International Journal of Industrial Engineering and Productional Research
Volume:34 Issue: 2, Jun 2023

In some applications, the number of quality characteristics is larger than the number of observations within subgroups. Common multivariate control charts to monitor the variability of such high-dimensional processes are unsuitable because the sample covariance matrix is not positive semi-definite and invertible. Moreover, the impact of gauge imprecision on detection capability of multivariate control charts under high-dimensional setting has been clearly neglected in the literature. To overcome these shortcomings, this paper develops a ridge penalized likelihood ratio chart for Phase II monitoring of high-dimensional process in the presence of measurement system errors. The developed control chart departures from the assumption of sparse variability shifts in which the assignable cause can only affects a few elements of the covariance matrix. Then, to compensate for the adverse impact of gauge impression, the developed chart is extended by employing multiple measurements on each sampled item. Simulation studies are carried out to study the impact of imprecise measurements on detectability of the developed monitoring scheme under different shift patterns. The results show that the gauge inability negatively affects the run-length distribution of the developed control chart. It is also found that the extended chart under multiple measurements strategy can effectively reduce the error impact.

Given the increasing human need for health systems and the costs of using such systems, the problem of optimizing health-related systems has attracted the attention of many researchers. One of the most critical cases in this area is the operating room scheduling. Much of the cost of health systems is related to operating room costs. Therefore, planning and scheduling of operating rooms can play an essential role in increasing the efficiency of health systems as well as reducing costs. Given the uncertain factors involved in such matters, attention to uncertainty in this problem is one of the most critical factors in the results. In this study, the problem of the daily scheduling of the operating room with uncertain surgical time was investigated. For minimizing overhead costs and maximizing the number of surgeries to reduce patients' waiting time, after introducing a mathematical model, a chance-constrained programming approach is used to deal with its uncertainty. In this study, also, a harmony search algorithm is proposed to solve the model because of its NP-Hardness. By performing the numerical analysis and comparing the presented algorithm result with a genetic algorithm, the results show that the proposed algorithm has a better performance.

The productivity and flexibility of current manufacturing systems (dedicated and flexible production systems) are no longer competitive as products are developed and brought to market in increasingly shorter cycles. As a result, a new generation of reconfigurable manufacturing systems (RMS) has emerged that should be responsive enough to cope with sudden market changes while maintaining excellent product quality at low prices. These systems could also leverage technologies at the heart of Industry 4.0, such as artificial intelligence and machine learning, the Internet of Things (IoT), and digital twins, to create a smart, dynamic, and most importantly, reconfigurable factory, dubbed the Reconfigurable Factory 4.0. This study provides an organized and up-to-date systematic review of the literature on reconfigurable manufacturing systems, from design to simulation, and from automation to the fourth industrial revolution (Industry 4.0) highlighting the application areas as well as the significant approaches and technologies that have contributed to the development of a Reconfigurable Factory 4.0.

In the field of scheduling and sequence of operations, one of the common assumptions is the availability of machines and workers on the planning horizon. In the real world, a machine may be temporarily unavailable for a variety of reasons, including maintenance activities, and the full capacity of human resources cannot be used due to their limited number and/or different skill levels. Therefore, this paper examines the Dual Resource Constrained Flexible Job Shop Scheduling Problem (DRCFJSP) considering the limit of preventive maintenance (PM). Due to various variables and constraints, the goal is to minimize the maximum completion time. In this regard, Mixed Integer Linear Programming (MILP) model is presented for the mentioned problem. To evaluate and validate the presented mathematical model, several small and medium-sized problems are randomly generated and solved using CPLEX solver in GAMS software. Because the solving of this problem on a large scale is complex and time-consuming, two metaheuristic algorithms called Genetic Algorithm (GA) and Vibration Damping Optimization Algorithm (VDO) are used. The computational results show that GAMS software can solve small problems in an acceptable time and achieve an accurate answer, and also meta-heuristic algorithms can reach appropriate answers. The efficiency of the two proposed algorithms is also compared in terms of computational time and the value obtained for the objective function.

