Appl. Syst. Innov., Volume 5, Issue 4 (August 2022) – 28 articles

Additive manufacturing (AM) technologies are growing more and more in the manufacturing industry; the increase in world energy consumption encourages the quantification and optimization of energy use in additive manufacturing processes. Orientation of the part to be printed is very important for reducing energy consumption. Our work focuses on defining the most appropriate direction for minimizing energy consumption. In this paper, twelve machine learning (ML) algorithms are applied to model energy consumption in the fused deposition modelling (FDM) process using a database of the FDM 3D printing of isovolumetric mechanical components. The adequate predicted model was selected using four performance criteria: mean absolute error (MAE), root mean squared error (RMSE), R-squared (R2), and explained variance score (EVS). It was clearly seen that the Gaussian process regressor (GPR) model estimates the energy consumption in FDM process with high accuracy: R² > 99%, EVS > 99%, MAE < 3.89, and RMSE < 5.8. Full article

The choice of technique for the creation of a 3D digital human bone model from natural specimens has a critical impact on the final result and usability of the obtained model. The cornerstone factor in 3D modeling is the number of faces of polygon mesh, along with topological accuracy, as well as resolution and level of detail of the texture map. Three different techniques (3D scanning, photogrammetry, and micro-computed tomography) have been used to create a digital 3D model of the human zygomatic bone. As implementation and use of 3D models can be divided into three main categories—visualization, simulation, and physical replication to obtain a functioning model (implant or prothesis)—the obtained models have been evaluated by the density and topological accuracy of the polygonal mesh, as well as by visual appearance by inspecting the obtained texture map. The obtained data indicate that for biomedical applications and computer biomechanical simulation the most appropriate technique of 3D model obtainment is micro-computed tomography, in its turn for visualization and educational purposes, the photogrammetry technique is a more preferable choice. Full article

Past research reveals that many lean implementation barriers hinder lean implementation in small and medium-sized enterprises (SMEs). Among many sectors, the manufacturing sector suffers more as it generates more waste while carrying out manufacturing processes. Many manufacturing units make unsuccessful attempts to implement lean principles in their manufacturing systems. Hence, such units must eliminate the prevailing lean barriers to accomplish successful lean implementation. Moreover, the contextual relationship of lean barriers must be studied to understand the effect of such barriers. This paper uses interpretive structural modeling (ISM) to explore lean barriers, their relationships, and their influence on other lean barriers. The present research also reveals the most significant classification of lean barriers into various categories of independent, dependent, autonomous, and linkage using the (MICMAC) Matrice d’Impacts Croisés Multiplication Appliquée á un Classement analysis. ISM and MICMAC together provide relationship modeling and reveal the interrelationship between each lean implementation barrier and its categories, respectively. The ISM model is validated using the Delphi technique. The interpretative ranking process (IRP) is used to rank the barriers. The three significant lean implementation barriers revealed through the IRP include “lack of lean understanding”, “lack of strong quality policy”, and “risk of sustainable practice implementation”. The present research will help practicing managers of SMEs in the manufacturing sector to understand the mutual influence of lean barriers before introducing lean implementation. It is suggested that SMEs work on independent barriers so that dependent barriers can also be overcome with the least amount of resources and effort. Full article

The modern sedentary lifestyle is negatively influencing human health, and the current guidelines recommend at least 150 min of moderate activity per week. However, the challenge is how to measure human activity in a practical way. While accelerometers are the most common tools to measure activity, current activity classification methods require calibration studies or labelled datasets—requirements that slow the research progress. Therefore, there is a pressing need to classify and quantify human activity efficiently. In this work, we propose an unsupervised approach to classify activities from accelerometer data using hidden semi-Markov models. We tune and infer the model parameters on accelerometer data from the UK Biobank and select the optimal model based on features used and informativeness of the prior. The best model achieves an average correlation of 0.4 between the inferred activities and the reference ones, with the overall physical activity obtaining a correlation of 0.8. Additionally, to prove the clinical significance of the method, we validate it by performing a linear regression between the inferred activities and anthropometric measures such as BMI and waist circumference. We show that for a sedentary behaviour and total physical activity, the proposed method achieves comparable regression coefficients to the reference labelled dataset. Moreover, the proposed method achieves a good agreement with a labelled dataset for daily time spent in a sedentary behaviour and total physical activity. The unsupervised nature of the method allows for a data-driven classification that does not require calibration studies or labelled datasets and can thus facilitate both clinical research as well as lifestyle recommendations. Full article

