International Journal of Industrial Engineering and Productional Research
Volume:18 Issue: 3, Sep 2006

In this paper, we apply the stochastic dynamic programming to approximate the mean project completion time in dynamic Markov PERT networks. It is assumed that the activity durations are independent random variables with exponential distributions, but some social and economical problems influence the mean of activity durations. It is also assumed that the social problems evolve in accordance with the independent semi-Markov processes over the planning horizon. By using the stochastic dynamic programming, we find a dynamic path with maximum expected length from the source node to the sink node of the stochastic dynamic network. The expected value of such path can be considered as an approximation for the mean project completion time in the original dynamic PERT network.

This paper presents an exact model and a genetic algorithm for the multi-mode resource constrained project scheduling problem with generalized precedence relations in which the duration of an activity is determined by the mode selection and the duration reduction (crashing) applied within the selected mode. All resources considered are renewable. The objective is to determine a mode, the amount of continuous crashing, and a start time for each activity so that all constraints are obeyed and the project duration is minimized. Project scheduling of this type occurs in many fields; for instance, predicting the resources and duration of activities in software development projects. A key feature of the model is that none of the typical models can cope with the continuous resource constraints. Computational results with a set of 100 generated instances have been reported and the efficiency of the proposed model has been analyzed.

In this paper a meta-heuristic approach has been presented to solve lot-size determination problems in a complex multi-stage production planning problems with production capacity constraint. This type of problems has multiple products with sequential production processes which are manufactured in different periods to meet customer’s demand. By determining the decision variables, machinery production capacity and customer’s demand, an integer linear program with the objective function of minimization of total costs of set-up, inventory and production is achieved. In the first step, the original problem is decomposed to several sub-problems using a heuristic approach based on the limited resource Lagrange multiplier. Thus, each sub-problem can be solved using one of the easier methods. In the second step, through combining the genetic algorithm with one of the neighborhood search techniques, a new approach has been developed for the sub-problems. In the third step, to obtain a better result, resource leveling is performed for the smaller problems using a heuristic algorithm. Using this method, each product’s lot-size is determined through several steps. This paper’s propositions have been studied and verified through considerable empirical experiments.

ط Nowadays knowledge is recognized as an important enabler for competitive advantages and many companies are beginning to establish knowledge management systems. Within the last few years many organizations tried to design a suitable knowledge management system and many of them were successful. This paper is to discover critical success factors (CSF) of knowledge management (KM) and their relationships in an effective way. A qualitative case study technique has been used in this paper for data collection and analysis. In this way, grounded theory (GT) research approach has been selected. The collected data are categorized and analyzed through specific stages of GT. A semantic network has been developed by categorized data showing the relationships between the extracted CSFs and finally a theory has been emerged. The semantic network and the emerged theory show the roadmap of success in KM area for the organizations.