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Collaborative scheduling problem pertaining to launch and recovery operations for carrier aircraft 认领 引用
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作者 GUO Fang HAN Wei +3 位作者 LIU Yujie SU Xichao LIU Jie LI Changjiu 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第1期287-306,共20页
The proliferation of carrier aircraft and the integration of unmanned aerial vehicles(UAVs)on aircraft carriers present new challenges to the automation of launch and recovery operations.This paper investigates a coll... The proliferation of carrier aircraft and the integration of unmanned aerial vehicles(UAVs)on aircraft carriers present new challenges to the automation of launch and recovery operations.This paper investigates a collaborative scheduling problem inherent to the operational processes of carrier aircraft,where launch and recovery tasks are conducted concurrently on the flight deck.The objective is to minimize the cumulative weighted waiting time in the air for recovering aircraft and the cumulative weighted delay time for launching aircraft.To tackle this challenge,a multiple population self-adaptive differential evolution(MPSADE)algorithm is proposed.This method features a self-adaptive parameter updating mechanism that is contingent upon population diversity,an asynchronous updating scheme,an individual migration operator,and a global crossover mechanism.Additionally,comprehensive experiments are conducted to validate the effectiveness of the proposed model and algorithm.Ultimately,a comparative analysis with existing operation modes confirms the enhanced efficiency of the collaborative operation mode. 展开更多
关键词 carrier aircraft collaborative scheduling problem launch recovery multiple population differential evolution
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Hyper-heuristic Evolutionary Algorithm Utilizing Q-learning for Addressing the Distributed Flexible Job-Shop Scheduling Problem in the Context of Worker Absenteeism 认领 引用
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作者 Zhiqing Li Yongquan Zhou Qifang Luo 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第3期1932-1969,共38页
The Distributed Flexible Job Shop Scheduling Problem(DFJSP)is critical in modern manufacturing;however,existing research has not sufficiently addressed dynamic disturbances,particularly unexpected worker absences.This... The Distributed Flexible Job Shop Scheduling Problem(DFJSP)is critical in modern manufacturing;however,existing research has not sufficiently addressed dynamic disturbances,particularly unexpected worker absences.This study extends the DFJSPW model to introduce an enhanced framework,DFJSPWA,which optimizes maximum makespan,worker workload,and total energy consumption by integrating worker load factors with random absenteeism.To solve this complex problem,we propose a Q-learning-based Hyper-heuristic Evolutionary Algorithm(QLHHEA).This algorithm features a segmented encoding scheme that implicitly captures absenteeism information,utilizing a decoding process tailored for both standard and rescheduling contexts.Additionally,we construct a pool of twelve efficient Low-Level Heuristics(LLHs)combined with Q-learning to enable the adaptive selection of operators.Furthermore,a Hybrid Rescheduling Method(HRM)is developed,employing three response strategies based on worker status and the urgency of the absenteeism.Comprehensive experiments on 58 benchmark instances demonstrate that QLHHEA significantly outperforms six established algorithms,including MOEA/D and NSGA-II.Statistical validation confirms the superiority of the proposed method.This research provides a robust theoretical and methodological framework for improving scheduling efficiency and resource utilization in distributed production systems facing worker absenteeism. 展开更多
关键词 Worker absenteeism Distributed flexible job shop scheduling problem Hyper-heuristic evolutionary algorithm Dynamic rescheduling Q-learning
Multi-Level Subpopulation-Based Particle Swarm Optimization Algorithm for Hybrid Flow Shop Scheduling Problem with Limited Buffers 认领 引用 被引量：1
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作者 Yuan Zou Chao Lu +1 位作者 Lvjiang Yin Xiaoyu Wen 《Computers, Materials & Continua》 SCIE EI 2025年第8期2305-2330,共26页
