Two-stage optimisation method for material flow and allocation management in cross-docking networks


KÜÇÜKOĞLU İ. , ÖZTÜRK N.

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, vol.55, no.2, pp.410-429, 2017 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 55 Issue: 2
  • Publication Date: 2017
  • Doi Number: 10.1080/00207543.2016.1184346
  • Title of Journal : INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
  • Page Numbers: pp.410-429
  • Keywords: cross-docking, material flow, two-dimensional loading, integer programming, genetic algorithm, SUPPLY CHAIN NETWORK, DISTRIBUTION PLANNING PROBLEM, PARTICLE SWARM OPTIMIZATION, GENETIC ALGORITHM, TRANSPORTATION PROBLEM, ASSIGNMENT PROBLEM, DESIGN, HYBRID, INVENTORY, HEURISTICS

Abstract

Cross-docking is a relatively new logistics strategy in which items are moved from suppliers to customers through cross-docking centres without putting them into long-term storage. An important decision during the planning of cross-docking operations is related to the material flow management in the network, which has great potential to reduce transportation costs. However, until now, there has been a lack of studies regarding operations for both transportation of trucks between locations and trans-shipment of items in cross-docking centres. This study presents a novel two-stage mixed integer linear mathematical model for the transportation problem of cross-docking network design integrated with truck-door assignments to minimise total transportation costs from suppliers to customers. This model also considers incoming/outgoing truck-loading plans and product allocations in the cross-docking area with regard to the two-dimensional physical constraints. Due to the complexity of the problem, a genetic algorithm (GA) is proposed to solve large-sized problems. Computational studies are conducted to examine the validity of the two-stage model and performance of the GA. The computational studies show that the introduced model provides a comprehensive plan for material flow management in cross-docking networks and proposed GA is capable of obtaining effective results for the problem within a short computational time.