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Hapsoro Agung Jatmiko
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Universitas Ahmad Dahlan, 4th Campus Jl. Ringroad Selatan, Kragilan, Tamanan, Banguntapan, Bantul, Yogyakarta, Indonesia 55191 Phone: +62 (274) 563515, 511830, 379418, 371120 ext. 4902, Fax: +62 274 564604
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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 6 Documents
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Search results for , issue "Vol. 5 No. 1 (2024)" : 6 Documents clear
Queuing analysis and optimization of public vehicle transport stations: A case of South West Ethiopia region vehicle stations Alem, Mequanint Birhan
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.v5i1.7963

Abstract

Modern urban environments present a dynamically growing field where, notwithstanding shared goals, several mutually conflicting interests frequently collide. However, it has a big impact on the city's socioeconomic standing, waiting lines and queues are common occurrences. This results in extremely long lines for vehicles and people on incongruous routes, service coagulation, customer murmuring, unhappiness, complaints, and looking for other options, sometimes illegally. The root cause is corruption, which leads to traffic jams, stops and packs vehicles beyond their safe carrying capacity, and violates passengers' human rights and freedoms. This study focused on optimizing the time passengers had to wait in public vehicle stations. This applied research employed both data-gathering sources and mixed approaches. Then, 166 samples of key informants of transport stations were taken using the Slovin sampling formula. The time vehicles, including the drivers and auxiliary drivers ‘Weyala', had to wait was also studied. To maximize the service level at vehicle stations, a queuing model was subsequently devised ‘Menaharya’. Time, cost, and quality encompass performance, scope, and suitability for the intended purposes. The study also focused on determining the minimal response time required for passengers and vehicles queuing to reach their ultimate destinations within the transportation stations in Tepi, Mizan, and Bonga. A new bus station system was modeled and simulated by Arena simulation software in the chosen study area. 84% improvement on cost reduced by 56.25%, time 4 hours to 1.5 hours, quality, safety and designed load performance calculations employed. Stakeholders are asked to implement the model and monitor the results obtained.
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.
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.

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