Hybrid Optimization Method for Large-Scale Multimode Resource-Constrained Project Scheduling Problem

Since Resource-Constrained Project Scheduling Problem (RCPSP) is a well-known NP-hard problem, it is difficult to solve large-scale practical cases by using traditional exact algorithms. Genetic algorithm (GA) is a kind of intelligent algorithm for approximate optimization, which can ascertain global optimization or suboptimal solution within a reasonable time. This article presented a new simulation algorithm by using GA for solving Resource-Constrained Project Scheduling Problem. In the algorithm, the activity adjacency matrix and priority-based preemptive resource conflict resolution are used to prevent chromosome from generating infeasible schedules. Finally, the method was tested with an actual machine and electricity project case, and the results show that the presented method is efficient and practical for practical project cases.

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

International Journal of Information Technology Project Management ◽

10.4018/ijitpm.2019010102 ◽

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.

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Application of Three Meta-Heuristic Algorithms for Maximizing the Net Present Value of a Resource-Constrained Project Scheduling Problem with Respect to Delay Penalties

International Journal of Applied Industrial Engineering ◽

10.4018/ijaie.2016010101 ◽

2016 ◽

Vol 3 (1) ◽

pp. 1-15 ◽

Cited By ~ 3

Author(s):

Masoud Rabbani ◽

Azadeh Arjmand ◽

Mohammad Mahdi Saffar ◽

Moeen Sammak Jalali

Keyword(s):

Project Scheduling ◽

Large Scale ◽

Net Present Value ◽

Heuristic Algorithms ◽

Optimal Solution ◽

Cash Flows ◽

Scheduling Problem ◽

Resource Constrained ◽

Resource Constrained Project Scheduling ◽

Project Scheduling Problem

The Resource Constrained Project Scheduling Problem (RCPSP) is been studied under different kinds of constraints and limitations. In this paper, we are going to consider the discounted cash flows for project activities, and delay penalties which occur when the project make span exceeds its deadline as the objective function of the RCPSP. To solve the model, we will take advantage of three different meta-heuristic algorithms - Genetic Algorithm (GA), Imperialist Competitive Algorithm (ICA), and Shuffled Frog Leaping Algorithm (SFLA) - to achieve the optimal solution of the problem. The evaluation of the algorithms performance reveals that, in comparison with ICA and SFLA, GA performs better, especially in large-scale problems.

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