Considering Product Structures and Stochasticity

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Complex System Modeling and Simulation 2024年第2期4卷 184-209页

作者： Fuquan Wang Yaping Fu Kaizhou Gao Yaoxin Wu Song Gao the School of Business Qingdao UniversityQingdao 266071China the Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China the Department of Industrial Engineering&Innovation Sciences Eindhoven University of TechnologyEindhoven5600 MBthe Netherlands the College of Information Science and Engineering Northeastern UniversityShenyang 110819China

Remanufacturing is regarded as a sustainable manufacturing paradigm of energy conservation and environment *** improve the efficiency of the remanufacturing process,this work investigates an integrated scheduling problem for disassembly and reprocessing in a remanufacturing process,where product structures and uncertainty are taken into ***,a stochastic programming model is developed to minimize the maximum completion time(makespan).Second,a Q-learning based hybrid meta-heuristic(Q-HMH)is specially *** each iteration,a Q-learning method is employed to adaptively choose a premium algorithm from four candidate ones,including genetic algorithm(GA),artificial bee colony(ABC),shuffled frog-leaping algorithm(SFLA),and simulated annealing(SA)*** last,simulation experiments are carried out by using sixteen instances with different scales,and three state-of-the-art algorithms in literature and an exact solver CPLEX are chosen for *** analyzing the results with the average relative percentage deviation(RPD)metric,we find that Q-HMH outperforms its rivals by 9.79%-26.76%.The results and comparisons verify the excellent competitiveness of Q-HMH for solving the concerned problems.

关键词： remanufacturing scheduling disassembly reprocessing meta-heuristic Q-learning

Modeling the Scheduling Problem in Cellular Manufacturing Systems Using Genetic Algorithm as an Efficient meta-heuristic Approach

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Journal of Artificial Intelligence and Technology 2021年第4期1卷 228-234页

作者： Amin Rezaeipanah Musa Mojarad Depiecement of Computer Engineering University of Rahjuyan Danesh BorazjanBushehrIran Depiecement of Computer Engineering Firoozabad BranchIslamic Azad UniversityFiroozabadIran

This paper presents a new,bi-criteria mixed_integer programming model for scheduling cells and pieces within each cell in a manufacturing cellular *** objective of this model is to minimize the makespan and intercell movements simultaneously,while considering sequence-dependent cell setup *** the cellular manufacturing systems design and planning,three main steps must be considered,namely cell formation(i.e,piece families and machine grouping),inter and intra-cell layouts,and scheduling *** to the fact that the cellular manufacturing systems problem is NP-Hard,a genetic algorithm as an efficient meta-heuristic method is proposed to solve such a hard ***,a number of test problems are solved to show the efficiency of the proposed genetic algorithm and the related computational results are compared with the results obtained by the use of an optimization tool.

关键词： scheduling cellular manufacturing system genetic algorithm meta-heuristic

A New meta heuristic Approach for Aircraft Landing Problem

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Transactions of Nanjing University of Aeronautics and Astronautics 2020年第2期37卷 197-208页

作者： ZHANG Junfeng ZHAO Pengli YANG Chunwei HU Rong College of Civil Aviation Nanjing University of Aeronautics and AstronauticsNanjing 211106P.R.China

A new meta-heuristic approach is proposed in this paper based on a new composite dispatching rule to tackle the aircraft landing problem(ALP).First,the ALP is modeled as a machine scheduling problem with the objective of minimizing the total penalty,i.e.,total weighted earliness plus total weighted ***,a composite dispatching rule,minimized penalty with due dates and set-ups(MPDS),is presented to determine the landing ***,an efficient heuristic approach is proposed to solve the problem by integrating the MPDS rule and CPLEX *** the first stage,the landing sequence is established based on the proposed MPDS *** the second stage,landing time is optimized using CPLEX ***,a new meta-heuristic strategy is introduced into the heuristic approach by conducting the local search from the potential landing sequences,which are generated by the proposed MPDS ***,the performance of the proposed approach is evaluated using a set of benchmark instances taken from the OR *** results demonstrate the effectiveness and efficiency of the proposed approaches.

关键词： arrival scheduling air traffic control decision support meta-heuristic local search

Hybridizing grey wolf optimization with differential evolution for global optimization and test scheduling for 3D stacked SoC

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Journal of Systems Engineering and Electronics 2015年第2期26卷 317-328页

作者： Aijun Zhu Chuanpei Xu Zhi Li Jun Wu Zhenbing Liu School of Mechano-Electronic Engineering Xidian University School of Electronic Engineering and Automation Guilin University of Electronic Technology Guangxi Key Laboratory of Automatic Detecting Technology and Instruments Guilin University of Aerospace Technology

A new meta-heuristic method is proposed to enhance current meta-heuristic methods for global optimization and test scheduling for three-dimensional （3D） stacked system-on-chip （SoC） by hybridizing grey wolf optimization with differential evo- lution （HGWO）. Because basic grey wolf optimization （GWO） is easy to fall into stagnation when it carries out the operation of at- tacking prey, and differential evolution （DE） is integrated into GWO to update the previous best position of grey wolf Alpha, Beta and Delta, in order to force GWO to jump out of the stagnation with DE＇s strong searching ability. The proposed algorithm can accele- rate the convergence speed of GWO and improve its performance. Twenty-three well-known benchmark functions and an NP hard problem of test scheduling for 3D SoC are employed to verify the performance of the proposed algorithm. Experimental results show the superior performance of the proposed algorithm for exploiting the optimum and it has advantages in terms of exploration.

