Ziv Katalan

Ziv Katalan
  • Adjunct Professor, Operations, Information and Decisions
  • Managing Director, Wharton Global Initiatives

Contact Information

  • office Address:

    3620 Locust Walk
    3454 Steinberg Hall - Dietrich Hall
    Philadelphia, PA 19104

Overview

Ziv Katalan is Managing Director for Wharton Global Initiatives and an Adjunct Professor of Operations, Information and Decisions. He received his BS in Mathematics from Tel-Aviv University (1987), and his PhD in Management Science/Operations Research from Columbia University (1995).

Since 1994, Dr. Katalan has taught courses in decision models and uncertainty, business analytics, operations management, supply chain management, mathematical modeling and its applications in finance, and quantitative finance at Wharton’s MBA and MBA for Executives programs and Penn’s EMTM program. He received a number of teaching awards from the Wharton MBA and MBA for Executives programs. During 2002-2007, Dr. Katalan served as Co-Director of the Executive Master’s in Technology Management (EMTM), a program jointly sponsored by the University of Pennsylvania’s School of Engineering and the Wharton School.

Dr. Katalan’s research interests include the design and analysis of production and service systems, and their interface with a company’s marketing strategy, scheduling and allocation of resources, and the impact of increasing product variety on manufacturing performance.  His research papers were published in Queuing Systems, Management Science, Operations Research, and Naval Research Logistics.

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Research

  • Sergei Savin, Morris A. Cohen, Noah Gans, Ziv Katalan (2005), Capacity Management in Rental Businesses with Two Customer Bases, Operations Research, 617-631.

    Abstract: We consider the allocation of capacity in a system in which rental equipment is accessed by two classes of customers. We formulate the problem as a continuous-time analogue of the one-shot allocation problems found in the more traditional literature on revenue management, and we analyze a queueing control model that approximates its dynamics. Our investigation yields three sets of results. First, we use dynamic programming to characterize properties of optimal capacity allocation policies. We identify conditions under which “complete sharing”—in which both classes of customers have unlimited access to the rental fleet—is optimal. Next, we develop a computationally efficient “aggregate threshold” heuristic that is based on a fluid approximation of the original stochastic model. We obtain closed-form expressions for the heuristic’s control parameters and show that the heuristic performs well in numerical experiments. The closed-form expressions also show that, in the context of the fluid approximation, revenues are concave and increasing in the fleet size. Finally, we consider the effect of the ability to allocate capacity on optimal fleet size. We show that the optimal fleet size under allocation policies may be lower, the same as, or higher than that under complete sharing. As capacity costs increase, allocation policies allow for larger relative fleet sizes. Numerical results show that, even in cases in which dollar profits under complete sharing may be close to those under allocation policies, the capacity reductions enabled by allocation schemes can help to lift profit margins significantly.

  • Ziv Katalan and A. Federgruen (1999), The Impact of Adding a Make-to-Order Product Line to a Make-to-Stock Production System, Management Science, Vol. 45, pp. 980-994.

  • Ziv Katalan (Work In Progress), The Effect of Product Variety on Manufacturing Performance, (with A. Federgruen and G. Gallego), The Wharton School, University of Pennsylvania, Philadelphia PA (1999).

  • Ziv Katalan and A. Federgruen (1998), Determining Production Schedules under Base-Stock Policies in Single Facility Multi-Item Production Systems, Operations Research, Vol. 46, pp. 883-898.

  • Ziv Katalan and A. Federgruen (1996), Customer Waiting Time Distributions under Base-Stock Policies in Single Facility Multi-Item Production Systems, Naval Research Logistics, Vol. 43, pp. 533-548.

  • Ziv Katalan (Work In Progress), Make-to-Stock: That is the Question; Novel Answers to An Ancient Debate, (with A. Federgruen), The Wharton School, University of Pennsylvania, Philadelphia PA (1996).

  • Ziv Katalan and A. Federgruen (1996), The Stochastic Economic Lot Scheduling Problem: Cyclical Base-Stock Policies with Idle Times, Management Science, Vol. 42, pp. 783-796.

  • Ziv Katalan and A. Federgruen (1996), The Impact of Setup Times on The Performance of Multi-Class Service and Production Systems, Operations Research, Vol. 44, pp. 989-1001.

  • Ziv Katalan and A. Federgruen (1994), Approximating Queue Size and Waiting Time Distributions in General Polling Systems, Queueing Systems, Vol. 18, pp. 353-386.