Last-mile delivery (LMD) is essential in supply chains, and providers of logistics services are aware that they must adapt to changing customer and society expectations, competition challenges, and modern technologies. In light of the importance of artificial intelligence (AI) in modern technologies, this article employed a narrative literature review to recognize impacts of AI-powered technologies that aid in optimizing the LMD component. For this study, two possible classes of technologies were considered: tangible technologies, such as robots, drones, and autonomous vehicles, and intangible technologies, such as decision support tools and operating systems. Thus, this article frames potential developments in modern LMD, taking into account the fact that technological advancement brings both opportunities and challenges. According to this literature review, the modern version of LMD is capable of providing services that are both more productive and more sustainable, thus satisfying demands for better services. Last but not least, another goal of this article was to look into the challenges that limit the benefits of modern LMDs. Finally, suggestions are provided as a resource for practitioners and policymakers working in the supply chain to improve performance. Full article

Cardiac diseases have increased over the years; thus, it is essential to predict their possible signs. Accurate prediction efficiently treats the patient’s medical history before the attack occurs. Sensors available in commonly used devices may strive for the proper and early identification of various cardiac diseases. The primary purpose of this review is to analyze studies related to gender discretization based on data from different sensors including electrocardiography and echocardiography. The analyzed studies were published between 2010 and 2022 in various scientific databases, including PubMed Central, Springer, ACM, IEEE Xplore, MDPI, and Elsevier, based on the analysis of different cardiovascular diseases. It was possible to verify that most of the analyzed studies measured similar parameters as traditional methods including the QRS complex and other waves that characterize the various individuals. Full article

In several fields nowadays, automated emotion recognition has been shown to be a highly powerful tool. Mapping different facial expressions to their respective emotional states is the main objective of facial emotion recognition (FER). In this study, facial expression recognition (FER) was classified using the ResNet-18 model and transformers. This study examines the performance of the Vision Transformer in this task and contrasts our model with cutting-edge models on hybrid datasets. The pipeline and associated procedures for face detection, cropping, and feature extraction using the most recent deep learning model, fine-tuned transformer, are described in this study. The experimental findings demonstrate that our proposed emotion recognition system is capable of being successfully used in practical settings. Full article

The wireless body sensor network (WBSN) is a wireless communication that might enable 24/7 patient monitoring and health findings through the online platform. Although BSN design is becoming simpler, building a secure BSN seems to be more challenging than designing conventional solutions, and the recent study provides little guidance to designers and developers. The proposed study summarizes the multivocal literature study of security mechanisms for BSN. The investigation found 10,871 academic publications and 697 grey content; duplicates were removed, and selection criteria were employed, resulting in 73 academic papers and 30 grey publications. Various conventional security techniques, scope, and security contexts were used to classify the stated security solutions within each publication. It was crucial to inquire about the frequency of publications, research methods, security mechanisms, and contexts to answer the proposed questions. Our survey concludes that security methods and assessments are categorized into 15 categories, with the most frequently referenced being authentication and authorization; the majority of strategies concentrate on preventing and mitigating security breaches, with a limited number of works focusing on detection and recovery; and the techniques used to conduct the survey vary between the two types of publications. This evaluation might be the first step toward making the BSN platform more consistent by giving professionals and researchers a complete set of security strategies and methods. Experts will apply these solutions to fix security issues while establishing a trustworthy BSN after they have been identified through the process of discovering the most commonly utilized security solutions. Full article

Large sports centers are characterized by special energy demand profiles compared to other facilities. The aim of this work is to assess the economic investment of photovoltaics (PVs) on a sport center microgrid using different charging methods and by efficiently exploiting the PV generation. The overall work is performed in the following three steps. The first step is the energy requirement analysis, the second focuses on a PEV charging strategy proposition in order to exploit the PV generation, grid electricity price, and Vehicle-to-Grid (V2G), and the third step is the net present value (NPV) analysis of the PV investment in the different scenarios. The simulations showed that the proposed charging strategy increases the NPV. In addition, the increment of PEV penetration rate leads to the maximization of the NPV. The energy and costs analysis are carried out for an application case in the Olympic Athletic Centre of Athens. Full article

