Sharif Digital Repository

This research attempts to develop the optimal solution for an inventory problem consisting of a single machine which produces items some of which may be defective. We assume that no shortages are allowed and all defective items are to be reworked. Setup cost for rework and waiting time of defectives are considered. This research consists of two different parts. In the first part we consider two different policies where in the first policy in each cycle after the regular production the machine is setup for the rework of defectives of the same cycle. In the second policy several cycles constitute a period. All the defectives produced in the period are reworked in the last cycle of that period.... 

Fulfilling the demands of customers, used to be the main purpose of Supply Chain Network Designers. Soon after that, the environmental concerns created the concept of Reverse Logistic in order to prevent goods from being scattered in the environment after their disposal. Governmental regulations and people's concerns, forced the factories to gather the created pollutions of the environment. They gradualy confessed the positive impacts of these ruturns on their financials. The flow of forward and reverse logistics establishes a Closed-Loop in which the returning economical circumstances were transferd into original factories. Later, Open-Loops were created. In open-loops the returns have to... 

Although inventory control under discrete demand in dynamic quantities and time intervals are very common in reality, existing literature has largely ignored them. In this thesis, we investigate the inventory control decisions in a two echelon supply chain. The vendor supplies a product to several retailers, buying discrete quantities in pre-determined time intervals. The order quantity and order cycles for each retailer can be different from others. We have developed a new efficient algorithm to find the optimal ordering policy involves the scheduling and the order quantity of vendor to his supplier to minimize his cost function in infinite time horizon. Moreover, we have designed a revenue... 

Generally, nowadays, machine learning methods are used in many different areas for their superiority over other methods of prediction. Although being a tough task, stock market prediction with machine learning approaches is being spread due to satisfying results published ever yday. Machine learning methods usually use varied kinds of data ,including structured data such as market data, technical indicators and some fundamental data as well as unstructured one entailing text and graph data in order to enhance their predictability capacity. In this thesis we aimed to find out more about the importance and the contribution of structured data in prediction and we tried to attain a framework...