PT Journal
AU Pagani, P
   Pfann, F
TI Deep Neural Networks for the Scheduling of Resource-Constrained Activity Sequences: A Preliminary Investigation
SO Logistics Journal : Proceedings
PY 2020
VL 2020
IS 12
DI 10.2195/lj_Proc_pagani_en_202012_01
DE Maschinelles Lernen; Planung; RCPSP; Resource-Constrained Project Scheduling Problem; artificial neural networks; künstliche neuronale Netze; machine learning; scheduling
AB The scheduling of activity sequences under resource constraints, also known as Resource-Constrained Project Scheduling Problem (RCPSP), is a well-known optimization problem that consists in finding an activity execution schedule that minimizes the total duration of the considered sequence. This problem is generally tackled with heuristic and meta-heuristic methods. This paper proposes a different approach based on artificial neural networks, used as decision tools, and machine learning. Moreover, it is shown that such methodology is able to provide good and fast activity execution schedules.
ER