A comparison of different solution approaches to the vehicle scheduling problem in a practical case
Computers and Operations Research
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Research on Benefits Distribution of Fourth Party Logistics Based on Nonprofit Organization
ICIRA '08 Proceedings of the First International Conference on Intelligent Robotics and Applications: Part II
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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In order to reduce logistic costs, the scheduling of logistic tasks and resources for fourth party logistics (4PL) is studied. Current scheduling models only consider costs and finish times of each logistic resource or task. Not generally considered are the joint cost and time between two adjacent activities for a resource to process and two sequential activities of a task for two different resources to process are ignored. Therefore, a multi-objective scheduling model aiming at minimizing total operation costs, finishing time and tardiness of all logistic tasks in a 4PL is proposed. Not only are the joint cost and time of logistic activities between two adjacent activities and two sequential activities included but the constraints of resource time windows and due date of tasks are also considered. An improved nondominated sorting genetic algorithm (NSGA-II) is presented to solve the model. The validity of the proposed model and algorithm are verified by a corresponding case study.