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

PhD student), Swati Gupta (ORC PhD student), Jeremy Kalas (EECS Undergraduate), Zachary

Leung (recently graduated ORC PhD student), Danny Segev (Visiting Scholar ORC/MIT Sloan

from U. Haifa), Kiran Panchamgam (Oracle RGBU), and Anthony Smith (formerly from Oracle

RGBU)

Abstract:

Pricing has been a field that has seen a lot of exciting developments in the recent years. A

particular area of pricing that has recently emerged is promotion pricing. In many important

settings, promotions are a key instrument for driving sales and profits. Important examples

include promotions in grocery retail among others. The Promotion Optimization Problem (POP)

is a challenging problem as the retailer needs to decide which products to promote, what is the

depth of price discounts, when to schedule the promotions and how to promote the product.

In this talk we will discuss how analytics can have a key impact on promotion pricing. This

presentation will reflect our ongoing collaboration over the past few years with Oracle RGBU.

We will describe the journey we took with them on introducing analytics tools for promotion

planning in the grocery industry. We will describe optimization models we have built in order to

determine which products to promote and when, as well as which vehicles to use to promote

each product (e.g. flyer versus radio announcements among others) and how deeply to promote

each product.

An important consumer behavior we will incorporate and which is a direct consequence of

promotions in grocery retail is that consumers stockpile the products on promotion and then

experience promotion fatigue after the promotion ends. Therefore, as a first step, we study

general classes of demand functions that capture this effect and can be directly estimated from

data. Using these demand functions, we model and study the promotion planning problem

through an optimization formulation. Unfortunately, the underlying formulation even for a single

product is NP-hard and highly nonlinear. We will first propose a linear approximation and show

how to solve the problem efficiently as a linear programming (LP) problem. We will illustrate

how this approximation idea has applications in many areas beyond pricing. We will discuss

analytical bounds on the accuracy of this LP approximation relative to exact problem solution.

We will also consider a graphical representation of the problem which will allow us to employ a

Dynamic Programming (DP) solution approach as an alternative. We will discuss the tradeoffs

between the two approaches (LP vs DP). Furthermore, we will incorporate in the optimization

formulations, apart from the pricing aspect, how to decide which vehicle to use each time in

order to promote which product. This further complicates the problem. We will introduce greedy

and integer optimization ideas in order to solve the vehicle selection problem in a tractable way.

These methods are computationally efficient and hence are easy to use in practice. We will also

discuss some performance guarantees for these methods.

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Together with our industry collaborators from Oracle Retail, our framework allows us to

develop a tool which can help supermarket managers to better understand promotions by testing

various strategies and business constraints. We show that the formulation we propose solves fast

in practice using actual data from a grocery retailer and that the accuracy is high. We calibrate

our models using actual data and determine that they can improve profits by 3% just by

optimizing the promotion schedule and up to 5% by slightly modifying some business

requirements.

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Academic Talk by Mark S. Daskin

Day 1, 2:15 pm – 3:00 pm

Mitigating Capacity Constraints in Facility Location Models

Mark S. Daskin and Kayse Lee Maass (Industrial and Operations Engineering, University of

Michigan)

Abstract:

Hard capacity constraints have been used for decades in facility location modeling and planning.

In this talk, we will argue that such constraints are unrealistic as a variety of operational tools can

be used to extend capacity in the short term. We will focus on the use of inventory as a short

term mitigation strategy as other strategies – overtime and outsourcing, for example – are easier

to model. At the same time, massive amounts of data are now available for planners. The models

we present enable the use of such databases.

We will present a basic model in which demands from a node must be assigned to a single

facility for all time intervals under consideration by the model. We show that the location

decisions made without allowing inventory to mitigate the capacity constraints are different, and

in some cases significantly worse, than those suggested by models that allow inventory

accumulation to mitigate capacity constraints. We then extend this model to allow for day of the

week assignments which can account for time-varying demands or for facilities that are only

open on selected days of the week.

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Academic Talk by Guillermo Gallego

Day 1, 3:30 pm – 4:15 pm

Do Consumers Benefit from Dynamic Pricing?

