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
The classical job shop scheduling problem(JSP) is the most popular machine scheduling model in practice and is known as NP-hard.The formulation of the JSP is based on the assumption that for each part type or job ther...The classical job shop scheduling problem(JSP) is the most popular machine scheduling model in practice and is known as NP-hard.The formulation of the JSP is based on the assumption that for each part type or job there is only one process plan that prescribes the sequence of operations and the machine on which each operation has to be performed.However,JSP with alternative machines for various operations is an extension of the classical JSP,which allows an operation to be processed by any machine from a given set of machines.Since this problem requires an additional decision of machine allocation during scheduling,it is much more complex than JSP.We present a domain independent genetic algorithm(GA) approach for the job shop scheduling problem with alternative machines.The GA is implemented in a spreadsheet environment.The performance of the proposed GA is analyzed by comparing with various problem instances taken from the literatures.The result shows that the proposed GA is competitive with the existing approaches.A simplified approach that would be beneficial to both practitioners and researchers is presented for solving scheduling problems with alternative machines.展开更多
It is of great significance to carry out effective scheduling for the carrier-based aircraft flight deck operations.In this paper,the precedence constraints and resource constraints in flight deck operations are analy...It is of great significance to carry out effective scheduling for the carrier-based aircraft flight deck operations.In this paper,the precedence constraints and resource constraints in flight deck operations are analyzed,then the model of the multi-aircraft integrated scheduling problem with transfer times(MAISPTT)is established.A dual population multi-operator genetic algorithm(DPMOGA)is proposed for solving the problem.In the algorithm,the dual population structure and random-key encoding modified by starting/ending time of operations are adopted,and multiple genetic operators are self-adaptively used to obtain better encodings.In order to conduct the mapping from encodings to feasible schedules,serial and parallel scheduling generation scheme-based decoding operators,each of which adopts different justified mechanisms in two separated populations,are introduced.The superiority of the DPMOGA is verified by simulation experiments.展开更多
The problem of simultaneous scheduling of machines and vehicles in flexible manufacturing system (FMS) was addressed.A spreadsheet based genetic algorithm (GA) approach was presented to solve the problem.A domain inde...The problem of simultaneous scheduling of machines and vehicles in flexible manufacturing system (FMS) was addressed.A spreadsheet based genetic algorithm (GA) approach was presented to solve the problem.A domain independent general purpose GA was used,which was an add-in to the spreadsheet software.An adaptation of the propritary GA software was demonstrated to the problem of minimizing the total completion time or makespan for simultaneous scheduling of machines and vehicles in flexible manufacturing systems.Computational results are presented for a benchmark with 82 test problems,which have been constructed by other researchers.The achieved results are comparable to the previous approaches.The proposed approach can be also applied to other problems or objective functions without changing the GA routine or the spreadsheet model.展开更多
The electromagnetic detection satellite （EDS） is a type of earth observation satellites （EOSs）. The Information collected by EDSs plays an important role in some fields, such as industry, science and military. The...The electromagnetic detection satellite （EDS） is a type of earth observation satellites （EOSs）. The Information collected by EDSs plays an important role in some fields, such as industry, science and military. The scheduling of EDSs is a complex combinatorial optimization problem. Current research mainly focuses on the scheduling of imaging satellites and SAR satellites, but little work has been done on the scheduling of EDSs for its specific characteristics. A multi-satellite scheduling model is established, in which the specific constrains of EDSs are considered, then a scheduling algorithm based on the genetic algorithm （GA） is proposed. To deal with the specific constrains of EDSs, a penalty function method is introduced. However, it is hard to determine the appropriate penalty coefficient in the penalty function. Therefore, an adaptive adjustment mechanism of the penalty coefficient is designed to solve the problem, as well as improve the scheduling results. Experimental results are used to demonstrate the correctness and practicability of the proposed scheduling algorithm.展开更多
A hybrid scheduling algorithm based on genetic algorithm is proposed in this paper for reconnaissance satellite data transmission.At first,based on description of satellite data transmission request,satellite data tra...A hybrid scheduling algorithm based on genetic algorithm is proposed in this paper for reconnaissance satellite data transmission.At first,based on description of satellite data transmission request,satellite data transmission task model and satellite data transmission scheduling problem model are established.Secondly,the conflicts in scheduling are discussed.According to the meaning of possible conflict,the method to divide possible conflict task set is given.Thirdly,a hybrid algorithm which consists of genetic algorithm and heuristic information is presented.The heuristic information comes from two concepts,conflict degree and conflict number.Finally,an example shows the algorithm's feasibility and performance better than other traditional展开更多
