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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 7 Documents
 1 
Search results for , issue "Vol. 4 No. 2 (2023)" : 7 Documents clear
Experimental design of steel bearings and ceramic bearings to find efficient energy consumption Suhariyanto Suhariyanto; Heru Mirmanto; Azlan Arifin Khan; Rohmadoni Rohmadoni
International Journal of Industrial Optimization Vol. 4 No. 2 (2023)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v4i2.7137

Abstract

A bearing is a vital machine component that supports shafts, enabling smooth rotation and minimizing friction. The level of friction is determined by the coefficient of friction, which varies based on the bearing material. In this study, we evaluated two types of bearings: steel and ceramic, with the aim of identifying the one with the lowest frictional force and, consequently, the least input power required. To conduct a comprehensive comparison, we performed comparative tests on the Nogogeni Evo V vehicle, analyzing the energy consumption impact of both steel and ceramic bearings. The tests involved measuring the input power of each bearing type at various throttle openings, ranging from 10% to 100%. The results revealed that ceramic bearings exhibited superior energy efficiency compared to their steel counterparts. At all throttle openings, the ceramic bearings consistently demanded lower input power, indicating their higher efficiency. For instance, at 100% throttle opening, the input power for steel bearings was 17,939 watts, while ceramic bearings required only 17,290 watts, representing a 3.6% reduction. Moreover, ceramic bearings achieved higher rotation speeds, with the ceramic bearing rotating at 598 rpm, a 3.5% increase compared to the steel bearing's 577 rpm. Based on these findings, it can be concluded that the implementation of ceramic bearings would significantly enhance the energy efficiency of the Mobil Nogogeni Evo V electric motor. Therefore, for improved performance and reduced energy consumption, we recommend the incorporation of ceramic bearings in the vehicle's design.
Delivery service order policy with the sharing economy concept using a discrete event simulation system Farida Nurmala Sihotang
International Journal of Industrial Optimization Vol. 4 No. 2 (2023)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v4i2.7483

Abstract

People's lifestyle, especially in shopping, has shifted from offline shopping, such as in supermarkets, traditional markets, stalls, and so on, to online shopping. Therefore, online business (e-commerce) has increased, resulting in a surge in the business potential of freight forwarder companies. When there is an increase in demand for the delivery of goods by sellers to consumers, companies need to make adjustments to improve their performance. This paper proposed the sequence of goods delivery services for XYZ companies by considering dynamic requests and conditions that vary discretely over time. This paper is based on a case company in Bandung, Indonesia. The method employed queuing models and discrete event simulations using hypothetical data with performance criteria to minimize the total cost of shipping goods. Simulations are carried out using one courier and one zone to compare customer service determination algorithms, namely first-come, first-served, proximity, and predictive control models. The simulation results show that the proximity algorithm produces a minimum total cost of Rp 1,785,749, the smallest cost compared to using first-come, first-served and predictive control models, respectively IDR  2,782,389, and IDR  2,639,291. Then, the Annova test was conducted, which provided information that one policy differed significantly from another, and a Turkey test was carried out, showing that the proximity algorithm produces better performance than other algorithms. The contribution of this paper is to present that the delivery service employed by the company provided a minimum total cost.
Dynamic small-series fashion order allocation and supplier selection: a ga-topsis-based model Nitin Harale; Sébastien Thomassey; Xianyi Zeng
International Journal of Industrial Optimization Vol. 4 No. 2 (2023)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v4i2.7640

Abstract

The fashion industry is currently confronted with significant economic and environmental challenges, necessitating the exploration of novel business models. Among the promising approaches is small series production on demand, though this poses considerable complexities in the highly competitive sector. Traditional supplier selection and production planning processes, known for their lengthy and intricate nature, must be replaced with more dynamic and effective decision-making procedures. To tackle this problem, GA-TOPSIS hybrid model is proposed as the methodology. The model integrates Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) evaluation into the fitness function of Genetic Algorithm (GA) to comprehensively consider both qualitative and quantitative criteria for supplier selection. Simultaneously, GA efficiently optimizes the order sequence for production planning. The model's efficacy is demonstrated through implementation on real orders, showcasing its ability to handle diverse evaluation criteria and support supplier selection in different scenarios. Moreover, the proposed model is employed to compute the Pareto front, which provides optimal sets of solutions for the given objective criteria. This allows for an effective demand-driven strategy, particularly relevant for fashion retailers to select supplier and order planning optimization decisions in dynamic and multi-criteria context. Overall, GA-TOPSIS hybrid model offers an innovative and efficient decision support system for fashion retailers to adapt to changing demands and achieve effective supplier selection and production planning optimization. The model's incorporation of both qualitative and quantitative criteria in a dynamic environment contributes to its originality and potential for addressing the complexities of the fashion industry's supply chain challenges
A new health-based metaheuristic algorithm: cholesterol algorithm Serap Ulusam Seçkiner; Şeyma Yilkici Yüzügüldü
International Journal of Industrial Optimization Vol. 4 No. 2 (2023)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v4i2.7651

