TY - CHAP
T1 - Adoption of genetic algorithm for cross-docking scheduling with time window
AU - Yang, L.
AU - Lee, Ka Man
PY - 2012
Y1 - 2012
N2 - Cross-docking is widely adopted as an alternative to traditional warehousing in many industries. It consolidates different deliveries from suppliers into specified shipments catered for respective customers, thus reducing transportation and inventory holding costs. This book chapter addresses the scheduling problem of delivery where the products are expected to ship from suppliers to cross-docking faculties to customers within time window. For generating online delivery scheduling for the distribution network, the problem, which is formulated with the objective of minimising the inventory, transportation and penalty cost, is solved by genetic algorithm. Experiments were conducted to study the robustness of the model and the performance of the important parameters. From the results, it was also found that as the number of deliveries, pickups, cross-docks, time horizon and product type increase, the number of variables involved increases which in turn increase the complexity of the model. With higher number of variables, the computational time elapsed increase tremendously and total cost increases with the number of product types.
AB - Cross-docking is widely adopted as an alternative to traditional warehousing in many industries. It consolidates different deliveries from suppliers into specified shipments catered for respective customers, thus reducing transportation and inventory holding costs. This book chapter addresses the scheduling problem of delivery where the products are expected to ship from suppliers to cross-docking faculties to customers within time window. For generating online delivery scheduling for the distribution network, the problem, which is formulated with the objective of minimising the inventory, transportation and penalty cost, is solved by genetic algorithm. Experiments were conducted to study the robustness of the model and the performance of the important parameters. From the results, it was also found that as the number of deliveries, pickups, cross-docks, time horizon and product type increase, the number of variables involved increases which in turn increase the complexity of the model. With higher number of variables, the computational time elapsed increase tremendously and total cost increases with the number of product types.
U2 - 10.1007/978-1-4471-4033-7_1
DO - 10.1007/978-1-4471-4033-7_1
M3 - Chapter in an edited book (as author)
SN - 9781447140320
T3 - Decision engineering
SP - 1
EP - 22
BT - Decision-making for supply chain integration : supply chain integration
PB - Springer
ER -