With the inception of high-speed internet data services and ever-growing technical advancement in manufacturing technology, the integration of production systems and the internet of things to produce different types of jobs via cloud computing has become possible. The internet-enabled advanced automatic production systems can be referred to as the cyber-physical production systems (CPPS). The use of CPPS via cloud computing and the internet of things (IoT) can offer high productivity and high flexibility for the production of jobs in a dynamic production environment with varying specifications. The aim of this paper is to present a generalized synergic framework between different production facilities locating at different geographical locations to realize an energy-saving and efficient cyber-physical production system for the production of different types of jobs in the context of the industry 4.0 and beyond. In addition to the above, the study also identifies a need to address large scale multi-objective optimization issues to make the best decisions for different combinatorial production scenarios by using CPPS that are functioning in smart production facilities at different geographical locations.

Building Maintenance plays an important role throughout the building lifecycle from devis-ing conceptual plans to the end. Due to the high cost of building maintenance and the direct impact of maintenance effectiveness on the quality of life of building occupants, special attention must be devoted. One of the most important issues in this field is building maintenance contractor selection. This issue becomes even more critical in public buildings, such as hospitals, offices, and military centers. The purpose of this study is to present a method that can be used to select the contractor in such a way that the response robustness is high and the employed method is the most accurate one among other similar methods. To do this, the contractors are ranked by 7 multi-criteria decision-making methods. Then, the Spearman correlation coefficients are obtained for each pair of methods. When there is a significant difference between the outcomes of the methods, the output of each method is compared with the output of the Artificial Neural Network (ANN) model. The method with the least difference with the neural network output is taken as the superior method. After selecting the best method, a robustness analysis is performed on it to verify the stability of the answer. The proposed model is implemented on a real case study. Statistical analysis shows that the implementation of this method has increased the satisfaction of the residents.

Computer TCP/IP networks are becoming critical in all aspects of life. As computer networks continue to improve, the levels of redundancy continue to increase. Modern network redun-dancy features can be complex and expensive. This leads to misconfiguration of the redun-dancy features. Monitoring everything is not always practical. Some redundancy features are easy to detect while others are more difficult. It is common for redundancy features to fail or contribute to a failure scenario. Incorrectly configured redundancy will lead to network downtime when the network is supposed to be redundant. This presents a false sense of se-curity to the network operators and administrators. This research will present two scenarios that are commonly left unmonitored and look at a practical way to deploy solutions to these two scenarios in such a way that the network uptime can be improved. Implementing a practical approach to monitor and mitigate these types of failures allows costs spent on re-dundancy to increase uptime, and thus increase overall quality that is critical to a modern digital company.

The computer numerical control (CNC) machines are chiefly used for the production of jobs with high accuracy and high speed. The CNC machining centers perform the machining operations according to the given program instructions which are commonly programmed by a CNC programmer. In this paper, a procedure to develop an automatic CNC program for machining of different types of holes by using different machine learning algorithms is developed. The machine learning algorithms namely support vector machine (SVM) and restricted boltzmann machine algorithm (RBM) with deep belief network (DBN) are used for the au-tomatic development of CNC machining programs of different types of holes. Initially, the position and other parameters of machining operations are identified and thereafter the CNC machining program is developed by using the MATLAB application. The automatically de-veloped CNC programs are tested on a CNC simulator. It is found that the application of RBM machine learning algorithm with DBN outperforms the SVM machine learning algo-rithm for the development of automatic CNC machining program for the machining of blind holes, through holes, counterbores and countersink operations.

Service quality is basically a comparison between expectations and the perceptions of the customer. The interrelationship between various aspects of information technology (IT) adoption and other basic characteristics of service quality is complex and dependent on the expected service composition. Reliability, conformance, durability and serviceability are taken as functions of service quality; whereas Tenure, utility and vendor are taken as functions of Information Technology. Substantial literature has examined the concept of service quality, its dimensions, and measurement methods. Statistical analysis of quality parameters has been performed and validation is done with different Artificial Neural Network (ANNs) architecture with different hidden layers. The model proposed in this study is designed to evaluate and improve service quality within a comprehensive framework.

