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International Journal of Industrial Optimization (IJIO)
Published by Universitas Ahmad Dahlan
ISSN : 27146006     EISSN : 27233022     DOI : https://doi.org/10.12928/ijio.v1i1.764
Core Subject : Science, Engineering,
Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering
The Journal invites original articles and not simultaneously submitted to another journal or conference. The whole spectrums of Industrial Engineering are welcome but are not limited to Metaheuristics, Simulation, Design of Experiment, Data Mining, and Production System. 1. Metaheuristics: Artificial Intelligence, Genetic Algorithm, Particle Swarm Optimization, etc. 2. Simulations: Markov Chains, Queueing Theory, Discrete Event Simulation, Simulation Optimization, etc. 3. Design of experiment: Taguchi Methods, Six Sigma, etc. 4. Data Mining: Clustering, Classification, etc. 5. Production Systems: Plant Layout, Production Planning, and Inventory Control, Scheduling, System Modelling, Just in Time, etc.
Arjuna Subject : Teknik (semua kategori) - Tenik Industri dan Manufaktur
Articles 94 Documents
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Research on bearing fault diagnosis technology based on machine learning Xia, Yu; Guo, XiaoJun; Su, ErChuan; Kong, LingPei
International Journal of Industrial Optimization Vol. 5 No. 1 (2024)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v5i1.8106

Abstract

As industrial equipment complexity continues to rise, the importance of bearings within these systems has become more critical, given their pivotal role in equipment functionality. Bearing faults can result in severe production accidents and safety issues. Hence, there is an urgent need for advanced bearing fault diagnosis technology. This study concentrates on rolling bearings, analyzing their structural characteristics and key parameters to classify fault types—inner race faults, rolling element faults, and outer race faults. Utilizing a dataset of 80 sets of bearing factory data, time and frequency domain analyses are conducted, establishing seven feature parameters (five in the time domain and two in the frequency domain). This data is organized into a 7-dimensional matrix for subsequent analysis and model development. The K-Means algorithm is chosen for its effectiveness in automatically recognizing fault patterns in rolling bearings. Training on the 7-dimensional matrix identifies four clustering centers corresponding to normal conditions, inner race faults, rolling element faults, and outer race faults. The fault diagnosis system is implemented using Python, and algorithm optimization improves efficiency. The study concludes with insights drawn from the analysis and proposes optimization methods, which contributing to advancing bearing fault diagnosis technology, particularly addressing industrial equipment reliability and safety concerns.
Multi-objective elitist spotted hyena resource optimized flexible job shop scheduling Senthilvel, A. N.; Hemamalini , T.; Geetha, G.
International Journal of Industrial Optimization Vol. 5 No. 1 (2024)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v5i1.8743

Abstract

The job shop scheduling problem (JSSP) has drained a lot of consideration since it is one of the most important optimization problems in the manufacturing domain. The scheduling method is crucial for optimizing the objective of minimizing makespan among thousands of jobs, but evaluating machine capacity for achieving this goal remains challenging despite the development of various population-based optimization algorithms for job shop scheduling problems. To improve the efficiency of Job shop scheduling, a novel Multi-objective Elitist Spotted Hyena Monotonic Scheduling (MESHS) technique is introduced. The proposed MESHS technique includes two major processes: machine selection and operation sequences. The number of jobs is considered for solving the scheduling problem. First, the machine selection is performed by applying the Multi-objective Elitist Spotted Hyena optimization technique. The optimization technique selects the optimal machines parallelly based on multiple objective functions such as energy consumption, CPU utilization, and job completion time. The fitness of every machine is calculated based on these multiple objective functions using Levenberg–Marquardt method. Then the Elitist strategy is applied to select the optimal machine based on fitness. After the machine selection, the rate-monotonic preemptive scheduling is modeled to provide a robust operation sequence by assigning high-priority jobs to the optimal machines. As a result, efficient job scheduling is achieved with minimum time. Finally, the experimental valuation is carried out using a benchmark OR-Library dataset with different factors such as job shop scheduling efficiency, job scheduling time, makespan, and memory consumption concerning a number of jobs.
Modeling and simulation of friction stir welding process: A neural approach Chaturvedi, Devendra Kumar; Suri , Atul
International Journal of Industrial Optimization Vol. 5 No. 1 (2024)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v5i1.9010

