Position Assignment on an Enterprise Level Using Combinatorial Optimization

AAAI Conferences

We developed a tool to solve a problem of position assignment within the IT Ford College Graduate program. This position assignment tool was first developed in 2012 and has been used successfully since then. The tool has since evolved for use with several other position assignment and related tasks with other similar programs in Ford Motor Company. This paper will describe the creation of this tool and how we have applied it, focusing on the need for developing such a tool, and how the continued development of this tool will benefit its users and the company.


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The Conference Paper Assignment Problem: Using Order Weighted Averages to Assign Indivisible Goods

AAAI Conferences

We propose a novel mechanism for solving the assignment problem when we have a two sided matching problem with preferences from one side (the agents/reviewers) over the other side (the objects/papers) and both sides have capacity constraints. The assignment problem is a fundamental in both computer science and economics with application in many areas including task and resource allocation. Drawing inspiration from work in multi-criteria decision making and social choice theory we use order weighted averages (OWAs), a parameterized class of mean aggregators, to propose a novel and flexible class of algorithms for the assignment problem. We show an algorithm for finding an SUM-OWA assignment in polynomial time, in contrast to the NP-hardness of finding an egalitarian assignment. We demonstrate through empirical experiments that using SUM-OWA assignments can lead to high quality and more fair assignments.


Linear Satisfiability Preserving Assignments

Journal of Artificial Intelligence Research

In this paper, we study several classes of satisfiability preserving assignments to the constraint satisfaction problem (CSP). In particular, we consider fixable, autark and satisfying assignments. Since it is in general NP-hard to find a nontrivial (i.e., nonempty) satisfiability preserving assignment, we introduce linear satisfiability preserving assignments, which are defined by polyhedral cones in an associated vector space. The vector space is obtained by the identification, introduced by Kullmann, of assignments with real vectors. We consider arbitrary polyhedral cones, where only restricted classes of cones for autark assignments are considered in the literature. We reveal that cones in certain classes are maximal as a convex subset of the set of the associated vectors, which can be regarded as extensions of Kullmann's results for autark assignments of CNFs. As algorithmic results, we present a pseudo-polynomial time algorithm that computes a linear fixable assignment for a given integer linear system, which implies the well known pseudo-polynomial solvability for integer linear systems such as two-variable-per-inequality (TVPI), Horn and q-Horn systems.


PeerReview4All: Fair and Accurate Reviewer Assignment in Peer Review

arXiv.org Machine Learning

We consider the problem of automated assignment of papers to reviewers in conference peer review, with a focus on fairness and statistical accuracy. Our fairness objective is to maximize the review quality of the most disadvantaged paper, in contrast to the commonly used objective of maximizing the total quality over all papers. We design an assignment algorithm based on an incremental max-flow procedure that we prove is near-optimally fair. Our statistical accuracy objective is to ensure correct recovery of the papers that should be accepted. We provide a sharp minimax analysis of the accuracy of the peer-review process for a popular objective-score model as well as for a novel subjective-score model that we propose in the paper. Our analysis proves that our proposed assignment algorithm also leads to a near-optimal statistical accuracy. Finally, we design a novel experiment that allows for an objective comparison of various assignment algorithms, and overcomes the inherent difficulty posed by the absence of a ground truth in experiments on peer-review. The results of this experiment corroborate the theoretical guarantees of our algorithm.