Several kinds of research conclude that the level of digital competence of students is mainly oriented to their daily activities. Therefore, we present the current paper which seeks to determine the impact of implementing a blended learning course designed to improve digital competence for research (DCR) among a group of undergraduate engineering students. With this approach, a quasi-experimental explanatory methodology with a causal-comparative scope was applied. For this reason, the results were analyzed before and after applying a specially designed course to the experimental group, comparing it with a passive control group by collecting data using three previously validated instruments. For data analysis, students’ t-tests and two-way ANOVA (Analysis of Variance) were used, estimating the effects with Cohen’s study. Given the results, there was a statistically significant improvement (p ≤ 0.05) in their skills and attitudes, but not in their knowledge, obtaining a significant effect size only in the procedural dimension (f = 0.41 y η² = 0.142). Therefore, the implementation of the course used in the blended learning modality is considered to significantly improve the DCR of a group of undergraduate engineering students, although the results should be evaluated with due reservations. Full article

When analyzing high-dimensional data with many elements, a data visualization that maps the data onto a low-dimensional space is often performed. By visualizing the data, humans can intuitively understand the structure of the data in the high-dimensional space. The self-organizing map (SOM) is one such data visualization method. We propose a spherical tree-structured SOM (S-TS-SOM), which speeds up the search for winner nodes and eliminates the unevenness of learning due to the position of the winner nodes by placing the nodes on a sphere and applying the tree search method. In this paper, we confirm that the S-TS-SOM can achieve the same results as a normal spherical SOM while reducing the learning time. In addition, we confirm the granularity of clustering on the tree structure of the S-TS-SOM. Full article

The recent literature reveals a dichotomy formed by a coevolution between cryptography and Artificial Intelligence (AI). This dichotomy consists of two sides, namely Crypto-Influenced AI (CIAI) and AI-Influenced Cryptography (AIIC). While it is pertinent to investigate this dichotomy from both sides, the first side has already been studied. In this review, we focused on AIIC. We identified and analyzed the stages on the evolutionary path of AIIC. Moreover, we attempted to anticipate what the future may hold for AIIC given the impact of quantum computing on the present and the future of AI. Full article

Planning and defining places for the installation of police facilities are fundamental to improving the public security service in the urban space. Geographic Information Systems connecting the spatial distribution of police occurrences, budgetary restrictions, and the maximum distance covered are state-of-the-art innovations addressing the need for preventive and responsive police management. The present work proposes a management information system to support the analysis and suggestion of potential police facility locations. The management information system is modeled using k-means for cluster analysis and the definition of candidate locations, and the maximal covering location problem is used to optimize the predefined locations. The proposed system allows the analysis of alternative locations and their impacts on public security. The application in Brazil demonstrates that it is possible to obtain an additional 22% gain in the coverage area of occurrences and an additional reduction of 920 m in terms of the average distance covered when comparing the management information system’s suggested locations to the current configuration. Thus, our assessment provides an efficient tool for supporting decisions regarding the location of police facilities and helps improve the public security service. Full article

Using technology to prevent cyber-attacks has allowed organisations to somewhat automate cyber security. Despite solutions to aid organisations, many are susceptible to phishing and spam emails which can make an unwanted impact if not mitigated. Traits that make organisations susceptible to phishing and spam emails include a lack of awareness around the identification of malicious emails, explicit trust, and the lack of basic security controls. For any organisation, phishing and spam emails can be received and the consequences of an attack could result in disruption. This research investigated the threat of phishing and spam and developed a detection solution to address this challenge. Deep learning and natural language processing are two techniques that have been employed in related research, which has illustrated improvements in the detection of phishing. Therefore, this research contributes by developing Phish Responder, a solution that uses a hybrid machine learning approach combining natural language processing to detect phishing and spam emails. To ensure its efficiency, Phish Responder was subjected to an experiment in which it has achieved an average accuracy of 99% with the LSTM model for text-based datasets. Furthermore, Phish Responder has presented an average accuracy of 94% with the MLP model for numerical-based datasets. Phish Responder was evaluated by comparing it with other solutions and through an independent t-test which demonstrated that the numerical-based technique is statistically significantly better than existing approaches. Full article

This study proposes a novel robust speech feature extraction technique to improve speech recognition performance in noisy environments. This novel method exploits the information provided by the original acoustic model in the automatic speech recognition (ASR) system to learn a deep neural network that converts the original speech features. This deep neural network is trained to maximize the posterior accuracy of the state sequences of acoustic models with respect to the speech feature sequences. Compared with the robustness methods that retrain or adapt acoustic models, the new method has the advantages of less computation load and faster training. In the experiments conducted on the medium-vocabulary TIMIT database and task, the presented method provides lower word error rates than the unprocessed baseline and speech-enhancement-based techniques. These results indicate that the presented method is promising and worth further developing. Full article

