Quantifying picker blocking in a bucket brigade order picking system accepted for publication in the International Journal of Production Economics



Soondo Hong (a 2010 graduate of the lab) continues to develop extension to his dissertation work which modeled and qualified picker blocking in narrow aisle order picking systems. He has now extend this work to consider bucket brigade operations. This paper addresses some of the unique challenges of the bucket brigade setting.


This paper we model and quantify picker blocking in bucket brigade order picking systems (OPSs). Bucket brigades improves throughput and reduces variability in OPSs. However, each order picking trip fills different orders and creates workload variation per order. We show that bucket brigade order picking experiences picker blocking when there is a workload imbalance per pick face. We derive a closed-form solution to quantify the level of blocking for two extreme walk speed cases. Additional simulation comparisons validate the picker blocking model which includes backward walk and hand-off delays. We identify the relationship between picker blocking in bucket brigade OPSs and picker blocking in a circular-aisle abstraction of an OPSs in which backward walk and hand-off delays as well as forward walk speed are considered. Our analytical model and simulations verify that aggregating orders into batches smoothes the workload variation by pooling the randomness of picks in each order and that slowest-to-fastest picker sequencing modulates picker blocking between two pickers, i.e., the interaction between neighboring pickers.

GAMS code for DEA



This file contains the set of GAMS codes for DEA that was developed for a GAMS tutorial at EWEPA XIII held in June 2013 in Helsinki Finland. Please see the introduction of codes inside the zip file.




GAMS code for CNLS




This file contains the set of GAMS codes for CNLS that was developed for a GAMS tutorial at EWEPA XIII held in June 2013 in Helsinki Finland. Please see the introduction of codes inside the zip file.



IIE Transactions accepts “Modeling Dependence in Health Behaviors”

Brandon Pope‘s (2011 graduate of the lab) dissertation is about modeling incentives in healthcare systems. This paper is a critical component where he explores the dependencies in health behaviors (diet, exercise and smoking) modeling as binary decisions and explores modeling the dependence through either joint attraction functions or probabilistic dependence. We find some evidence of superior performance of the joint attraction function approach for our data.

The prediction and control of distributed healthcare behaviors within a population such as smoking, diet, and physical activity are of great concern to those who pay for healthcare, including employers, insurers, and public policy makers given the significant effect on costs. In considering the selection of multiple health behaviors, the nature of dependence between behaviors must be considered because simplifying assumptions such as independence are untenable. Using data from the National Heart, Lung, and Blood Institute, we find strong evidence to reject the hypothesis of independence between the aforementioned behaviors, while finding some evidence of conditional independence. In this paper, several alternatives to the assumption of independence are presented, each of which signi cantly improves the ability to predict combined behavior. We present models of dependence through marginal probabilities and, taking inspiration from non-expected utility maximizing behavior, through attractions to behavioral alternatives. We find that consistently healthy (or unhealthy) combinations of behaviors are more likely to occur relative to the assumption of independence. We discuss how our results could be used in designing policies to curtail costs and improve health.

MATLAB code for Convex Nonparametric Least Square

The first version of this code was created by Bodhisattva Sen at Columbia University. What is presented here is a slightly adapted version to aid in the estimation of CNLS.

Adaptations done by Andrew L. Johnson ajohnson@tamu.edu

Function to compute the CONVEX Regression of the response y on the data matrix x
Function NEEDS the PACKAGE cvx (see http://cvxr.com/cvx/) to be installed in the computer

y: the response variable — a column vector of length n
x: the data matrix of the predictors (of size n*p);
Note that the vector of ones is NOT required in the data matrix

th: the value of the fitted response

The function also gives estimates of the sub-gradients at each data point and is stored in the ‘beta1’ matrix

Note that for sample sizes of 200 or more, the function can be very slow!
It involves n(n-1) many constraints





MATLAB code for A Birth-Death Markov Chain Monte Carlo Method to Estimate the Number of States in a State-Contingent Production Frontier Model

To run BDMCMC with the Tarlac rice dataset

  1. Place all contents of this package on the same folder and make it the active folder in Matlab.
  2. BDMCMC_Dummy_Trend_Mono runs the monotonicity-constrained dummy time trend model.
  3. BDMCMC_Linear_Trend_Mono runs the monotonicity-constrained linear time trend model.
  4. RiceIM contains the dataset scaled to input means (used on the estimation process).
  5. Rice contains the dataset in its original units of measurement. This is a subset of the dataset used in Villano, O’Donnell and Battesse (2004) and the same dataset used by ODG.


Villano, R.A., C.J. O’Donnell, and G.E. Battese. 2004. “An Investigation of Production Risk, Risk Preferences and Technical Efficiency: Evidence from Rainfed Lowland Rice Farms in the Philippines.” Article presented at Asia-Pacific Productivity Conference, Brisbane, 14–16 July.





November 9th – Informs Annual Conference: A Multivariate Seminonparametric Bayesian Concave Regression Method to Estimate Stochastic Frontiers


This presentation discusses a method that incorporates the latest advances in the Bayesian constrained regression literature offering an alternative to the current Least Squares-based and Kernel Regression-based Stochastic frontier constrained estimation methods, both in terms of runtime and of data capacity.

Although monotonicity constraints can be applied in a simple manner by the specification of sign constraints on the regression coefficients in both parametric and nonparametric settings, estimation of concavity-constrained production frontiers for flexible functional has proven to be more challenging. Imposition of such constraints in the nonparametric setting was discussed by Banker and Mandiratta (1992) and has been conducted using Least Squares approaches using Convex Nonparametric Least Squares (CNLS) (Kuosmanen, 2008). Du, Parmeter and Racine (2013) develop a Kernel regression method that also allows for imposition of a vast array of derivative-based constraints, including global concavity. This presentation discusses an extension to Multi-variate Bayesian Convex Regression (MBCR) developed by Hannah and Dunson (2013) to the setting with inefficiency. This extension offers benefits both in terms of runtime and of data capacity. Moreover, this method includes some extensions to the known Bayesian regression techniques and further enriches the Bayesian literature.


DEA Cluster at Informs


Informs held its Annual meeting in San Francisco this year from November 9-12. The DEA track had 8 invited session and 4 contributed sessions, with more than 40 papers presented. This is largest the track has been in the past 4 years.

The Informs meeting will be held in Philadelphia from November 1-4. I hope everyone will be able to join again. If you are interested in presenting in the DEA cluster at Informs, please contact me.

The DEA Journal publishes first issue


After many years in development, the DEA Journal published its first issue which was available at the NOW Publishing booth at Informs. This provides another field specific outlet for efficiency and productivity papers.

The mission of the new journal is to publish the best and most insightful papers on the theory and practice of Data Envelopment Analysis (DEA). The DEA Journal is the official journal of the International Data Envelopment Analysis Society (iDEAs). Click here, for more details and access to the articles in the first issue.

October 4 and 5: College Industry Council on Material Handling Education Semi-annual Meeting in San Diego


The College Industry Council on Material Handling Education (CICMHE) is a group of 15 academic and 8 industry members that work to increase awareness, understanding, exploration, and development of material handling and logistics through projects and events.

CICMHE meet in San Diego as part of the MHI‘s annual conference. The two-day event resulted in a set of projects MHI will fund to promote material handling and logistics education along with allowing considering progress on planning the upcoming events for CICMHE such as Promat.