I Know What You Bought At Chipotle for $9.81 by Solving A Linear Inverse Problem

Michael  Fleder and Devavrat D. Shah, I Know What You Bought At Chipotle for $9.81 by Solving A Linear Inverse Problem, Proceedings of the ACM on Measurement and Analysis of Computing Systems, November 2020, Article No. 47.  

We consider the question of identifying which set of products are purchased and at what prices in a given transaction by observing only the total amount spent in the transaction, and nothing more. The ability to solve such an inverse problem can lead to refined information about consumer spending by simply observing anonymized credit card transactions data. Indeed, when considered in isolation, it is impossible to identify the products purchased and their prices from a given transaction just based on the transaction total. However, given a large number of transactions, there may be a hope.

Special Issue — M&SOM 20th Anniversary

Special Issue — M&SOM 20th Anniversary, Volume 22, Issue 1, January-February 2020 (online)

This special issue contains invited and review articles by eminent researchers in the field.

Data-Driven Modeling and Optimization of the Order Consolidation Problem in E-Warehousing

Fatma Gzara, Samir Elhedhli, Ugur Yildiz, and Gohram Baloch, Data-Driven Modeling and Optimization of the Order Consolidation Problem in E-Warehousing,  INFORMS Journal on Optimization, Vol. 2, No. 4, Fall 2020, pp. 273–296. (online pdf)

We analyze data emanating from a major e-commerce warehouse and provided by a third-party warehouse logistics management company to replicate flow diagrams, assess order fulfillment efficiency, identify bottlenecks, and suggest improvement strategies. Without access to actual layouts and process-flow diagrams and purely based on data, we are able to describe the processes in detail and prescribe changes. By investigating the characteristics of orders, the wave-sorting operation, and the order-preparation process, we find that products from different orders are picked in batches for efficiency. Similar products are picked in small containers called totes. Totes are then stored in a buffer area and routed to be emptied of their contents at induction lines. Orders are then consolidated at the put wall, where each order is accumulated in a cubby. This order consolidation process depends on the sequence in which totes are processed and has a huge impact on order-completion time. We, therefore, present a generalization of the parallel machine–scheduling problem that we call the order consolidation problem to determine the tote-processing sequence that minimizes total order completion time. We provide mathematical formulations and devise heuristic and exact solution methods. We propose a fast simulated annealing metaheuristic and a branch-and-price approach in which the subproblems are variants of the single machine-scheduling problem and are solved using dynamic programming. We also devise a new branching rule, compare it against the literature, and test it on randomly generated and industry data. Applied to the data and the warehouse under study, optimizing the order consolidation is found to decrease the completion time of 75.66% of orders and achieve average improvements of up to 28.77% in order consolidation time and 21.92% in cubby usage.

計算機程式補考後的人生 (2/2)：對教學的影響

因為自己失敗的經驗，所以課程中特別強調實作和電腦軟體的使用。

首先，我的講義中，寫下使用軟體的步驟，並請學生於學期初影印所有的上課筆記；課堂中，使用放大鏡或是 Excel 下檢視 200%，讓最後一排的學生也看得清楚，所有的展示或步驟一定操作兩遍。

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

C. Rudin, Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead, Nature Machine Intelligence, 1, 206–215 (2019). 

Black box machine learning models are currently being used for high-stakes decision making throughout society, causing problems in healthcare, criminal justice and other domains. Some people hope that creating methods for explaining these black box models will alleviate some of the problems, but trying to explain black box models, rather than creating models that are interpretable in the first place, is likely to perpetuate bad practice and can potentially cause great harm to society. The way forward is to design models that are inherently interpretable. This Perspective clarifies the chasm between explaining black boxes and using inherently interpretable models, outlines several key reasons why explainable black boxes should be avoided in high-stakes decisions, identifies challenges to interpretable machine learning, and provides several example applications where interpretable models could potentially replace black box models in criminal justice, healthcare and computer vision.

Key contents:

• Key issues with explainable ML
• Key issues with interpretable ML
• Encouraging responsible ML governance
• Algorithmic challenges in interpretable ML
• Discussion on interpretability for specific domains