The R-PSO algorithm solving multi-skill resource-constrained project scheduling problem

2021 ◽  
pp. 71-82
Author(s):  
Dang Quoc Huu
Keyword(s):  
Project Scheduling ◽  
Optimal Solution ◽  
Simulated Data ◽  
Pso Algorithm ◽  
Scheduling Problem ◽  
Resource Constrained ◽  
Feasible Schedule ◽  
Approximate Methods ◽  
Resource Constrained Project Scheduling ◽  
Project Scheduling Problem

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.

scholarly journals A Modified PSO Algorithm for Minimizing the Total Costs of Resources in MRCPSP

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
Mohammad Khalilzadeh ◽  
Fereydoon Kianfar ◽  
Ali Shirzadeh Chaleshtari ◽  
Shahram Shadrokh ◽  
Mohammad Ranjbar
Keyword(s):  
Project Scheduling ◽  
Renewable Resource ◽  
Pso Algorithm ◽  
Scheduling Problem ◽  
Due Dates ◽  
Resource Constrained ◽  
Nonrenewable Resource ◽  
Penalty Cost ◽  
Resource Constrained Project Scheduling ◽  
Project Scheduling Problem

We introduce a multimode resource-constrained project scheduling problem with finish-to-start precedence relations among project activities, considering renewable and nonrenewable resource costs. We assume that renewable resources are rented and are not available in all periods of time of the project. In other words, there is a mandated ready date as well as a due date for each renewable resource type so that no resource is used before its ready date. However, the resources are permitted to be used after their due dates by paying penalty costs. The objective is to minimize the total costs of both renewable and nonrenewable resource usage. This problem is called multimode resource-constrained project scheduling problem with minimization of total weighted resource tardiness penalty cost (MRCPSP-TWRTPC), where, for each activity, both renewable and nonrenewable resource requirements depend on activity mode. For this problem, we present a metaheuristic algorithm based on a modified Particle Swarm Optimization (PSO) approach introduced by Tchomté and Gourgand which uses a modified rule for the displacement of particles. We present a prioritization rule for activities and several improvement and local search methods. Experimental results reveal the effectiveness and efficiency of the proposed algorithm for the problem in question.

scholarly journals Mode-Based versus Activity-Based Search for a Nonredundant Resolution of the Multimode Resource-Constrained Project Scheduling Problem

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Daniel Morillo ◽  
Federico Barber ◽  
Miguel A. Salido
Keyword(s):  
Energy Consumption ◽  
Project Scheduling ◽  
Scheduling Problem ◽  
Resource Constrained ◽  
Exact Methods ◽  
Approximate Methods ◽  
Resource Constrained Project Scheduling ◽  
Np Hard Problem ◽  
Project Scheduling Problem ◽  
Metaheuristic Techniques

This paper addresses an energy-based extension of the Multimode Resource-Constrained Project Scheduling Problem (MRCPSP) called MRCPSP-ENERGY. This extension considers the energy consumption as an additional resource that leads to different execution modes (and durations) of the activities. Consequently, different schedules can be obtained. The objective is to maximize the efficiency of the project, which takes into account the minimization of both makespan and energy consumption. This is a well-known NP-hard problem, such that the application of metaheuristic techniques is necessary to address real-size problems in a reasonable time. This paper shows that the Activity List representation, commonly used in metaheuristics, can lead to obtaining many redundant solutions, that is, solutions that have different representations but are in fact the same. This is a serious disadvantage for a search procedure. We propose a genetic algorithm (GA) for solving the MRCPSP-ENERGY, trying to avoid redundant solutions by focusing the search on the execution modes, by using the Mode List representation. The proposed GA is evaluated on different instances of the PSPLIB-ENERGY library and compared to the results obtained by both exact methods and approximate methods reported in the literature. This library is an extension of the well-known PSPLIB library, which contains MRCPSP-ENERGY test cases.

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

2016 ◽  
Vol 3 (1) ◽  
pp. 1-15 ◽  
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.

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

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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