Healthcare service configuration based on project scheduling Abstract   Configuration has been recognized as an effective methodology to provide high product variety that caters to individual customerxe2x80x99s needs in the manufacturing industry. For healthcare service configuration, this decisionmaking process can be taken as service package creation and treated analogously as a project, which is defined as a collection of tasks. This research develops a decision support model to integrate individual patients into the healthcare service configuration process. The healthcare service configuration is formulated as a Resource Constrained Project Scheduling Problem (RCPSP), and a bilevel optimization algorithm based on Genetic Algorithm (GA) is developed for problem solving. The methodology and algorithm are implemented with a case study based on the data obtained from a general hospital in Singapore, which has demonstrated the applicability of healthcare service configuration.

A LazyConstraints Approach to ResourceConstrained Project Scheduling We study the complex combinatorial optimization problem to schedule the activities of a single project with the objective to complete the project within the shortestpossible amount of time such that the limited resource capacities as well as the prescribed precedence relations between pairs of the activities are taken into account. In addition to various specific solution algorithms, the related literature proposes several MixedInteger Linear Programming (MILP) models, but these models remain complex to solve even for smallsized instances. We present a novel approach based on an MILP model in which the resourcecapacity constraints are formulated for all inclusionminimal sets of activities which, due to the limited capacities of the resources, cannot be processed simultaneously. We propose to remove these constraints from the model and iteratively add those constraints back which are violated in the solutions obtained. For a set of test instances from the literature, our computational results indicate that with respect to both, the deviation of the project duration obtained from the lower bound devised from the criticalpath length and the number of instances solved to optimality, the novel lazyconstraints approach outperforms ten stateoftheart MILP models.