Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.680747
Title: Multi-stage stochastic modelling for global supply chain and logistics under uncertainty
Author: Zhu, Lin
ISNI:       0000 0004 5916 9548
Awarding Body: University of Southampton
Current Institution: University of Southampton
Date of Award: 2015
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Abstract:
This research focuses on the applications of multi-stage stochastic models for global supply chain and logistics, especially in global production planning problems and international air cargo forwarding problems under uncertainties. We first exam a multi-period, multi-product and multi-plant production planning problem under uncertain demand and quota limitations and develop a multi-stage stochastic model to handle this problem. Then we present three types of robust models for the same problem: the robust optimization model with solution robustness, the robust optimization model with model robustness, and the robust optimization model with the trade-off between solution robustness and model robustness. Results show that multi-stage models will bring more benefits to their decision-makers. The second problem we look at is an international air cargo forwarding problem under uncertainty, which means the cargoes need to be transported from regions to destinations via a hub. The air forwarders not only have to make a decision about the number of containers to be booked for the regions and hub in advance before accurate customers’ information becomes available, but also have to decide the number of extra containers to be required or the containers to be returned after the realisation of uncertainty. We develop stochastic models and three types of robust models for one day’s flights per week and multi-days’ flights per week cases for this air cargo forwarding problem. For the large scale problem which means the computer software cannot give the optimal solution, we also present a new way to design the genetic algorithm to get the better solutions. Computational results show that the stochastic models can provide effective and cost-efficient solutions; the robust optimization models can provide a more responsive and flexible system with less risk.
Supervisor: Wu, Yue Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.680747  DOI: Not available
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