A convolutional neural network for the resource-constrained project scheduling problem (RCPSP): A new approach

Abstract

All projects require a structure to meet project requirements and achieve established goals. This framework is called project management. Therefore, project management plays an important role in national development and economic growth. Project management includes various knowledge areas such as project integration management, project scope management, project schedule management, etc. The article focuses on the resource-constrained project scheduling known as problem so- called the resource-constrained project scheduling problem (RCPSP). The RCPSP is a part of schedule management. The standard RCPSP has two important constraints, resource constraints and precedence relationships of activities during project scheduling. The objective of the problem is to optimize and minimize the project duration, subject to the above constraints. In this paper, we develop a convolutional neural network approach to solve the standard single mode RCPSP. The advantage of this algorithm over conventional methods such as metaheuristics is that it does not need to generate many solutions or populations. In this paper, the serial schedule generation scheme (SSGS) is used to schedule the project activities using an evolved convolutional neural network (CNN) as a tool to select an appropriate priority rule to filter out a candidate activity. The evolved CNN learns according to the eight project parameters, namely network complexity, resource factor, resource strength, average work per activity, etc. The above parameters are the inputs of the network and are recalculated at each step of the project planning. Moreover, the developed network has priority rules which are the outputs of the developed neural network. Therefore, after the learning process, the network can automatically select an appropriate priority rule to filter an activity from the eligible activities. In this way, the algorithm is able to schedule all project activities according to the given project constraints. Finally, the performance of the Convolutional Neural Network (CNN) approach is investigated using standard benchmark problems from PSPLIB in comparison to the MLFNN approach and standard metaheuristics.

An Agile Adaptive Biased-Randomized Discrete-Event Heuristic for the Resource-Constrained Project Scheduling Problem

“…This method has demonstrated superior performance in computational experiments on PSPLIB, showcasing its potential in evolutionary algorithm research for RCPSP. Golab et al [31] proposed a convolutional neural network (CNN) approach that bypasses the need for generating multiple solutions or populations, instead using an evolved CNN within a serial schedule generation scheme to effectively prioritize project activities.…”

Multi-skill resource-constrained multi-modal project scheduling problem based on hybrid quantum algorithm

“…In response, the Resource Constrained Project Scheduling Problem (RCPSP), a class of project scheduling techniques that consider resource constraints, was introduced Kelley to produce a schedule that optimizes management objectives while satisfying activity tightness and resource constraints 13 . Since then, many scholars have launched the solution for RCPSP, for example, Amir developed a convolutional neural network method to solve RCPSP and investigated the performance of the convolutional neural network (CNN) method using standard benchmark problems in PSPLIB and compared it with the MLFNN method and standard meta-heuristics 14 ; Feng et al proposed an extended genetic algorithm for solving the RCPSP, and experiments showed that the extended genetic algorithm can solve the RCPSP faster and more accurately than the traditional genetic algorithm 15 . For example, Hua et al proposed an improved genetic algorithm based on time window decomposition in order to solve the RCPSP problem more efficiently, which employs three derived methods to increase population diversity 16 .…”