The capacitated arc routing problem (CARP) is an important vehicle routing problem with numerous real world applications. In this paper, an extended version of CARP, the capacitated arc routing problem with priority edges is presented. The new introduced CARP is more general and closer to reality, and thus is more worthwhile to be solved. In this problem, a set of important priority edges is given and the task is to service of all edges with positive demand in such a way that the higher priority edges are visited as soon as possible. The capacitated arc routing problem with priority edges is an NP-hard problem, so we propose an algorithm that can quickly obtain optimal or near-optimal solution for the defined problem. Another important contribution is that our proposed algorithm is fast and easy to apply. In this paper, through some examples, efficiency of the proposed algorithm has been showed and some guidelines for the future studies have been given

The research aimed to determine the best components of Malaysia-Counsellor Performance Indicator in measuring the counsellor’s performance in Malaysia. This is the first development phase of the M-CPI. This study involved two type of research designs; quantitative and qualitative approach (Mixed Method). The quantitative data has been obtained from 102 respondents and interview with eight (8) counsellors from different settings. Stratified random sampling technique was utilized to select the respondent and proportional stratification was used to determine the sample size of each stratum. A Need Assessment questionnaire has been developed by the researchers as well as the protocol interview. These two instruments were developed based on the literature reviews of previous instruments that have been invented from the western perspective to measure the performance and competency of counsellors. The results of the study were analysed using the descriptive analysis and thematic analysis. Findings have shown that majority counsellors possessed knowledge and skills in conducting counselling session. Most counsellors in the study demonstrated good interpersonal relationship, interaction, multicultural and religiosity and ethics and professionalism. Through this study, to measure the performance of counsellors, the researchers have found that they must equip themselves with knowledge, skill, interpersonal relationship, interaction, multicultural and religiosity and ethics and professionalism aspects. Based on the interview data, there were new  components that have been identified to be added in the Malaysia Counsellor Performance Indicator (M-CPI) which include knowledge (theoretical and knowledge transfer), skills (case management, practical skills and academic/professional writing), interpersonal relationship and interaction, cultural and religiosity, professional roles and expertise, ethics and legality, attitudes and personality, referral and articulate philosophy of profession. In future, research should also focus on the validity and reliability of the components listed in the M-CPI.

Sales and operations planning (S&OP) is considered as an important tool at the planning strategic level. Its models vary depending on industries. The Asian model is known to be very developed. Having several parameters, the Asian model proves to be an effective tool, precisely for the study of capacity. However, after several searches made in various databases, we did not find any concrete model actually used in industry and whose parameters are presented and which defines the analysis logic to better align supply and demand. In this article, we will carry out various simulations on the basis of the data of a model of sales and operations planning used in a wire harnesses factory, in order to explain the decision-making process during S&OP meetings. The parameters of the model and the various constraints that were facing the sales and operations planning team are presented and discussed as well as the financial consequences of certain decisions. As a result of this study, we can notice that S&OP is indeed a powerful tool that makes it possible to detect in advance the various constraints whose resolution concludes in an optimal alignment between customer demand and factory capacity.

Leadership is an important factor in the social relationships of line in the workplace. Consider as the main factor that affects and forms group behavior in every organization known since the time of ancient. Practically, it involves employees who appreciate the feeling of a strong commitment to accomplishing organizational goals and long-term objectives inside the company. The objective of this study was to determine the influence of micromanagement leadership style on employee perception on job satisfaction in the manufacturing industry at Malacca. Thus, leadership styles can fortunately, influence or greatly affect job satisfaction in workplace performance. Micromanagement leadership style has comparatively more negative effects on an employee’s behavior and commitment towards the effort in the workplace. This creates a sense of perceived stress managing to behave in an ineffective approach. A descriptive study was used to understand employee perceptions of micromanage leadership styles that affect job satisfaction. A total of 97 respondents among manager level from the Malacca state of Malaysia’s manufacturing industry was collected with the level of manager’s range 27 to 55 years of age. Situational leadership theory conducted this study to discover how a micromanaged leadership style influences employee perception that impacts an employee’s job satisfaction. The primary research question focused on positive and negative employee perceptions related to managers’ leadership behaviors and attributes. The study found that employee perception by micromanaging leadership style affects an employee’s job satisfaction with the correlation coefficient between overall job satisfaction and main factors for job satisfaction recognition at work and personal growth were (0.79) and (0.85) respectively. There were statistically significant differences in age group, working experience and position (P<0.05). The result showed Cronbach alpha 0.708 internal consistency acceptable affect the variables. Micromanagement had reduced productivity, lower morality, loss of trust, less teamwork involvement, less personal growth and reduced innovation. Therefore, consideration of an employee's knowledge, skills, experience, attitude, and motivation is essential for job satisfaction to enhance high productivity and efficiency.

