252 research outputs found
Sort by
Predicting Human Reliability based on Individual’s Resting Period: Effect of Physical Workload Rate
Full text linkWhen a person is exposed to a prolonged workload, he/she enters a fatigue phase, the indication is the decline of cognitive performance that leading to human error. As an integral part of a system, human contributes to system reliability; therefore, it plays an important role in potential failure. Those, it is necessary to investigate how human reliability relates to physical workload rate, in order to predict maximum work duration to eliminate potential failure. A physical experiment involving 20 participants was conducted to generate medium workload, followed by Stroop test to observe selective attention and cognitive control as a form of cognitive performance. The physical workload was observed through energy expenditure and oxygen consumption during physical activity, and cognitive performance through response error time on the Stroop test. The usage of Weibull distribution was aimed to obtain reliabilities for each participant. There was a decline in reliability for all participants from one test to the other. Based on scale and form parameters, the prediction of resting time was based on mean time to human error (MTTHE), and from this experiment, varied MTTHE from each participant were obtained. The variation was created by differences in physical performance, cognitive capabilities, and other contributing factors such as environment and time of the implementation of the experiment. From this research, it was evident that human reliability can be utilized to predict potential failure in humans, which then implies a preventive action is necessitated to prevent failure from manifesting in the shape of taking a break/rest or reducing work rhythm. The application of human reliability in human resource management can be directed towards fatigue management and operator-related operational management
Evaluation of Driving Comparative Life Cycle Cost Assessment of Conventional and Electric Motorcycles in Indonesia: Monte Carlo Analysis
Full text linkThe adoption of electric vehicles (EVs) is one of the solutions to reduce emission problems. Vehicle cost analysis is one of the keys to seizing the Indonesian market. As a consumer, it is not only the purchase price that needs to be considered, but the life cycle costs throughout ownership also need to be considered in the purchase. This study discusses the life cycle cost (LCC) of EVs in Indonesia, especially electric motorcycles (EMs), which will be compared with conventional motorcycles (CMs). In particular, this study aims to encourage the government's target for ownership of 2.1 million EMs in Indonesia by 2025. The novelty of this research is to develop a more comprehensive LCC model by considering the costs in terms of tangible and intangible to compare the two types of motorcycles using Monte Carlo simulation. This simulation is used to coordinate the behavioral uncertainty of motorcycle users. As a result, the value of an EM is more economical than CM for various users. The average value percentage of EMs is lower than CMs by 45% (IDR 30,6 million). In addition, several scenarios are also analyzed to maximize consumer welfare in Indonesia
Do Job Boredom and Distress Influence Self-Report Individual Work Performance? Case Study in an Indonesia Muslim Fashion Industry
Full text linkA creative and innovative workforce is a key determinant of the sustainability of the fashion industry in a highly competitive market. Such characteristics have been linked to employees’ well-being. This study aimed at examining to what extent the employees’ boredom, stress, and work performance levels in a medium-scale Muslim fashion Industry. We employed a cross-sectional study design by administering a set of questionnaires consisting of the Dutch Boredom Scale; Depression, Anxiety, and Stress Scale; and Individual Work Performance in a total sampling of 75 female workers. The association between key variables and demographic factors was analyzed using non-parametric tests while the relationship between boredom, stress, and work performance was analyzed using the regression. Less-educated employees reported more stress and lower work performance while their boredom levels were similar, compared to their counterparts. Job boredom and stress were higher among newly hired employees but no significant difference in self-reported productivity between the two job experience groups was observed. There are also no differences in job boredom, stress, and work performance between sales and non-sales groups. Our regression model shows that job boredom and stress were significant predictors to work performance after controlling age, education, job experience, and type of occupations. These findings support the importance of improving employees’ well-being for better individual performance which may, in turn, lead to any tangible organizational outcomes. Regardless of the case study design, our study may provide insights for other industrial sectors and beyond the context of small and medium enterprises
