Heuristic optimization of operations at a cross-dock warehouse
Cross-docking is a logistics strategy that streamlines the supply chain and moves the goods to the market faster using an efficient distribution system. A cross dock warehouse receives shipment from various suppliers, consolidates and ships it to the customers directly as quickly as possible, with m...
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| Format: | Theses and Dissertations |
| Language: | English |
| Published: |
2019
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| Online Access: | http://hdl.handle.net/10356/78693 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Cross-docking is a logistics strategy that streamlines the supply chain and moves the goods to the market faster using an efficient distribution system. A cross dock warehouse receives shipment from various suppliers, consolidates and ships it to the customers directly as quickly as possible, with minimal or no storage in between. The implementation of cross-dock warehousing has many advantages including shorter lead time and faster delivery to customer, reduced inventory holding cost and space for warehouse, increased customer satisfaction and increased cost savings.
This dissertation addresses the issues involved in the optimisation of a cross-dock warehouse which is the vehicle routing and scheduling problem. The objective of this study is to perform cluster analysis to find the optimal number of clusters for different sets of customers and hence find the optimal route. The approach used in this study is cluster first – route second methodology where the customers are first clustered using K-medoid clustering and the optimal route within each cluster is found using Tabu search. The aim of this study is two-fold: to reduce the number of vehicles required to satisfy the customer demand using cluster analysis, reduce the total distance covered and, deliver the goods within the customer specified time; and to minimise the total operational costs in the cross-dock warehouse such as the transportation cost, inventory holding cost, tardiness cost and earliness cost using Tabu search.
This paper provides a clear understanding of how cluster analysis produces effective results for a large data set using Tabu search in solving the vehicle routing and scheduling problem. |
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