The shop scheduling problem with limited buffers has broad applications in real-world production scenarios,so this research direction is of great practical significance.However,there is currently little research on th... The shop scheduling problem with limited buffers has broad applications in real-world production scenarios,so this research direction is of great practical significance.However,there is currently little research on the hybrid flow shop scheduling problem with limited buffers(LBHFSP).This paper deeply investigates the LBHFSP to optimize the goal of the total completion time.To better solve the LBHFSP,a multi-level subpopulation-based particle swarm optimization algorithm(MLPSO)is proposed,which is founded on the attributes of the LBHFSP and the shortcomings of the basic PSO(particle swarm optimization)algorithm.In MLPSO,firstly,considering the impact of the limited buffers on the process of subsequent operations,a specific circular decoding strategy is developed to accommodate the characteristics of limited buffers.Secondly,an initialization strategy based on blocking time is designed to enhance the quality and diversity of the initial population.Afterward,a multi-level subpopulation collaborative search is developed to prevent being trapped in a local optimum and improve the global exploration capability.Additionally,a local search strategy based on the first blocked job is designed to enhance the MLPSO algorithm’s exploitation capability.Lastly,numerous experiments are carried out to test the performance of the proposed MLPSO by comparing it with classical intelligent optimization and popular algorithms in recent years.The results confirm that the proposed MLPSO has an outstanding performance when compared to other algorithms when solving LBHFSP. 展开更多
关键词 Hybrid flow shop scheduling problem limited buffers PSO algorithm collaborative search blocking phenomenon
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A Q-Learning-Assisted Co-Evolutionary Algorithm for Distributed Assembly Flexible Job Shop Scheduling Problems 认领 引用
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作者 Song Gao Shixin Liu 《Computers, Materials & Continua》 SCIE EI 2025年第6期5623-5641,共19页
With the development of economic globalization,distributedmanufacturing is becomingmore andmore prevalent.Recently,integrated scheduling of distributed production and assembly has captured much concern.This research s... With the development of economic globalization,distributedmanufacturing is becomingmore andmore prevalent.Recently,integrated scheduling of distributed production and assembly has captured much concern.This research studies a distributed flexible job shop scheduling problem with assembly operations.Firstly,a mixed integer programming model is formulated to minimize the maximum completion time.Secondly,a Q-learning-assisted coevolutionary algorithmis presented to solve themodel:(1)Multiple populations are developed to seek required decisions simultaneously;(2)An encoding and decoding method based on problem features is applied to represent individuals;(3)A hybrid approach of heuristic rules and random methods is employed to acquire a high-quality population;(4)Three evolutionary strategies having crossover and mutation methods are adopted to enhance exploration capabilities;(5)Three neighborhood structures based on problem features are constructed,and a Q-learning-based iterative local search method is devised to improve exploitation abilities.The Q-learning approach is applied to intelligently select better neighborhood structures.Finally,a group of instances is constructed to perform comparison experiments.The effectiveness of the Q-learning approach is verified by comparing the developed algorithm with its variant without the Q-learning method.Three renowned meta-heuristic algorithms are used in comparison with the developed algorithm.The comparison results demonstrate that the designed method exhibits better performance in coping with the formulated problem. 展开更多
关键词 Distributed manufacturing flexible job shop scheduling problem assembly operation co-evolutionary algorithm Q-learning method
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Clonal Selection Based Memetic Algorithm for Job Shop Scheduling Problems 认领 引用 被引量：4
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作者 Jin-hui Yang Liang Sun +2 位作者 Heow Pueh Lee Yun Qian Yan-chun Liang 《Journal of Bionic Engineering》 SCIE EI 2008年第2期111-119,共9页
A clonal selection based memetic algorithm is proposed for solving job shop scheduling problems in this paper. In the proposed algorithm, the clonal selection and the local search mechanism are designed to enhance exp... A clonal selection based memetic algorithm is proposed for solving job shop scheduling problems in this paper. In the proposed algorithm, the clonal selection and the local search mechanism are designed to enhance exploration and exploitation. In the clonal selection mechanism, clonal selection, hypermutation and receptor edit theories are presented to construct an evolutionary searching mechanism which is used for exploration. In the local search mechanism, a simulated annealing local search algorithm based on Nowicki and Smutnicki＇s neighborhood is presented to exploit local optima. The proposed algorithm is examined using some well-known benchmark problems. Numerical results validate the effectiveness of the proposed algorithm. 展开更多
