Generalized best-first search strategies and the optimality of A*
Journal of the ACM (JACM)
Principles of artificial intelligence
Principles of artificial intelligence
The CDP: A unifying formulation for heuristic search, dynamic programming, and branch-and-bound
Search in Artificial Intelligence
Dynamic programming with convexity, concavity and sparsity
Theoretical Computer Science - Selected papers of the Combinatorial Pattern Matching School
Linear-space best-first search
Artificial Intelligence
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
A hybrid dynamic programming/branch-and-bound algorithm for the multiple-choice knapsack problem
Journal of Computational and Applied Mathematics
Space-efficient search algorithms
ACM Computing Surveys (CSUR)
A military reserve manpower planning model
Computers and Operations Research
A class of greedy algorithms for the generalized assignment problem
Discrete Applied Mathematics
Efficient dynamic programming using quadrangle inequalities
STOC '80 Proceedings of the twelfth annual ACM symposium on Theory of computing
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In this paper, we focus on the lower level allocation problem of a hierarchical time-constrained product development situation. Commonly found in the industrial practice, the type of product development process we consider is the radical/experiential model of product development of Eisenhardt and Tabrizi, (Administr. Sci. Q. 40 (1995) 84). The description of the main characteristics of the process follows the line of the recent research of Bowers et al. (in: M.T. Brannick, E. Salas, C. Prince (Eds.), Team Performance Assessment and Measurement: Theory, Research, and Applications, Lawrence Erlbaum Associates, Inc., Publishers, New Jersey, 1997, pp. 85-108) and Oorschot (Analysing Radical NPD Projects from an Operational Control Perspective, Ph.D. Thesis, Eindhoven University of Technology, The Netherlands, 2001).Starting from the dynamic programming techniques, we propose a solution to this optimization problem by employing an A* monotonic heuristic evaluation function for best-search algorithms. This function is based on aggregate information on the design task set. We discuss the efficiency of the general best-first search algorithm using an A* evaluation function, and of an RBFS variant of it, which also searches in best-first order, using the same A* evaluation function. We prove that the algorithms find an optimal feasible allocation, provided that there exists a feasible allocation. We conclude by commenting upon some experimental results.