Guillermo Gallego (Department of Industrial Engineering and Operations Research, Columbia

University) and Ningyuan Chen (Yale University)

Abstract:

We have asked this question to seasoned practitioners and leading academics and the answer is

often "I don't know, but my intuition is that if the seller is benefiting from dynamic pricing then

it may be because more surplus is being extracted from consumers." This perception may even

prevent the use of dynamic pricing if companies feel that it may hurt their image in the eyes of

consumers. It was precisely the expression of this sentiment that leads us to this investigation.

We asked ourselves: Do consumers prefer random prices? In other words, is the consumer

surplus a decreasing convex function of price? Is the price an increasing concave function of cost?

Is the composite function a decreasing convex function of cost? And if this is true, is dynamic

pricing (which responds to random changes in marginal cost of capacity), better than fixed

pricing for consumers? We show that the answers are mostly yes, so dynamic pricing is win-win

most of the time, but there are some exceptions as we will describe in our talk.

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Academic Talk by Stephen C. Graves

Day 1, 4:15 pm – 5:00 pm

Modeling and Design of a Material Recovery Facility

Stephen C. Graves (Sloan School of Management, Massachusetts Institute of Technology); joint

work with Timothy Gutowski (MIT), Anne Raymond (MIT), and Mariapaola Testa (MIT)

Abstract:

The production of municipal solid waste (MSW) has increased world-wide, in both developed

and developing countries. In developed countries, MSW is usually sent to material recovery

facilities (MRFs), which use mechanical and manual sorting units to extract valuable

components. In this ongoing work, we formulate and develop a network flow model to represent

an MRF that sorts wastes with various sortation processes and with recirculating streams. For

each material flow in the system, we define a matrix to describe the sorting process. We use data

from an operating MRF to estimate the sortation parameters for the model, as well as to

characterize the input waste stream. We then formulate a genetic algorithm (GA) that generates

alternative configurations of an MRF for a given set of sorting units with known separation

parameters and selects those with highest profit and efficiency. The GA incorporates a heuristic

for personnel allocation to manual units. We report on our experience to date experimenting with

the model.

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Academic Talk by Caro Felipe

Day 2, 9:15 am – 10:00 am

Managing Online Content to Build a Follower Base

Caro Felipe (Anderson School of Management, UCLA); joint work with Victor Martínez-de-

Albéniz (IESE Business School)

Abstract:

Content providers typically manage a dual objective of generating interest for current followers

and at the same time reaching out to new audiences that may become repeat visitors. The pace at

which content is created must thus take into account how much it contributes to maintain the

follower base. We formulate a simple model to study follower base build-up dynamics under the

assumption that the attractiveness of past content decays over time. Using stochastic dynamic

programming we develop heuristics for content release and an upper bound to assess

performance. We then apply our model to the case of blogs: we analyze four blogs and their

daily traffic for over 3 years. Our findings show that adjusting posting frequency to the optimum

can raise total traffic levels by generating a larger follower base.

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Academic Talk by Nir Halman

Day 2, 10:00 am – 10:45 am

Multi-Parameter Approximation Schemes for Hard Newsvendor Problems

Nir Halman (Jerusalem School of Business Administration, The Hebrew University of Jerusalem)

Abstract:

For every given real value , a Fully Polynomial Time Approximation Scheme (FPTAS)

computes in polynomial time (in both the input size and ) a feasible solution that is close to

the optimal solution within ratio . As can be chosen arbitrarily small, and the running time is

polynomial, FPTASs are considered as the “Holy grail” of approximation algorithms.

In this talk I will study 3 hard variants of the classical newsvendor problem, each of which

cannot admit an FPTAS. However, by expanding the notion of FPTASs to multi-parameter

approximation schemes, it is possible to compute for them provably near-optimal feasible

solutions.

The talk is based upon 3 papers by (a subset of) Nir Halman, Giacomo Nannicini, Jim Orlin

and David Simchi-Levi.

