Designing a smart environment is crucial stakes. It starts with analysing the environment goals, functional and information requirements, and the process end with decision to employ the smart objects and enabling technologies. One issue is that the risk involved in such investment in these technologies which necessitates ensuring the justification, efficiency, and effectiveness of these employed technologies. Subsequently, there is a need to test these technologies prior to the investment decision, which is difficult to achieve in reality. One feasible solution is to use a simulation model which is capable to test the impact of these technologies. This research presents a new and pioneering methodology for simulating the implementation and testing smart enabling technologies.

This article presents a comprehensive plan for creating a research model of a neural network for barcode recognition in a warehouse environment. The introduced new methodology enables the development of an efficient model with high accuracy for barcode recognition and decoding. In the proposed methodology, we consider current approaches and present a research plan focusing on convolutional neural networks and the Roboflow tool. The new methodology has been divided into several steps, including data collection, dataset compilation, selection of a suitable neural network model, network training, validation, and optimization, and finally, testing the model on a test dataset.

Multi-agent systems are systems that can perceive the environment through their sensors and perform actions through their actuators. Multi-agent systems are thus an interesting alternative or complement to artificial intelligence. One of the key problems of these systems is designing the principles of agent behavior through coordination, cooperation, and communication, which bring order to the actions of agents and ensure that there are no contradictions in the system. The development of OLAP technology to support online analytical data processing enables the use multidimensional data stores directly by individual agents. In this way, the agent can streamline the analytical processing of the data to find a match between its intention and the plan. For the resulting multi-agent system to find the maximum possible agreement between its agents, we propose a new conceptual approach, which is based on the use of OLAP technology for storing analytical data by specific agents, and we propose the so-called Compromise Decision Agent, which calculates compromise values from all agents in the system.

This study introduces a new application of aggregate production planning (APP) in the manufacturing of carbon steel pipes and hot induction bends. Given the strategic importance of this type of industry, enhancing productivity through cost-effectiveness, and economic performance optimization has become crucial in such industry. The study proposes an APP optimization model that is both inspiring and realistic, aimed at increasing profitability by minimizing both production and inventory costs. The model is formulated as a deterministic, multi-product, multi-period model, and three alternative optimization techniques were applied: linear programming, genetic algorithms, and hybrid genetic algorithms, as a case study in a steel pipes manufacturing company. The results indicate that linear programming yields the same results as hybrid genetic algorithms, but in less time. Additionally, a feasibility study evaluated the effectiveness of the proposed model against the original planning system in the company, revealing a 12% decrease in overtime wages and a 9% increase in profit.

This article, discuss the effect of finishing polyester/cotton blended fabric (PET/C) with alkali and Titanium dioxide nanoparticles (TiO2 NPs) simultaneously. The treatment conditions such as NaOH and TiO2 NPs concentrations, reaction temperature and duration will be investigated. The effect of addition NPs on alkaline treatment conditions will prove through weight loss and carboxylic content. The ability of PET/C fabrics for loading with NPs during alkaline treatment was investigated by using SEM, EDX, and FTIR measurements. The effect of finishing of PET/C blended fabric with the suggested method on antimicrobial activity and ultraviolet protection was investigated. The simultaneous finishing of PET/C blended fabrics with alkali and TiO2 NPs showed excellent ultraviolet protection and high antimicrobial activity against Gram-positive ( Bacillus mycoides), Gram-negative ( Escherichia coli), and nonfilamentous fungus ( Candida albicans). The functional performance imparted to PET/C fabrics by the suggested approach are durable in repeated laundering processes, even after five Launder-Ometer washes.

The investment in the clinic and hospitals environments while becoming necessary very attractive, it must be justifiable with obvious level of reliability based on logical and systematically understandable steps. In this paper, we present a simple, systematic, and objective fuzzy relation-based approach for the design of a smart healthcare system. The proposed approach establishes fuzzy relations among three model building blocks: the smart enabling technologies, the healthcare system smartness features, and the healthcare system operational objectives that are desirable to realize. The max-min composition operator is utilized for combining the aforementioned relations for attaining the target relation among the enabling technologies and the operational objectives. A priority of the smart enabling technology is computed based on the total impact relation of each enabling technology on all the operational objectives. Then, the design of the smart healthcare system is reached by adopting the enabling technologies in order of priority.