Integration of Analytic Network Process and PROMETHEE in Supplier Performance Evaluation
Full text linkSupplier performance evaluation is one of the important factors in the supply chain because it is one of the company's strategies for increasing customer satisfaction and also maintaining the company's services in meeting consumer demand. This study proposes the integration of the Analytic Network Process (ANP) and the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) to evaluate supplier performance. The integration of the two methods is proposed to obtain more complex assessment results because the combination of the two methods considers various criteria derived from ANP and various preferences from PROMETHEE, so both methods are very good to use instead of using just ANP or PROMETHEE or other methods. ANP exhibit more complex relationships between criteria and levels in the decision hierarchy, while PROMETHEE provides decision-makers with flexible and straightforward outranking to analyze multi-criteria problems. In this study, ANP is used to weight the criteria, and PROMETHEE is used to rank suppliers in evaluating supplier performance. Integrating these two methods provides more objective and accurate results in multi-criteria decision-making. The proposed method is validated by solving an industrial case of supplier evaluation problem using the real data from the skewer industry. Finally, some useful implications for managerial decision-making are discussed
Total Tardiness Minimization in a Single-Machine with Periodical Resource Constraints
Full text linkIn this paper we introduce a variant of the single machine considering resource restriction per period. The objective function to be minimized is the total tardiness. We proposed an integer linear programming modeling based on a bin packing formulation. In view of the NP-hardness of the introduced variant, heuristic algorithms are required to find high-quality solutions within an admissible computation times. In this sense, we present a new hybrid matheuristic called Relax-and-Fix with Variable Fixing Search (RFVFS). This innovative solution approach combines the relax-and-fix algorithm and a strategy for the fixation of decision variables based on the concept of the variable neighborhood search metaheuristic. As statistical indicators to evaluate the solution procedures under comparison, we employ the Average Relative Deviation Index (ARDI) and the Success Rate (SR). We performed extensive computational experimentation with a testbed composed by 450 proposed test problems. Considering the results for the number of jobs, the RFVFS returned ARDI and SR values of 35.6% and 41.3%, respectively. Our proposal outperformed the best solution approach available for a closely-related problem with statistical significance
Inventory Policy for Retail Stores: A Multi-Item EOQ Model Considering Permissible Delay in Payment and Limited Warehouse Capacity
Full text linkThe retail industry such as minimarkets has many products consisting of several types of products that have expiration dates. Their warehouses have limited capacity, making it difficult to make decision about optimum inventory. Most of the suppliers will give permissible delay in payment, that can be used to increase income potential through earned by considering the risk of fines imposed if payments are exceeded and help companies raise capital before generating sales. These three factors must be considered when developing the inventory model. The purpose of this study is to develop a multi-item inventory model by considering perishable or damaged products, permissible delay in payment in limited warehouse. Model development is carried out in 2 stages. The first stage was the development of a multi-item EOQ model by considering product defects and permissible delay in payment. The second stage model is by adding a capacity constraint factor to the model. The results obtained are getting the optimal order quantity by considering the number of product types, product damage factors, late payments in limited warehouses, the best ordering policy can be found, and it is known that the total inventory costs to changes in parameters are good and sensitive to changes in percentage, interest percentage, payment allowances, and warehouse capacity through sensitivity tests
Application of Genetic Algorithms to Solve MTSP Problems with Priority (Case Study at the Jakarta Street Lighting Service)
Full text linkTransportation is one thing that is very important and is the highest cost in the supply chain. One way to reduce these costs is to optimize vehicle routes. The Multiple Traveling Salesman Problem (MTSP) and Capacitated Vehicle Routing Problem (CVRP) are models that have been extensively researched to optimize vehicle routes. In its development based on actual events in the real world, some priorities must be visited first in optimizing vehicle routes. Several studies on MTSP and CVRP models have been conducted with exact solutions and algorithms. In a real case in the Jakarta City Street Lighting Section, the problem of determining the route in three shifts is a crucial problem that must be resolved to increase worker productivity to improve services. Services in MCB (Miniature Circuit Breaker) installation and maintenance activities for general street lights and priority is given to light points that require replacement. Because, in this case, the delivery capacity is not taken into account, the priority of the lights visited is random, and the number of street light points is enormous, in this study, we use the MTSP method with priority and solve by a genetic algorithm assisted by the nearest neighbor algorithm. From the resolution of this problem, it was found that the travel time reduction was 32 % for shift 1, 24 % for shift 2, and 23 % for shift 3. Of course, this time reduction will impact worker productivity so that MCB installation can be done faster for all lights and replace a dead lamp
