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Applications of Modern Heuristic Search Methods to Pattern Sequencing Problems
 COMPUTERS & OPERATIONS RESEARCH
, 1999
"... This article describes applications of modern heuristic search methods to pattern sequencing problems, i.e., problems seeking for a permutation of the rows of a given matrix with respect to some given objective function. We consider two di#erent objectives: Minimization of the number of simultane ..."
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Cited by 21 (6 self)
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This article describes applications of modern heuristic search methods to pattern sequencing problems, i.e., problems seeking for a permutation of the rows of a given matrix with respect to some given objective function. We consider two di#erent objectives: Minimization of the number of simultaneously open stacks and minimization of the average order spread. Both objectives require the adaptive evaluation of changed solutions to allow an e#cient application of neighbourhood search techniques.
Caching search states in permutation problems
 In van Beek [19
"... Abstract. When the search for a solution to a constraint satisfaction problem backtracks, it is not usually worthwhile to remember the assignment that failed, because the same assignment will not occur again. However, we show that for some problems recording assignments is useful, because other assi ..."
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Cited by 6 (0 self)
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Abstract. When the search for a solution to a constraint satisfaction problem backtracks, it is not usually worthwhile to remember the assignment that failed, because the same assignment will not occur again. However, we show that for some problems recording assignments is useful, because other assignments can lead to the same state of the search. We demonstrate this in two classes of permutation problem, a satisfaction problem and an optimization problem. Caching states visited has proved effective in reducing both search effort and runtime for difficult instances of each class, and the space requirements are manageable. 1
Constraint Programming in Practice: Scheduling a Rehearsal Report
, 2003
"... The basic principles of constraint programming (constraint satisfaction problems, search, constraint propagation) are introduced by discussing how constraint programming can be used to solve a specific optimization problem. A set of orchestral pieces is to be rehearsed and the problem requires fi ..."
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Cited by 4 (1 self)
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The basic principles of constraint programming (constraint satisfaction problems, search, constraint propagation) are introduced by discussing how constraint programming can be used to solve a specific optimization problem. A set of orchestral pieces is to be rehearsed and the problem requires finding a sequence that will minimize the time that players are at the rehearsal but not playing, if they arrive for the first piece they are involved in and leave after the last. A constraint programming model of this problem is presented. A similar problem arises in `talent scheduling' in shooting a film; improvements to the basic model are given that allow a larger instance of this kind to be solved.
Solving Talent Scheduling with Dynamic Programming
"... We give a dynamic programming solution to the problem of scheduling scenes to minimize the cost of the talent. Starting from a basic dynamic program, we show a number of ways to improve the dynamic programming solution, by preprocessing and restricting the search. We show how by considering a bounde ..."
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Cited by 4 (3 self)
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We give a dynamic programming solution to the problem of scheduling scenes to minimize the cost of the talent. Starting from a basic dynamic program, we show a number of ways to improve the dynamic programming solution, by preprocessing and restricting the search. We show how by considering a bounded version of the problem, and determining lower and upper bounds, we can improve the search. We then show how ordering the scenes from both ends can drastically reduce the search space. The final dynamic programming solution is, orders of magnitude faster than competing approaches, and finds optimal solutions to larger problems than were considered previously.
Improving Combinatorial Optimization (Extended Abstract)
 PROCEEDINGS OF THE TWENTYTHIRD INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE
, 2013
"... Combinatorial Optimization is an important area of computer science that has many theoretical and practical applications. In the thesis [Chu, 2011], we present important contributions to several different areas of combinatorial optimization, including nogood learning, symmetry breaking, dominance, r ..."
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Combinatorial Optimization is an important area of computer science that has many theoretical and practical applications. In the thesis [Chu, 2011], we present important contributions to several different areas of combinatorial optimization, including nogood learning, symmetry breaking, dominance, relaxations and parallelization. We develop a new nogood learning technique based on constraint projection that allows us to exploit subproblem dominances that arise when two different search paths lead to subproblems which are identical on the remaining unfixed variables. We present a new symmetry breaking technique called SBDS1UIP, which extends Symmetry Breaking During Search (SBDS) by using the more powerful 1UIP nogoods generated by Lazy Clause Generation (LCG) solvers. We present two new general methods for exploiting almost symmetries by modifying SBDS1UIP and by using conditional symmetry breaking constraints.