The evolution of information technology and the great advances in artificial intelligence are leading to a level of automation that has never been reached before. A large part of this level of automation is due to the use of robotics, which in turn ends up both hindering and accelerating the process of Industry 4.0. Industry 4.0 is driven by innovative technologies that have an effect on production systems and business models. Although technologies are the driving motors of production within Industry 4.0, many production systems require collaboration between robotics and humans, and safety is required for both parties. Given the need for robots to collaborate with humans simultaneously or in parallel, a new generation of robots, called cobots, “Collaborative Robots”, are gaining prominence to face these challenges. With cobots, it is possible to overcome security barriers and envisage working safely side-by-side with humans. This paper presents the development and testing of a low-cost, within standards, 6-axis collaborative robot that can be used for educational purposes in different task-specific applications. The development of this collaborative robot involves the design and 3D printing of the structure (connections and parts), sizing and selection of circuits and/or electronic components, programming, and control. Furthermore, this study considers the development of a user interface application with the robotic arm. Thus, the application of technological solutions, as well as of the scientific and educational approaches used in the development of cobots can foster the wide implementation of Industry 4.0. Full article

To thrive, an organization must adapt to the fast and constant change in the economic environment caused by an aging society, technological changes, and the pandemic crisis. Innovation becomes important for the adaptation of industries. Healthcare is one of them. Innovation development in hospitals is effective and acceptable when its management is effective and aligns with the healthcare quality context since quality is a philosophy of work in life-related settings. To the best of our knowledge, quality management and innovation management in healthcare have never been integrated. Therefore, this research aimed to create an integrated framework of quality and innovation management in healthcare (TQIM-H). To establish the effectiveness of applying TQIM-H for the development of effective healthcare innovation, this study developed a TQIM-H conceptual framework using multiple methodologies including a literature review, multiple case studies analysis, Delphi study with healthcare experts, Technology Acceptance Model (TAM), and triangulation with an external dataset. We constructed a TQIM-H conceptual framework, consisting of seven dimensions, that can be used in developing innovation projects in hospitals and which agrees with safety and quality principles in hospitals. Full article

Several statistical techniques for analyzing data matrices use lower rank approximations to these matrices, for which, in general, the appropriate rank must first be estimated depending on the objective of the study. The estimation can be conducted by cross-validation (CV), but most methods are not designed to cope with the presence of outliers, a very common problem in data matrices. The literature suggests one option to circumvent the problem, namely, the elimination of the outliers, but such information removal should only be performed when it is possible to verify that an outlier effectively corresponds to a collection or typing error. This paper proposes a methodology that combines the robust singular value decomposition (rSVD) with a CV scheme, and this allows outliers to be taken into account without eliminating them. For this, three possible rSVD’s are considered and six resistant criteria are proposed for the choice of the rank, based on three classic statistics used in multivariate statistics. To test the performance of the various methods, a simulation study and an analysis of real data are described, using an exclusively numerical evaluation through Procrustes statistics and critical angles between subspaces of principal components. We conclude that, when data matrices are contaminated with outliers, the best estimation of rank is the one that uses a CV scheme over a robust lower rank approximation (RLRA) containing as many components as possible. In our experiments, the best results were obtained when this RLRA was calculated using an rSVD that minimizes the L₂ norm. Full article

Endodontic reciprocating motors are largely used to support a series of procedures in dentistry treatments, useful for those different circular movement patterns. In the last years, different motors have been available on the market, with varying costs and promised performance for the users; however, since their reciprocating angles and rotation speeds may have significant outcomes on the employed endodontic files, there should be an affordable and practical way to assess the actual performance of such motors concerning their expected operation. Actually, endodontic files attached to reciprocating motors will move too fast to be easily accounted, which has fostered the development of computational methods to allow the proper validation of their movements according to their official datasheets. In this scenario, this paper describes a feasible method to detect the movement patterns of different reciprocating endodontic motors by the processing of a set of consecutive images taken by a high-speed camera. The performed experiments for three different off-the-shelf reciprocating endodontic motors showed that their actual movement characteristics are slightly different from their specifications, and that each considered motor has a particular movement pattern. Full article

