This paper presents a parallel composite local search algorithm based on multiple search neighborhoods to solve a special kind of timetable problem. The new algorithm can also effectively solve those problems that can...This paper presents a parallel composite local search algorithm based on multiple search neighborhoods to solve a special kind of timetable problem. The new algorithm can also effectively solve those problems that can be solved by general local search algorithms. Experimental results show that the new algorithm can generate better solutions than general local search algorithms.展开更多
This paper presents two optimization methods for solving the passenger train timetabling problem to minimize the total delay time in the single track railway networks. The goal of the train timetable problem is to det...This paper presents two optimization methods for solving the passenger train timetabling problem to minimize the total delay time in the single track railway networks. The goal of the train timetable problem is to determine departure and arrival times to or from each station in order to prevent collisions between trains and effective utilization of resources. The two proposed methods are based on integration of a simulation and an optimization method to simulate train traffic flow and generate near optimal train timetable under realistic con- straints including stops for track maintenance and praying. The first proposed method integrates a cellular automata (CA) simulation model with genetic algorithm optimiza- tion method. In the second proposed approach, a CA simulation model combines with dynamically dimensioned search optimization method. The proposed models are applied to hypothetical case study to demonstrate the merit of them. The Islamic Republic of Iran Railways (IRIR) data and regulations have been used to optimize train timetable. The results show the first method is more effi- cient than the second method to obtain near optimal train timetabling.展开更多
[Objective]We aimed to study the influencing factors and comprehensive management of environmental problems in Dongping Lake.[Method]Based on field investigation and historical data,environmental problems of Dongping ...[Objective]We aimed to study the influencing factors and comprehensive management of environmental problems in Dongping Lake.[Method]Based on field investigation and historical data,environmental problems of Dongping Lake and their influencing factors were analyzed,and seven measures about comprehensive management of environmental issues and ecological protection of Dongping Lake were put forward.[Result]During recent years,the content of total nitrogen and total phosphorus in Dongping Lake showed a decreasing trend,but the water was still in the eutrophic state.The environmental problems of Dongping Lake,like flood,drought and pollution,are closely linked to natural and man-made factors,such as regional seasonal precipitation,industrial and agricultural pollution,etc.In order to control the environmental problems of Dongping Lake,we should strengthen the construction and management of wetland nature reserves,develop ecological agriculture,establish an integral system for environmental monitoring and information management,reinforce propaganda and education to improve residents’ environmental consciousness,etc.[Conclusion]The research could provide scientific references for the comprehensive management of environmental problems in Dongping Lake and its basin.展开更多
A real-life problem is the rostering of nurses at hospitals.It is a famous nondeterministic,polynomial time(NP)-hard combinatorial optimization problem.Handling the real-world nurse rostering problem(NRP)constraints i...A real-life problem is the rostering of nurses at hospitals.It is a famous nondeterministic,polynomial time(NP)-hard combinatorial optimization problem.Handling the real-world nurse rostering problem(NRP)constraints in distributing workload equally between available nurses is still a difficult task to achieve.The international shortage of nurses,in addition to the spread of COVID-19,has made it more difficult to provide convenient rosters for nurses.Based on the literature,heuristic-based methods are the most commonly used methods to solve the NRP due to its computational complexity,especially for large rosters.Heuristic-based algorithms in general have problems striking the balance between diversification and intensification.Therefore,this paper aims to introduce a novel metaheuristic hybridization that combines the enhanced harmony search algorithm(EHSA)with the simulated annealing(SA)algorithm called the annealing harmony search algorithm(AHSA).The AHSA is used to solve NRP from a Malaysian hospital.The AHSA performance is compared to the EHSA,climbing harmony search algorithm(CHSA),deluge harmony search algorithm(DHSA),and harmony annealing search algorithm(HAS).The results show that the AHSA performs better than the other compared algorithms for all the tested instances where the best ever results reported for the UKMMC dataset.展开更多
