A Hybrid Differential Evolution with Cuckoo Search for Solving Resource Constrained Project Scheduling Problems The Resource Constrained Project Scheduling problem (RCPSP) has been considered as a scheduling problem which has a wide range of applications in construction industries, manufacturing, production planning and project management domains. To solve such RCPSPs, in this paper we propose a hybrid algorithm that utilizes the strengths of both differential evolution (DE) and cuckoo search (CS) algorithm in one framework called hybrid differential evolution with cuckoo search (DECS) algorithm. In it, a selection mechanism based on the solutionsu0027 quality and populationsu0027 diversity is used to select the most appropriate algorithm during the evolutionary process. A linear population reduction mechanism is utilized to update the DE population size. A number of data sets of singlemode RCPSPs from the project scheduling library (PSPLIB) have been considered and solved by the proposed hybrid DECS algorithm. Computational results and comparisons with some recent stateoftheart algorithms show that DECS is able to produce very high quality results.

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.