Optimizing Truck Scheduling in Cross Docking System under Uncertainty, Using Simulation Technique (Case Study: Central Fruit and Vegetable Warehouse of Tehran Municipality)

Document Type : Original Article

Authors

1 Ph. D student, Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran.

2 Associate Professor, Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran.

3 Assistant Professor, Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran.

4 Professor, Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran.

Abstract

Cross docking is a distribution system in which goods are transported to destinations with the aim of preparing trucks at full capacity and reducing inventory costs. The truck scheduling problem has recently been studied by many researchers, but mostly in deterministic cases and situations, while there are many uncertainties in this process. This paper intends to optimize the process related to cross docking operations in a logistics center (Central Fruit and Vegetable Warehouse of Tehran Municipality) with the aim of minimizing operation time, inventory holding, and determining the optimal number of operators under uncertainties in truck arrival time, loading and unloading operations time, handling time and availability of resources and equipment. Due to the computational complexity of precision solving and evaluation of various options, a simulation-based optimization approach has been used, which is a combination of simulation, design of experiment and response surface methodology (RSM). The response surface methodology shows that the best-case scenario includes composition, direct movement between docks, exclusive service mode and "first come-first served" method. Numerical results also confirm that the proposed scenario greatly improves the performance measures in the studied warehouse.

Keywords

Main Subjects

References

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