A lagrangian relaxation algorithm for facility location of resource-constrained decentralized multi-project scheduling problems

2017 ◽  
Vol 20 (2) ◽  
pp. 857-897 ◽  
Author(s):  
Mohammad Rostami ◽  
Morteza Bagherpour
Keyword(s):  
Facility Location ◽  
Lagrangian Relaxation ◽  
Project Scheduling ◽  
Scheduling Problems ◽  
Resource Constrained ◽  
Relaxation Algorithm ◽  
Lagrangian Relaxation Algorithm

Multi Project Scheduling and Material Planning Using Lagrangian Relaxation Algorithm

2021 ◽  
Vol 20 (4) ◽  
pp. 580-587
Author(s):  
Alim Al Ayub Ahmed ◽  
Ngakan Ketut Acwin Dwijendra ◽  
NareshBabu Bynagari ◽  
A.K. Modenov ◽  
M. Kavitha ◽  
...  
Keyword(s):  
Lagrangian Relaxation ◽  
Project Scheduling ◽  
Relaxation Algorithm ◽  
Lagrangian Relaxation 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.

Applying Genetic Algorithm to Resource Constrained Multi-Project Scheduling Problems

2009 ◽  
Vol 419-420 ◽  
pp. 633-636 ◽  
Author(s):  
James C. Chen ◽  
Wun Hao Jaong ◽  
Cheng Ju Sun ◽  
Hung Yu Lee ◽  
Jenn Sheng Wu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Project Scheduling ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Total Tardiness ◽  
Scheduling Problems ◽  
Test Bed ◽  
Resource Constrained ◽  
Multiple Resources ◽  
Practical Constraints

Resource-constrained multi-project scheduling problems (RCMPSP) consider precedence relationship among activities and the capacity constraints of multiple resources for multiple projects. RCMPSP are NP-hard due to these practical constraints indicating an exponential calculation time to reach optimal solution. In order to improve the speed and the performance of problem solving, heuristic approaches are widely applied to solve RCMPSP. This research proposes Hybrid Genetic Algorithm (HGA) and heuristic approach to solve RCMPSP with an objective to minimize the total tardiness. HGA is compared with three typical heuristics for RCMPSP: Maximum Total Work Content, Earliest Due Date, and Minimum Slack. Two typical RCMPSP from literature are used as a test bed for performance evaluation. The results demonstrate that HGA outperforms the three heuristic methods in term of the total tardiness.

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