Michael Roe, Wei Xu, Dongping Song

Optimizing Supply Chain Performance

Information Sharing and Coordinated Management

• Gebundenes Buch

Produktbeschreibung

Optimizing Supply Chain Performance takes industrial case studies from SMEs in China to examine the importance of information sharing and coordinated management as essential mechanisms to improve supply chain performance.

Produktdetails

• Verlag: Macmillan Education / Palgrave Macmillan UK / Springer Palgrave Macmillan
• Artikelnr. des Verlages: 978-1-137-50113-4
• 1st ed. 2015
• Seitenzahl: 214
• Erscheinungstermin: 8. Juli 2015
• Englisch
• Abmessung: 216mm x 140mm x 14mm
• Gewicht: 418g
• ISBN-13: 9781137501134
• ISBN-10: 1137501138
• Artikelnr.: 42459111

Autorenporträt

Michael Roe is Professor of Maritime Governance at Plymouth University, UK, with research interests in policy-making in the maritime sector and supply chain management. He has published over 15 other books and numerous research papers. Dongping Song is Professor of Supply Chain Management at the University of Liverpool Management School, UK. His research interests include modelling, control and optimization of container shipping logistics, supply chain systems and manufacturing and service systems with uncertainties. He has widely published in supply chain management. Wei Xu is a founder of Broadtek Innovation Ltd (BTI) and China Material System Cooperation (CMSC). Her research focuses on Supply Chain Performance Optimisation. Her industrial expertise is in technology management and transfer for 3D Printing and materials for the health care industry.

Inhaltsangabe

1. Introduction
2. Literature Review
3. A Methodology for the Exploration of Supply Chain Management
4. The Case Company Supply Chains
5. A Generalised Domestic and International Supply Chain Model
6. Mathematical Modelling for the Supply Chain System
7. Single Objective and Multiple Objective Genetic Algorithms
8. The Impact of Information Sharing
9. Evaluating the Single Objective Genetic Algorithm (SOGA)
10. Evaluating the Single Objective Genetic Algorithm (MOGA)
11. Conclusions