In this paper, a new implementation of genetic algorithms (GAs) is developed for the machine scheduling problem, which is abundant among the modern manufacturing systems. The performance measure of early and tardy com...In this paper, a new implementation of genetic algorithms (GAs) is developed for the machine scheduling problem, which is abundant among the modern manufacturing systems. The performance measure of early and tardy completion of jobs is very natural as one's aim, which is usually to minimize simultaneously both earliness and tardiness of all jobs. As the problem is NP-hard and no effective algorithms exist, we propose a hybrid genetic algorithms approach to deal with it. We adjust the crossover and mutation probabilities by fuzzy logic controller whereas the hybrid genetic algorithm does not require preliminary experiments to determine probabilities for genetic operators. The experimental results show the effectiveness of the GAs method proposed in the paper.展开更多
Electromagnetic detection satellite（EDS） is a type of Earth observation satellite（EOS）. Satellites observation and data down-link scheduling plays a significant role in improving the efficiency of satellite observ...Electromagnetic detection satellite（EDS） is a type of Earth observation satellite（EOS）. Satellites observation and data down-link scheduling plays a significant role in improving the efficiency of satellite observation systems. However, the current works mainly focus on the scheduling of imaging satellites, little work focuses on the scheduling of EDSes for its specific requirements.And current works mainly schedule satellite resources and data down-link resources separately, not considering them in a globally optimal perspective. The EDSes and data down-link resources are scheduled in an integrated process and the scheduling result is searched globally. Considering the specific constraints of EDS, a coordinate scheduling model for EDS observation tasks and data transmission jobs is established and an algorithm based on the genetic algorithm is proposed. Furthermore, the convergence of our algorithm is proved. To deal with some specific constraints, a solution repairing algorithm of polynomial computing time is designed. Finally, some experiments are conducted to validate the correctness and practicability of our scheduling algorithms.展开更多
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
考虑到当前梯级水库蓄水调度研究尚未开展碳减排调度,基于碳排放因子法提出了梯级水库蓄水期水碳多目标调度模型,制定了梯级水库提前蓄水策略,并以防洪风险最小化、发电量最大化和温室气体排放量最小化为调度目标,采用NSGA-II求解调度...考虑到当前梯级水库蓄水调度研究尚未开展碳减排调度,基于碳排放因子法提出了梯级水库蓄水期水碳多目标调度模型,制定了梯级水库提前蓄水策略,并以防洪风险最小化、发电量最大化和温室气体排放量最小化为调度目标,采用NSGA-II求解调度模型推求了梯级水库蓄水期优化调度方案,在金沙江中下游6座水库与三峡水库组成的梯级水库开展了实例研究。结果表明:相较于现行调度方案,优化调度方案集在防洪库容占用率为0~4.92%的情况下,发电量提升了7.23~40.26亿kW·h/a(0.65%~3.60%),弃水量减少了15.82~55.03亿m^(3)/a(6.45%~22.43%),温室气体排放量降低了38.55~45.63 Gg CO_(2e)/a(8.33%~9.85%),碳排放强度降低了0.39~0.47 kg CO_(2e)/(MW·h)(9.49%~11.44%),显著提升了梯级水库的发电量、抗旱供水能力并减少了温室气体排放。研究成果为实现梯级水库蓄水期水碳协同调度提供了技术支撑。展开更多
基金supported by the National Key R&D Program of China(2018AAA0101700)the Program for HUST Academic Frontier Youth Team(2017QYTD04).
文摘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.
文摘The classical job shop scheduling problem(JSP) is the most popular machine scheduling model in practice and is known as NP-hard.The formulation of the JSP is based on the assumption that for each part type or job there is only one process plan that prescribes the sequence of operations and the machine on which each operation has to be performed.However,JSP with alternative machines for various operations is an extension of the classical JSP,which allows an operation to be processed by any machine from a given set of machines.Since this problem requires an additional decision of machine allocation during scheduling,it is much more complex than JSP.We present a domain independent genetic algorithm(GA) approach for the job shop scheduling problem with alternative machines.The GA is implemented in a spreadsheet environment.The performance of the proposed GA is analyzed by comparing with various problem instances taken from the literatures.The result shows that the proposed GA is competitive with the existing approaches.A simplified approach that would be beneficial to both practitioners and researchers is presented for solving scheduling problems with alternative machines.
基金supported by the National Natural Science Foundation of China(61671462).
文摘It is of great significance to carry out effective scheduling for the carrier-based aircraft flight deck operations.In this paper,the precedence constraints and resource constraints in flight deck operations are analyzed,then the model of the multi-aircraft integrated scheduling problem with transfer times(MAISPTT)is established.A dual population multi-operator genetic algorithm(DPMOGA)is proposed for solving the problem.In the algorithm,the dual population structure and random-key encoding modified by starting/ending time of operations are adopted,and multiple genetic operators are self-adaptively used to obtain better encodings.In order to conduct the mapping from encodings to feasible schedules,serial and parallel scheduling generation scheme-based decoding operators,each of which adopts different justified mechanisms in two separated populations,are introduced.The superiority of the DPMOGA is verified by simulation experiments.
