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We develop a multi-objective model for the time-cost trade-off problem in PERT networks with generalized Erlang distributions of activity durations, using a genetic algorithm. The mean duration of each activity is assumed to be a non-increasing function and the direct cost of each activity is assumed to be a non-decreasing function of the amount of resource(More)
In this paper we develop an open queueing network for optimal design of multi-stage assemblies, in which each service station represents a manufacturing or assembly operation. The arrival processes of the individual parts of the product are independent Poisson processes with equal rates. In each service station, there is a server with exponential(More)
In this paper, we develop a multi-objective model to optimally control the lead time of a multi-stage assembly system, using genetic algorithms. The multi-stage assembly system is modelled as an open queueing network. It is assumed that the product order arrives according to a Poisson process. In each service station, there is either one or infinite number(More)
In this paper, we develop a multi-objective model to optimally control the lead time of a multistage assembly system, using an interactive method. The multistage assembly system is modelled as an open queueing network, whose service stations represent manufacturing or assembly operations. It is assumed that the product order arrives according to a Poisson(More)
In this paper, we apply the stochastic dynamic programming to find the dynamic shortest path from the source node to the sink node in stochastic dynamic networks, in which the arc lengths are independent random variables with exponential distributions. In each node there is an environmental variable, which evolves in accordance with a continuous time Markov(More)
We develop a multi-objective model for resource allocation problem in PERT networks with exponentially or Erlang distributed activity durations, where the mean duration of each activity is a non-increasing function and the direct cost of each activity is a non-decreasing function of the amount of resource allocated to it. The decision variables of the model(More)
This paper presents a nonlinear mathematical programming model for a stochastic job shop scheduling problem. Due to the complexity of the proposed model, traditional algorithms have low capability in producing a feasible solution. Therefore, a hybrid method is proposed to obtain a near-optimal solution within a reasonable amount of time. This method uses a(More)