A Cross-Entropy Based Population Learning Algorithm for Multi-mode Resource-Constrained Project Scheduling Problem with Minimum and Maximum Time Lags

Abstract

Abstract. The multi-mode resource-constrained project scheduling problem with minimum and maximum time lags is considered in the paper. An activity is performed in a mode, which determines the demand of renewable and nonrenewable resources required for its processing and minimum and maximum time lags between adjacent activities. The goal is to find a mode assignment to the activities and their start times such that all constraints are satisfied and the project duration is minimized. Because the problem is NP-hard a populationlearning algorithm (PLA2) is proposed to tackle the problem. PLA2 is a population-based approach which takes advantage of the features common to the social education system rather than to the evolutionary processes. The proposed approach perfectly suits for multi-agent systems because it is based on the idea of constructing a hybrid algorithm integrating different optimization techniques complementing each other and producing a synergetic effect. Results of the experiment were compared to the results published in Project Scheduling Problem Library.

Bi-objective optimization of a multi-mode, multi-site resource-constrained project scheduling problem

“…Schnell and Hartl (2016) considered generalized precedence relations along with time lags. Jędrzejowicz and Skakovski (2010) and Ballestín et al (2013) Some research studies add assumptions on the resources like multiple skills or renewability and non-renewability, e.g. Varma et al (2007), Afshar-Nadjafi (2014) and Maghsoudlou et al (2016).…”

“…Schnell and Hartl (2016) considered generalized precedence relations along with time lags. Jędrzejowicz and Skakovski (2010) and Ballestín et al (2013) are other research examples for time lag assumptions.…”