关键词 job shop scheduling problem clonal selection algorithm simulated annealing global search local search
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Solving flexible job shop scheduling problem by a multi-swarm collaborative genetic algorithm 认领 引用 被引量：15
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作者 WANG Cuiyu LI Yang LI Xinyu 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2021年第2期261-271,共11页
The flexible job shop scheduling problem(FJSP),which is NP-hard,widely exists in many manufacturing industries.It is very hard to be solved.A multi-swarm collaborative genetic algorithm(MSCGA)based on the collaborativ... The flexible job shop scheduling problem(FJSP),which is NP-hard,widely exists in many manufacturing industries.It is very hard to be solved.A multi-swarm collaborative genetic algorithm(MSCGA)based on the collaborative optimization algorithm is proposed for the FJSP.Multi-population structure is used to independently evolve two sub-problems of the FJSP in the MSCGA.Good operators are adopted and designed to ensure this algorithm to achieve a good performance.Some famous FJSP benchmarks are chosen to evaluate the effectiveness of the MSCGA.The adaptability and superiority of the proposed method are demonstrated by comparing with other reported algorithms. 展开更多
关键词 flexible job shop scheduling problem(FJSP) collaborative genetic algorithm co-evolutionary algorithm
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An Improved Genetic Algorithm for Solving the Mixed⁃Flow Job⁃Shop Scheduling Problem with Combined Processing Constraints 认领 引用 被引量：4
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作者 ZHU Haihua ZHANG Yi +2 位作者 SUN Hongwei LIAO Liangchuang TANG Dunbing 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2021年第3期415-426,共12页
The flexible job-shop scheduling problem(FJSP)with combined processing constraints is a common scheduling problem in mixed-flow production lines.However,traditional methods for classic FJSP cannot be directly applied.... The flexible job-shop scheduling problem(FJSP)with combined processing constraints is a common scheduling problem in mixed-flow production lines.However,traditional methods for classic FJSP cannot be directly applied.Targeting this problem,the process state model of a mixed-flow production line is analyzed.On this basis,a mathematical model of a mixed-flow job-shop scheduling problem with combined processing constraints is established based on the traditional FJSP.Then,an improved genetic algorithm with multi-segment encoding,crossover,and mutation is proposed for the mixed-flow production line problem.Finally,the proposed algorithm is applied to the production workshop of missile structural components at an aerospace institute to verify its feasibility and effectiveness. 展开更多
关键词 mixed-flow production flexible job-shop scheduling problem(FJSP) genetic algorithm encoding
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Self-adaptive large neighborhood search algorithm for parallel machine scheduling problems 认领 引用 被引量：8
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作者 Pei Wang Gerhard Reinelt Yuejin Tan 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2012年第2期208-215,共8页
A self-adaptive large neighborhood search method for scheduling n jobs on m non-identical parallel machines with mul- tiple time windows is presented. The problems＇ another feature lies in oversubscription, namely no... A self-adaptive large neighborhood search method for scheduling n jobs on m non-identical parallel machines with mul- tiple time windows is presented. The problems＇ another feature lies in oversubscription, namely not all jobs can be scheduled within specified scheduling horizons due to the limited machine capacity. The objective is thus to maximize the overall profits of processed jobs while respecting machine constraints. A first-in- first-out heuristic is applied to find an initial solution, and then a large neighborhood search procedure is employed to relax and re- optimize cumbersome solutions. A machine learning mechanism is also introduced to converge on the most efficient neighborhoods for the problem. Extensive computational results are presented based on data from an application involving the daily observation scheduling of a fleet of earth observing satellites. The method rapidly solves most problem instances to optimal or near optimal and shows a robust performance in sensitive analysis. 展开更多
关键词 non-identical parallel machine scheduling problem with multiple time windows （NPMSPMTW） oversubscribed self- adaptive large neighborhood search （SALNS） machine learning.
