A genetic algorithm with local search using activity list characteristics for solving resource‐constrained multiproject scheduling problem

Abstract

In this paper, we aim to solve the resource-constrained multiproject scheduling problem (rc-mPSP), in which more than one project are scheduled simultaneously, projects share global resources, and the average project delay and total project time are minimized as objectives. In order to solve this problem by a centralized scheduling method, we present a new genetic algorithm (GA) approach. In this procedure, we follow the GA described in Okada et al . (2014) and improve its genetic operators, such as crossover and mutation, and local search so as to work better on rc-mPSP. Furthermore, in order to evaluate the proposed approach, we compare the results of the computational experiment on certain standard project instances with the several competing decentralized methods and centralized methods presented in the literature. We show that our procedure is one of the most competitive among such algorithms.

Triple‐chromosome genetic algorithm for unrelated parallel machine scheduling under time‐of‐use tariffs

“…Holland initiated the GA methodology, and afterward, GA became widely adopted to solve combinatorial optimization problems such as resource‐constraint multiproject scheduling and assignment . The UPMSTOU is the extension of UPMS in which we must determine not only the job sequencing and machine assignment, but also the starting time of jobs.…”