Warehouses

Distribution centers and warehouses are becoming more and more dependent on advanced computer technologies to establish and maintain competitiveness in a global economy. Neural network represent a new technology with a wide scope of potential warehouses applications, ranging from planning and forecasting to overall performance. In this dissertation, numerous results are showing increases in warehouse performance, when using neural network technology. The neural network system is used as a forecasting tool. It is then compared to time series forecasting analysis. The comparison process is designed to increase the warehouse performance understudy. At the end of this process, the results are forecasting variables needed to eventually increase warehouse performance and efficiency. Initially, neural networks along with time series are used to make the forecast on inventory control. Then the following step is to let different neural network modules perform the forecasting analysis on other management operations like inventory adjustments, accuracy and turnover, customer complaints and labor productivity for any distribution center or warehouse. The concept of benchmarking is also used, in order to provide tools which will help warehouse management determining performance levels for each subcomponent of the warehouse operations, and consequently the overall performance of the warehouse or distributor center taken into consideration after feeding in the appropriate data to the system.

In recent years, there has been an exponential increase in container volume shipment within intermodal transportation systems. Container terminals as part of the global port system represent important hubs within this intermodal transportation system. Thus, the need to improve the operational efficiency is the most important issue for container terminals from an economic standpoint. Moreover, intermodal transportation systems, ports and inland transport facilities should all be integrated into one coordinated plan. More specifically, a method to schedule different types of handling equipment in an integrated way within a container terminal is a popular topic for researchers. However, not many researchers have addresses this topic in relationship to the simulation aspect which will test feasible solutions under real container terminal environment parameters. In order to increase the efficiency of operations, the development of mathematical models and algorithms is critical in finding the best feasible solution. The objective of this study is to evaluate the feasible solution to find the proper number of Yard Trailers (YTs) with the minimal cost for the container terminals. This study uses the Dynamic YTs operation's method as a background for modeling. A mathematical model with various constraints related to the integrated operations among the different types of handling equipment is formulated. This model takes into consideration both serving time of quay cranes and yard cranes, and cost reduction strategies by decreasing use of YTs with the specific objective of minimum total cost including utilization of YTs and vessel berthing. In addition, a heuristic algorithm combined with Monte Carlo Method and Brute-Force Search are employed. The early Stage Technique of Monte Carlo method is proposed to generate vast random numbers to replicate simulation for real cases.