The practice of developing product or system safety standards is currently conducted with high level of subjectivity, which affects the reliability of development outcomes. The decision to adopt specific safety requirements into the standard is usually based on the knowledge, intuition and experience of the technical expert involved in developing the body of the safety standard made up of those requirements or specifications. This is usually carried out without analyzing risk attributes or factors addressed by each safety requirement constituting a safety standard. This can lead to inaccuracy and lack of reliability due to either under-estimation or over-estimation of risks involved. This research presents unprecedented, novel, structured and objective methodology, that aims to improve the practice of developing safety standards. In order to accomplish the above end, the proposed methodology acts to prioritize the system’ safety requirements of each system’s design elements based on a set of commonly identified risk factors they address. We have investigated the currently existing risk prioritization and multi-criterion decision making techniques, and found that machine learning data clustering approaches can be adequate especially in case of relatively large number of assessed decision alternatives (i.e., the assessed safety requirements). We have elaborated the merits of the proposed clustering-based methodology over existing prominent ones. The proposed methodology starts with analyzing product or system safety to identify common risk factors. A numerical scale is used to enable objective quantification of risk factors’ values for each safety requirement by multiple experts. The obtained experts’ numerical assessments of each safety requirement are then averaged to represent the raw data set containing risk profiles of each safety requirement assessed. This risk factors data set is then used by fuzzy c-means clustering algorithm, to organize them into groups of different level of priorities, so as to prioritize their corresponding safety requirements. We have applied the proposed methodology on a real case study of developing school bus safety standard. The main benefit of the proposed methodology includes its capability to support the safety standards development practitioners and policy makers, as well as technical experts in objectively and systematically carry out the critical decision of adoption, exclusion and update of the safety requirements into the developed safety standard. Additionally, it can efficiently guide safety engineers and inspectors to undertake more reliable and structured risk assessment process. Over and above, the implementation of the proposed methodology is guaranteed to result in more reliable, accurate and risk-informed safety standard.

In today's competitive word, the automation of material handling, the manufacturing processes have replaced the mundane work in the various industries where the mass production of the part has taken place. The plastic product manufacturing is one of the mass production industry. A flow-packing machine is an essential equipment used for packing the final goods. It facilitates the material handling of bulk and unit material. Automation of flow-packing machine reduces labor requirement, lead time, workplace injuries, and by so allows maximum possible production with best quality. In this paper an automatic feeding mechanism is designed and fabricated for manually operated flow-packing machine. Before automation of the feeding mechanism, parts were placed on the conveyor manually. This results in a large waste of products and a rise possibility of workplace injury. Design of an automatic feeding mechanism overcome these problems. The proposed design is validated in real life scenario.

The problem of assessment and adoption of automotive tyre design specifications has not been addressed sufficiently in literature. This is in spite of its significance as a crucial component relevant to design and safety of the automobile. In this paper, a multi-objective optimization model of the tyre design trademark adoption decision is proposed. Multi-attribute or multi-criterion decision making techniques are heuristics providing good solution, but do not guarantee optimum solution. Up to date, there is no optimal yielding method for selection of vehicle tyre manufacturer or trademark based on prespecified design targets. The proposed model is formulated as a binary goal programming model for optimizing tyre trademark design selection decision by adopting an optimal tyre design trademark that best achieve design targets. The model is solved by the branch and bound algorithm. One advantage of the proposed model is flexibility to incorporate multiple design targets, tolerance limits and different constraints. The proposed model can support efficient and effective decision making concerning the adoption of tyre trademark design for new automobile or to re-adopt new design for new road vehicle operating conditions.

The design of the progressive die stamping process is optimized through minimizing the number of die stamping stations in the strip layout to reduce the die cost. In order to accomplish such end, in this study, a graph-theoretic based method is implemented to model and optimize the strip layout design. This method starts with mapping stamping features into stamping operations. This step is followed by constructing two graphs to model the precedence and adjacency constraints among stamping operations based on a set of manufacturing rules. These two graphs are called: operation precedence graph and operation adjacency graph. In the next step, a topological sorting algorithm clusters the operations into partially ordered sets. Then, a graph coloring algorithm clusters the partially ordered operations sets into final sequence of operations. The graph-theoretic technique has been implemented on a part currently manufactured by laser cutting process technology in some Egyptian factory in Cairo. This study indicated that the graph-theoretic technique offers several advantages including the ease of programming and transparency in understanding the obtained strip layout design. This is besides being a systematic and logically approach to obtain an optimized strip layout design. In general, the progressive die manufacturing can increase productivity of sheet metal works in Egypt, only in situations of mass production. The limitation is that it requires considerable skill level and training for labor to conduct die strip layout design.