Employing sunlight to produce electrical energy has been demonstrated to be one of the most promising solutions to the world’s energy crisis. The device to convert solar energy to electrical energy, a solar cell, must be reliable and cost-effective to compete with traditional resources. This paper reviews many basics of photovoltaic (PV) cells, such as the working principle of the PV cell, main physical properties of PV cell materials, the significance of gallium arsenide (GaAs) thin films in solar technology, their prospects, and some mathematical analysis of p-n junction solar cells. Furthermore, the paper presents the standard model of solar cells with the application of this model to different PV technologies together with the main findings. Moreover, the paper explores the role of numerical and mathematical modelling of PV cells by MATLAB/Simulink and COMSOL in evaluating the power conversion efficiency (PCE) of the PV cells and determining the main parameters affecting the power output at various conditions. Full article

Business process reengineering (BPR) has been widely known for its effectiveness in generating chances for organizational improvement. Understanding and modeling the relationship between the factors that contribute to the BPR and organizational culture (OC) is critical for the success of its implementation. This paper introduces a fuzzy-based analytical hierarchy process, named FAHP, for integrating the factors affecting BPR with OC to ensure the BPR’s success. First, a real case study was conducted to investigate the operational effectiveness and applicability of the proposed approach. Then, the BPR factors were validated and prioritized by a panel of experts from the American Society for Quality (ASQ) members network-Qatar chapter and a group of quality directors in Qatar. On the other side, the OC factors were investigated via a systematic literature review. Finally, an analytical correlation study was conducted to understand the relationship between the corresponding factors better. The correlation analysis study has shown that the organizational strategy, leadership, and transfer of knowledge are highly correlated with respect to people, innovation, and supportiveness. The team orientation, outcome orientation, and attention to detail factors have a medium correlation with the BPR factors. Moreover, the aggressiveness showed a weak correlation with all the BPR factors. The outcomes of this study provide decision-makers with guidelines for successful integration of the BPR and OC factors to ensure effective implementation of the BPR initiatives. Full article

A newly introduced term in the field of simulating an artificial or physical system is that of the “Digital Twin” concept method. It employs a digital representation and modeling method, capable of expanding and improving the life cycle of complex items, systems, and processes. Nowadays, digital twin technology has become a key research field worldwide. In this context, it is applied and utilized in various fields. One such field is the automotive industry, a technological field that has great implications in users’ everyday life. Digital twin technology not only has great contributions from the initial stages of design until the final construction stages of vehicles, but also during its use, drawing useful information from its daily functions and making the driving experience more enjoyable, comfortable, and safe. It is worth noting that the vehicles that can greatly benefit from the use of digital twins are electric vehicles, which has tended to acquire greater shares in the last decade. Full article

Urban planning is a valuable tool for growth control and city development, both to maintain the local urban identity and provide life quality for inhabitants. To regulate it, local governments have defined standards for a proper city’s growth through municipal and detailed urban plans. Such instruments identify a set of rules which constructions and buildings must fulfil for a careful and smooth integration within the urban areas. New projects are required to comply with such rules, and designers must adapt and guarantee that their projects fulfil all the local urban requirements. However, the verification process of construction projects is still a manual procedure and often a time-consuming process, with high possibilities for inaccurate measures. Thus, this paper aims to streamline the verification procedure of construction projects’ code compliance to enhance project design efficiency and save designers time. To do so, the Building Information Modelling (BIM) method will be used through the Dynamo programming software. By creating a Dynamo routine to check the building code and urban plan compliance from Brazilian municipalities, specific BIM models will be automatically analysed to detect and evaluate if it is according to local urban legislation. Results have provided a real-time decision support tool, where designers can assess if their buildings are complying with local urban codes at any time of the design stage, making it easy to innovate and integrate innovative design options, as well as to precisely communicate their buildings’ code compliance. Such a method can also support municipality authorities to verify project compliance, reducing assessment and calculation errors, as well as the required time and bureaucracy for project appraisal. Full article

