Abstract:
Supply chain operations are the vital functions of today’s huge and complex organizations. Although most of these activities are inevitable due to the different needs of customers, eliminating some non-value-adding activities provides significant cost reduction opportunities. Considering internal logistics activities, it is seen that material-receiving process includes many types of inefficiencies. To illustrate; waiting trucks, unloading materials at unsuitable docks or huge distances from docks to the warehouses are the inefficiencies resulting in extra costs without adding value for the product. The presented study is based on truck unloading and material acceptance processes in an automotive plant. After entering the factory, trucks wait in queues if all unloading docks are busy in that time and then they are directed to suitable docks. “What kind of a queuing system should be designed?”, “Which truck should be selected from the queue when an unloading dock becomes idle? or “Which unloading dock should be selected if there are some idle docks?” are the main decisions to be made in daily operations. This study aims to investigate different truck routing and dock selection policies supporting the decision making process. By using ARENA simulation software, studied policies are simulated in various environments with different input parameters. Resulting average waiting time per truck and average covered distance to carry unloaded materials from unloading docks to warehouses are investigated as main performance measures of the system. Due to the multi-objective setting of the study, pareto analysis is utilized by using two-dimensional trade-off curves. Analyzing system outputs, some generalized conclusions about the effectiveness of the proposed routing policies are drawn. Then, an actual data set is obtained from an automotive factory and a real life case study is investigated. After simulating the process with different routing policies, the efficient ones are determined for the automotive factory.