This paper proposes a data-driven method, using Artificial Neural Networks, to price financial options and compute volatilities, which speeds up the corresponding numerical methods. Prospects of the Stock Market are priced by the Black Scholes model, with the difference that the volatility is considered stochastic. So, we propose an innovative hybrid method to forecast the volatility and returns in Stock Market indices, which declare a model with a generalized autoregressive conditional heteroscedasticity framework. In addition, this research analyzes the impact of COVID-19 on the option, return, and volatility of the stock market indices. It also incorporates the long short-term memory network with a traditional artificial neural network and COVID-19 to generate better volatility and option pricing forecasts. We appraise the models' performance using the root second-order quadratic function means of the out-of-sample returns powers. The results illustrate that the autoregressive conditional heteroscedasticity forecasts can serve as informative features to significantly increase the predictive power of the neural network model. Integrating the long short-term memory and COVID-19 is an effective approach to construct proper neural network structures to boost prediction performance. Finally, we interpret the sensitivity of option prices concerning the market or model parameters, which are essential in practice.

Numerous studies have examined the increasing number of virtual team communication usage, especially during the Covid-19 pandemic. However, little research has been conducted on the factors affecting its effectiveness in improving task performance, seeing the virtual team's rapid development today. Therefore, this study examines the effect of direct and indirect employee preferences and organizational support on task performance through virtual teamwork communication. The research method used was a survey of 156 employees in the fields of education, telecommunications, transportation, and health in Banjarmasin city, who work from home, interact with colleagues who also work from home, and with colleagues who work in the office. The analysis was carried out using path analysis. The results showed that employee preferences and organizational support directly affected task performance. Virtual team communication can mediate the influence of employee preferences and organizational support on task performance. The research implies that virtual team communication that runs well can improve work performance. Therefore, it requires collaborative support, both from individuals and the organization.

Advances in the field of technology can be applied or utilized to streamline the production process, making it easier for producers to make a product. The existence of technological changes also affects the output produced by the home industry. Data collected using questionnaires then analyzed. This study aims to identify the role of technology adoption using Technology Acceptance Model (TAM) analysis in home industries. This type of research is quantitative approach research with positivistic methods and associative descriptive approaches. The sample in this study was home industry owners totaling 168 people obtained by incidental sampling technique. The results showed that perceived useful, perceived case of use, attitude and intention factors affect actual system use in the form of technology adoption in the home industry.

This paper presents a model for minimizing the makespan in the flow shop scheduling problem. Due to the impact of increased workloads, flow shops are becoming more popular and widely used in industries. To solve the challenge of minimizing makespan, a Hybrid-Heuristic-Metaheuristic-Genetic-Algorithm (HHMGA) is proposed. The proposed HHMGA algorithm is tested using the simulation software and demonstrated with steel industry data. The results are compared with those of the best available flow shop problem algorithms such as Palmer’s slope index, Campbell-Dudek-Smith (CDS), Nawaz-Enscore-Ham (NEH), genetic algorithm (GA) and particle swarm optimization (PSO). According to empirical results and relative differences from the lower bound, the proposed technique outperforms the three heuristics and two metaheuristics algorithms in three of six cases, while the remaining three produce the same results as the NEH heuristic. In comparison to the steel industry's regular job scheduling technique, the simulation model based on HHMGA can save 4642 hours. It was discovered that the suggested model enhanced the job sequence based on the makespan requirements.

This research study was conducted to determine the effect of knowledge management strategies (knowledge creation, knowledge acquisition, knowledge sharing, and knowledge application) on green innovation practices. The instrument adapted from the previous study was distributed among employees in Public Sector. The sampling technique employed was purposive sampling. A total of 256 data have been successfully collected and analyzed. The finding of this study indicates knowledge management strategies which are knowledge creation, knowledge acquisition, knowledge sharing, and knowledge application have a positive and significant influence on green innovation practices, where the knowledge sharing factor is the most influential factor that affects green innovation practices.