关键词： meta-heuristic global optimization NP hard problem

Binary Hybrid Artificial Hummingbird with Flower Pollination Algorithm for Feature Selection in Parkinson’s Disease Diagnosis

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Journal of Bionic Engineering 2024年第2期21卷 1003-1021页

作者： Liuyan Feng Yongquan Zhou Qifang Luo College of Artificial Intelligence Guangxi University for NationalitiesNanning530006China Xiangsihu College of Guangxi University for Nationalities Nanning532100GuangxiChina Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis Nanning530006China

Parkinson’s disease is a neurodegenerative disorder that inflicts irreversible damage on *** experimental data regarding Parkinson’s patients are redundant and irrelevant,posing significant challenges for disease ***,there is a need to devise an effective method for the selective extraction of disease-specific information,ensuring both accuracy and the utilization of fewer *** this paper,a Binary Hybrid Artificial Hummingbird and Flower Pollination Algorithm(FPA),called BFAHA,is proposed to solve the problem of Parkinson’s disease diagnosis based on speech ***,combining FPA with Artificial Hummingbird Algorithm(AHA)can take advantage of the strong global exploration ability possessed by FPA to improve the disadvantages of AHA,such as premature convergence and easy falling into local ***,the Hemming distance is used to determine the difference between the other individuals in the population and the optimal individual after each iteration,if the difference is too significant,the cross-mutation strategy in the genetic algorithm(GA)is used to induce the population individuals to keep approaching the optimal individual in the random search process to speed up finding the optimal ***,an S-shaped function converts the improved algorithm into a binary version to suit the characteristics of the feature selection(FS)*** this paper,10 high-dimensional datasets from UCI and the ASU are used to test the performance of BFAHA and apply it to Parkinson’s disease *** with other state-of-the-art algorithms,BFAHA shows excellent competitiveness in both the test datasets and the classification problem,indicating that the algorithm proposed in this study has apparent advantages in the field of feature selection.

关键词： Artificial Hummingbird Algorithm Flower pollination algorithm Feature selection Parkinson’s disease meta-heuristic

The Colony Predation Algorithm

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Journal of Bionic Engineering 2021年第3期18卷 674-710页

作者： Jiaze Tu Huiling Chen Mingjing Wang Amir H.Gandomi College of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou 325035China Faculty of Engineering and Information Technology University of Technology SydneyUltimoNSW 2007Australia

This paper proposes a new stochastic optimizer called the Colony Predation Algorithm(CPA)based on the corporate predation of animals in *** utilizes a mathematical mapping following the strategies used by animal hunting groups,such as dispersing prey,encircling prey,supporting the most likely successful hunter,and seeking another ***,the proposed CPA introduces new features of a unique mathematical model that uses a success rate to adjust the strategy and simulate hunting animals'selective abandonment *** paper also presents a new way to deal with cross-border situations,whereby the optimal position value of a cross-border situation replaces the cross-border value to improve the algorithm's exploitation *** proposed CPA was compared with state-of-the-art metaheuristics on a comprehensive set of benchmark functions for performance verification and on five classical engineering design problems to evaluate the algorithm's efficacy in optimizing engineering *** results show that the proposed algorithm exhibits competitive,superior performance in different search landscapes over the other ***,the source code of the CPA will be publicly available after publication.

关键词： Colony Predation Algorithm optimization nature-inspired computing meta-heuristic engineering problems

Stochastic Bi-level Programming Model for Home Healthcare Scheduling Problems Considering the Degree of Satisfaction with Visit Time

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Journal of Systems Science and Systems Engineering 2021年第5期30卷 572-599页

作者： Huichao Chen Xinggang Luo Zhongliang Zhang Qing Zhou School of Management Hangzhou Dianzi University310018 HangzhouChina

Home health care(HHC)includes a wide range of healthcare services that are performed in customers'homes to help them *** the constantly increasing demand for health care,HHC policymakers are eager to address routing and scheduling problems from the perspective of *** this paper,a bi-level programming model for HHC routing and scheduling problems with stochastic travel times is proposed,in which the degree of satisfaction with the visit time is simultaneously *** upper-level model is formulated for customer assignment with the aim of minimizing the total operating cost,and the lower-level model is formulated as a routing problem to maximize the degree of satisfaction with the visit *** with Stackelberg game decision-making,the trade-off relationship between these two objectives can be achieved spontaneously so as to reach an equilibrium state.A three-stage hybrid algorithm combining an iterated local search framework,which uses a large neighborhood search procedure as a sub-heuristic,a set-partitioning model,and a post-optimization method is developed to solve the proposed *** experiments on a set of instances including 10 to 100 customers verify the effectiveness of the proposed model and algorithm.

