scholarly journals An Efficient Particle Swarm Optimizer with Application to Man-Day Project Scheduling Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Ruey-Maw Chen ◽  
Frode Eika Sandnes
Keyword(s):  
Project Scheduling ◽  
Particle Swarm ◽  
Scheduling Problem ◽  
Np Hard ◽  
Scheduling Problems ◽  
Particle Swarm Optimizer ◽  
Np Hard Problem ◽  
Hard Problems ◽  
Project Scheduling Problem ◽  
Mode Assignment

The multimode resource-constrained project scheduling problem (MRCPSP) has been confirmed to be an NP-hard problem. Particle swarm optimization (PSO) has been efficiently applied to the search for near optimal solutions to various NP-hard problems. MRCPSP involves solving two subproblems: mode assignment and activity priority determination. Hence, two PSOs are applied to each subproblem. A constriction PSO is proposed for the activity priority determination while a discrete PSO is employed for mode assignment. A least total resource usage (LTRU) heuristic and minimum slack (MSLK) heuristic ensure better initial solutions. To ensure a diverse initial collection of solutions and thereby enhancing the PSO efficiency, a best heuristic rate (HR) is suggested. Moreover, a new communication topology with random links is also introduced to prevent slow and premature convergence. To verify the performance of the approach, the MRCPSP benchmarks in PSPLIB were evaluated and the results compared to other state-of-the-art algorithms. The results demonstrate that the proposed algorithm outperforms other algorithms for the MRCPSP problems. Finally, a real-world man-day project scheduling problem (MDPSP)—a MRCPSP problem—was evaluated and the results demonstrate that MDPSP can be solved successfully.

A Practical Algorithmic Approach Towards Multi-Modal Resource Constrained Multi-Project Scheduling Problems (MMRCMPSP)

2015 ◽  
Author(s):  
Michael Völker ◽  
Taiba Zahid ◽  
Thorsten Schmidt
Keyword(s):  
Project Scheduling ◽  
Decision Support Tool ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Decision Strategy ◽  
Resource Constrained ◽  
Support Tool ◽  
Decision Variables ◽  
Np Hard Problem ◽  
Project Scheduling Problem

The literature concerning resource constrained project scheduling problems (RCPSP) are mainly based on series or parallel schedule generation schemes with priority sequencing rules to resolve conflicts. Recently, these models have been extended for scheduling multi-modal RCPSP (MMRCPSP) where each activity has multiple possibilities to be performed thus providing decision managers a useful tool for manipulating resources and activities. Nonetheless, this further complicates the scheduling problem inflicted by increase of decision variables. Multiple heuristics have been proposed for this NP-hard problem. The main solution strategy adopted by such heuristics is a two loops decision strategy. Basically the problem is split between two parts where first part is conversion of MMRCPSP to RCPSP (mode fix) while second is finding feasible solution for a resource constrained project and is restricted to single project environments. This research aims on the development of scheduling heuristics, exploring the possibilities of scheduling MMRCPSP with parallel assignment of modes while sequencing the activities. The work addresses Multi-Mode Resource Constrained Multi-Project Scheduling Problem, (MMRCMPSP) by formulating a mathematical model that regards practical requirements of working systems. The algorithm is made intelligent and flexible in order to adopt and shift among various defined heuristic rules under different objectives to function as a decision support tool for managers.

scholarly journals An Improved Differential Evolution Solution for Software Project Scheduling Problem

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
A. C. Biju ◽  
T. Aruldoss Albert Victoire ◽  
Kumaresan Mohanasundaram
Keyword(s):  
Differential Evolution ◽  
Project Scheduling ◽  
Optimization Technique ◽  
Solution Procedure ◽  
Solution Technique ◽  
Software Project ◽  
Scheduling Problem ◽  
Np Hard Problem ◽  
Random Instances ◽  
Project Scheduling Problem

This paper proposes a differential evolution (DE) method for the software project scheduling problem (SPSP). The interest on finding a more efficient solution technique for SPSP is always a topic of interest due to the fact of ever growing challenges faced by the software industry. The curse of dimensionality is introduced in the scheduling problem by ever increasing software assignments and the number of staff who handles it. Thus the SPSP is a class of NP-hard problem, which requires a rigorous solution procedure which guarantees a reasonably better solution. Differential evolution is a direct search stochastic optimization technique that is fairly fast and reasonably robust. It is also capable of handling nondifferentiable, nonlinear, and multimodal objective functions like SPSP. This paper proposes a refined DE where a new mutation mechanism is introduced. The superiority of the proposed method is experimented and demonstrated by solving the SPSP on 50 random instances and the results are compared with some of the techniques in the literature.

