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Joel Sokol

Bio: Joel Sokol is an academic researcher from Georgia Institute of Technology. The author has contributed to research in topics: Flow network & Routing (electronic design automation). The author has an hindex of 18, co-authored 36 publications receiving 905 citations. Previous affiliations of Joel Sokol include Massachusetts Institute of Technology.

Papers
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Journal ArticleDOI
TL;DR: In this article, the authors exploit properties of protein-based maximum clique problems to develop specialized preprocessing techniques and show how they can be used to solve contact map overlap instances to optimality.
Abstract: In biology, the protein structure alignment problem answers the question of how similar two proteins are. Proteins with strong physical similarities in their tertiary (folded) structure often have similar functions, so understanding physical similarity could be a key to developing protein-based medical treatments. One of the models for protein structure alignment is the maximum contact map overlap (CMO) model. The CMO model of protein structure alignment can be cast as a maximum clique problem on an appropriately defined graph. We exploit properties of these protein-based maximum clique problems to develop specialized preprocessing techniques and show how they can be used to more quickly solve contact map overlap instances to optimality.

121 citations

Journal ArticleDOI
TL;DR: Over the past 6 years, the combined logistic regression/Markov chain model has been significantly more successful than the other common methods such as tournament seedings, the AP and ESPN/USA Today polls, the RPI, and the Sagarin and Massey ratings.
Abstract: Each year, more than $3 billion is wagered on the NCAA Division 1 men's basketball tournament. Most of that money is wagered in pools where the object is to correctly predict winners of each game, with emphasis on the last four teams remaining (the Final Four). In this paper, we present a combined logistic regression/Markov chain model for predicting the outcome of NCAA tournament games given only basic input data. Over the past 6 years, our model has been significantly more successful than the other common methods such as tournament seedings, the AP and ESPN/USA Today polls, the RPI, and the Sagarin and Massey ratings. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006.

101 citations

Journal ArticleDOI
TL;DR: The main goal for MIRPLib is to help maritime inventory routing gain maturity as an important and interesting class of planning problems, and to provide the mixed-integer linear programming community with a set of optimization problem instances from the maritime transportation domain in LP and MPS format.

89 citations

Journal ArticleDOI
TL;DR: It is found that changing assumptions about the degree of carrier control can significantly impact the feasibility of routing decisions made by individual carriers when operating under the mechanism, as well as the properties of the profit allocations.
Abstract: When cargo carriers form an alliance, sharing network capacity in order to improve profitability, a key issue is how to provide incentive for carriers to make decisions that are optimal for the alliance as a whole. We propose a mechanism that allocates both alliance resources and profits by appropriately setting resource prices. Clearly, it is important to understand the impact of these prices on the behavior of an individual carrier. We analyze the performance of our mechanism using a modeling approach that makes use of realistic control parameters, investigating theoretical and practical properties of profit allocations obtained. Experimental results confirm that our proposed mechanism is robust with respect to variability in alliance composition and cargo demand, yielding solutions that retain a high proportion of optimal profit and achieve a stable distribution of revenue across members of the alliance. We also study an alternative modeling approach in which we assume that each carrier can make load selection decisions for other carriers. We find that changing assumptions about the degree of carrier control can significantly impact the feasibility of routing decisions made by individual carriers when operating under our mechanism, as well as the properties of the profit allocations.

85 citations

Journal ArticleDOI
TL;DR: This work presents an optimization methodology for finding cost-effective schedules for regional daily drayage operations and presents numerical results for real-world data which demonstrate that the methodology produces low cost solutions in a reasonably short time.
Abstract: Daily drayage operations involve moving loaded or empty equipment between customer locations and rail ramps Our goal is to minimize the cost of daily drayage operations in a region on a given day Drayage orders are generally pickup and delivery requests with time windows The repositioning of empty equipment may also be required in order to facilitate loaded movements The drayage orders are satisfied by a heterogeneous fleet of drivers Driver routes must satisfy various operational constraints We present an optimization methodology for finding cost-effective schedules for regional daily drayage operations The core of the formulation is a set partitioning model whose columns represent routes Routes are added to the formulation by column generation We present numerical results for real-world data which demonstrate that our methodology produces low cost solutions in a reasonably short time

59 citations


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31 Dec 1994
TL;DR: A partially enumerative algorithm is presented for the maximum clique problem which is very simple to implement and Computational results for an efficient implementation on an IBM 3090 computer are provided.
Abstract: We present an exact partial enumerative algorithm for the maximum clique problem. The pruning device used is derived from graph colorings. Pruning of the search tree is accomplished not only by the number of colors used to color a tree subproblem but also by using information gained in the process of coloring. This leads to increased pruning which translates into improved computational performance. Experimental results on test problems are presented.

467 citations

Posted Content
TL;DR: In this paper, a combination of reinforcement learning and graph embedding is proposed to learn heuristics for combinatorial optimization problems over graphs, such as Minimum Vertex Cover, Maximum Cut and Traveling Salesman problems.
Abstract: The design of good heuristics or approximation algorithms for NP-hard combinatorial optimization problems often requires significant specialized knowledge and trial-and-error. Can we automate this challenging, tedious process, and learn the algorithms instead? In many real-world applications, it is typically the case that the same optimization problem is solved again and again on a regular basis, maintaining the same problem structure but differing in the data. This provides an opportunity for learning heuristic algorithms that exploit the structure of such recurring problems. In this paper, we propose a unique combination of reinforcement learning and graph embedding to address this challenge. The learned greedy policy behaves like a meta-algorithm that incrementally constructs a solution, and the action is determined by the output of a graph embedding network capturing the current state of the solution. We show that our framework can be applied to a diverse range of optimization problems over graphs, and learns effective algorithms for the Minimum Vertex Cover, Maximum Cut and Traveling Salesman problems.

455 citations

Journal ArticleDOI
TL;DR: This work surveys the state of the art in the field of Vehicle Routing Problem research, summarizing problem combinations, constraints defined, and approaches found and concludes that the Rich VRP arises: combining multiple constraints for tackling realistic problems.
Abstract: The Vehicle Routing Problem (VRP) is a well-known research line in the optimization research community. Its different basic variants have been widely explored in the literature. Even though it has been studied for years, the research around it is still very active. The new tendency is mainly focused on applying this study case to real-life problems. Due to this trend, the Rich VRP arises: combining multiple constraints for tackling realistic problems. Nowadays, some studies have considered specific combinations of real-life constraints to define the emerging Rich VRP scopes. This work surveys the state of the art in the field, summarizing problem combinations, constraints defined, and approaches found.

255 citations

Journal ArticleDOI
TL;DR: The purpose of the paper is to provide a comprehensive and relevant taxonomy for the RVRP literature and to propose an elaborate definition of RVRPs.

243 citations