关键词： Home health care bi-level programming stochastic travel times routing meta-heuristic

THE EFFECT OF WORKER LEARNING ON SCHEDULING JOBS IN A HYBRID FLOW SHOP： A BI-OBJECTIVE APPROACH

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Journal of Systems Science and Systems Engineering 2018年第3期27卷 265-291页

作者： Farzad Pargar Mostafa Zandieh Osmo Kauppila Jaakko Kujala Industrial Engineering and Management Faculty of Technology University ofOulu Oulu Finland Department of Industrial Management Management and Accounting Faculty Shahid Beheshti University G.C.Tehran Iran

This paper studies learning effect as a resource utilization technique that can model improvement in worker＇s ability as a result of repeating similar tasks. By considering learning of workers while performing setup times, a schedule can be determined to place jobs that share similar tools and fixtures next to each other. The purpose of this paper is to schedule a set of jobs in a hybrid flow shop （HFS） environment with learning effect while minimizing two objectives that are in conflict： namely maximum completion time （makespan） and total tardiness. Minimizing makespan is desirable from an internal efficiency viewpoint, but may result in individual jobs being scheduled past their due date, causing customer dissatisfaction and penalty costs. A bi-objective mixed integer programming model is developed, and the complexity of the developed bi-objective model is compared against the bi-criteria one through numerical examples. The effect of worker learning on the structure of assigned jobs to machines and their sequences is analyzed. Two solution methods based on the hybrid water flow like algorithm and non-dominated sorting and ranking concepts are proposed to solve the problem. The quality of the approximated sets of Pareto solutions is evaluated using several performance criteria. The results show that the proposed algorithms with learning effect perform well in reducing setup times and eliminate the need for setups itself through proper scheduling.

关键词： Bi-objective scheduling hybrid flow shop learning effect meta-heuristic

An Improved Lung Cancer Segmentation Based on Nature-Inspired Optimization Approaches

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Computer Modeling in Engineering & Sciences 2024年第2期138卷 1051-1075页

作者： Shazia Shamas Surya Narayan Panda Ishu Sharma Kalpna Guleria Aman Singh Ahmad Ali AlZubi Mallak Ahmad AlZubi Chitkara University Institute of Engineering and Technology Chitkara UniversityRajpuraPunjab140401India Higher Polytechnic School Universidad Europea del AtlánticoC/Isabel Torres 21Santander39011Spain Department of Engineering Universidad Internacional IberoamericanaAreciboPR00613USA Uttaranchal Institute of Technology Uttaranchal UniversityDehradunUttarakhand248007India Computer Science Department King Saud UniversityRiyadhSaudi Arabia Faculty of Medicine Jordan University of Science and TechnologyIrbidJordan

The distinction and precise identification of tumor nodules are crucial for timely lung cancer diagnosis andplanning intervention. This research work addresses the major issues pertaining to the field of medical imageprocessing while focusing on lung cancer Computed Tomography (CT) images. In this context, the paper proposesan improved lung cancer segmentation technique based on the strengths of nature-inspired approaches. Thebetter resolution of CT is exploited to distinguish healthy subjects from those who have lung cancer. In thisprocess, the visual challenges of the K-means are addressed with the integration of four nature-inspired swarmintelligent techniques. The techniques experimented in this paper are K-means with Artificial Bee Colony (ABC),K-means with Cuckoo Search Algorithm (CSA), K-means with Particle Swarm Optimization (PSO), and Kmeanswith Firefly Algorithm (FFA). The testing and evaluation are performed on Early Lung Cancer ActionProgram (ELCAP) database. The simulation analysis is performed using lung cancer images set against metrics:precision, sensitivity, specificity, f-measure, accuracy,Matthews Correlation Coefficient (MCC), Jaccard, and *** detailed evaluation shows that the K-means with Cuckoo Search Algorithm (CSA) significantly improved thequality of lung cancer segmentation in comparison to the other optimization approaches utilized for lung cancerimages. The results exhibit that the proposed approach (K-means with CSA) achieves precision, sensitivity, and Fmeasureof 0.942, 0.964, and 0.953, respectively, and an average accuracy of 93%. The experimental results prove thatK-meanswithABC,K-meanswith PSO,K-meanswith FFA, andK-meanswithCSAhave achieved an improvementof 10.8%, 13.38%, 13.93%, and 15.7%, respectively, for accuracy measure in comparison to K-means segmentationfor lung cancer images. Further, it is highlighted that the proposed K-means with CSA have achieved a significantimprovement in accuracy, hence can be utilized by resea

关键词： Lesion lung cancer segmentation medical imaging meta-heuristic Artificial Bee Colony(ABC) Cuckoo Search Algorithm(CSA) Particle Swarm Optimization(PSO) Firefly Algorithm(FFA) segmentation