scholarly journals A dual scheduling model for optimizing robustness and energy consumption in manufacturing systems

2016 ◽  
Vol 232 (1) ◽  
pp. 5-16 ◽  
Author(s):  
Joan Escamilla ◽  
Miguel A Salido
Keyword(s):  
Energy Consumption ◽  
Manufacturing Systems ◽  
Optimal Solution ◽  
Combinatorial Problems ◽  
Dual Model ◽  
Scheduling Problems ◽  
Huge Number ◽  
Energy Aware ◽  
Knowledge Based ◽  
Manufacturing Problems

Manufacturing systems involve a huge number of combinatorial problems that must be optimized in an efficient way. One of these problems is related to task scheduling problems. These problems are NP-hard, so most of the complete techniques are not able to obtain an optimal solution in an efficient way. Furthermore, most of real manufacturing problems are dynamic, so the main objective is not only to obtain an optimized solution in terms of makespan, tardiness, and so on but also to obtain a solution able to absorb minor incidences/disruptions presented in any daily process. Most of these industries are also focused on improving the energy efficiency of their industrial processes. In this article, we propose a knowledge-based model to analyse previous incidences occurred in the machines with the aim of modelling the problem to obtain robust and energy-aware solutions. The resultant model (called dual model) will protect the more dynamic and disrupted tasks by assigning buffer times. These buffers will be used to absorb incidences during execution and to reduce the machine rate to minimize energy consumption. This model is solved by a memetic algorithm which combines a genetic algorithm with a local search to obtain robust and energy-aware solutions able to absorb further disruptions. The proposed dual model has been proven to be efficient in terms of energy consumption, robustness and stability in different and well-known benchmarks.

scholarly journals Linear Weighted Regression and Energy-Aware Greedy Scheduling for Heterogeneous Big Data

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 554
Author(s):  
Suresh Kallam ◽  
Rizwan Patan ◽  
Tathapudi V. Ramana ◽  
Amir H. Gandomi
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Total Energy ◽  
Optimal Solution ◽  
Data Locality ◽  
Weighted Regression ◽  
Heterogeneous Environments ◽  
Energy Aware ◽  
Total Energy Consumption ◽  
Greedy Scheduling

Data are presently being produced at an increased speed in different formats, which complicates the design, processing, and evaluation of the data. The MapReduce algorithm is a distributed file system that is used for big data parallel processing. Current implementations of MapReduce assist in data locality along with robustness. In this study, a linear weighted regression and energy-aware greedy scheduling (LWR-EGS) method were combined to handle big data. The LWR-EGS method initially selects tasks for an assignment and then selects the best available machine to identify an optimal solution. With this objective, first, the problem was modeled as an integer linear weighted regression program to choose tasks for the assignment. Then, the best available machines were selected to find the optimal solution. In this manner, the optimization of resources is said to have taken place. Then, an energy efficiency-aware greedy scheduling algorithm was presented to select a position for each task to minimize the total energy consumption of the MapReduce job for big data applications in heterogeneous environments without a significant performance loss. To evaluate the performance, the LWR-EGS method was compared with two related approaches via MapReduce. The experimental results showed that the LWR-EGS method effectively reduced the total energy consumption without producing large scheduling overheads. Moreover, the method also reduced the execution time when compared to state-of-the-art methods. The LWR-EGS method reduced the energy consumption, average processing time, and scheduling overhead by 16%, 20%, and 22%, respectively, compared to existing methods.

scholarly journals Energy-Aware Autonomic Resource Scheduling Framework for Cloud

2019 ◽  
Vol 4 (1) ◽  
pp. 41-55 ◽  
Author(s):  
Bhupesh Kumar Dewangan ◽  
Amit Agarwal ◽  
Venkatadri M. ◽  
Ashutosh Pasricha
Keyword(s):  
Energy Consumption ◽  
Resource Utilization ◽  
Data Centers ◽  
Virtual Machines ◽  
Service Providers ◽  
Optimal Solution ◽  
Cloud Service ◽  
Cloud Services ◽  
Energy Aware ◽  
Increasing Demand

Cloud computing is a platform where services are provided through the internet either free of cost or rent basis. Many cloud service providers (CSP) offer cloud services on the rental basis. Due to increasing demand for cloud services, the existing infrastructure needs to be scale. However, the scaling comes at the cost of heavy energy consumption due to the inclusion of a number of data centers, and servers. The extraneous power consumption affects the operating costs, which in turn, affects its users. In addition, CO2 emissions affect the environment as well. Moreover, inadequate allocation of resources like servers, data centers, and virtual machines increases operational costs. This may ultimately lead to customer distraction from the cloud service. In all, an optimal usage of the resources is required. This paper proposes to calculate different multi-objective functions to find the optimal solution for resource utilization and their allocation through an improved Antlion (ALO) algorithm. The proposed method simulated in cloudsim environments, and compute energy consumption for different workloads quantity and it increases the performance of different multi-objectives functions to maximize the resource utilization. It compared with existing frameworks and experiment results shows that the proposed framework performs utmost.

