Sponsored by: The Department of Industrial Engineering and Operations Research, University of California, Berkeley The MIT Forum for Supply Chain Innovation, Massachusetts Institute of Technology Workshop on Supply Chain Analytics − Honoring David Simchi-Levi on the Occasion of His 60th Birthday October 15-16, 2015 University of California, Berkeley
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Sponsored by:
The Department of Industrial Engineering and Operations Research, University of California, Berkeley
The MIT Forum for Supply Chain Innovation, Massachusetts Institute of Technology
Workshop on Supply Chain Analytics − Honoring David Simchi-Levi on the
Occasion of His 60th Birthday
October 15-16, 2015
University of California, Berkeley
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Program Rundown All academic talks take place in 290 Hearst Memorial Mining Building (HMMB)
Time Activity
Day 1 (Thursday, October 15, 2015)
8:15 am – 9:15 am Registration (HMMB Lobby)
9:15 am – 9:25 am Welcoming Remarks by Philip Kaminsky
9:25 am – 10:00 am Special Remarks by Pierre Haren, Wallace J. Hopp, and
Narendra P. Mulani
10:00 am – 10:45 am Academic Talk by Opher Baron and Oded Berman
10:45 am – 11:15 am Coffee break (HMMB Lobby)
11:15 am – 12:00 noon Academic Talk by Xiuli Chao
12:00 noon – 1:30 pm Lunch (HMMB Lobby)
1:30 pm – 2:15 pm Academic Talk by Georgia Perakis
2:15 pm – 3:00 pm Academic Talk by Mark S. Daskin
3:00 pm – 3:30 pm Coffee break (HMMB Lobby)
3:30 pm – 4:15 pm Academic Talk by Guillermo Gallego
4:15 pm – 5:00 pm Academic Talk by Stephen C. Graves
5:00 pm Cocktails and Banquet (Berkeley Faculty Club)
Day 2 (Friday, October 16, 2015)
9:15 am – 10:00 am Academic Talk by Caro Felipe
10:00 am – 10:45 am Academic Talk by Nir Halman
10:45 am – 11:15 am Coffee break (HMMB Lobby)
11:15 am – 12:00 noon Academic Talk by Zvi Drezner
12:00 noon – 1:30 pm Lunch (HMMB Lobby)
1:30 pm – 2:15 pm Academic Talk by Max Shen
2:15 pm – 3:00 pm Academic Talk by Chung Piaw Teo
3:00 pm – 3:10 pm Closing Remarks by Chung-Lun Li
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Special Remarks
Day 1, 9:25 am – 10:00 am
Pierre Haren Vice-President, Global Leader Advanced Analytics, GBS, IBM and former founder of ILOG
Wallace J. Hopp Senior Associate Dean for Faculty & Research and Herrick Professor of Business, Stephen M.
Ross School of Business, University of Michigan
Narendra P. Mulani Chief Analytics Officer, Accenture Analytics
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Academic Talk by Opher Baron and Oded Berman
Day 1, 10:00 am – 10:45 am
Strategic Idling and Dynamic Scheduling in Open-Shop Service Network:
Case Study and Analysis
Opher Baron, Oded Berman, D. Krass (Rotman School of Management, University of Toronto),
and J. Wang (Nanyang Business School, Nanyang Technological University)
Abstract:
In Open-shop service networks customers would like to obtain service from a set of stations,
most of them without a specific order. This paper is motivated by XYZ (not the real name), a
company in the healthcare service industry that operates a stochastic open shop network where
the stations of the networks administer medical tests that customers can take within several hours
of the same day. According to senior management of XYZ there are two types of complaints
about the service that customers of XYZ experience. One is with respect to the total time that
customers spend in the system; the other is with respect to the long waiting time at a specific
station. In fact the company believes that customers get upset when they wait more than 20
minutes for a particular station (such customers appear on the computer screen of the schedulers
with red faces).
We focus in this paper on two types of service levels: the more traditional macro-level
measures such as minimizing total waiting time or total system time (waiting plus service times)
or minimizing total tardiness, and the “micro-level” measure of reducing excessive long waits at
any individual workstation within the process. The only paper we are aware of that discusses
systematically and analytically micro service level is [1] where a strategic idling (SI) scheduling
policy is suggested.
The idea behind SI is that when a downstream station is very congested operating the
upstream station in a normal rate may increase the congestion at the downstream station. Instead,
idling the upstream station until the downstream station is less congested could be beneficial.
Therefore while work-conserving policies are optimal for macro-level measures, scheduling
policies with SI might be helpful for the micro-level measure. In [1] we showed the benefits of
SI for the two stations in tandem network where customers arrive to the network according to a
Poisson process and services at the stations are exponentially distributed. In the current paper we
examine whether similar ideas can be applied to a much more complex environment of a
stochastic open shop network.
There was no official policy of using SI in XYZ. However using the empirical data we found
statistical evidence that SI is in fact used by the schedulers to effectively manage the micro-level
measure. This SI was done using only intuition of the schedulers of XYZ. We provide in this
paper an efficient way to combine the SI and Dynamic Scheduling Policies (DSPs)so that the
resulting policies can simultaneously address both macro- and micro-level measures.
For deciding which customer should be assigned to the next freed-up station we use 6 DSPs
that include among others rules such as: “Longest System time first” and “Longest Current
Waiting time first”. In all of the 6 DSPs used the station that is just freed-up and has the highest
remaining workload is assigned to a waiting customer.
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Since the stochastic open-shop networks are very difficult to analyze analytically, we
developed two simulation models. The first simulation model is based on the empirical data (ED)
for arrivals and service times. We compared the micro and macro service levels for the following
policies: ED; ED with no idling; the six DSPs with no idling and the six DSPs with SI. The main
findings are: (1) ED with no idling results in better macro service levels than ED but with much
worse micro-level performance, (2) The DSPs with no idling are much better than ED in their
macro service levels but perform worse on micro-level than ED, (3) The DSPs with SI result in
worse (but not by a lot) macro service levels than those without idling but are much better than
the DSPs without SI and quite close to the ED policy in the micro-level performance. The second
simulation model is based on randomly generated open-shop networks aiming to show benefits
of using SI with DSPs for general networks. The results obtained with the second simulation are
in line with those of the first model and show that combining DSPs with SI is a promising
strategy in general stochastic open-shop environments.
Reference
[1] O. Baron, O. Berman, D. Krass and J. Wang. Using strategic idleness to improve customer
service experience in service networks. Operations Research, 2014, 62(1), 123-140.
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Academic Talk by Xiuli Chao
Day 1, 11:15 am – 12:00 noon
Nonparametric Algorithm for Joint Pricing and Inventory Control with Lost-
Sales and Censored Demand
Xiuli Chao (Department of Industrial and Operations Engineering, University of Michigan); joint
work with Beryl Chen and Cong Shi
Abstract:
We consider the classic joint pricing and inventory control problem with lost-sales and censored
demand in which the customer response to selling price and the demand distribution is unknown
to the firm a priori. The major difficulty in this problem lies in that, the estimate of the expected
profit function from data can never be unimodal, even though the expected profit function is
unimodal under certain conditions. We develop a data-driven algorithm that conducts active
exploration and exploitation in carefully designed cycles, and show it converges to the optimal
policy when the planning horizon becomes long. The regret rate of the algorithm is also given.
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Academic Talk by Georgia Perakis
Day 1, 1:30 pm – 2:15 pm
The Impact of Analytics on Promotion Pricing
Georgia Perakis (Sloan School of Management, Massachusetts Institute of Technology); joint
work with Lennart Baardman (ORC PhD student), Maxime Cohen, (recently graduated ORC