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Academic Talk by Zvi Drezner

Day 2, 11:15 am – 12:00 noon

An Iterative Procedure for Solving Non-Linear Programs

Zvi Drezner and Pawel Kalczynski (Steven G. Mihaylo College of Business and Economics,

California State University-Fullerton)

Abstract:

Non-linear programming problems of minimizing a convex objective function subject to convex

constraints are convex, have one local optimum which is the global one, and can be solved by

numerous approaches and canned programs. Non-convex programs such as a maximization of a

convex objective subject to constraints which are outside or on the outsides of convex regions

usually have many local optima and are generally difficult to solve. Many such problems are

solved by global optimization methods which exploit the special structure of the problem and

usually require excessive processing times that restrict the size of the problems that can be

practically solved. We found that such problems can be solved iteratively by a multi-start

heuristic approach based on solving a sequence of linear programs. It is interesting that this

general approach is suitable for solving non-convex problems but cannot be applied for solving

convex problems. Our iterative approach is much faster than a direct multi-start approach (one to

three orders of magnitude) and provided better results on four test problems and 116 instances.

Since the iterative procedure is much faster and provides better results, the multi-start approach

can be implemented with more starts providing excellent results in relatively short computing

times.

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Academic Talk by Max Shen

Day 2, 1:30 pm – 2:15 pm

Service Region Design for Urban Electric Vehicle Sharing Systems

Max Shen (Department of Industrial Engineering and Operations Research, and Department of

Civil and Environmental Engineering, University of California-Berkeley)

Abstract:

Emerging collaborative consumption business models have shown promise in both generating

business opportunities and enhancing efficient use of resources. In the transportation domain, car

sharing models are adopted at mass scale in major metropolitan areas worldwide. This mode of

servicized mobility bridges the resource efficiency of public transit and the flexibility of personal

transportation. Beyond significant potential to reduce car ownership, car sharing shows promise

in supporting adoption of fuel efficient vehicles, such as electric vehicles (EVs), due to these

vehicles special cost structure with high purchase but low operating costs. Recently, it has

become a trend for key players in the car sharing business, such as Car2Go and Autolib, to

employ EVs in an operations model that accommodates one-way trips. On the one hand, the one-

way model brings about significant improvements in coverage of travel needs and therefore

adoption potential, compared with the conventional round-trip-only model (advocated by ZipCar,

for example). On the other hand, it poses tremendous planning and operational challenges. In this

work, we study the planning problem faced by service providers in designing the geographical

service region in which to operate the service. This decision encompasses the trade-off between

maximizing customer catchment by covering travel needs, and controlling fleet operations costs.

We develop a mathematical programming model that incorporates details of both customer

adoption behavior and fleet management (including EV repositioning and charging) operations

under spatially-imbalanced and time-varying travel patterns. To address inherent planning

uncertainty with regard to adoption patterns, we employ a distributionally-robust optimization

framework that informs robust decisions to avoid possible ambiguity (or lack) of data.

Mathematically, the problem can be approximated by a mixed integer second-order cone

program, which is computationally- tractable with practical scale data. Applying this approach to

the case of Car2Go’s service in San Diego, California, with real operations data, we address a

number of planning questions and suggest potential for future development of the service.

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Academic Talk by Chung Piaw Teo

Day 2, 2:15 pm – 3:00 pm

Risk Exposure Index Revisited − Prioritising and Mitigating Disruption Risks

in Supply Chains

Chung Piaw Teo (Department of Decision Sciences, National University of Singapore); joint

work with Zhenzhen Yan and Yini Sarah Gao (National University of Singapore)

Abstract:

In recent years, the concept of REI introduced by David has slowly gained traction among supply

chain professionals to study risk management. In this paper, we propose an extension to the

concept and show that the new index can model the supply chain time to survive distribution

profile more accurately. Furthermore, we propose a new model to design mitigation strategy

using the related time to recovery concept.

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Organizing Committee

Philip Kaminsky (Chair), University of California, Berkeley

Chung-Lun Li (Co-chair), The Hong Kong Polytechnic University

Xin Chen, University of Illinois at Urbana-Champaign

Youhua (Frank) Chen, City University of Hong Kong

Ye Lu, City University of Hong Kong

Max Shen, University of California, Berkeley

Miao Song, The Hong Kong Polytechnic University

Venue

Registration and Academic Talks: 290 Hearst Memorial Mining Building

Banquet: The Faculty Club

Hearst Memorial Mining Building

The Faculty Club