This paper presents a hybrid genetic algorithm for the job shop scheduling problem. The chromosome representation of the problem is based on random keys. The schedules are constructed using a priority rule in which the priorities are defined by the genetic algorithm. Schedules are constructed using a procedure that generates parameterized active schedules. After a schedule is obtained a local search heuristic is applied to improve the solution. The approach is tested on a set of standard instances taken from the literature and compared with other approaches. The computation results validate the effectiveness of the proposed algorithm.

This paper presents a genetic algorithm for the Resource Constrained Multi-Project Scheduling Problem (RCMPSP). The chromosome representation of the problem is based on random keys. The schedules are constructed using a heuristic that builds parameterized active schedules based on priorities, delay times, and release dates defined by the genetic algorithm. The approach is tested on a set of randomly generated problems. The computational results validate the effectiveness of the proposed algorithm.

In this chapter the Basic Multi-Project Scheduling Problem (BMPSP) is described, an overview of the literature on multi-project scheduling is provided, and a solution approach based on a biased random-key genetic algorithm (BRKGA) is presented. The BMPSP consists in finding a schedule for all the activities belonging to all the projects taking into account the precedence constraints and the availability of resources, while minimizing some measure of performance. The representation of the problem is based on random keys. The BRKGA generates priorities, delay times, and release dates, which are used by a heuristic decoder procedure to construct parameterized active schedules. The performance of the proposed approach is validated on a set of randomly generated problems. Keywords Genetic algorithm • Meta-heuristics • Multi-project scheduling • Random keys IntroductionManaging multiple projects is a complex decision-making process, where a number of projects must share concurrently a set of limited resources. Examples of multiproject environments are new product development, multi-product manufacturing, infrastructure constructions, and maintenance of systems.