scholarly journals Project Scheduling Heuristics-Based Standard PSO for Task-Resource Assignment in Heterogeneous Grid

2011 ◽  
Vol 2011 ◽  
pp. 1-20 ◽  
Author(s):  
Ruey-Maw Chen ◽  
Chuin-Mu Wang
Keyword(s):  
Grid Computing ◽  
Task Scheduling ◽  
Project Scheduling ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Resource Assignment ◽  
Grid Environment ◽  
Standard Particle Swarm Optimization ◽  
Communication Topology ◽  
Np Complete

The task scheduling problem has been widely studied for assigning resources to tasks in heterogeneous grid environment. Effective task scheduling is an important issue for the performance of grid computing. Meanwhile, the task scheduling problem is an NP-complete problem. Hence, this investigation introduces a named “standard“ particle swarm optimization (PSO) metaheuristic approach to efficiently solve the task scheduling problems in grid. Meanwhile, two promising heuristics based on multimode project scheduling are proposed to help in solving interesting scheduling problems. They are the best performance resource heuristic and the latest finish time heuristic. These two heuristics applied to the PSO scheme are for speeding up the search of the particle and improving the capability of finding a sound schedule. Moreover, both global communication topology and local ring communication topology are also investigated for efficient study of proposed scheme. Simulation results demonstrate that the proposed approach in this investigation can successfully solve the task-resource assignment problems in grid computing and similar scheduling problems.

scholarly journals The Complexity Landscape of Resource-Constrained Scheduling

2020 ◽  
Author(s):  
Robert Ganian ◽  
Thekla Hamm ◽  
Guillaume Mescoff
Keyword(s):  
Artificial Intelligence ◽  
Polynomial Time ◽  
Project Scheduling ◽  
Scheduling Problem ◽  
Comprehensive Understanding ◽  
Resource Constrained ◽  
Special Cases ◽  
Project Scheduling Problem ◽  
Np Complete ◽  
New Algorithms

The Resource-Constrained Project Scheduling Problem (RCPSP) and its extension via activity modes (MRCPSP) are well-established scheduling frameworks that have found numerous applications in a broad range of settings related to artificial intelligence. Unsurprisingly, the problem of finding a suitable schedule in these frameworks is known to be NP-complete; however, aside from a few results for special cases, we have lacked an in-depth and comprehensive understanding of the complexity of the problems from the viewpoint of natural restrictions of the considered instances. In the first part of our paper, we develop new algorithms and give hardness-proofs in order to obtain a detailed complexity map of (M)RCPSP that settles the complexity of all 1024 considered variants of the problem defined in terms of explicit restrictions of natural parameters of instances. In the second part, we turn to implicit structural restrictions defined in terms of the complexity of interactions between individual activities. In particular, we show that if the treewidth of a graph which captures such interactions is bounded by a constant, then we can solve MRCPSP in polynomial time.

scholarly journals Task Scheduling Model of Double-Deep Multi-Tier Shuttle System

Processes ◽  
2019 ◽  
Vol 7 (9) ◽  
pp. 604 ◽  
Author(s):  
Yanyan Wang ◽  
Rongxu Zhang ◽  
Hui Liu ◽  
Xiaoqing Zhang ◽  
Ziwei Liu
Keyword(s):  
Task Scheduling ◽  
Production Scheduling ◽  
Current System ◽  
Storage System ◽  
Complex Structure ◽  
Vertical Movement ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
High Flexibility ◽  
Shuttle System

As a new type of part-to-picker storage system, the double-deep multi-tier shuttle system has been developed rapidly in the e-commerce industry because of its high flexibility, large storage capacity, and robustness. The system consists of a multi-tier shuttle sub-system that controls horizontal movement and a lift sub-system that manages vertical movement. The combination of shuttles and lifts, instead of a stacker crane in conventional automated storage and retrieval system, undertakes inbound/outbound tasks. Because of the complex structure and numerous equipment of the system, task scheduling has become a major difficulty in the outbound operation of the double-deep multi-tier shuttle system. Figuring out methods to improve the overall efficiency of task scheduling operations is the focus of current system application enterprises. This paper introduces the task scheduling problem for the shuttle system. Inspired from workshop production scheduling problems, we minimize the total time of a batch of retrieval tasks as the objective function, applying the modified Simulated Annealing Algorithms (SAAs) to solve the task scheduling problem. In conclusion, we verified the proposed model and the algorithm efficiency, using case studies.

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 Constraint-Based Approach for Multi-Skilled Project Scheduling Problem

2014 ◽  
Vol 681 ◽  
pp. 265-269
Author(s):  
Yan Li ◽  
Zhi Run Xiao
Keyword(s):  
Constraint Programming ◽  
Project Scheduling ◽  
Complex Problem ◽  
Programming Approach ◽  
Scheduling Problem ◽  
Computational Results ◽  
Resource Assignment ◽  
Software Company ◽  
Scheduling Model ◽  
Project Scheduling Problem

The problem of multi-skilled project scheduling (MSPSP) is a complex problem of task scheduling and resource assignment that comes up in the daily management of many software company. In this paper we present a constraint programming (CP) approach for the MSPSP. We extend the project scheduling literature by developing a project scheduling model that accounts for differing skills among workers. The computational results for the MSPSP show that the constraint programming approach increases the performance of the model solving processes. The results for the MSPSP is effective in solving the proposed problem.

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 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 Task Scheduling Problem Using Fuzzy Graph

2016 ◽  
Vol 1 (1-2) ◽  
pp. 16-20
Author(s):  
Vivek Raich ◽  
Shweta Rai
Keyword(s):  
Task Scheduling ◽  
Single Machine ◽  
Scheduling Problem ◽  
Scheduling Theory ◽  
Scheduling Problems ◽  
Fuzzy Graph ◽  
Minimum Value ◽  
Chromatic Sum ◽  
Completion Times

The concept of obtaining fuzzy sum of fuzzy colorings problem has a natural application in scheduling theory. The problem of scheduling N jobs on a single machine and obtain the minimum value of the job completion times is equivalent to finding the fuzzy chromatic sum of the fuzzy graph modeled for this problem. The aim of this paper is to solve task scheduling problems using fuzzy graph.

scholarly journals Static vs. Dynamic List-Scheduling Performance Comparison

10.14311/490 ◽  
2003 ◽  
Vol 43 (6) ◽  
Author(s):  
T. Hagras ◽  
J. Janeček
Keyword(s):  
Task Scheduling ◽  
Optimal Solution ◽  
Scheduling Algorithms ◽  
Low Complexity ◽  
Performance Comparison ◽  
List Scheduling ◽  
Scheduling Problem ◽  
Computing Environments ◽  
Np Complete ◽  
List Scheduling Algorithms

The problem of efficient task scheduling is one of the most important and most difficult issues in homogeneous computing environments. Finding an optimal solution for a scheduling problem is NP-complete. Therefore, it is necessary to have heuristics to find a reasonably good schedule rather than evaluate all possible schedules. List-scheduling is generally accepted as an attractive approach, since it pairs low complexity with good results. List-scheduling algorithms schedule tasks in order of priority. This priority can be computed either statically (before scheduling) or dynamically (during scheduling). This paper presents the characteristics of the two main static and the two main dynamic list-scheduling algorithms. It also compares their performance in dealing with random generated graphs with various characteristics.

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