Christiane Barz

Assistant Professor in Decisions, Operations, and Technology Management

Phone: (310) 825-0296

Fax: (310) 206-3337

christiane.barz@anderson.ucla.edu

Gold Hall, Room B-520

Biography

Christiane Barz is an assistant professor of the Decisions, Operations & Technology Management group. Prior to joining UCLA Anderson, she spent two years as a postdoctoral researcher with the Operations Management group at the Chicago Booth School of Business. Professor Barz holds a doctorate degree and a diploma in industrial engineering from the University of Karlsruhe, Germany.

Her main research interests are revenue management, dynamic pricing, healthcare services, inventory theory, and production scheduling. In her dissertation, she analyzed the impact of an airline’s risk-attitude on the availability of low-cost tickets. The thesis was published in the Springer Lecture Notes in Economics and Mathematical Systems and won several prizes including the dissertation prize of the German Operations Research Society 2007. Recent projects focus on the solution of large-scale dynamic optimization problems using approximate dynamic programming techniques. She applies these methods in production scheduling and healthcare applications.

Christiane Barz teaches "Data and Decisions" in the Full Time MBA Program and "Applied Stochastic Processes" in the PhD Program and "Data Analysis and Management Decisions" in the Global Executive MBA Program for the Americas. Her teaching was recognized by the George Robbins Assistant Professor Teaching Award in 2012.

Education

Dr. rer. pol. (PhD equivalent), 2006, University of Karlsruhe (Germany)
Diploma (MS-MBA equivalent), 2004, University of Karlsruhe (Germany)

Interests

Revenue Management, Dynamic Pricing, Approximate Dynamic Programming, Health Care, Production Scheduling, Inventory Theory
  • Dan Adelman and Christiane Barz. (2014). A Price-Directed Heuristic for the Economic Lot Scheduling Problem. IIE Transactions, to appear.
  • Dan Adelman and Christiane Barz. (2013). A Unifying Approximate Dynamic Programming Model for the Economic Lot Scheduling Problem. Mathematics of Operations Research, to appear.
  • Christiane Barz and Rainer Kolisch. (2013). Hierarchical multi-skill resource assignment in the telecommunications industry. Production and Operations Management, to appear. [ Link ]
  • Christiane Barz and Alfred Muller. (2012). Comparison and Bounds for Functions of Future Lifetimes Consistent with Mortality Tables. Insurance: Mathematics and Economics, Vol. 50, pp. 229-235. [ Link ]
  • Christiane Barz and Alfred Muller. (2012). A Tilting Algorithm for the Estimation of Fractional Age Survival Probabilities. Lifetime Data Analysis, Vol. 18, pp. 234-246. [ Link ]
  • Christiane Barz. (2007). Risk-Averse Capacity Control in Revenue Management (Monograph). Lecture Notes in Economics and Mathematical Systems, Vol. 597, Springer. [ Link ]
  • Christiane Barz and Karl-Heinz Waldmann. (2007). Risk-Sensitive Capacity Control in Revenue Management. Mathematical Methods of Operations Research, Vol. 65, pp. 565-579. [ Link ]
  • Catherine Duda, Kumar Rajaram, Christiane Barz, Thomas Rosenthal. (2013). A Framework for Improving Access and Customer Service Times in Health Care: Application and Analysis at the UCLA Medical Center. The Health Care Manager, Vol. 32(3), pp. 212-26.
  • Christiane Barz and Kumar Rajaram. Elective Patient Admission and Scheduling under Multiple Resource Constraints. [ Download ] [ Show Abstract ]
    We consider a patient admission problem to a hospital with multiple resource constraints (e.g. OR and beds) and a stochastic evolution of patient care requirements across multiple resources. There is a small but significant proportion of emergency patients who arrive randomly and have to be accepted at the hospital. However, the hospital needs to decide whether to accept, postpone or even reject the admission from a random stream of non-emergency elective patients. We formulate the control process as a Markov Decision Process to maximize expected contribution net of overbooking costs. We develop bounds using approximate dynamic programming and use this to construct heuristics. We test our methods on data from the Ronald Reagan UCLA Medical Center.
  • Christiane Barz and Rainer Hoffmann. Network Air Cargo Revenue Management. [ Download ] [ Show Abstract ]
    In this paper, we discuss heuristics for network air cargo revenue management. Starting point of our analysis is a dynamic programming formulation of a cargo revenue management model for carriers that operate flights on a network. Since the size of the state space makes this problem intractable, we suggest several upper bound problems and heuristics, based on both linear programming, approximate dynamic programming, and decomposition approaches. Two results turn out to be surprising differences to the passenger revenue management problem: First, the well-known randomized linear programming approach need not give a tighter upper bound that the deterministic linear programming approach. Second, bid prices that are obtained through an affine approximation of the value function may return bid prices for weight or volume that are negative on some legs. s. In a numerical study, we find that a dynamic programming decomposition approach, which is based on bid-prices, dominates other approaches by giving tighter bounds and higher expected revenues both when applied on the single-leg and on the network cargo problem.