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School of Business and Management

Dr Eun-Seok Kim

Eun-Seok

Senior Lecturer in Operations Management

Email: e.kim@qmul.ac.uk
Telephone: +44 (0)20 7882 2351
Room Number: Room 4.28b, The Bancroft Building, Mile End Campus
Office Hours: Tuesday, 2pm-3pm Thursday, 3pm-4pm

Profile

Roles

Programme Director for MSc International Business

Dr Eun-Seok Kim is a Senior Lecturer (Associate Professor) in Operations Management at Queen Mary, University of London. He has BSc, MSc and PhD degrees in Operational Research from KAIST (Korea Advanced Institute of Science and Technology), South Korea. Before joining Queen Mary, he was a Senior Lecturer in Operations Management and Programme Leader in MSc Global Supply Chain Management at Middlesex University.

Teaching

Undergraduate

BUS002: Operations Management

BUS260: Quantitative Analytics

Research

Research Interests:

  • Methodologies: Mathematical Programming, Combinatorial Optimisation, Stochastic Analysis.
  • Applications: Operations and Supply Chain Management

Dr Kim’s main research interest falls within the field of operations and supply chain management specialising in combinatorial optimisation and scheduling theory. He is also interested in applications of operational research, especially optimisation, to various problem areas including telecommunication and actuarial science.

Publications

Publications:

 

  • Kazemi, A., E.-S. Kim and M.-H. Kazemi, "Identifying and prioritizing delay factors in Iran’s oil construction projects", To appear in International Journal of Energy Sector Management.
  • Asimit, A.V., T. Gao, J. Hu and E.-S. Kim (2018), “Optimal Risk Transfer: Numerical Optimisation for Actuarial Applications”, North American Actuarial Journal, 22(3), 341 – 364. (ABS 2)
  • Eun, J., C. S. Sung and E.–S. Kim (2017), “Maximizing total job value on a single machine with job selection”, Journal of the Operational Research Society, 68, 998 – 1005. (SSCI, ABS 3)
  • Asimit, A.V., V. Bignozzi, K.C. Cheung, J. Hu and E.-S. Kim (2017), “Robust and Pareto Optimality of Insurance Contracts”, European Journal of Operational Research, 262(2), 720 – 732. (SCIE, ABS 4)
  • Asimit, A. V., A. M. Badescu, S. Haberman and E.–S. Kim (2016), “Efficient Risk Allocation within a Non-life Insurance Group under Solvency II Regime”, Insurance: Mathematics and Economics, 66, 60-76. (SSCI, ABS 3)
  • Kim, E.–S. and C. A. Glass (2015), “Perfect periodic scheduling for binary tree routing in wireless networks”, European Journal of Operational Research, 247(2), 389-400. (SCIE, ABS 4)
  • Kim, E.–S. and D. Oron (2015), “Minimizing total completion time on a single machine with step improving jobs”, Journal of the Operational Research Society, 66(9), 1481-1490. (SSCI, ABS 3)
  • Kim, E.–S. and C. A. Glass (2014), “Perfect periodic scheduling for three basic cycles”, Journal of Scheduling, 17(1), 47-65. (SCIE, ABS 3)
  • Kim, E.–S. and D. Oron (2013), “Multi-Location production and delivery with job selection”, Computers and Operations Research, 40(5), 1461-1466. (SCIE, ABS 3)
  • Kim, E.–S. and D. Oron (2013), “Coordinating multi-location production and customer delivery”, Optimization Letters, 7(1), 39-50. (SCIE)
  • Kim, E.–S. (2011), “Scheduling of uniform parallel machines with s-precedence constraints”, Mathematical and Computer Modelling, 54(1-2), 576-583. (SCIE)
  • Kim, E.–S and I.–S. Lee (2011), “Integrated inventory-distribution planning in a (1:N) supply chain system with heterogeneous vehicles incorporated”, International Journal of Management Science, 17(2),1-22.
  • Kim, E.–S. and M. E. Posner (2010), “Parallel machine scheduling with s-precedence constraints”, IIE Transactions, 42(7), 525-537. (SCI, ABS 3)
  • Kim, E.-S., C. S. Sung and I–S. Lee (2009), “Scheduling of parallel machines to minimize total completion time subject to s-precedence constraints”, Computers and Operations Research, 36(3), 698-710. (SCIE, ABS 3)

 

Supervision

Dr Eun-Seok Kim welcomes prospective doctoral students with a research interest in applications of operational research, especially optimisation, to various problem areas including logistics and supply chains. Students with background in mathematics, computer science, or industrial engineering are particularly welcome to apply.