In this paper we consider the labor constrained scheduling problem (LCSP), in which a set of jobs to be processed is subject to precedence and labor requirement constraints. Each job has a specified processing time and a labor requirements profile, which typically varies as the job is processed. Given the amount of labor available at each period, the problem consists in determining starting times so as to minimize the overall makespan, subject to the precedence and labor constraints. We propose two parallel cooperative algorithms for LCSP: an asynchronous team and a parallel tabu search strategy. Both algorithms make use of cooperative processes that asynchronously exchange information gathered along their execution. Computational experiments on benchmark instances show that these parallel algorithms produce significantly better solutions than all sequential algorithms previously proposed in the literature.
To appear in Essays and Surveys in Metaheuristics (C.C. Ribeiro and P. Hansen, editors), Kluwer, 2001.