| The central task of railway terminal is use equipment to complete the transportation of cargo and passenger flows.Placing-in and taking-out wagons helps the cargo transportation and it is an important work of the large railway freight terminal.The demand for freight transportation in China’s railway terminals is increasing.Therefore,it is of great significance to study the optimization of placing-in and taking-out wagons with multiple engines.Reasonably arranging engine resources to complete the placing-in and taking-out wagons work can accelerate the turnover of trucks.Meanwhile,a good arrangement plan can improve transportation efficiency and economic efficiency.In this paper,the problem of placing-in and taking-out wagons with multiple engines on branch-shaped siding network is further studied.The study consider the working environment of branch-shaped siding network and multiple shunting engines simultaneous operate.According to the characteristics of the problem,a bi-level model is constructed by the idea of bi-level programming.The model is solved by using the HP-CCI&IGA(hybrid procedure combining comprehensive correlation indicator and improved genetic algorithm)fusion solving strategy.The upper level model aiming at the balance of each engine’s traction is established,and it considers the workload of handling station.The lower level model with the goal of minimizing the total cost which comprise the walking cost produced by engine and staying cost produced by wagons is established.The lower level model considers the factors such as the work sequence,the time window,the maximum wagon number that engine can draw,and the requirements of batch division.For the solution of the upper model,we use reasonable method to calculate the optimal engine amount.Then,the central clustering algorithm to divide the working area is designed.It helps assign the working area and work to each engine.The work allocation information solved by the upper level model is used to solve the lower model.A coding scheme based on the traffic flow is designed when use improved genetic algorithm to solve the lower model.Firstly,the initial solution is generated by the rule of placing-in first andtaking-out later in the IGA algorithm.Secondly,the multiple wheel roulette method of sequencing type without putting back is used to select the solution.Then,the variation operation is designed to modify the order.At the end,the bi-level model and HP-CCI&IGA fusion solving strategy are verified by simulation experiments.A comparative experiment is performed by using GA and IGA.Both the algorithm set different work quantity scales and parameters.The results show that the IGA has the better ability to solve the study problem in the paper,and it has a better performance on large-scale problem. |
