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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 69 Documents
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Quality by design of yogurt product using taguchi multi responses method Ali Parkhan; Muhammad Ridwan Andi Purnomo
International Journal of Industrial Optimization Vol. 1 No. 2 (2020)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v1i2.2442

Abstract

Quality of yogurt could be determined based on its flavor and texture which could be tested using an organoleptic test. The quality objective of the yogurt flavor and texture is larger the better. As one of the intermediate products that could be processed to become various other products, the demand for yogurt is continuously increasing in Indonesia. Along with the increase in demand, the demand for quality of yogurt has also increased. In this study, experiments have been conducted to improve the quality of a yogurt product. The experiments were designed based on the Taguchi method with Multi Responses Signal to Noise (MRSN) that involves 7 factors consists of 6 controllable factors and 1 uncontrollable factor. Every factor has 2 levels of an experiment. The 6 controllable factors are heating temperature, heating duration, number of yogurt seeds, incubation temperature, incubation duration, number of sugars, while the uncontrollable factor is the weather condition. Result of the experiments showed when weight for flavor and texture is 0.437 and 0.563 respectively, the levels of the optimum factors are 950C for milk heating temperature, 20 minutes for the duration of milk heating process, 75 ml of yogurt seeds, 450C of the temperature of incubation, 6 hours of incubation duration and 12.5 grams of sugar weight. Based on the organoleptic test that conducted by a group of experienced testers, the new optimum factors combination could improve the yogurt’s quality in term of flavor and quality up to 16.24% and 11.37% respectively. It could be concluded that the proposed method could improve the quality of yogurt-based on preferences from the experienced testers that have been expressed by the weight of every quality response.
Gas lift optimization in the oil and gas production process: a review of production challenges and optimization strategies Ikenna Tobechukwu Okorocha; Chuka Emmanuel Chinwuko; Chika Edith Mgbemena; Chinedum Ogonna Mgbemena
International Journal of Industrial Optimization Vol. 1 No. 2 (2020)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v1i2.2470

Abstract

Gas Lift operation involves the injection of compressed gas into a low producing or non-performing well to maximize oil production. The oil produced from a gas lift well is a function of the gas injection rate. The optimal gas injection rate is achieved by optimization. However, the gas lift, which is an artificial lift process, has some drawbacks such as the deterioration of the oil well, incorrect production metering, instability of the gas compressor, and over injection of gas. This paper discusses the various optimization techniques for the gas lift in the Oil and Gas production process. A systematic literature search was conducted on four databases, namely Google Scholar, Scopus, IEE Explore and DOAJ, to identify papers that focused on Gas lift optimizations. The materials for this review were collected primarily via database searches. The major challenges associated with gas lift were identified, and the different optimization strategies available in the literature reviewed. The strategies reviewed were found to be based on artificial intelligence (AI) and machine learning (ML). The implementation of any of the optimization strategies for the gas lift will enhance profitability, reduce operational cost, and extend the life of the wells.
Currency movement forecasting using time series analysis and long short-term memory Kristina Sanjaya Putri; Siana Halim
International Journal of Industrial Optimization Vol. 1 No. 2 (2020)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v1i2.2490

Abstract

Foreign exchange is one type of investment, which its goal is to minimize losses that could occur. Forecasting is a technique to minimize losses when investing. The purpose of this study is to make foreign exchange predictions using a time series analysis called Auto-Regressive Integrated Moving Average (ARIMA) and Long Short-term memory methods. This study uses the daily EUR / USD exchange rates from 2014 to March 2020. The data are used as the model to predict the value of the foreign exchange market in April 2020. The model obtained will be used for predictions in April 2020, where the RMSE values obtained from time series analysis (ARIMA) with a window size of 100 days and LSTM sequentially as follows 0.00527 and 0.00509. LSTM produces lower RMSE values than ARIMA. LSTM has better prediction results; this is because the LSTM has the ability to learn so that it can utilize a large amount of data while ARIMA cannot use it. ARIMA does not have the ability to learn even though given a large amount of data it gives poor forecasting results. The ARIMA prediction is the same as the values of the previous day.
Integration of lot sizing and scheduling models to minimize production cost and time in the automotive industry Huda Muhamad Badri; Nor Kamaliana Khamis; Mariyam Jameelah Ghazali
International Journal of Industrial Optimization Vol. 1 No. 1 (2020)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v1i1.2753