Smart environments have proven very supportive to the improvement of the performance of people in different workplaces. Plenty of applications have been introduced spanning different settings including healthcare, ambient assisted living, homes, offices, and manufacturing environment, etc. However, subjectivity and ambiguity prevail in the majority of research, and still, up to date, rare approaches found quantitatively and objectively constructing or assessing the impact of smart enabling technologies on the performance of the subject environment. Further, no approaches have considered optimizing the adoption of those smart technologies with respect to objectives achievement. This article presents a novel optimization methodology for designing a smart workplace environment in conditions of ambiguity or fuzziness. The methodology begins with defining and weighing the overall goals and objectives of the workplace. The Prometthe multi-criterion decision-making technique is used to weigh the operational objectives with respect to the overall workplace goals. Next, the relation among basic building blocks of the model; namely: the operational objectives, smartness features, and smart enabling technologies are quantified, utilizing fuzzy relations. Then, the fuzzy goal programming techniques will be utilized to optimize the impact relation values while considering the budget constraint. The proposed optimization methodology is implemented on the development and optimization of the smart clinic, as a typical instance of the workplace.

The multiple, different and specific expertises are often needed in making YES-or-NO (YES/NO) decisions for treating a variety of business, economic, and agricultural decision problems. This is due to the nature of such problems in which decisions are influenced by multiple factors, and accordingly multiple corresponding expertises are required. Fuzzy expert systems (FESs) are widely used to model expertise due to its capability to model real world values which are not always exact, but frequently vague, or uncertain. In addition, they are able to incorporate qualitative factors. The problem of integrating multiple fuzzy expert systems involves several independent and autonomous fuzzy expert systems arranged synergistically to suit a varying problem context. Every expert system participates in judging the problem based on a pre- defined match between problem context and the required specific expertises. In this research, multiple FESs are integrated through combining their crisp numerical outputs, which reflect the degree of bias to the Yes/No subjective answers. The reasons for independency can be related to maintainability, decision responsibility, analyzability, knowledge cohesion and modularity, context flexibility, sensitivity of aggregate knowledge, decision consistency, etc. This article presents simple al - gorithms to integrate multiple parallel FES under specific requirements: preserving the extreme crisp output values, provi- ding for null or non-participating expertises, and considering decision-related expert systems, which are true requirements of a currently held project. The presented results provides a theoretical framework, which can bring advantage to decision making is many disciplines, as e.g. new product launching decision, food quality tracking, monitoring of suspicious devia- tion of the business processes from the standard performance, tax and customs declaration issues, control and logistic of food chains/networks, etc.

There is no currently known systematic and quantitative procedure developed to design smart workplace environments through guiding efficient and effective adoption and exploitation of the currently available wide variety of smart-environment-enabling technologies. This article presents a novel meth odology to design a smart workplace environment objectively to improve, for example, productivity and safety. The proposed methodology starts by analysing the given subject workplace environment in terms of tasks and goals, and then structures and quantifies effect relations among workplace overall goals and the smart-environment-enabling technologies through linking them to the intermediate task objectives, potential improvement opportunities and smart characteristics affected by those smart technologies. Fuzzy relations are utilized to express vague effect relations among related goals, objectives, concepts and technology elements of the proposed design model. The Max–Min composition is used to compose relations to obtain the final overall goal-technology relationships. The methodology eventually yields a composed relation among workplace overall performance goals and the investigated smart-environment-enabling technologies. This overall composed relation reveals promising enabling technologies that are relevant to achievement of the specified goals. Identification of goals promising smart technologies can be used directly as a guide to realize a goal-based smart workplace environment design. Promising technology relations to task goals also help to deploy those promising technologies to the relevant tasks. A comprehensive example case is used to demonstrate the applicability of the methodology.

In spite of their continual prove of success in several environments, few attempts have considered developing systematic approaches for designing of smart manufacturing environments. In this article, attributed to uncertainty or vagueness associated with their impact assessment, fuzzy screening approach is proposed to thoroughly assess the potential of the smart enabling technologies with respect to the objectives of manufacturing workplace tasks and the elicited logical improvement opportunities. Expert multi-criterion assessment of a set of candidate smart technologies is carried out through employing fuzzy linguistics. A set of promising smart technologies will be proposed.

