In this paper we consider a queueing network consisting of two parallel servers and threearrival streams generated by independent Poisson sources. Each server has its own queue and receivescustomers from its own arriv...In this paper we consider a queueing network consisting of two parallel servers and threearrival streams generated by independent Poisson sources. Each server has its own queue and receivescustomers from its own arrival stream. A third arrival stream consists of customers which place resourcedemands on both servers, which are handled separately by each server once the request is made. Eachservice time is independent and exponentially distributed. Each customer in the system pays a holdingcost per unit time. The objective is to dynamically determine the optimal scheduling policy to the thirdstream of conupled customers. based on the state of the system, so as to minimize the average cost. Thismodel is new, and has Policy implications for computer or communication networks. A fuzzy approachis presented to solve this problem. Simulation shows that the approach is efficient and promising.展开更多
This paper considers a reentrant scheduling problem on parallel primary machines with a remote server machine, which is required to carry out the setup operation. In this problem, each job has three operations. The fi...This paper considers a reentrant scheduling problem on parallel primary machines with a remote server machine, which is required to carry out the setup operation. In this problem, each job has three operations. The first and last operations are performed by the same primary machine, implying the reentrance, and the second operation is processed on the single server machine. The order of jobs is predetermined in our context. The challenge is to assign jobs to the primary machines to minimize the makespan. We develop a genetic algorithm(GA) to solve this problem. Based on a simple strategy of assigning jobs in batches on the parallel primary machines, the standardized random key vector representation is employed to split the jobs into batches. Comparisons among the proposed algorithm, the branch and bound(BB) algorithm and the heuristic algorithm, coordinated scheduling(CS), which is only one heuristic algorithm to solve this problem in the literature, are made on the benchmark data. The computational experiments show that the proposed genetic algorithm outperforms the heuristic CS and the maximum relative improvement rate in the makespan is 1.66%.展开更多
文摘In this paper we consider a queueing network consisting of two parallel servers and threearrival streams generated by independent Poisson sources. Each server has its own queue and receivescustomers from its own arrival stream. A third arrival stream consists of customers which place resourcedemands on both servers, which are handled separately by each server once the request is made. Eachservice time is independent and exponentially distributed. Each customer in the system pays a holdingcost per unit time. The objective is to dynamically determine the optimal scheduling policy to the thirdstream of conupled customers. based on the state of the system, so as to minimize the average cost. Thismodel is new, and has Policy implications for computer or communication networks. A fuzzy approachis presented to solve this problem. Simulation shows that the approach is efficient and promising.
基金Supported by National Natural Science Foundation of China(No.61271374)Beijing Natural Science Foundation(No.4122068)
文摘This paper considers a reentrant scheduling problem on parallel primary machines with a remote server machine, which is required to carry out the setup operation. In this problem, each job has three operations. The first and last operations are performed by the same primary machine, implying the reentrance, and the second operation is processed on the single server machine. The order of jobs is predetermined in our context. The challenge is to assign jobs to the primary machines to minimize the makespan. We develop a genetic algorithm(GA) to solve this problem. Based on a simple strategy of assigning jobs in batches on the parallel primary machines, the standardized random key vector representation is employed to split the jobs into batches. Comparisons among the proposed algorithm, the branch and bound(BB) algorithm and the heuristic algorithm, coordinated scheduling(CS), which is only one heuristic algorithm to solve this problem in the literature, are made on the benchmark data. The computational experiments show that the proposed genetic algorithm outperforms the heuristic CS and the maximum relative improvement rate in the makespan is 1.66%.