scholarly journals O Problema de Alocação de Recursos e Seleção de Múltiplos Projetos de TI

2013 ◽  
Vol 4 (2) ◽  
pp. 27-49
Author(s):  
Marcos Negreiros ◽  
Willame Tiberio Barbosa
Keyword(s):  
Project Scheduling ◽  
Scheduling Problem ◽  
Np Hard ◽  
Resource Constrained ◽  
Resource Constrained Project Scheduling ◽  
De Se ◽  
Project Scheduling Problem

As organizações que possuem projetos ocorrendo simultaneamente se deparam com a tarefa de selecioná-los segundo algum critério, bem como, a necessidade de alocação de recursos certos aos projetos certos nos tempos certos. O problema de RCPSP (Resource-Constrained Project Scheduling Problem) é um problema difícil que pertence à classe de problemas NP-Hard. Nele pretende-se criar cronogramas com restrições de recursos. Este problema continua desafiador, mesmo tendo origem na década de 1960, devido essencialmente a sua natureza combinatória. O objetivo deste trabalho é avaliar a utilização de técnicas exatas de otimização, no intuito de obter uma alocação ótima de recursos às atividades de múltiplos projetos de TI que usam metodologias ágeis (Scrum), considerando restrição de recursos (analistas e programadores), visando à construção de cronogramas de múltiplos projetos integrados. Utiliza-se neste trabalho uma nova abordagem no sentido de se encontrar a atribuição ótima de projetos considerando as suas prioridades relativas, e a avaliação poder ser realizada para um horizonte de planejamento ou enquanto estão em andamento. Mostramos que nosso modelo promove uma adequada avaliação de atribuição ótima de recursos de TI num contexto real de gestão de projetos para instâncias com até 9 projetos.DOI:10.5585/gep.v4i2.125

scholarly journals An Enhanced Discrete Bees Algorithm for Resource Constrained Optimization Problems

2019 ◽  
Vol 22 (64) ◽  
pp. 123-134
Author(s):  
Mohamed Amine Nemmich ◽  
Fatima Debbat ◽  
Mohamed Slimane
Keyword(s):  
Project Scheduling ◽  
Optimization Problems ◽  
Solution Space ◽  
Bees Algorithm ◽  
Problem Instance ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Resource Constrained ◽  
Proposed Model ◽  
Project Scheduling Problem

In this paper, we propose a novel efficient model based on Bees Algorithm (BA) for the Resource-Constrained Project Scheduling Problem (RCPSP). The studied RCPSP is a NP-hard combinatorial optimization problem which involves resource, precedence, and temporal constraints. It has been applied to many applications. The main objective is to minimize the expected makespan of the project. The proposed model, named Enhanced Discrete Bees Algorithm (EDBA), iteratively solves the RCPSP by utilizing intelligent foraging behaviors of honey bees. The potential solution is represented by the multidimensional bee, where the activity list representation (AL) is considered. This projection involves using the Serial Schedule Generation Scheme (SSGS) as decoding procedure to construct the active schedules. In addition, the conventional local search of the basic BA is replaced by a neighboring technique, based on the swap operator, which takes into account the specificity of the solution space of project scheduling problems and reduces the number of parameters to be tuned. The proposed EDBA is tested on well-known benchmark problem instance sets from Project Scheduling Problem Library (PSPLIB) and compared with other approaches from the literature. The promising computational results reveal the effectiveness of the proposed approach for solving the RCPSP problems of various scales.

scholarly journals An Efficient Simulation Algorithm for Resource-Constrained Project Scheduling Problem

2014 ◽  
Vol 8 (1) ◽  
pp. 9-13
Author(s):  
L. Peng ◽  
P. Wuliang
Keyword(s):  
Project Scheduling ◽  
Large Scale ◽  
Exact Algorithms ◽  
Scheduling Problem ◽  
Simulation Algorithm ◽  
Resource Constrained ◽  
Resource Conflict ◽  
Resource Constrained Project Scheduling ◽  
Np Hard Problem ◽  
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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