A multi-agent genetic algorithm for resource constrained project scheduling problems

2013 ◽  
Author(s):  
Xiaoxiao Yuan ◽  
Chuanfu Xiao ◽  
Xiyu Lv ◽  
Jing Liu
Keyword(s):  
Genetic Algorithm ◽  
Project Scheduling ◽  
Scheduling Problems ◽  
Resource Constrained ◽  
Resource Constrained Project Scheduling ◽  
Multi Agent

A Multi-Agent Optimization Method for Preemptive Resource-Constrained Project Scheduling

2019 ◽  
Vol 10 (1) ◽  
pp. 21-33
Author(s):  
Yongyi Shou ◽  
Wenjin Hu ◽  
Changtao Lai ◽  
Ying Ying
Keyword(s):  
Project Scheduling ◽  
Optimization Method ◽  
Negotiation Process ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Project Schedule ◽  
Resource Constrained ◽  
Resource Constrained Project Scheduling ◽  
Multi Agent ◽  
Project Scheduling Problem

A multi-agent optimization method is proposed to solve the preemptive resource-constrained project scheduling problem in which activities are allowed to be preempted no more than once. The proposed method involves a multi-agent system, a negotiation process, and two types of agents (activity agents and schedule agent). The activity agents and the schedule agent negotiate with each other to allocate resources and optimize the project schedule. Computational experiments were conducted using the standard project scheduling problem sets. Compared with prior studies, results of the proposed method are competitive in terms of project makespan. The method can be extended to other preemptive resource-constrained project scheduling problems.

Improved genetic algorithm for resource-constrained scheduling of large projects

2009 ◽  
Vol 36 (6) ◽  
pp. 1016-1027 ◽  
Author(s):  
Jin-Lee Kim
Keyword(s):  
Genetic Algorithm ◽  
Project Management ◽  
Project Scheduling ◽  
Selection Process ◽  
Transformation Method ◽  
Scheduling Problems ◽  
Improved Genetic Algorithm ◽  
Resource Constrained ◽  
Practical Applications ◽  
Resource Constrained Project Scheduling

The generalized model of the resource-constrained project scheduling problem (RCPSP) is valuable because it can be incorporated into the advanced computational methods of commercial project management software for practical applications. A construction schedule generated by most commercial project management programs does not guarantee its optimality when the resources are limited. This paper presents an improved elitist genetic algorithm (GA) for resource-constrained scheduling of large projects. The proposed algorithm allocates multiple renewable resources to activities of a single large-sized project to achieve the objective of minimizing the project duration. A permutation-based decoding procedure is developed using the improved parallel schedule generation scheme. A new parameter, named transformation power, is created in the transformation method of the improved algorithm to ensure that the individual selection process performs correctly. Extensive computational results using a standard set of large-sized multiple resource-constrained project scheduling problems are presented to demonstrate the performance and accuracy of the algorithm.

scholarly journals Integrating Schedule Risk Analysis with Multi-Skilled Resource Scheduling to Improve Resource-Constrained Project Scheduling Problems

2021 ◽  
Vol 11 (2) ◽  
pp. 650
Author(s):  
Muritala Adebayo Isah ◽  
Byung-Soo Kim
Keyword(s):  
Risk Analysis ◽  
Project Scheduling ◽  
Resource Scheduling ◽  
Scheduling Problems ◽  
Project Schedule ◽  
Risk And Uncertainty ◽  
Resource Constrained ◽  
Resource Constrained Project Scheduling ◽  
Schedule Risk ◽  
Schedule Risk Analysis

Construction projects are planned in a complex and dynamic environment characterized by high risks and uncertainties amidst resource constraints. Assessing construction schedule risk facilitates informed decision-making, especially in a resource-constrained situation, and allows proactive actions to be taken so that project objectives are not jeopardized. This study presents a stochastic multiskilled resource scheduling (SMSRS) model for resource-constrained project scheduling problems (RCSPSP) considering the impact of risk and uncertainty on activity durations. The SMSRS model was developed by integrating a schedule risk analysis (SRA) model (developed in MS Excel) with an existing multiskilled resource scheduling (MSRS) algorithm for the development of a feasible and realistic schedule. The computational experiment carried out on three case projects using the proposed SMSRS model revealed an average percentage deviation of 10.50%, indicating the inherent risk and uncertainty in activity durations of the project schedule. The core contribution of the proposed SMSRS model is that it: (1) presents project practitioners with a simple tool for assessing the risks and uncertainty associated with resource-constrained project schedules so that necessary response actions can be taken to ensure project success; (2) provides the small-scale construction businesses with an affordable tool for evaluating schedule risk and developing a feasible and realistic project schedule.

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