This paper presents a new bidirectional search algorithm to solve the shortest path problem. The new algorithm uses an iterative deepening technique with a consistent heuristic to improve lower bounds on path costs. The new algorithm contains a novel technique of filtering nodes to significantly reduce the memory requirements. Computational experiments on the pancake problem, sliding tile problem, and Rubik’s cube show that the new algorithm uses significantly less memory and executes faster than A* and other state-of-the-art bidirectional algorithms. Summary of Contribution: Quickly solving single-source shortest path problems on graphs is important for pathfinding applications and is a core problem in both artificial intelligence and operations research. This paper attempts to solve large problems that do not easily fit into the available memory of a desktop computer, such as finding the optimal shortest set of moves to solve a Rubik’s cube, and solve them faster than existing algorithms.

This paper proves several new properties of the Meet in the Middle (MMe) bidirectional heuristic search algorithm when applied to graphs with unit edge costs. Primarily, it is shown that the length of the first path discovered by MMe never exceeds the optimal length by more than one and that if the length of the first path found is odd, then it must be optimal. These properties suggest that the search strategy should emphasize finding a complete path as soon as possible. Computational experiments demonstrate that fully exploiting these new properties can decrease the number of nodes expanded by anywhere from twofold to over tenfold.

Branch-and-price algorithms combine a branch-and-bound search with an exponentially sized LP formulation that must be solved via column generation. Unfortunately, the standard branching rules used in branch and bound for integer programming interfere with the structure of the column generation routine; therefore, most such algorithms employ alternate branching rules to circumvent this difficulty. This paper shows how a zero-suppressed binary decision diagram can be used to solve the pricing problem in a branch-and-price algorithm for the graph coloring problem, even in the presence of constraints imposed by branching decisions. This approach facilitates a much more direct solution method and can improve convergence of the column generation subroutine.

Branch-and-price algorithms for the graph coloring problem use an exponentially sized independent set-based integer programming formulation to produce usually tight lower bounds to enable more aggressive pruning in the branch-and-bound tree. One major problem inherent to any branch-and-price scheme for graph coloring is that to avoid destroying the pricing problem structure during column generation, difficult-to-implement branching rules that modify the underlying graph must be used. This paper proposes an alternative branching strategy that does not change the graph to solve the pricing problem but rather modifies the search tree to require fewer calls to difficult instances of the pricing problem. This approach, called wide branching, generates many subproblems at each node in the branch-and-price tree; this significantly reduces the length of any path through the search tree. In contrast, traditional deep branching only creates two subproblems per node, assigning a variable to either 0 or 1. A delayed branching procedure is introduced that prevents the branching factor at any particular node from growing too large in this scheme. Finally, computational results are presented that show the wide branching strategy to be competitive with state-of-the-art graph coloring solvers.

Matching is widely used in the estimation of treatment effects in observational studies. However, the matching paradigm may be too restrictive in many cases because exact matches often do not exist in the available data. One mechanism for overcoming this issue is to relax the requirement of exact matching on some or all of the covariates (attributes that may affect the response to treatment) to a requirement of balance on the covariate distributions for the treatment and control groups. The balance optimization subset selection (BOSS) model can be used to identify a control group featuring optimal covariate balance. This paper explores the relationship between the matching and BOSS models and shows how BOSS subsumes matching. Complexity and approximation results are presented for the resulting models. Computational results demonstrate some of the important trade-offs between matching and BOSS. Data, as supplemental material, are available at http://dx.doi.org/10.1287/ijoc.2013.0583 . There is a video associated with this paper. Click here to view the Video Overview . To save the file, right click and choose “Save Link As” from the menu.

We present a new exact algorithm for the assembly line balancing problem. The algorithm finds and verifies the optimal solution for every problem in the combined benchmarks of Hoffmann, Talbot, and Scholl in less than one-half second per problem, on average, including one problem that has remained open for over 10 years. The previous best-known algorithm is able to solve 257 of the 269 benchmarks. The new algorithm is based on a branch-and-bound method that uses memory to eliminate redundant subproblems.

This article describes the motivation, development, and implementation of a software tool, www.vaccineselection.com, introduced to assist health care professionals and public health administrators in managing pediatric vaccine purchase decisions and making economically sound formulary choices. The tool integrates general operations research methodologies with specific local practice choices to solve for the lowest overall cost set of vaccines required to immunize a child according to the Recommended Childhood Immunization Schedule. A description of the tool's capabilities is provided. RESULTS on the use of the software tool are reported and discussed.

We present a branch and bound algorithm for finding a maximum stable set in a graph. The algorithm uses properties of the stable set polytope to construct strong upper bounds. Specifically, it uses cliques, odd cycles, and a maximum matching on the remaining nodes. The cliques are generated via standard coloring heuristics, and the odd cycles are generated from blossoms found by a matching algorithm. We report computational experience on two classes of randomly generated graphs and on the DIMACS Challenge Benchmark graphs. These experiments indicate that the algorithm is quite effective, particularly for sparse graphs.