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An effective discrete artificial bee colony algorithm for flow shop scheduling problem with intermediate buffers 认领 引用 被引量：6
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作者 张素君 顾幸生 《Journal of Central South University》 SCIE EI CAS CSCD 2015年第9期3471-3484,共14页
An effective discrete artificial bee colony(DABC) algorithm is proposed for the flow shop scheduling problem with intermediate buffers(IBFSP) in order to minimize the maximum completion time(i.e makespan). The effecti... An effective discrete artificial bee colony(DABC) algorithm is proposed for the flow shop scheduling problem with intermediate buffers(IBFSP) in order to minimize the maximum completion time(i.e makespan). The effective combination of the insertion and swap operator is applied to producing neighborhood individual at the employed bee phase. The tournament selection is adopted to avoid falling into local optima, while, the optimized insert operator embeds in onlooker bee phase for further searching the neighborhood solution to enhance the local search ability of algorithm. The tournament selection with size 2 is again applied and a better selected solution will be performed destruction and construction of iterated greedy(IG) algorithm, and then the result replaces the worse one. Simulation results show that our algorithm has a better performance compared with the HDDE and CHS which were proposed recently. It provides the better known solutions for the makespan criterion to flow shop scheduling problem with limited buffers for the Car benchmark by Carlier and Rec benchmark by Reeves. The convergence curves show that the algorithm not only has faster convergence speed but also has better convergence value. 展开更多
关键词 discrete artificial bee colony algorithm flow shop scheduling problem with intermediate buffers destruction and construction tournament selection
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Improvement of Lagrangian relaxation performance for open pit mines constrained long-term production scheduling problem 认领 引用 被引量：2
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作者 E.Moosavi J.Gholamnejad +1 位作者 M.Ataee-pour E.Khorram 《Journal of Central South University》 SCIE EI CAS 2014年第7期2848-2856,共9页
Constrained long-term production scheduling problem(CLTPSP) of open pit mines has been extensively studied in the past few decades due to its wide application in mining projects and the computational challenges it pos... Constrained long-term production scheduling problem(CLTPSP) of open pit mines has been extensively studied in the past few decades due to its wide application in mining projects and the computational challenges it poses become an NP-hard problem.This problem has major practical significance because the effectiveness of the schedules obtained has strong economical impact for any mining project.Despite of the rapid theoretical and technical advances in this field,heuristics is still the only viable approach for large scale industrial applications.This work presents an approach combining genetic algorithms(GAs) and Lagrangian relaxation(LR) to optimally determine the CLTPSP of open pit mines.GAs are stochastic,parallel search algorithms based on the natural selection and the process of evolution.LR method is known for handling large-scale separable problems; however,the convergence to the optimal solution can be slow.The proposed Lagrangian relaxation and genetic algorithms(LR-GAs) combines genetic algorithms into Lagrangian relaxation method to update the Lagrangian multipliers.This approach leads to improve the performance of Lagrangian relaxation method in solving CLTPSP.Numerical results demonstrate that the LR method using GAs to improve its performance speeding up the convergence.Subsequently,highly near-optimal solution to the CLTPSP can be achieved by the LR-GAs. 展开更多
关键词 constrained long-term production scheduling problem open pit mine Lagrangian relaxation genetic algorithm
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Solving Job-Shop Scheduling Problem Based on Improved Adaptive Particle Swarm Optimization Algorithm 认领 引用 被引量：4
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作者 顾文斌 唐敦兵 郑堃 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2014年第5期559-567,共9页