People born with disabilities (PWDs) face many problems because of their inability to accomplish simple daily tasks. However, the evolution of technological solutions has helped the PWDs to accomplish their tasks independently. Although a significant amount of the research work can be found on these technologies still, the authors believe that there is a lack of a rational method to quantitatively assess the solutions for their applicability to the respective impairment. In this paper, a quantitative and a qualitative assessment approach for the evaluation of the various technologies is proposed. It measures the applicability of the technological solutions for the different impairment groups. Based on the scoring model known as the multi-weighted scoring model (MWSM), the proposed evaluation approach carries out comparative judgment of clinical experts and input from PWD's using a well-defined criteria. The technique is applied to 16 research papers which are dealing with the application of technologies that assist PWDs suffering from various impairment conditions. The assessment technique has successfully identified the relevant criteria that can be used to assess technological research papers. It has also identified the research gaps which require further attention. The proposed quantitative approach can be a very useful tool to assess the applicability and the quality of the previous relevant works.

A little if not rare work has been considered empowering the PWD with smart universal assistive technologies at the workplace. Most researches focus on specific or single impairment condition such as smart solutions for blind or low vision persons, physically disabled persons (PDP), deaf or mute persons, and mostly with home or building places. This paper present the models of interaction and control of a universal interface solution for PWD, called SMARTUNIVERS. The SMARTUNIVERS is currently being developed within the of SMARTDISABLE's research project activities implemented at the Riyadh Techno Valley, King Saud University, Riyadh, KSA. IT includes two smart interface modules: Smart Help (SMARTHELP) and Smart editor (SMARTED-IT). The SMARTHELP module provides personalized smart help and communication services for the PWD at workplace. The SMARTEDIT module is a multimodal editor interface that provides the capability for wide spectra of PWD groups (11 groups with various combination of disabilities) to edit documents using multi-model ways of interactions and commanding through use of speech recognition engine, text-to-speech, Mic, virtual mouse/ keyboard and Braille keyboard. We shall present in this paper the high level design of the SMARTUNIVERS and the two smart component modules, together with the interaction models and scenarios for some typically covered PWD groups. The SMARTUNIVERS provides a flexible dynamic interface that adjusts itself according to the impairment conditions associated with the eleven supported groups of PWD.

The scheduling zero-wait (ZW) and no intermediate storage transfer (NIS) policy of multi-product batch processes in order to produce a number of low volume high value-added chemical products because of its economic impact.It involves various parameters such as makespan (completion time) which is recognized as one of the important design parameter as it helps to decide for the best scheduling design and normally used as the main parameter for selecting the optimal production sequence which involves various parameters such as batch process recipes, sequence of production and transfer policy for product intermediates.In this paper, we present a development, solution and computational performance evaluation of optimal scheduling for multiproduct batch process with two commonly used transfer policies namely zero wait (ZW) and no intermediate storage (NIS) by using computer program language software (Java) which simplify and improve the determination of Makespan and select the optimum sequence due to the minimum Makespan.

The potentially attractive exploitation of Ambient Intelligence (AmI) seeks improving performance and quality of life of people inside workplaces (e.g., offices, manufacturing work centers, homes). To succeed at making the implementation of AmI fruitful it is important to understand and objectively quantify the logical relationship among the following relevant elements: AmI key enabling technologies, AmI features, basic workplace functions or tasks, and performance measures of the activities of the workplace. Such relationships are fully characterized by ill-structuredness, subjectivity and vagueness. In this article we structure these ill-defined relationships and offer a generalized conceptual model as a foundation for understanding and objectively quantifying such relationships. We then propose fuzzy numbers as an adequate means for expressing the vagueness that is inherent with the subjective nature of the AmI features, technology impacts and characteristics, and relationships with workplace performance measures. The fuzzy numbers are adequately employed through the Analytical Hierarchically Process (AHP) in the form of a Fuzzy-AHP model. We give some example applications from a manufacturing system workplace. The results of the AmI technologies-performance measure assessment frameworks-can be used as a guide in designing smart workplaces and as a valuable insight in adopting the most significant AmI technologies.