Heuristic allocation based on a dynamic programming state-space representation

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 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 evaluation function, and of an RBFS variant of it, which also searches in best-first order, using the same 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