A Network Model for Time Management in Overhaul of Sugarcane Harvester

Introduction Mechanized harvesting of sugarcane by harvesters and the lack of proper management of harvesting, increase the cost of production and eventually become unprofitable. In the case of sugarcane harvester, because the systems are used to be repaired, increasing in system consumption will reduce machine reliability (Failure rate will increase). So, timely annual overhaul has effective role in enhancing the reliability of sugarcane harvesting machines. Because of time importance indicator for reducing timeliness cost and work breakdown, project scheduling techniques and work study especially network models are used. In this study, because of the ability of GERT networks capabilities in planning and scheduling, GERT networks were used and overhaul scheduling of sugarcane harvester in Amir Kabir Agro-Industry of Khuzestan province, Iran as a case study was analyzed. Materials and Methods The study was carried out in Khuzestan province of Iran in 2016. Data were collected from variety sources such as opinions and comments of experts and reports and statistics of Sugarcane Agro-Industry. All activity times are given in hour. At first, the project activities are determined and the work breakdown structure was drawn. Finally, GERT network was plotted and analyzed. GERT is a procedure, which combines the disciplines of the flow graph theory, Moment Generating Function (MGF) and Project Evaluation and Review Technique (PERT) for analyzing stochastic networks having logical nodes and directed branches. Each branch has a probability that the activity associated with it will be performed. Therefore, GERT provides a visual picture of the system by means of the corresponding graph and makes it possible to analyze the given system in a less inductive manner. The following steps are employed, when applying GERT: 1. Convert a qualitative description of a system or problem to a model in a stochastic network form. 2. Collect the necessary data to describe the transmittances of the network. 3. Apply Mason’s rule to determine the equivalent function or functions of the network. 4. Convert the equivalent function into the following two performance measures of the network: (a) The probability that a specific node is realized. (b) The moment generating function of the time associated with a node, if it is realized. 5. Make inferences concerning the system under study from the information obtained in the Step 4. Results and Discussion In this paper the GERT method has been