An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal ... An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal factor(HF),composed of an adaptive local hormonal factor(H l)and an adaptive global hormonal factor(H g),is devised to strengthen the information connection between particles.Using HF,each particle of the swarm can adjust its position self-adaptively to avoid premature phenomena and reach better solution.The computational results validate the effectiveness and stability of the proposed IAPSO,which can not only find optimal or close-to-optimal solutions but also obtain both better and more stability results than the existing particle swarm optimization(PSO)algorithms. 展开更多
关键词 job-shop scheduling problem(JSP) hormone modulation mechanism improved adaptive particle swarm optimization(IAPSO) algorithm minimum makespan
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Solving Resource-constrained Multiple Project Scheduling Problem Using Timed Colored Petri Nets 认领 引用 被引量：2
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作者 吴瑜 庄新村 +2 位作者 宋国辉 许晓栋 李从心 《Journal of Shanghai Jiaotong university(Science)》 EI 2009年第6期713-719,共7页
To solve the resource-constrained multiple project scheduling problem(RCMPSP) more effectively,a method based on timed colored Petri net(TCPN) was proposed.In this methodology,firstly a novel mapping mechanism between... To solve the resource-constrained multiple project scheduling problem(RCMPSP) more effectively,a method based on timed colored Petri net(TCPN) was proposed.In this methodology,firstly a novel mapping mechanism between traditional network diagram such as CPM(critical path method)/PERT(program evaluation and review technique) and TCPN was presented.Then a primary TCPN(PTCPN) for solving RCMPSP was modeled based on the proposed mapping mechanism.Meanwhile,the object PTCPN was used to simulate the multiple projects scheduling and to find the approximately optimal value of RCMPSP.Finally,the performance of the proposed approach for solving RCMPSP was validated by executing a mould manufacturing example. 展开更多
关键词 timed colored Petri nets resource-constrained multiple project scheduling problem （RCMPSP） mapping mechanism
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Scheduling Rules Based on Gene Expression Programming for Resource-Constrained Project Scheduling Problem 认领 引用 被引量：3
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作者 贾艳 李晋航 《Journal of Donghua University(English Edition)》 EI CAS 2015年第1期91-96,共6页
In order to minimize the project duration of resourceconstrained project scheduling problem( RCPSP), a gene expression programming-based scheduling rule( GEP-SR) method is proposed to automatically discover and select... In order to minimize the project duration of resourceconstrained project scheduling problem( RCPSP), a gene expression programming-based scheduling rule( GEP-SR) method is proposed to automatically discover and select the effective scheduling rules( SRs) which are constructed using the project status and attributes of the activities. SRs are represented by the chromosomes of GEP, and an improved parallel schedule generation scheme( IPSGS) is used to transform the SRs into explicit schedules. The framework of GEP-SR for RCPSP is designed,and the effectiveness of the GEP-SR approach is demonstrated by comparing with other methods on the same instances. 展开更多
关键词 resource-constrained project scheduling problem(RCPSP) gene expression programming(GEP) scheduling rules(SRs)
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Modified bottleneck-based heuristic for large-scale job-shop scheduling problems with a single bottleneck 认领 引用 被引量：22
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作者 Zuo Yan Gu Hanyu Xi Yugeng 《Journal of Systems Engineering and Electronics》 SCIE EI 2007年第3期556-565,共10页
A modified bottleneck-based （MB） heuristic for large-scale job-shop scheduling problems with a welldefined bottleneck is suggested, which is simpler but more tailored than the shifting bottleneck （SB） procedure. I... A modified bottleneck-based （MB） heuristic for large-scale job-shop scheduling problems with a welldefined bottleneck is suggested, which is simpler but more tailored than the shifting bottleneck （SB） procedure. In this algorithm, the bottleneck is first scheduled optimally while the non-bottleneck machines are subordinated around the solutions of the bottleneck schedule by some effective dispatching rules. Computational results indicate that the MB heuristic can achieve a better tradeoff between solution quality and computational time compared to SB procedure for medium-size problems. Furthermore, it can obtain a good solution in a short time for large-scale jobshop scheduling problems. 展开更多
关键词 job shop scheduling problem bottleneck shifting bottleneck procedure.