As the first attempt,this paper proposes a model for the Chinese high school timetabling problems(CHSTPs)under the new curriculum innovation which was launched in 2011 by the Chine6e government.Aooording 10 the new ou...As the first attempt,this paper proposes a model for the Chinese high school timetabling problems(CHSTPs)under the new curriculum innovation which was launched in 2011 by the Chine6e government.Aooording 10 the new our riculum innovation,students in high school can choose subjects that they are interested in instead of being forced to select one of the two study directions,namely,Science and Liberal Arts.Meanwhile,they also need to attend compulsory subjects as traditions.CHSTPs are student-oriented and involve more student constraints that make them more complex than the typi-cal"Class-Teacher model",in which the element"Teacher"is the primary constraint.In this paper,we first describe in detail the mathematical model of CHSTPs and then design a new two-part representation for the candidate solution.Based on the new representation,we adopt a two-phase simulated annealing(SA)algorithm to solve CHSTPs.A total number of 45 synthetic instances with different amounts of classes,teachers,and levels of student constraints are generated and used to ilustrate the characteristics of the CHSTP model and the effectiveness of the designed representation and algorithm.Finally,we apply the proposed model,the designed two-part representation and the two-phase SA on10 real high schools.展开更多
Purpose–The examination timetabling problem is an NP-hard problem.A large number of approaches for this problem are developed to find more appropriate search strategies.Hyper-heuristic is a kind of representative met...Purpose–The examination timetabling problem is an NP-hard problem.A large number of approaches for this problem are developed to find more appropriate search strategies.Hyper-heuristic is a kind of representative methods.In hyper-heuristic,the high-level search is executed to construct heuristic lists by traditional methods(such as Tabu search,variable neighborhoods and so on).The purpose of this paper is to apply the evolutionary strategy instead of traditional methods for high-level search to improve the capability of global search.Design/methodology/approach–This paper combines hyper-heuristic with evolutionary strategy to solve examination timetabling problems.First,four graph coloring heuristics are employed to construct heuristic lists.Within the evolutionary algorithm framework,the iterative initialization is utilized to improve the number of feasible solutions in the population;meanwhile,the crossover and mutation operators are applied to find potential heuristic lists in the heuristic space(high-level search).At last,two local search methods are combined to optimize the feasible solutions in the solution space(low-level search).Findings–Experimental results demonstrate that the proposed approach obtains competitive results and outperforms the compared approaches on some benchmark instances.Originality/value–The contribution of this paper is the development of a framework which combines evolutionary algorithm and hyper-heuristic for examination timetabling problems.展开更多
文摘This paper presents a parallel composite local search algorithm based on multiple search neighborhoods to solve a special kind of timetable problem. The new algorithm can also effectively solve those problems that can be solved by general local search algorithms. Experimental results show that the new algorithm can generate better solutions than general local search algorithms.
文摘This paper presents two optimization methods for solving the passenger train timetabling problem to minimize the total delay time in the single track railway networks. The goal of the train timetable problem is to determine departure and arrival times to or from each station in order to prevent collisions between trains and effective utilization of resources. The two proposed methods are based on integration of a simulation and an optimization method to simulate train traffic flow and generate near optimal train timetable under realistic con- straints including stops for track maintenance and praying. The first proposed method integrates a cellular automata (CA) simulation model with genetic algorithm optimiza- tion method. In the second proposed approach, a CA simulation model combines with dynamically dimensioned search optimization method. The proposed models are applied to hypothetical case study to demonstrate the merit of them. The Islamic Republic of Iran Railways (IRIR) data and regulations have been used to optimize train timetable. The results show the first method is more effi- cient than the second method to obtain near optimal train timetabling.
基金Supported by National Natural Science Foundation of China(40901276,40871239,41072258)Doctoral Foundation of Shandong Province,China (2011BSB01256)
文摘[Objective]We aimed to study the influencing factors and comprehensive management of environmental problems in Dongping Lake.[Method]Based on field investigation and historical data,environmental problems of Dongping Lake and their influencing factors were analyzed,and seven measures about comprehensive management of environmental issues and ecological protection of Dongping Lake were put forward.[Result]During recent years,the content of total nitrogen and total phosphorus in Dongping Lake showed a decreasing trend,but the water was still in the eutrophic state.The environmental problems of Dongping Lake,like flood,drought and pollution,are closely linked to natural and man-made factors,such as regional seasonal precipitation,industrial and agricultural pollution,etc.In order to control the environmental problems of Dongping Lake,we should strengthen the construction and management of wetland nature reserves,develop ecological agriculture,establish an integral system for environmental monitoring and information management,reinforce propaganda and education to improve residents’ environmental consciousness,etc.[Conclusion]The research could provide scientific references for the comprehensive management of environmental problems in Dongping Lake and its basin.
