Depo sistemlerinde sipariş gruplama probleminin modellenmesi ve optimizasyonu

Order batching problem (OBP) is a combinatorial optimization problem which arises in warehouse order picking process. In OBP, the aim is to find groups of orders and picking routes of these groups to minimize distance travelled by the order picker. This problem is encountered especially in manual order picking systems where the capacity of picking facility is limited. Solving the OBP becomes more important when size of the problem (e.g. number of storage locations, number of aisles, number of customer orders, etc.) is large. Most of the time spent during retrieval of the orders depends on content of the batch and picking route. Therefore, an effective batching and routing approach is essential in reducing the time needed to collect ordered items. The main objective of this study is identified as developing fast and effective metaheuristic approaches that can solve the OBP in different warehouse configurations. For this purpose, three solution approaches based on genetic algorithm and local search are proposed. The numerical study of proposed algorithms is implemented with generated data sets, and also with instances from literature. Results of the benchmark study demonstrate competitiveness of developed approaches. It is believed that proposed methods will be useful to solve real case applications of the problem in different warehouse configurations. Accordingly, a real case application in a distribution center of a well-known retailer in Turkey is implemented. The case study also includes storage assignment process of incoming products. The results demonstrate that algorithms are practical and useful in real life problems.