Abstract

Friction Stir Welding (FSW) stands out as a groundbreaking method in solid-state joining for aluminum alloys, presenting an innovative way to achieve joints of exceptional quality. This research delves into the application of FSW for bonding, focusing on plates that are 6mm thick and made from aluminum alloys Al6063, Al5083, and AL6061, aiming to produce a variety of FSW joints. To evaluate the quality of these joints, the study compares mechanical properties such as tensile strength, safe bending strength, and bending toughness necessary for achieving a 90° bend. The investigation leverages welding data to formulate a neural model, starting with using a conventional feedforward neural model (CFNM). It tackles the limitations of CFNM, including its intensive training requirements and the challenge of dealing with unknown configurations, by proposing a new, more adaptable neural network model known as FNNM. When comparing the two models, it becomes evident that CFNM is constrained by a root mean square error (RMSE) of 7-15%, whereas FNNM marks a significant improvement with a minimal RMSE of 1-3%. This indicates that FNNM improves accuracy and effectively navigates the complexities of modeling with unknown parameters. Through this study, insightful contributions are made to understanding FSW in joining aluminum alloys and developing an advanced neural model capable of predicting the outcomes of welding with greater precision.
A heterogeneous fleet electric vehicle routing model with soft time windows Kinanti, Yoanda Astri Ayu; Bakhtiar, Toni; Hanum, Farida
International Journal of Industrial Optimization Vol. 5 No. 2 (2024)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v5i2.9014

Abstract

The emergence of electric vehicles in distribution and logistics activities has brought significant benefits due to their unique characteristics, such as energy-efficient and lower carbon emissions. In the perspective of vehicle routing problem, electric vehicles pose challenging constraints regarding the limited battery capacity, and thus their traveling ranges, and the availability of charging stations. In this paper, we propose a model of the fleet electric vehicle routing problem (EVRP) with soft time windows, where a mixed integer linear programming framework is implemented in model formulation. The objective of mathematical programming is to minimize the total operational cost, which consists of a fixed cost, a traveling cost, a battery charging cost, and probably a penalty cost due to time window violation. We implement our model in two simple cases, namely homogeneous and heterogeneous fleets EVRPs, characterized by loading and battery capacities. Each case consists of one depot, five customers, two electric vehicles, and two charging stations. Optimal routes are obtained using the well-known branch-and-bound method under Lingo 17.0. It is found that the existence of charging stations may affect the routes selection and the implementation of soft time windows rather than hard time windows has been proven to increase the feasibility of routing problem.
Optimizing welding parameters for high deposition efficiency in waam by using the taguchi method Abdullah, Ahmad Baharuddin; Wani, Zarirah Karrim; Jaafar, Noor Azam
International Journal of Industrial Optimization Vol. 5 No. 2 (2024)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v5i2.9046

Abstract

Wire arc additive manufacturing (WAAM) is a type of additive manufacturing technology that offers high flexibility in shaping products and is cost-effective due to its low material consumption and rapid time to market. Material consumption can be evaluated by assessing deposition efficiency during welding. The efficiency of a deposited metal depends on various processes and welding parameters, including travel speed, wire feed rate, voltage, distance of the torch from the base, and many others. Therefore, process capability can be efficiently achieved by crucially determining the key parameters that have the most significant effect. In this study, the main objective is to determine the most significant parameters to obtain the optimum deposition efficiency of a gas metal arc welding-based 3D welding machine. The Taguchi experimental design method is used to determine the optimal welding parameters. Results showed that the distance of the torch from the base is the most significant parameter, followed by welding speed and wire feed rate. The observation is validated via a confirmation test.
Decision support system in determining the location of new supermarket branches using the copras method Tsaqila, Siti Lathifah; Winiarti, Sri; Widaningrum, Ida
International Journal of Industrial Optimization Vol. 5 No. 1 (2024)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v5i1.9061

Abstract

Supermarkets are one of the ideal and profitable retail business sectors to try because they are located in various urban and rural areas. This causes many people to be interested in setting up a supermarket. However, determining a strategic location is not easy and requires many strategic location considerations. The research objective is to develop a Decision Support System (DSS) to determine the location of new supermarket branches using the Complex Proportional Assessment (COPRAS) method, which is expected to be helpful for management and supermarket partners as a business strategy. The COPRAS method excels in calculating alternative utilities and selecting the best alternative. There are nine criteria (land rental price, distance to competitors, security, distance to education, warehouse distance, cleanliness, land area, building price, crowd) and five alternative locations (Juanda, Hos Cokroaminoto, Bayangkara, Batoro Katong, Sumoroto) are considered. This research created a web-based DSS that selects the best location for supermarket, with Juanda (A1) ranked first and scored 100, followed by Somoroto (location A5) with a score of 99.861, Bayangkara (A3) with a score of 97.099, Batoro Katong (A4) with a score of 91.293, and HOS Cokroaminoto (A2) with a score of 88.877. From the results of the COPRAS calculation, it can be concluded that Juanda is the best location to build a new supermarket branch location. This result provides a valuable tool for management and supermarket partners seeking to make informed decisions about branch expansion strategies.
Sustainable waste solutions: Optimizing location-allocation of 3R waste management sites in Gondokusuman, Yogyakarta, Indonesia through multi-maximal covering location approach Leuveano, Achmad Chairdino; Kasih, Puji Handayani; Ridho, Muhammad Ihsan; Lisan, Ahmad Rif’an Khoirul; Muhamed , Ariff Azly; Rafique , Muhammad Zeeshan
International Journal of Industrial Optimization Vol. 5 No. 1 (2024)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v5i1.9251