文摘A real-life problem is the rostering of nurses at hospitals.It is a famous nondeterministic,polynomial time(NP)-hard combinatorial optimization problem.Handling the real-world nurse rostering problem(NRP)constraints in distributing workload equally between available nurses is still a difficult task to achieve.The international shortage of nurses,in addition to the spread of COVID-19,has made it more difficult to provide convenient rosters for nurses.Based on the literature,heuristic-based methods are the most commonly used methods to solve the NRP due to its computational complexity,especially for large rosters.Heuristic-based algorithms in general have problems striking the balance between diversification and intensification.Therefore,this paper aims to introduce a novel metaheuristic hybridization that combines the enhanced harmony search algorithm(EHSA)with the simulated annealing(SA)algorithm called the annealing harmony search algorithm(AHSA).The AHSA is used to solve NRP from a Malaysian hospital.The AHSA performance is compared to the EHSA,climbing harmony search algorithm(CHSA),deluge harmony search algorithm(DHSA),and harmony annealing search algorithm(HAS).The results show that the AHSA performs better than the other compared algorithms for all the tested instances where the best ever results reported for the UKMMC dataset.
基金This work was supported in part by the Outstanding Young Scholar Program of National Natural Science Foundation of China(NSFC)(Grant No.61522311)in part by the General Program of NSFC(Grant No.61773300)+1 种基金in part by the Key Program of Fundamental Research Project of Natural Science of Shaanxi Province,China(2017JZ017)in part by the Doctoral Students'Short-Term Study Abroad Scholarship Fund of Xidian University.
文摘As the first attempt,this paper proposes a model for the Chinese high school timetabling problems(CHSTPs)under the new curriculum innovation which was launched in 2011 by the Chine6e government.Aooording 10 the new our riculum innovation,students in high school can choose subjects that they are interested in instead of being forced to select one of the two study directions,namely,Science and Liberal Arts.Meanwhile,they also need to attend compulsory subjects as traditions.CHSTPs are student-oriented and involve more student constraints that make them more complex than the typi-cal"Class-Teacher model",in which the element"Teacher"is the primary constraint.In this paper,we first describe in detail the mathematical model of CHSTPs and then design a new two-part representation for the candidate solution.Based on the new representation,we adopt a two-phase simulated annealing(SA)algorithm to solve CHSTPs.A total number of 45 synthetic instances with different amounts of classes,teachers,and levels of student constraints are generated and used to ilustrate the characteristics of the CHSTP model and the effectiveness of the designed representation and algorithm.Finally,we apply the proposed model,the designed two-part representation and the two-phase SA on10 real high schools.
文摘Purpose–The examination timetabling problem is an NP-hard problem.A large number of approaches for this problem are developed to find more appropriate search strategies.Hyper-heuristic is a kind of representative methods.In hyper-heuristic,the high-level search is executed to construct heuristic lists by traditional methods(such as Tabu search,variable neighborhoods and so on).The purpose of this paper is to apply the evolutionary strategy instead of traditional methods for high-level search to improve the capability of global search.Design/methodology/approach–This paper combines hyper-heuristic with evolutionary strategy to solve examination timetabling problems.First,four graph coloring heuristics are employed to construct heuristic lists.Within the evolutionary algorithm framework,the iterative initialization is utilized to improve the number of feasible solutions in the population;meanwhile,the crossover and mutation operators are applied to find potential heuristic lists in the heuristic space(high-level search).At last,two local search methods are combined to optimize the feasible solutions in the solution space(low-level search).Findings–Experimental results demonstrate that the proposed approach obtains competitive results and outperforms the compared approaches on some benchmark instances.Originality/value–The contribution of this paper is the development of a framework which combines evolutionary algorithm and hyper-heuristic for examination timetabling problems.