"A Tabu Search Heuristic for Multi-Period Clustering to Rationalize Delivery Operations"

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Title:	"A Tabu Search Heuristic for Multi-Period Clustering to Rationalize Delivery Operations"
Author:	Khambhampati, Surya Sudha
Description:	Delivery operations use centralized warehouses to serve geographically distributed customers. Resources (e.g. personnel, trucks, stock, and equipment) are scheduled from the warehouses to distributed locations with the aim of: (a) meeting customer demands and, (b) rationalizing delivery operation costs. My thesis investigates the problem of clustering customers based on their geographical vicinity and their multi-period demands, while optimally scheduling resources. The problem addresses with-and-without capacity constraints of vehicles at the warehouse. This problem is proven to be NP-Hard. Hence, solutions using state-of-the-art exact methods such as branch and bound are not pertinent due to the computation complexity involved. We develop a K-means clustering algorithm for the initial solution and a tabu search heuristic that combines three advanced neighborhood search algorithms: (i) shift move, (ii) shift move with supernodes, and (iii) ejection chain with supernodes, to accelerate convergence.
Permanent Link:	http://rave.ohiolink.edu/etdc/view?acc_num=wright1210959864 http://hdl.handle.net/2374.OX/19652
Date:	2008

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