Enhancement Material Removal Rate Optimization of Sinker EDM Process Parameters Using a Rectangular Graphite Electrode
Full text linkThis article discusses the optimization of sinker electrical discharge machining (sinker EDM) processes using SPHC material that has been hardened. The sinker EDM method is widely employed, for example, in the production of moulds, dies, and automotive and aeronautical components. There is neither contact nor a cutting force between the electrode and the work material in sinker EDM. The disadvantage of the sinker EDM is its low material removal rate. This work aims to optimize the material removal rate (MRR) using graphene electrodes in a rectangular configuration. The SPHC material was selected to determine the optimum MRR model of the sinker EDM input parameter. The Taguchi experimental design was chosen. The Taguchi technique used three input parameters and three experimental levels. Pulse current (I), spark on time (Ton), and gap voltage were among the input parameters (Vg). The graphite rectangle was chosen as an electrode material. The input parameter effect was evaluated by S/N ratio analysis. The result showed that pulse current has the most significant impact on material removal rate in the initial study, followed by spark on time and gap voltage. All input parameters are directly proportional to the MRR. For optimal material removal rate, the third level of pulse current, spark on time, and gap voltage must be maintained. In addition, the proposed Taguchi optimization model could be applied to an existing workshop floor as a simple and practical electronic tool for predicting wear and future research
Factors Affecting Millennials Purchase Intention and Sustainable Consumption of Organic Food
Full text linkOrganic food refers to the products produced in conventional way, without hazardous materials. Millennials are the generation most attractive to organic food market. This study aims to analyze whether factors such as environmental knowledge, environmental awareness, health awareness and social awareness affecting purchase intention and sustainable consumption of the millennials towards organic food. A questionnaire used to evaluate the relationships between the six constructs. The findings showed that 340 respondents have met requirements for analysis. The method used was Structural Equation Model (SEM). The research findings find out the main factors that influence purchase intention and sustainable consumption of the millennial generation towards organic food. This research is expected to help non-governmental organizations increase purchase intention and investigate factors that influence the sustainable consumption of organic food in the millennial generation. This research has implications for the organic food industry, especially organic food producers, namely by applying the packaging sustainability method to reduce waste in the environment
Grey-based Taguchi Method to Optimize the Multi-response Design of Product Form Design
Full text linkThis paper presents a multi-response optimization method that uses the grey-based Taguchi method as the integrative product form design optimization method, and it serves as a tool for product form design to determine the optimal combination of design parameters in Kansei engineering (KE). This method is unique in that it combines the Taguchi method (TM) and grey relational analysis (GRA), allowing it to take advantage of the benefits of both methods. The TM is used to design experiments and generate combinative product form design samples which can be used to improve product quality. The GRA is applied to multi-response optimization problems. Factor effect analysis and analysis of variance (ANOVA) are used to determine which combinations of design parameters will result in the optimal product design. To demonstrate the applicability of the grey-based TM, a case study of a car form design is presented, and a confirmation test is performed to verify the performance of the optimal product design. The results show that the grey-based TM can deal with optimization problems with multiple Kansei responses and determine an optimal car form design that is representative of the consumers' perception in a systematic manner. The confirmation test results also show that the optimal product design generated by the grey-based TM can be used to improve the overall quality of a product form