文摘The problem of simultaneous scheduling of machines and vehicles in flexible manufacturing system (FMS) was addressed.A spreadsheet based genetic algorithm (GA) approach was presented to solve the problem.A domain independent general purpose GA was used,which was an add-in to the spreadsheet software.An adaptation of the propritary GA software was demonstrated to the problem of minimizing the total completion time or makespan for simultaneous scheduling of machines and vehicles in flexible manufacturing systems.Computational results are presented for a benchmark with 82 test problems,which have been constructed by other researchers.The achieved results are comparable to the previous approaches.The proposed approach can be also applied to other problems or objective functions without changing the GA routine or the spreadsheet model.
基金supported by the National Natural Science Foundation of China(6110118461174159)
文摘The electromagnetic detection satellite （EDS） is a type of earth observation satellites （EOSs）. The Information collected by EDSs plays an important role in some fields, such as industry, science and military. The scheduling of EDSs is a complex combinatorial optimization problem. Current research mainly focuses on the scheduling of imaging satellites and SAR satellites, but little work has been done on the scheduling of EDSs for its specific characteristics. A multi-satellite scheduling model is established, in which the specific constrains of EDSs are considered, then a scheduling algorithm based on the genetic algorithm （GA） is proposed. To deal with the specific constrains of EDSs, a penalty function method is introduced. However, it is hard to determine the appropriate penalty coefficient in the penalty function. Therefore, an adaptive adjustment mechanism of the penalty coefficient is designed to solve the problem, as well as improve the scheduling results. Experimental results are used to demonstrate the correctness and practicability of the proposed scheduling algorithm.
文摘A hybrid scheduling algorithm based on genetic algorithm is proposed in this paper for reconnaissance satellite data transmission.At first,based on description of satellite data transmission request,satellite data transmission task model and satellite data transmission scheduling problem model are established.Secondly,the conflicts in scheduling are discussed.According to the meaning of possible conflict,the method to divide possible conflict task set is given.Thirdly,a hybrid algorithm which consists of genetic algorithm and heuristic information is presented.The heuristic information comes from two concepts,conflict degree and conflict number.Finally,an example shows the algorithm's feasibility and performance better than other traditional
文摘In this paper, a new implementation of genetic algorithms (GAs) is developed for the machine scheduling problem, which is abundant among the modern manufacturing systems. The performance measure of early and tardy completion of jobs is very natural as one's aim, which is usually to minimize simultaneously both earliness and tardiness of all jobs. As the problem is NP-hard and no effective algorithms exist, we propose a hybrid genetic algorithms approach to deal with it. We adjust the crossover and mutation probabilities by fuzzy logic controller whereas the hybrid genetic algorithm does not require preliminary experiments to determine probabilities for genetic operators. The experimental results show the effectiveness of the GAs method proposed in the paper.
基金supported by the National Natural Science Foundation of China(6110118461174159)
文摘Electromagnetic detection satellite（EDS） is a type of Earth observation satellite（EOS）. Satellites observation and data down-link scheduling plays a significant role in improving the efficiency of satellite observation systems. However, the current works mainly focus on the scheduling of imaging satellites, little work focuses on the scheduling of EDSes for its specific requirements.And current works mainly schedule satellite resources and data down-link resources separately, not considering them in a globally optimal perspective. The EDSes and data down-link resources are scheduled in an integrated process and the scheduling result is searched globally. Considering the specific constraints of EDS, a coordinate scheduling model for EDS observation tasks and data transmission jobs is established and an algorithm based on the genetic algorithm is proposed. Furthermore, the convergence of our algorithm is proved. To deal with some specific constraints, a solution repairing algorithm of polynomial computing time is designed. Finally, some experiments are conducted to validate the correctness and practicability of our scheduling algorithms.
文摘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.
文摘考虑到当前梯级水库蓄水调度研究尚未开展碳减排调度,基于碳排放因子法提出了梯级水库蓄水期水碳多目标调度模型,制定了梯级水库提前蓄水策略,并以防洪风险最小化、发电量最大化和温室气体排放量最小化为调度目标,采用NSGA-II求解调度模型推求了梯级水库蓄水期优化调度方案,在金沙江中下游6座水库与三峡水库组成的梯级水库开展了实例研究。结果表明:相较于现行调度方案,优化调度方案集在防洪库容占用率为0~4.92%的情况下,发电量提升了7.23~40.26亿kW·h/a(0.65%~3.60%),弃水量减少了15.82~55.03亿m^(3)/a(6.45%~22.43%),温室气体排放量降低了38.55~45.63 Gg CO_(2e)/a(8.33%~9.85%),碳排放强度降低了0.39~0.47 kg CO_(2e)/(MW·h)(9.49%~11.44%),显著提升了梯级水库的发电量、抗旱供水能力并减少了温室气体排放。研究成果为实现梯级水库蓄水期水碳协同调度提供了技术支撑。
