Task scheduling for FMS based on genetic algorithm

A Flexible Manufacturing System (FMS) consisting of p automated guided vehicles (AGV\u27s), m workstations and n tasks is studied. The main problem investigated in this thesis is to find an optimal or suboptimal task scheduling for p AGV\u27s among m workstations to complete n tasks. An efficient approach based on genetic algorithms has been designed and implemented to solve the problem of task scheduling for a FMS. Near-optimal, or even optimal, task scheduling is accomplished by genetic algorithms. Simulation results on the algorithm are also discussed

The trade off between diversity and quality for multi-objective workforce scheduling

In this paper we investigate and compare multi-objective and weighted single objective approaches to a real world workforce scheduling problem. For this difficult problem we consider the trade off in solution quality versus population diversity, for different sets of fixed objective weights. Our real-world workforce scheduling problem consists of assigning resources with the appropriate skills to geographically dispersed task locations while satisfying time window constraints. The problem is NP-Hard and contains the Resource Constrained Project Scheduling Problem (RCPSP) as a sub problem. We investigate a genetic algorithm and serial schedule generation scheme together with various multi-objective approaches. We show that multi-objective genetic algorithms can create solutions whose fitness is within 2% of genetic algorithms using weighted sum objectives even though the multi-objective approaches know nothing of the weights. The result is highly significant for complex real-world problems where objective weights are seldom known in advance since it suggests that a multi-objective approach can generate a solution close to the user preferred one without having knowledge of user preferences

The Task Scheduling Problem: A NeuroGenetic Approach

This paper addresses the task scheduling problem which involves minimizing the makespan in scheduling n tasks on m machines (resources) where the tasks follow a precedence relation and preemption is not allowed.  The machines (resources) are all identical and a task needs only one machine for processing.  Like most scheduling problems, this one is NP-hard in nature, making it difficult to find exact solutions for larger problems in reasonable computational time.  Heuristic and metaheuristic approaches are therefore needed to solve this type of problem.   This paper proposes a metaheuristic approach - called NeuroGenetic - which is a combination of an augmented neural network and a genetic algorithm.  The augmented neural network approach is itself a hybrid of a heuristic approach and a neural network approach.  The NeuroGenetic approach is tested against some popular test problems from the literature, and the results indicate that the NeuroGenetic approach performs significantly better than either the augmented neural network or the genetic algorithms alone.

Evolutionary algorithm-based multi-objective task scheduling optimization model in cloud environments

© 2015, Springer Science+Business Media New York. Optimizing task scheduling in a distributed heterogeneous computing environment, which is a nonlinear multi-objective NP-hard problem, plays a critical role in decreasing service response time and cost, and boosting Quality of Service (QoS). This paper, considers four conflicting objectives, namely minimizing task transfer time, task execution cost, power consumption, and task queue length, to develop a comprehensive multi-objective optimization model for task scheduling. This model reduces costs from both the customer and provider perspectives by considering execution and power cost. We evaluate our model by applying two multi-objective evolutionary algorithms, namely Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Genetic Algorithm (MOGA). To implement the proposed model, we extend the Cloudsim toolkit by using MOPSO and MOGA as its task scheduling algorithms which determine the optimal task arrangement among VMs. The simulation results show that the proposed multi-objective model finds optimal trade-off solutions amongst the four conflicting objectives, which significantly reduces the job response time and makespan. This model not only increases QoS but also decreases the cost to providers. From our experimentation results, we find that MOPSO is a faster and more accurate evolutionary algorithm than MOGA for solving such problems

Performance evaluation of task scheduling using hybrid meta-heuristic in heterogeneous cloud environment

Cloud computing is a ubiquitous platform that offers a wide range of online services to clients including but not limited to information and software over the Internet. It is an essential role of cloud computing to enable sharing of resources on-demand over the network including servers, applications, storage, services, and database to the end-users who are remotely connected to the network. Task scheduling is one of the significant function in the cloud computing environment which plays a vital role to sustain the performance of the system and improve its efficiency. Task scheduling is considered as an NP-complete problem in many contexts, however, the heterogeneity of resources in the cloud environment negatively influence on the job scheduling process. Furthermore, on the other side, the heuristic algorithms have satisfying performance but unable to achieve the desired level of efficiency for optimizing the scheduling in a cloud environment. Thus, this paper aims at evaluating the effectiveness of the hybrid meta-heuristic that incorporate genetic algorithm along with DE algorithm (GA-DE) in terms of make-span. In addition, the paper also intends to enhance the performance of the task scheduling in the heterogeneous cloud environment exploiting the scientific workflows (Cybershake, Montage, and Epigenomics). The proposed algorithm (GA-DE) has been compared against three heuristic algorithms, namely: HEFT-Upward Rank, HEFT – Downward Rank, and HEFT – Level Rank. The simulation results prove that the proposed algorithm (GA-DE) outperforms the other existing algorithms in all cases in terms of make-span