Concurrent engineering has been widely used in managing design projects to speed up the design process by concurrently performing multiple tasks. Since the progress of a design task often depends on the knowledge about other tasks and requires effective communication, tasks and communication activities need to be properly coordinated to avoid delays caused by waiting for information or the need for rework. This paper presents a novel formulation for design project scheduling with explicit modeling of task dependencies and the associated communication activities. General dependencies are modeled as combinations of three basic types representing sequential, concurrent, and independent processes. Communication activities are also modeled as tasks, and their interactions with design tasks are described by sets of intertask constraints. The objective is to achieve timely project completion with limited resources. To improve algorithm convergence and schedule quality, penalties on the violation of constraints coupling design tasks are added to the objective function. A solution methodology that combines Lagrangian relaxation, dynamic programming, and heuristic is developed to schedule design and communication tasks, and a surrogate optimization framework is used to overcome the ldquoinseperabilityrdquo caused by nonadditive penalties. A heuristic procedure is then developed to obtain scheduling policies from optimization results and to dynamically construct schedules. Numerical results show that the approach is effective to handle various task dependencies and the associated communication activities to provide high-quality schedules.

Pressed by market globalization, a recent trend for manufacturers is to have their design teams at different locations to better serve local markets and reduce design costs. Under the concurrent engineering paradigm, tasks in a design project are required to be performed in parallel, however, are often interdependent in a complex way. As a result, effective communication and coordination among teams become vital for a project to be successful. The complex interdependencies have not been adequately addressed in the literature. This paper presents a novel optimization formulation that explicitly models the interdependencies among tasks and the communication activities required. A solution methodology that combines Lagrangian relaxation and the surrogate subgradient method has been developed to solve the optimization problem that is inseparable. Backward/forward Dynamic programming is used to solve task subproblems. Numerical results demonstrate that complex dependencies among tasks are satisfied via communication activities, and near-optimal schedules are efficiently obtained.