ProGen/πx – An instance generator for resource-constrained project scheduling problems with partially renewable resources and further extensions

2000 ◽  
Vol 125 (1) ◽  
pp. 59-72 ◽  
Author(s):  
Andreas Drexl ◽  
Rüdiger Nissen ◽  
James H. Patterson ◽  
Frank Salewski
Keyword(s):  
Renewable Resources ◽  
Project Scheduling ◽  
Scheduling Problems ◽  
Resource Constrained ◽  
Resource Constrained Project Scheduling ◽  
Partially Renewable Resources

scholarly journals On some implementations of solving the resource constrained project scheduling problems

2019 ◽  
Vol 29 (1) ◽  
pp. 31-42 ◽  
Author(s):  
E.Kh. Gimadi ◽  
E.N. Goncharov ◽  
D.V. Mishin
Keyword(s):  
Renewable Resources ◽  
Project Scheduling ◽  
Resource Constraints ◽  
Scheduling Problems ◽  
Makespan Minimization ◽  
Resource Constrained ◽  
Resource Constrained Project Scheduling ◽  
Project Scheduling Problem ◽  
Technological Constraints

We consider a resource-constrained project scheduling problem with respect to the makespan minimization criterion. The problem accounts for technological constraints of activities precedence together with resource constraints. Activities pre- emptions are not allowed. The problem with renewable resources is NP-hard in the strong sense. We propose an exact branch and bound algorithm for solving the problem with renewable resources. It uses our new branching scheme based on the representation of a schedule in form of the activity list. We use two approaches of constructing the lower bound. We present results of numerical experiments, illustrating the quality of the proposed lower bounds. The test instances are taken from the library of test instances PSPLIB.

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.

scholarly journals Probability Mechanism Based Particle Swarm Optimization Algorithm and Its Application in Resource-Constrained Project Scheduling Problems

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Shuai Li ◽  
Zhicong Zhang ◽  
Xiaohui Yan ◽  
Liangwei Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Project Scheduling ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Scheduling Problems ◽  
Resource Constrained ◽  
Swarm Optimization ◽  
Resource Constrained Project Scheduling ◽  
Combinatorial Optimization Problems ◽  
New Particles

In this paper, a new probability mechanism based particle swarm optimization (PMPSO) algorithm is proposed to solve combinatorial optimization problems. Based on the idea of traditional PSO, the algorithm generates new particles based on the optimal particles in the population and the historical optimal particles in the individual changes. In our algorithm, new particles are generated by a specially designed probability selection mechanism. We adjust the probability of each child element in the new particle generation based on the difference between the best particles and the elements of each particle. To this end, we redefine the speed, position, and arithmetic symbols in the PMPSO algorithm. To test the performance of PMPSO, we used PMPSO to solve resource-constrained project scheduling problems. Experimental results validated the efficacy of the algorithm.

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