Abstract

This paper seeks to explore the effectiveness of a new health-based metaheuristic algorithm inspired by the cholesterol metabolism of the human body. In the study, the main idea is the focus on the performance of the cholesterol algorithm on unconstrained continuous optimization problems. The performances of the proposed cholesterol algorithm are evaluated based on 23 comparison tests and results were compared with Particle Swarm Optimization, Genetic Algorithm, Grey Wolf Optimization, Whale Optimization Algorithm, Harris Hawks Optimization, Differential Evolution, FireFly Algorithm, Cuckoo Search, Multi-Verse Optimizer, and JAYA algorithms. Results showed that this novel cholesterol algorithm implementation can compete effectively with the best-known solution to test functions.
Parametric optimization for hardness of tig welded duplex stainless steel Sandip Mondal; Goutam Kumar Bose
International Journal of Industrial Optimization Vol. 4 No. 2 (2023)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v4i2.7756

Abstract

Achieving optimal mechanical properties in welding joints hinges on employing precise parametric conditions. This is particularly crucial for Tungsten Inert Gas (TIG) welding of ASTM/UNS 2205 Duplex Stainless Steel (DSS), where attributes like hardness, ultimate tensile strength, and yield strength are paramount. Maintaining high Hardness Value (HV) demands proper welding parameters such as welding current, gas flow rate, and welding speed. To enhance DSS welding quality, especially hardness, this study utilizes the Taguchi method to optimize welding process parameters. The importance of each factor is assessed through Annova statistical analysis. The outcomes highlight the positive impact of parametric optimization on HV, as evidenced by the analysed data. Parametric optimization proves to be a potent approach for refining industrial processes like welding, with particular relevance in TIG welding of duplex stainless steel due to its mechanical robustness and corrosion resistance. Nevertheless, challenges arise due to the material's elevated hardness and low thermal conductivity, resulting in potential defects like cracks and porosity. The identification of optimal welding parameters, encompassing current, voltage, speed, and gas flow rate, helps address these challenges and advances high-quality welds. Through systematic variations and analysis of these parameters, researchers and engineers can pinpoint the optimal combination that mitigates defects while maximizing desired joint attributes. Within the realm of TIG welding of duplex stainless steel, metric optimization holds the potential to elevate welding quality, curtail costs and waste, and heighten productivity and safety. Consequently, organizations can attain enhanced performance, efficiency, and profitability within their welding processes
Augmented tour construction heuristics for the travelling salesman problem Ziauddin Ursani; Ahsan Ahmad Ursani
International Journal of Industrial Optimization Vol. 4 No. 2 (2023)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v4i2.7875

Abstract

Tour construction heuristics serve as fundamental techniques in optimizing the routes of a traveling salesman. These heuristics remain significant as foundational methods for generating initial solutions to the Traveling Salesman Problem (TSP), facilitating subsequent applications of tour improvement heuristics. These heuristics effectively comprise the iterative application of city node selection and insertion. However, thus far, no attempts have been made to enhance the basic structure of tour construction heuristics to bring a better initial solution for the advanced heuristics. This study aims to enhance tour construction heuristics without compromising their theoretical complexity. Specifically, an iterative step of partial tour deconstruction has been introduced to the existing heuristics. This additional step has been implemented and evaluated with three highly performing tour construction heuristics: the farthest insertion heuristic, the max difference insertion heuristic, and the fast max difference insertion heuristic. The results demonstrate that augmenting these heuristics with the partial tour deconstruction step improves the best, worst, and average solutions while preserving their theoretical complexity
Supplier's selection of plate material using analytical hierarchy process and additive ratio assessment methods Ja'iza Salsabila; Dira Ernawati
International Journal of Industrial Optimization Vol. 4 No. 2 (2023)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v4i2.8127

Abstract

PT PAL Indonesia (PERSERO) is among the prominent shipyard companies in Indonesia that currently employs a simplistic supplier selection weighting system, prioritizing low prices and material specification conformity. This approach often leads to subjective assessments, making it challenging for the company to identify suitable suppliers from a large pool. Therefore, this study proposed a methodology to enhance supplier selection by incorporating additional criteria based on Dickson's criteria and company policies. The Analytical Hierarchy Process (AHP) and Additive Ratio Assessment (ARAS) methods were utilized for this purpose. The findings indicate that PT Krakatau Steel (A1) emerges as the top-ranked supplier with a Ki value of 0.19, followed by PT Diansakti Sejahtera (A5) in second place with a Ki value of 0.157, and PT Gunawan Dianjaya Steel (A3) in third place with a Ki value of 0.152. PT Jastindo Raya (A4) secures the fourth position with a Ki value of 0.151, while PT Gunung Raja Paksi (A2) takes the fifth and final spot with a Ki value of 0.15. This research helps the company effectively select the best suppliers, particularly in the procurement sector, by employing the AHP-ARAS method and considering Dickson's criteria, thereby addressing existing gaps the company encounters.

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