Abstract

Developing a Multi-Maximal Covering Location Model (MMCLM) for waste management in Gondokusuman Sub-district, Yogyakarta, Indonesia, is urgently needed. The closure of the Piyungan landfill has resulted in the need for additional Reduce, Reuse, and Recycle Waste Management Sites (3R-WMSs) to reduce waste that the landfill cannot accommodate. The primary objective of this model is to optimize the location and allocation of demand volume nodes, representing the resident population, to a specific set of 3R-WMS. These demand nodes are located at different distances from 3R-WMSs, including high and low coverage areas. The research in the Gondokusuman Sub-district employed MMCLM with facility capacity constraints and was developed using mixed integer linear programming methodology. The study identified five optimal locations for a 3R-WMS establishment that comprehensively cover all demand nodes (15301) and population clusters (45903) in the sub-district, including both high (5085) and low coverage areas (10216). This research represents a significant step forward in developing a sustainable environment by ensuring easy access to reducing, reusing, and recycling-based waste management facilities for residents.
Prediction analysis of retail store sales level using neural network algorithm method based on customer segments Yuniar, Mylenia Martina; Ambarwati, Rita
International Journal of Industrial Optimization Vol. 5 No. 2 (2024)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v5i2.9889

Abstract

Marketing activities are of significant importance to business operations, as they are uniquely positioned to provide value to consumers. The marketing mix represents one of the strategic approaches employed to attain these organizational objectives. However, the company's sales data is only available for consultation in the archives. By understanding customer preferences and requirements, the company can readily develop an effective marketing strategy to compete with similar businesses. Accordingly, this study employs the neural network methodology to forecast sales based on the company's historical sales data. The research method employs a neural network due to its capacity for processing substantial data sets with flexibility. Moreover, the Root Mean Square Error (RMSE) must be employed to ascertain the precision of the utilized model. The findings of this study indicate that the discrepancy between the actual and predicted values is minimal, suggesting that the model is able to accurately represent the data. Similarly, the results of the RMSE (Root Mean Square Error) demonstrate that the model's accuracy is improving, with minimal values observed in each segment. A 4P marketing mix strategy may be employed to enhance the company's sales potential. Based on the findings of the research, it can be posited that the results of the prediction data set, the visual prediction results, and the RMSE using the Neural Network method can be utilized effectively and accurately to forecast sales and assist company owners and management in considering target sales levels in the future.
Implementation of 5s and kaızen methods for developing a novel wage assessment method in a steel construction factory: an application in Turkey Önay, Mehmet Burçin; Seçkiner, Serap Ulusam
International Journal of Industrial Optimization Vol. 5 No. 2 (2024)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v5i2.10023

Abstract

The study aims to implement the 5S (seiri, seiton, seiso, seiketsu, and shitsuke) method and KAIZEN for emphasizing the troubles and defective products, establishing work standards, implementing fair wage assessments based on job analysis and job evaluations in a steel construction factory. A more objective wage assessment method is developed, and workers' unrest can be resolved fairly. 5S and KAIZEN studies have been applied for two years in a steel construction factory. Then, the evaluation of success factors within the internal structure of wage brackets utilized last year's 5S scores to enhance employees' confidence in the objectiveness of the wage system assessment. A reformer method for assessing wages has been created and implemented, integrating lean manufacturing principles and a job analysis and evaluation system. The framework has been tested and implemented only for a steel construction factory. In the future, studies could be conducted to assess different sector factories. The proposed framework has been successfully implemented in a medium-large scale manufacturing factory. A novel wage assessment framework that involves lean application studies integrated into the job evaluation method has been proposed in a medium-sized manufacturing factory.
Development of genetic algorithm for human-robot collaboration assembly line design Ma'ruf, Anas; Budhiarti, Diniarie
International Journal of Industrial Optimization Vol. 5 No. 2 (2024)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v5i2.10027

Abstract

An assembly line requires flexibility due to a shorter product life cycle. A way to increase flexibility is to utilize collaborative robots or cobots. Due to frequent product changes, redesigning an assembly line requires an efficient algorithm. This research aims to develop a genetic algorithm (GA) for solving a human-cobots assembly line design. The setup time of cobots is considered due to the flexibility of conducting multiple tasks by exchanging tools / end-effectors. The main contribution of the research is the efficient GA for solving assembly lines considering setup time. Secondly, the study proposed an upper limit parameter that enables faster computation without sacrificing the quality of the solution. The computational results showed that the algorithm could achieve an optimal solution with the number of tasks less than 35. Experiments of several data prove the proposed GA obtained solutions with an average gap of 3.83% to the optimal solution. Also, a faster computation time with an average difference of 64.66%. The proposed GA obtained a reasonable solution with fast computing time that helps improve efficiency and effectiveness in decision-making related to frequent redesigning of assembly lines.

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