scholarly journals Variable Neighborhood Strategy Adaptive Search to Solve Parallel-Machine Scheduling to Minimize Energy Consumption While Considering Job Priority and Control Makespan

2021 ◽  
Vol 11 (11) ◽  
pp. 5311
Author(s):  
Rujapa Nanthapodej ◽  
Cheng-Hsiang Liu ◽  
Krisanarach Nitisiri ◽  
Sirorat Pattanapairoj
Keyword(s):  
Energy Consumption ◽  
Manufacturing Industry ◽  
Optimal Solution ◽  
Machine Scheduling ◽  
Parallel Machine Scheduling ◽  
Parallel Machine ◽  
Adaptive Search ◽  
Energy Prices ◽  
Scheduling Problems ◽  
Save Energy

Environmental concerns and rising energy prices put great pressure on the manufacturing industry to reduce pollution and save energy. Electricity is one of the main machinery energy sources in a plant; thus, reducing energy consumption both saves energy costs and protects our planet. This paper proposes the novel method called variable neighborhood strategy adaptive search (VaNSAS) in order to minimize energy consumption while also considering job priority and makespan control for parallel-machine scheduling problems. The newly presented neighborhood strategies of (1) solution destroy and repair (SDR), (2) track-transition method (TTM), and (3) multiplier factor (MF) were proposed and tested against the original differential evaluation (DE), current practice procedure (CU), SDR, TTM, and MF for three groups of test instances, namely small, medium, and large. Experimental results revealed that VaNSAS outperformed DE, CU, SDR, TTM, and MF, as it could find the optimal solution and the mathematical model in the small test instance, while the DE could only find 25%, and the others could not. In the remaining test instances, VaNSAS performed 16.35–19.55% better than the best solution obtained from Lingo, followed by DE, CU, SDR, TTM, and MF, which performed 7.89–14.59% better. Unfortunately, the CU failed to improve the solution and had worse performance than that of Lingo, including all proposed methods.

Scheduling Problems of Flexible Manufacturing Systems: Review, Classification and Opportunities

2016 ◽  
Vol 26 ◽  
pp. 142-160 ◽  
Author(s):  
Ghita Lebbar ◽  
Abdellah El Barkany ◽  
Abdelouahhab Jabri
Keyword(s):  
Comparative Analysis ◽  
Flexible Manufacturing ◽  
Flexible Manufacturing Systems ◽  
Manufacturing Systems ◽  
Optimal Solution ◽  
Type Systems ◽  
Scheduling Problems ◽  
Hybrid Flowshop ◽  
Calculation Time

This paper will relate initially to the scheduling characteristics of flexible manufacturing systems, and more specifically, the scheduling problems in flowshop and hybrid flowshop type systems representing interesting structures for the modeling of several problems resulting from the industrial world. Subsequently, we will focus our attention on the principal methods for solving scheduling problems, while presenting in the following the main published works for the aforementioned systems. Lastly, a comparative analysis will be carried out to highlight the fundamental ideas leading to the adoption of an effective approach capable of producing an optimal solution in a reasonable calculation time.

Energy-Aware Virtual Data Center Migration

2019 ◽  
Vol 23 (2) ◽  
pp. 209-218
Author(s):  
Xiao Ma ◽  
◽  
Zhongbao Zhang ◽  
Sen Su
Keyword(s):  
Energy Consumption ◽  
Success Rate ◽  
Data Center ◽  
Optimal Solution ◽  
Energy Aware ◽  
Physical Data ◽  
Virtual Links ◽  
Virtual Data Center ◽  
Changes Over Time ◽  
Virtual Data

Recently, the concept of virtual data center (VDC) has attracted significant attention from researchers. VDC is made up of virtual nodes and virtual links with guaranteed bandwidth. It offers elasticity and flexibility, which means VDC can adjust resources dynamically according to different requirements. Existing studies focus on how to design the optimal embedding algorithm to achieve high success rate for the virtual data center request. However, due to the resource of physical data center changes over time, the optimal solution may become sub-optimal. In this paper, we study the problem of virtual data center migration and propose an energy-aware virtual data center migration algorithm, called CA-VDCM-ACO. This novel algorithm leverages the migration technique to further reduce the energy consumption with the success rate for the physical data center guaranteed. The extensive experiments show that our algorithm is very effective to reduce the energy consumption.

Sustainable Development in Manufacturing Systems

2017 ◽  
pp. 201-224
Author(s):  
Farnaz Ghazi Nezami ◽  
Ali Ghazinezami ◽  
Krishna K. Krishnan
Keyword(s):  
Decision Making ◽  
Sustainable Development ◽  
Energy Consumption ◽  
Operations Management ◽  
Manufacturing Systems ◽  
Industrial Sector ◽  
Maintenance Strategy ◽  
Energy Aware ◽  
Planning Decisions ◽  
Management Approaches