The aim of the research was to evaluate the performance of smartphone depth sensors (Time of Flight Camera(ToF) and Light Detection and Ranging (LiDAR)) from Android (Huawei P30 Pro) and iOS (iPhone 12 Pro and iPAD 2021 Pro) devices in order to build a 3D point cloud. In particular, the smartphones were tested in several case studies involving the scanning of several objects: 10 building material samples, a statue, an interior room environment and the remains of a Doric column in a major archaeological site. The quality of the point clouds was evaluated through visual analysis and using three eigenfeatures: surface variation, planarity and omnivariance. Based on this approach, some issues with the point clouds generated by smartphones were highlighted, such as surface splitting, loss of planarity and inertial navigation system drift problems. In addition, it can finally be deduced that, in the absence of scanning problems, the accuracies achievable from this type of scanning are ~1–3 cm. Therefore, this research intends to describe a method of quantifying anomalies occurring in smartphone scans and, more generally, to verify the quality of the point cloud obtained with these devices. Full article

Organizations are transforming their linear models into circular models in order to become more sustainable. Remanufacturing is an essential element of the circular model; thus, there is an urgent need to adopt remanufacturing. It can offer organizations economic and environmental advantages and facilitate the transition to a circular economy (CE). Several aspects are crucial to the use of remanufacturing methods in order to transition to the CE. Therefore, in this study, we aimed to develop a framework for investigating the causal relationship among determinants of adopting remanufacturing processes for the circular economy. Through an integrated approach comprising a literature review and the Modified Delphi Method, we identified ten remanufacturing adoption determinants. The causal relationship among these determinants was established using the DEMATEL method. Furthermore, we classified these determinants into cause and effect groups. Five determinants, “consumer preferences”, “remanufacturing adoption framework”, “market opportunities”, “management commitment”, and “preferential tax policies”, belong to the cause group, and the remaining five belong to the effect group based on the effect score. To implement remanufacturing processes and transition to a circular economy, it is necessary to pay greater attention to these identified determinants, especially those that belong to the cause group. The outcomes of this study may aid management and policy makers in formulating strategies for effectively implementing remanufacturing methods within their organizations. Full article

In recent years, neural networks and cryptographic schemes have come together in war and peace; a cross-impact that forms a dichotomy deserving a comprehensive review study. Neural networks can be used against cryptosystems; they can play roles in cryptanalysis and attacks against encryption algorithms and encrypted data. This side of the dichotomy can be interpreted as a war declared by neural networks. On the other hand, neural networks and cryptographic algorithms can mutually support each other. Neural networks can help improve the performance and the security of cryptosystems, and encryption techniques can support the confidentiality of neural networks. The latter side of the dichotomy can be referred to as the peace. There are, to the best of our knowledge, no current surveys that take a comprehensive look at the many ways neural networks are currently interacting with cryptography. This survey aims to fill that niche by providing an overview on the state of the cross-impact between neural networks and cryptography systems. To this end, this paper will highlight the current areas where progress is being made as well as the aspects where there is room for future research to be conducted. Full article

For making full use of the discoloration function of electrochromic (EC) devices and better show the charge and discharge states of supercapacitors (SCs), electrochromic supercapacitors (ECSCs) have attracted much attention and expectations in recent years. The research progress of tungsten-oxide-based electrochromic supercapacitors (ECSCs) in recent years is reviewed in this paper. Nanostructured tungsten oxide is widely used to facilitate ion implantation/extraction and increase the porosity of the electrode. The low-dimensional nanostructured tungsten oxide was compared in four respects: material scale, electrode life, coloring efficiency, and specific capacitance. Due to the mechanics and ductility of nano-tungsten oxide electrodes, they are very suitable for the preparation of flexible ECSCs. With the application of an organic protective layer and metal nanowire conductive electrode, the device has higher coloring efficiency and a lower activation voltage. Finally, this paper indicates that in the future, WO₃-based ECSCs will develop in the direction of self-supporting power supply to meet the needs of use. Full article

Due to the shortage and high cost of shipping containers, manufacturers that distribute products for worldwide customers must optimize their delivery. Commonly, they complete their delivery schedule first before creating the production schedule. This article tries to address the scheduling problem arising in the process of creating the production schedule to meet the delivery schedule. The complexity of the situation is increased since there is concern about producing as few product types as possible in a single period and having a balance of production load over multiple periods. A mathematical programming model for addressing this production scheduling problem is proposed in the form of integer programming. This model can be solved using LINGO, a commercial mathematical programming software. Microsoft Excel^® spreadsheets are used to store both the data input and the output of the mathematical programming model. The advantages of using Excel are its easy-to-read format and familiarity with most users in various companies. Computational experiments in several cases show that the proposed method is effective to create the production and delivery schedule in various problem settings as expected. Full article