In a real manufacturing environment, the set of tasks that should be scheduled is changing over the time, which means that scheduling problems are dynamic. Also, in order to adapt the manufacturing systems with fluctuations, such as machine failure and create bottleneck machines, various flexibilities are considered in this system. For the first time, in this research, we consider the operational flexibility and flexibility due to Parallel Machines (PM) with non-uniform speed in Dynamic Job Shop (DJS) and in the field of Flexible Dynamic Job-Shop with Parallel Machines (FDJSPM) model. After modeling the problem, an algorithm based on the principles of Genetic Algorithm (GA) with dynamic two-dimensional chromosomes is proposed. The results of proposed algorithm and comparison with meta-heuristic data in the literature indicate the improvement of solutions by 1.34 percent for different dimensions of the problem.

Arun Kumar S

Department of Information Security

School of Computer Science and Engineering

Vellore Campus

Mohsen Yaghoubi Suraki

Mehdi Sadeghilalimi

Seyed Mostafa Bozorgi

Ali Asghar Rahmani Hosseinabadi

Jin Wang

Fulltext

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Symmetry

With the development of technology and computer systems, web services are used to develop business processes. Since a web service only performs a simple operation, web services composition has become important to respond to these business processes. In recent times, the number of existing web services has grown increasingly; therefore, similar services are presented increasingly. These similar web services are discriminated based on the various quality of service (QoS) parameters. These quality parameters include cost, execution time, availability, and reliability. In order to have the best QoS, each user should select a subset of services that presents best quality parameters. On the other hand, due to huge number of services, selecting web services for composition is an NP-hard optimization problem. This paper presents an efficient method for solving this problem using biogeography-based optimization (BBO). BBO is a very simple algorithm with few control parameters and effective exploit. The proposed method offers promising solutions to this problem. Evaluation and simulation results indicate efficiency and feasibility of the proposed algorithm. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

Soft Computing

A novel quality-of-service-aware web services composition using biogeography-based optimization algorithm

The Internet of Things (IoT) is a distributed system that connects everything via internet. IoT infrastructure contains multiple resources and gateways. In such a system, the problem of optimizing IoT resource allocation and scheduling (IRAS) is vital, because resource allocation (RA) and scheduling deals with the mapping between recourses and gateways and is also responsible for optimally allocating resources to available gateways. In the IoT environment, a gateway may face hundreds of resources to connect. Therefore, manual resource allocation and scheduling is not possible. In this paper, the whale optimization algorithm (WOA) is used to solve the RA problem in IoT with the aim of optimal RA and reducing the total communication cost between resources and gateways. The proposed algorithm has been compared to the other existing algorithms. Results indicate the proper performance of the proposed algorithm. Based on various benchmarks, the proposed method, in terms of “total communication cost”, is better than other ones.

Sensors

IoT Resource Allocation and Optimization Based on Heuristic Algorithm

Optimal resource allocation in cloud systems is NP-hard due to the involvement of several conflicting objectives and unpredictable cloud traffic. To improve user satisfaction and resource utilization while minimizing end-user cost, the joint allocation of cloud resources is inevitable. In this work, we model end-user cost in cloud as the optimization objective using bandwidth and compute allocation as the decision variables. To solve the joint Virtual Machine Placement (VMP) problem we propose a single point, greedy, software-defined network (SDN)-based solution that minimizes end-user cost by making certain changes to the fat-tree datacenter architecture. Mathematically, we show that the overall objective function is convex hence solving it using a weighted-sum greedy approach will induce solutions that are Pareto-optimal. Experimental evaluations confirm up to 15% reduction in the response time and up to 14% increase in the efficiency of resources. To analyse the risks involved in deploying delay-sensitive applications over cloud and to show the effects of resource allocation approaches, we perform a risk analysis of delay-sensitivity in cloud using real-time CVD detection. The results confirm the reduced response time due to the proposed approach while maintaining the efficiency of detection. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

Journal of Ambient Intelligence and Humanized Computing

Pareto-optimal cost optimization for large scale cloud systems using joint allocation of resources

Greenhouse gases (GHG) are the main reason for the global warming during the past decades. On the other hand, establishing a well-structured transportation system will yield to create least cost-pollution. This paper addresses a novel model for the multi-trip Green Capacitated Arc Routing Problem (G-CARP) with the aim of minimizing total cost including the cost of generation and emission of greenhouse gases, the cost of vehicle usage and routing cost. The cost of generation and emission of greenhouse gases is based on the calculation of the amount of carbon dioxide emitted from vehicles, which depends on such factors as the vehicle speed, weather conditions, load on the vehicle and traveled distance. The main applications of this problem are in municipalities for urban waste collection, road surface marking and so forth. Due to NP-hardness of the problem, a Hybrid Genetic Algorithm (HGA) is developed, wherein a heuristic and simulated annealing algorithm are applied to generate initial solutions and a Genetic Algorithm (GA) is then used to generate the best possible solution. The obtained numerical results indicate that the proposed algorithm could present desirable performance within a suitable computational run time. Finally, a sensitivity analysis is implemented on the maximum available time of the vehicles in order to determine the optimal policy. © 2018 by the authors.

Sustainability

A Hybrid Genetic Algorithm for Multi-Trip Green Capacitated Arc Routing Problem in the Scope of Urban Services

Computers & Industrial Engineering

Recent advances in hybrid evolutionary algorithms for multiobjective manufacturing scheduling

A reinforcement learning approach to parameter estimation in dynamic job shop scheduling

Optimizing the sum of maximum earliness and tardiness of the job shop scheduling problem

The Proceedings of the 5th International Conference on Industrial Application Engineering 2017

Improvement the System to Fold Laundry that has be Hung up

A New Meta-Heuristic Algorithm for Solving the Flexible Dynamic Job-Shop Problem with Parallel Machines

Journal	Symmetry
Publisher	MDPI AG
ISSN	20738994
Open Access	Yes