The Preemptive Stochastic Resource-Constrained Project Scheduling Problem: An Efficient Globally Optimal Solution Procedure

The Multi-Skill Resource-Constrained Project Scheduling Problem (MS-RCPSP) is a combinational optimization problem with many applications in science and practical areas. This problem aims to find out the feasible schedule for the completion of projects and workflows that is minimal duration or cost (or both of them - multi objectives). The researches show that MS-RCPSP is classified into NP-Hard classification, which could not get the optimal solution in polynomial time. Therefore, we usually use approximate methods to carry out the feasible schedule. There are many publication results for that problem based on evolutionary methods such as GA, Greedy, Ant, etc. However, the evolutionary algorithms usually have a limitation that is fallen into local extremes after a number of generations. This paper will study a new method to solve the MS-RCPSP problem based on the Particle Swarm Optimization (PSO) algorithm that is called R-PSO. The new improvement of R-PSO is re-assigning the resource to execute solution tasks. To evaluate the new algorithm's effectiveness, the paper conducts experiments on iMOPSE datasets. Experimental results on simulated data show that the proposed algorithm finds a better schedule than related works.

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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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Network Traffic Statistics Method for Resource-Constrained Industrial Project Group Scheduling under Big Data

Wireless Communications and Mobile Computing ◽

10.1155/2021/5594663 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Zongjie Huo ◽

Wei Zhu ◽

Pei Pei

Keyword(s):

Big Data ◽

Network Traffic ◽

Project Scheduling ◽

Ant Colony Algorithm ◽

Optimal Solution ◽

Ant Colony ◽

Scheduling Problem ◽

Resource Constrained ◽

Resource Constrained Project Scheduling ◽

Project Scheduling Problem

With the advent of the Internet era, the demand for network in various fields is growing, and network applications are increasingly rich, which brings new challenges to network traffic statistics. How to carry out network traffic statistics efficiently and accurately has become the focus of research. Although the current research results are many, they are not very ideal. Based on the era background of big data and machine learning algorithm, this paper uses the ant colony algorithm to solve the typical resource-constrained project scheduling problem and finds the optimal solution of network traffic resource allocation problem. Firstly, the objective function and mathematical model of the resource-constrained project scheduling problem are established, and the ant colony algorithm is used for optimization. Then, the project scheduling problem in PSPLIB is introduced, which contains 10 tasks and 1 renewable resource. The mathematical model and ant colony algorithm are used to solve the resource-constrained project scheduling problem. Finally, the data quantity and frequency of a PCU with a busy hour IP of 112.58.14.66 are analyzed and counted. The experimental results show that the algorithm can get the unique optimal solution after the 94th generation, which shows that the parameters set in the solution method are appropriate and the optimal solution can be obtained. The schedule of each task in the optimal scheduling scheme is very compact and reasonable. The peak time of network traffic is usually between 9 : 00 and 19 : 00-21 : 00. We can reasonably schedule the network resources according to these time periods. Therefore, the network traffic statistics method based on the solution of resource constrained industrial project group scheduling problem under big data can effectively carry out network traffic statistics and trend analysis.

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