A mathematical model for outbound truck scheduling with multiple trips in multi-door cross docking system

© IEOM Society International. This paper presents the novel mathematical model which considered the outbound scheduling problem in the multi-door cross docking system. The proposed model is different from other truck scheduling models due to the characteristic of the outbound trucks in which each ou...

Full description

Saved in:
Bibliographic Details
Main Authors: Nattapong Kamsura, Warisa Wisittipanich
Format: Conference Proceeding
Published: 2018
Subjects:
Online Access:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85051545302&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/58382
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Chiang Mai University
Description
Summary:© IEOM Society International. This paper presents the novel mathematical model which considered the outbound scheduling problem in the multi-door cross docking system. The proposed model is different from other truck scheduling models due to the characteristic of the outbound trucks in which each outbound truck can make multiple trips to deliver products to different sets of customers. In addition, it is desired that outbound trucks leave the dock doors as close to their predetermined due time to ensure customer satisfaction. To determine optimal solutions for this problem, the problem is formulated as a Mixed Integer Programming (MIP) model. The objectives are to minimize the total tardiness and the total earliness of all outbound trucks. The model is solved using the exact method of LINGO programming solver. The experimental results are executed in two phases. First, the optimal truck schedules are obtained by optimizing two objectives separately. Then, the multi-objective approach is used to find a set of solutions so that the decision makers can make a decision based on their preferences. The numerical results illustrate that, the model can only find optimal solutions for the small-size problems, however; it could not find optimal solutions for the large size problems within reasonable time.