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Hybrid artificial immune system and extremal optimization algorithm for permutation flowshop scheduling problem 认领 引用 被引量：2
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作者 孙凯 杨根科 《Journal of Shanghai University(English Edition)》 2008年第4期352-357,共6页
The permutation flowshop scheduling problem （PFSP） is one of the most well-known and well-studied production scheduling problems with strong industrial background. This paper presents a new hybrid optimization algor... The permutation flowshop scheduling problem （PFSP） is one of the most well-known and well-studied production scheduling problems with strong industrial background. This paper presents a new hybrid optimization algorithm which combines the strong global search ability of artificial immune system （AIS） with a strong local search ability of extremal optimization （EO） algorithm. The proposed algorithm is applied to a set of benchmark problems with a makespan criterion. Performance of the algorithm is evaluated. Comparison results indicate that this new method is an effective and competitive approach to the PFSP. 展开更多
关键词 artificial immune system （AIS） extremal optimization （EO） permutation flowshop scheduling problem （PFSP）
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Deep Reinforcement Learning Solves Job-shop Scheduling Problems 认领 引用 被引量：2
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作者 Anjiang Cai Yangfan Yu Manman Zhao 《Instrumentation》 2024年第1期88-100,共13页
To solve the sparse reward problem of job-shop scheduling by deep reinforcement learning,a deep reinforcement learning framework considering sparse reward problem is proposed.The job shop scheduling problem is transfo... To solve the sparse reward problem of job-shop scheduling by deep reinforcement learning,a deep reinforcement learning framework considering sparse reward problem is proposed.The job shop scheduling problem is transformed into Markov decision process,and six state features are designed to improve the state feature representation by using two-way scheduling method,including four state features that distinguish the optimal action and two state features that are related to the learning goal.An extended variant of graph isomorphic network GIN++is used to encode disjunction graphs to improve the performance and generalization ability of the model.Through iterative greedy algorithm,random strategy is generated as the initial strategy,and the action with the maximum information gain is selected to expand it to optimize the exploration ability of Actor-Critic algorithm.Through validation of the trained policy model on multiple public test data sets and comparison with other advanced DRL methods and scheduling rules,the proposed method reduces the minimum average gap by 3.49%,5.31%and 4.16%,respectively,compared with the priority rule-based method,and 5.34%compared with the learning-based method.11.97%and 5.02%,effectively improving the accuracy of DRL to solve the approximate solution of JSSP minimum completion time. 展开更多
关键词 job shop scheduling problems deep reinforcement learning state characteristics policy network
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MODIFIED BOTTLENECK-BASED PROCEDURE FOR LARGE-SCALE FLOW-SHOP SCHEDULING PROBLEMS WITH BOTTLENECK 认领 引用
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作者 ZUO Yan GU Hanyu XI Yugeng 《Chinese Journal of Mechanical Engineering》 EI CAS 2006年第3期356-361,共6页
A new bottleneck-based heuristic for large-scale flow-shop scheduling problems with a bottleneck is proposed,which is simpler but more tailored than the shifting bottleneck(SB)procedure.In this algorithm,a schedule fo... A new bottleneck-based heuristic for large-scale flow-shop scheduling problems with a bottleneck is proposed,which is simpler but more tailored than the shifting bottleneck(SB)procedure.In this algorithm,a schedule for the bottleneck machine is first constructed optimally and then the non-bottleneck machines are scheduled around the bottleneck schedule by some effective dispatching rules.Computational results show that the modified bottleneck-based procedure can achieve a tradeoff between solution quality and computational time comparing with SB procedure for medium-size problems.Furthermore it can obtain a good solution in quite short time for large-scale scheduling problems. 展开更多
关键词 Flow-shop scheduling problem Heuristic Bottleneck machine
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A new polynomial algorithm for a parallelidentical scheduling problem 认领 引用
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作者 Yumei LI Hongxing LI Vincent C. YEN 《控制理论与应用（英文版）》 2007年第4期345-350,共6页