This chapter discusses sustainable development (SD) planning in manufacturing facilities. The industrial sector uses half of the world's energy, and manufacturing, as the core of this sector, contributes significantly to energy consumption and environmental footprints. In this chapter, in the first step, energy consumption, as one of the main factors influencing SD in manufacturing, is analyzed from different perspectives, and its impact on SD is studied. Thereafter, several energy-aware operations management approaches are proposed. These approaches integrate energy consumption into classic production planning and scheduling decisions. In the second step, a generic sustainability-based decision-making framework is proposed for maintenance strategy selection problem, considering three pillars of sustainability. For this purpose, various indicators are proposed for each sustainability factor that has an impact on maintenance planning decisions. The maintenance strategy alternatives are evaluated for each indicator and the best alternative is selected using a decision-making method.

scholarly journals Economical Speed for Optimizing the Travel Time and Energy Consumption in Train Scheduling using a Fuzzy Multi-Objective Model

2021 ◽  
Author(s):  
Ahmad Reza Jafarian-Moghaddam
Keyword(s):  
Energy Consumption ◽  
Travel Time ◽  
Pollutant Emissions ◽  
Scheduling Problems ◽  
Train Scheduling ◽  
Multi Objective ◽  
Consumption Ratio ◽  
Proposed Model ◽  
Objective Model ◽  
Time And Energy

AbstractSpeed is one of the most influential variables in both energy consumption and train scheduling problems. Increasing speed guarantees punctuality, thereby improving railroad capacity and railway stakeholders’ satisfaction and revenues. However, a rise in speed leads to more energy consumption, costs, and thus, more pollutant emissions. Therefore, determining an economic speed, which requires a trade-off between the user’s expectations and the capabilities of the railway system in providing tractive forces to overcome the running resistance due to rail route and moving conditions, is a critical challenge in railway studies. This paper proposes a new fuzzy multi-objective model, which, by integrating micro and macro levels and determining the economical speed for trains in block sections, can optimize train travel time and energy consumption. Implementing the proposed model in a real case with different scenarios for train scheduling reveals that this model can enhance the total travel time by 19% without changing the energy consumption ratio. The proposed model has little need for input from experts’ opinions to determine the rates and parameters.

scholarly journals Energy-aware very fast decision tree

2021 ◽  
Author(s):  
Eva García-Martín ◽  
Niklas Lavesson ◽  
Håkan Grahn ◽  
Emiliano Casalicchio ◽  
Veselka Boeva
Keyword(s):  
Energy Consumption ◽  
Decision Tree ◽  
Concept Drift ◽  
Algorithm Design ◽  
Energy Aware ◽  
Battery Capacity ◽  
Very Fast Decision Tree ◽  
Public Datasets ◽  
Additional Constraints ◽  
Fast Decision

AbstractRecently machine learning researchers are designing algorithms that can run in embedded and mobile devices, which introduces additional constraints compared to traditional algorithm design approaches. One of these constraints is energy consumption, which directly translates to battery capacity for these devices. Streaming algorithms, such as the Very Fast Decision Tree (VFDT), are designed to run in such devices due to their high velocity and low memory requirements. However, they have not been designed with an energy efficiency focus. This paper addresses this challenge by presenting the nmin adaptation method, which reduces the energy consumption of the VFDT algorithm with only minor effects on accuracy. nmin adaptation allows the algorithm to grow faster in those branches where there is more confidence to create a split, and delays the split on the less confident branches. This removes unnecessary computations related to checking for splits but maintains similar levels of accuracy. We have conducted extensive experiments on 29 public datasets, showing that the VFDT with nmin adaptation consumes up to 31% less energy than the original VFDT, and up to 96% less energy than the CVFDT (VFDT adapted for concept drift scenarios), trading off up to 1.7 percent of accuracy.

scholarly journals Hybrid PSO-SA Type Algorithms for Multimodal Function Optimization and Reducing Energy Consumption in Embedded Systems

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Lhassane Idoumghar ◽  
Mahmoud Melkemi ◽  
René Schott ◽  
Maha Idrissi Aouad
Keyword(s):  
Energy Consumption ◽  
Embedded Systems ◽  
Evolutionary Algorithm ◽  
Optimal Solution ◽  
Function Optimization ◽  
Time Saving ◽  
Local Optima ◽  
Hybrid Evolutionary Algorithm ◽  
Multimodal Function Optimization ◽  
Multimodal Function

The paper presents a novel hybrid evolutionary algorithm that combines Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithms. When a local optimal solution is reached with PSO, all particles gather around it, and escaping from this local optima becomes difficult. To avoid premature convergence of PSO, we present a new hybrid evolutionary algorithm, called HPSO-SA, based on the idea that PSO ensures fast convergence, while SA brings the search out of local optima because of its strong local-search ability. The proposed HPSO-SA algorithm is validated on ten standard benchmark multimodal functions for which we obtained significant improvements. The results are compared with these obtained by existing hybrid PSO-SA algorithms. In this paper, we provide also two versions of HPSO-SA (sequential and distributed) for minimizing the energy consumption in embedded systems memories. The two versions, of HPSO-SA, reduce the energy consumption in memories from 76% up to 98% as compared to Tabu Search (TS). Moreover, the distributed version of HPSO-SA provides execution time saving of about 73% up to 84% on a cluster of 4 PCs.

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