Research On The Vehicle And Dock Synchronized Scheduling Problem In A Milk Run System

In “Industry 4.0” era,automotive logistics is moving in direction of intelligence,interconnection and integration.In this context,the paper analyzes key aspects for improvement in milk run.Actually,integrated planning and scheduling is one of the most important aspects in milk run operations that need strengthening.After analyzing the business process of milk run,it’s found that the vehicle scheduling and unloading dock scheduling are two mostly related parts.The integrated scheduling of vehicles and docks is a key problem faced by the automotive inbound logistics transportation planners in daily operations.However,it’s rarely discussed in existing literature.Therefore,the paper proposes the milk run Vehicle and Dock Synchronized Scheduling Problem(VDSSP).Firstly,a mixed integer programming model is established for the VDSSP,considering vehicle scheduling constraints,dock scheduling constraints and synchronized constraints.The VDSSP is a complicated combinatorial optimization problem;it’s difficult to obtain an optimal solution theoretically or practically.Thus,two algorithms are proposed.The first one is a decomposition algorithm(H1),which splits the integrated problem and uses a feedback mechanism;the second one is a list-based simulated annealing algorithm(H2),which combines list scheduling and simulated annealing algorithm.Furthermore,computational experiments are conducted based on the simulated data of a third-party logistics enterprise.The numerical results indicate that feasible solutions of VDSSP can be obtained efficiently and effectively via the two algorithms.Besides,the two proposed algorithms can both achieve more efficient utilization of the pick-up vehicles and unloading docks,compared to the results of Gurobi.Nevertheless,the algorithms have different performances in different problem size.H1 performs better in small-scale problems.When the number of routes is larger than 30,H2 has an advantage and can maintain good solving capability.The algorithms presented in this paper can be used for VDSSP.Thus they can provide decision support for the transportation planners in milk run daily operations.They can also help to achieve more efficient resource utilization and finally realize cost savings in logistics.After all,the paper is only making a small step in the development of more intelligent,interconnected and integrated automotive logistics.