A precedence order is defined based on the release dates of jobs＇ direct successors. Using the defined precedence order and Heap Sort, a new polynomial algorithm is provided which aims to solve the parallel schedulin... A precedence order is defined based on the release dates of jobs＇ direct successors. Using the defined precedence order and Heap Sort, a new polynomial algorithm is provided which aims to solve the parallel scheduling problem P｜pj = 1, r j, outtree｜ ∑Cj Cj. The new algorithm is shown to be more compact and easier to implement. 展开更多
关键词 Scheduling problem Precedence order Heap sort Algorithm
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A Pre-Selection-Based Ant Colony System for Integrated Resources Scheduling Problem at Marine Container Terminal 认领 引用
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作者 Rong Wang Xinxin Xu +2 位作者 Zijia Wang Fei Ji Nankun Mu 《Computers, Materials & Continua》 SCIE EI 2024年第8期2363-2385,共23页
Marine container terminal(MCT)plays a key role in the marine intelligent transportation system and international logistics system.However,the efficiency of resource scheduling significantly influences the operation pe... Marine container terminal(MCT)plays a key role in the marine intelligent transportation system and international logistics system.However,the efficiency of resource scheduling significantly influences the operation performance of MCT.To solve the practical resource scheduling problem(RSP)in MCT efficiently,this paper has contributions to both the problem model and the algorithm design.Firstly,in the problem model,different from most of the existing studies that only consider scheduling part of the resources in MCT,we propose a unified mathematical model for formulating an integrated RSP.The new integrated RSP model allocates and schedules multiple MCT resources simultaneously by taking the total cost minimization as the objective.Secondly,in the algorithm design,a pre-selection-based ant colony system(PACS)approach is proposed based on graphic structure solution representation and a pre-selection strategy.On the one hand,as the RSP can be formulated as the shortest path problem on the directed complete graph,the graphic structure is proposed to represent the solution encoding to consider multiple constraints and multiple factors of the RSP,which effectively avoids the generation of infeasible solutions.On the other hand,the pre-selection strategy aims to reduce the computational burden of PACS and to fast obtain a higher-quality solution.To evaluate the performance of the proposed novel PACS in solving the new integrated RSP model,a set of test cases with different sizes is conducted.Experimental results and comparisons show the effectiveness and efficiency of the PACS algorithm,which can significantly outperform other state-of-the-art algorithms. 展开更多
关键词 Resource scheduling problem(RSP) ant colony system(ACS) marine container terminal(MCT) pre-selection strategy
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APPLYING PARTICLE SWARM OPTIMIZATION TO JOB-SHOPSCHEDULING PROBLEM 认领 引用 被引量：6
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作者 XiaWeijun WuZhiming ZhangWei YangGenke 《Chinese Journal of Mechanical Engineering》 EI CAS 2004年第3期437-441,共5页
A new heuristic algorithm is proposed for the problem of finding the minimummakespan in the job-shop scheduling problem. The new algorithm is based on the principles ofparticle swarm optimization (PSO). PSO employs a ... A new heuristic algorithm is proposed for the problem of finding the minimummakespan in the job-shop scheduling problem. The new algorithm is based on the principles ofparticle swarm optimization (PSO). PSO employs a collaborative population-based search, which isinspired by the social behavior of bird flocking. It combines local search (by self experience) andglobal search (by neighboring experience), possessing high search efficiency. Simulated annealing(SA) employs certain probability to avoid becoming trapped in a local optimum and the search processcan be controlled by the cooling schedule. By reasonably combining these two different searchalgorithms, a general, fast and easily implemented hybrid optimization algorithm, named HPSO, isdeveloped. The effectiveness and efficiency of the proposed PSO-based algorithm are demonstrated byapplying it to some benchmark job-shop scheduling problems and comparing results with otheralgorithms in literature. Comparing results indicate that PSO-based algorithm is a viable andeffective approach for the job-shop scheduling problem. 展开更多
关键词 Job-shop scheduling problem Particle swarm optimization Simulated annealingHybrid optimization algorithm
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