The main objective of this research article is to select the best mobile model among various alternatives available on the market. For this analysis 10 alternative models from different brands are selected from different online shopping website having different specifications and ranging from low budget to medium budget in terms of price. For this selection purposes two multiple criteria decision making tools (MCDM) has been adopted i.e. Complex Propor-tional Assessment (COPRAS) and Additive Ratio Assessment (ARAS). The selection process is done based on four important criteria i.e. price, internal storage, RAM and brand. The weightages of the criteria are calculated by using Analytic Hierarchy Process (AHP) and these weightages are further used in COPRAS and ARAS methods. Individual COPRAS and ARAS method is applied for the selection of the best mobile and the preference ranking order of the models are also proposed by each process. The proposed ranking order by both the methods are compared and it is found that the outcome results are more or less the same using both techniques but there is a slight change in ranking of the middle-order alternatives. Both processes give model 1 and model 4 as the best and the worst models respectively among 10 alternatives.

In precision manufacturing, especially in making of holes on the parts during drilling process for precision assembling of parts facing problems with burr formation. Drilling is one of the finishing operations in the production cycle, removal of burrs during drilling process is a time consuming and non-value added process to the manufacturing sector. In this paper, we developed an analytical models for thrust force, torque and burr size with variable clearance angle of drill bit geometry especially for Aluminium alloys used for automotive applications. An analytical model results were obtained by writing a code in MATLAB @ 2010 software for aluminium alloy series such as Al 6061, Al 2014 and Al 7075. In an economic front, de-veloped mathematical models are benefitted to reduce experimentation, these results reveal that at the beginning of the drilled hole due to indentation of chisel edge, more thrust and torque was identified, beyond that suddenly thrust force was lowered, cutting forces by the lip edges were increased from beginning of the process to progress of drilling process. The proposed model approach is helpful to the budding entrepreneurs in the related areas to select optimal combination of drilling parameters to attain multiple performance characteristics of responses especially in burr size to prevent the post finishing operations up to certain extent.

Over the past number of years, competence management has become a key issue for companies. Similarly, to the importance of knowledge management, competence management considers an individual’s performance, based on his or her knowledge. Thus, knowledge management becomes an important aspect for companies to manage resources, through the ability of characterizing key competences and evaluating how they are improved through past experiences and roles, thus selecting project team members according to the respective existing skills. This paper focuses on the coordination of design activities in order to propose a tool dedicated to operation-level project managers to help prepare their technical teams based on the required skills. The goal is to enhance the effectiveness of the teams, as well as their performance in the short-term, as well as the long-term, while coordinating with the human resources department. This work is based on the results of the Aides et assisTances pour la conception, la conduite et leur coupLage par les connAissanceS – Help and support for design, coordination and their coupling by knowledge (ATLAS, 2008 - 2011). This project studies the coupling between systems design and management. We propose an initial tool to manage skills in a design project.

Most of the time, the government of the affected country cannot handle the entire relief operations. Under such conditions, international organizations act independently but are obliged to obey the law of the country. In this paper, we propose a dynamic multi-level programming where the affected country and international organizations dynamically change their roles, being leader or follower, according to a game. The application of the proposed model is investigated for a case problem where real data are utilized to design a network for humanitarian logistics during potential earthquakes. The advantages of using multi-level modeling against considering just one player's point of view are provided to guide decision-makers under a variety of conditions. The results show for the first three periods, the gov-ernment of the affected area cannot handle the demand for the rescue operation. Thus, the international suppliers act as the leader for the first three periods. However, by decreasing the demand for the rescue operation in the last three periods, the government can manage the evacuation operation properly.

Electrical Energy is an essential commodity which significantly contributes to the economic development of any country. Many non-linear factors contribute to the final output of electrical energy demand. In order to efficiently predict electrical energy demand, many time-series analysis and multivariate techniques have been suggested. In order for these methods to accurately work, an enormous quantity of historical dataset is essential which sometimes are not available, inadequate and inaccurate. To overcome some of these challenges, this paper presents an Artificial Neural Network based method for Electrical Energy Demand Forecasting using a case study of Lagos state, Nigeria. The predicted values are compared with actual values to estimate the performance of the proposed technique.