Teaching

Current Courses

  • OIDD353 - Math Mdlng Appl In Fnce

    Quantitative methods have become fundamental tools in the analysis and planning of financial operations. There are many reasons for this development: the emergence of a whole range of new complex financial instruments, innovations in securitization, the increased globalization of the financial markets, the proliferation of information technology and the rise of high-frequency traders, etc. In this course, models for hedging, asset allocation, and multi-period portfolio planning are developed, implemented, and tested. In addition, pricing models for options, bonds, mortgage-backed securities, and other derivatives are studied. The models typically require the tools of statistics, optimization, and/or simulation, and they are implemented in spreadsheets or a high-level modeling environment, MATLAB. This course is quantitative and will require extensive computer use. The course is intended for students who have strong interest in finance. The objective is to provide students the necessary practical tools they will require should they choose to join the financial services industry, particularly in roles such as: derivatives, quantitative trading, portfolio management, structuring, financial engineering, risk management, etc. Prospective students should be comfortable with quantitative methods such as basic statistics and the methodologies (mathematical programming and simulation) taugh tin OIDD612 Business Analytics and OIDD321 Management Science (or equivalent). Students should seek permission from the instructor if the background requirements are not met.

    OIDD353401 ( Syllabus )

  • OIDD653 - Math Mdlng Appl In Fnce

    Quantitative methods have become fundamental tools in the analysis and planning of financial operations. There are many reasons for this development: the emergence of a whole range of new complex financial instruments, innovations in securitization, the increased globalization of the financial markets, the proliferation of information technology and the rise of high-frequency traders, etc. In this course, models for hedging, asset allocation, and multi-period portfolio planning are developed, implemented, and tested. In addition, pricing models for options, bonds, mortgage-backed securities, and other derivatives are studied. The models typically require the tools of statistics, optimization, and/or simulation, and they are implemented in spreadsheets or a high-level modeling environment, MATLAB. This course is quantitative and will require extensive computer use. The course is intended for students who have strong interest in finance. The objective is to provide students the necessary practical tools they will require should they choose to join the financial services industry, particularly in roles such as: derivatives, quantitative trading, portfolio management, structuring, financial engineering, risk management, etc. Prospective students should be comfortable with quantitative methods quantitative methods, such as basic statistics and the methodologies (mathematical programming and simulation) taught in OPIM612 Business Analytics or OPIM321 Management Science (or equivalent). Students should seek permission from the instructor if the background requirements are not met.

    OIDD653401 ( Syllabus )

Past Courses

  • OIDD353 - MATH MDLNG APPL IN FNCE

    Quantitative methods have become fundamental tools in the analysis and planning of financial operations. There are many reasons for this development: the emergence of a whole range of new complex financial instruments, innovations in securitization, the increased globalization of the financial markets, the proliferation of information technology and the rise of high-frequency traders, etc. In this course, models for hedging, asset allocation, and multi-period portfolio planning are developed, implemented, and tested. In addition, pricing models for options, bonds, mortgage-backed securities, and other derivatives are studied. The models typically require the tools of statistics, optimization, and/or simulation, and they are implemented in spreadsheets or a high-level modeling environment, MATLAB. This course is quantitative and will require extensive computer use. The course is intended for students who have strong interest in finance. The objective is to provide students the necessary practical tools they will require should they choose to join the financial services industry, particularly in roles such as: derivatives, quantitative trading, portfolio management, structuring, financial engineering, risk management, etc. Prospective students should be comfortable with quantitative methods such as basic statistics and the methodologies (mathematical programming and simulation) taugh tin OIDD612 Business Analytics and OIDD321 Management Science (or equivalent). Students should seek permission from the instructor if the background requirements are not met.

  • OIDD653 - MATH MDLNG APPL IN FNCE

    Quantitative methods have become fundamental tools in the analysis and planning of financial operations. There are many reasons for this development: the emergence of a whole range of new complex financial instruments, innovations in securitization, the increased globalization of the financial markets, the proliferation of information technology and the rise of high-frequency traders, etc. In this course, models for hedging, asset allocation, and multi-period portfolio planning are developed, implemented, and tested. In addition, pricing models for options, bonds, mortgage-backed securities, and other derivatives are studied. The models typically require the tools of statistics, optimization, and/or simulation, and they are implemented in spreadsheets or a high-level modeling environment, MATLAB. This course is quantitative and will require extensive computer use. The course is intended for students who have strong interest in finance. The objective is to provide students the necessary practical tools they will require should they choose to join the financial services industry, particularly in roles such as: derivatives, quantitative trading, portfolio management, structuring, financial engineering, risk management, etc. Prospective students should be comfortable with quantitative methods quantitative methods, such as basic statistics and the methodologies (mathematical programming and simulation) taught in OPIM612 Business Analytics or OPIM321 Management Science (or equivalent). Students should seek permission from the instructor if the background requirements are not met.

Awards and Honors

  • Wharton MBA Program Core Teaching Award, The Wharton School, University of Pennsylvania, 2013
  • Wharton MBA Program for Executives Elective Teaching Award, The Wharton School, University of Pennsylvania, Class of 1999-2003, 2006, 2011
  • MBA Core Curriculum Cluster Teaching Award, The Wharton School, University of Pennsylvania, 1997, 1999, 2001
  • First Place Student Paper Award, The ORSA Technical Section on Manufacturing and Operations Management, 1994
  • Honorable Mention in George E. Nicholson Student Paper Competition, 1994

Activity

Awards and Honors

Wharton MBA Program Core Teaching Award, The Wharton School, University of Pennsylvania 2013
All Awards