| Summary: | 碩士 === 國立東華大學 === 資訊管理碩士學位學程 === 101 === Traditional unloading problem merely concentrate on optimizing the usage rate of the container and it is only applicable in the point to point delivery problem. In practical, there are a lot of packing problems in logistics which belong to multiple point delivery. Therefore, we should also consider the unloading sequence of each goods when we pack the goods. Recently, because of the rising price of oil and awareness of environmental protection, how to cost down the expenses of logistics to achieve green transportation has also become an essential issue.
In order to classify all the delivery destinations, we also improve traditional K-means through considering the amount of goods and the total volume of goods that per delivery destination received. And we use TSP to arrange the sequence of all the delivery destinations. Finally, we apply genetic programming to create packing heuristic which considering the routing costs, unloading costs and the amount of trucks we sent at the same time. We use these costs to evaluate the performance of our multi-objective heuristics to reduce the expense that enterprise spends in logistics. In the experiment, we found out that our method could effectively improve the unloading costs and the routing costs by arranging the packing sequence of goods.
|
|---|