Abstract

Lot planning and production scheduling are important processes in the manufacturing industry. This study is based on the case study of automotive spare parts manufacturing firm (Firm-A), which produces various products based on customer demand. Several complex problems have been identified due to different production process flows for different products with different machine capability considerations at each stage of the production process. Based on these problems, this study proposes three integrated models that include lot planning and scheduling to minimize production costs, production times, and production costs and time simultaneously. These can be achieved by optimizing model solutions such as job order decisions and production quantities on the production process. Next, the genetic algorithm (GA) and the Taguchi approach are used to optimize the models by finding the optimal model solution for each objective. Model testing is presented using numerical examples and actual case data from Firm-A. The model testing analysis is performed using Microsoft Excel software to develop a model based on mathematical programming to formulate all three objective functions. Meanwhile, GeneHunter software is used to represent the optimization process using GA. The results show production quantity and job sequence play an essential role in reducing the cost and time of production by Rp 42.717.200,00 and 31392.82 minutes (65.4 days), respectively. The findings of the study contribute to the production management of Firm-A in helping to make decisions to reduce the time and costs of production strategically, where it provides a guideline for complex production activities.
Using machine learning to predict the number of alternative solutions to a minimum cardinality set covering problem Brooks Emerick; Yun Lu; Francis J. Vasko
International Journal of Industrial Optimization Vol. 2 No. 1 (2021)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v2i1.2948

Abstract

Although the characterization of alternative optimal solutions for linear programming problems is well known, such characterizations for combinatorial optimization problems are essentially non-existent. This is the first article to qualitatively predict the number of alternative optima for a classic NP-hard combinatorial optimization problem, namely, the minimum cardinality (also called unicost) set covering problem (MCSCP). For the MCSCP, a set must be covered by a minimum number of subsets selected from a specified collection of subsets of the given set. The MCSCP has numerous industrial applications that require that a secondary objective is optimized once the size of a minimum cover has been determined. To optimize the secondary objective, the number of MCSCP solutions is optimized. In this article, for the first time, a machine learning methodology is presented to generate categorical regression trees to predict, qualitatively (extra-small, small, medium, large, or extra-large), the number of solutions to an MCSCP. Within the machine learning toolbox of MATLAB®, 600,000 unique random MCSCPs were generated and used to construct regression trees. The prediction quality of these regression trees was tested on 5000 different MCSCPs. For the 5-output model, the average accuracy of being at most one off from the predicted category was 94.2%. 
Optimizing the clinker production by using an automation model in raw material feed Ahmad Hidayat Sutawijaya; Abdul Kayi
International Journal of Industrial Optimization Vol. 2 No. 1 (2021)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v2i1.3002

Abstract

The clinker production process involves much equipment and material flow; thus, an operating system is needed to regulate and manage the production process. XYZ company uses an operating system for clinker production called Cement Management Quality (CMQ). The CMQ operation on clinker production is considered semi-automatic because it requires many interventions from the operator. Furthermore, the program is limited under specific condition. As a result, the quality of the clinker is decreased, and the energy consumption is increased. The failure of clinker production is related to the CMQ system, and it is vital to solving the problem appropriately. Since the CMQ system is connected with many aspects, it is essential to find the root cause. Root Cause Analysis (RCA) method is suitable to find the root of the problem for a complex system. After researching using RCA, the main problems on the CMQ system is the data not appropriately integrated, and the process algorithm is insufficient. The new integration of data transfer and new algorithms are developed as an attempt to solve the issues. The new data integration model and algorithm are applied through the simulation method as a test case before taking complete corrective action on the CMQ system. The new model's application shows the standard deviation of the process is decreased under the specified threshold. The method provides good results for improving the quality of the clinker production process. It can be used as an essential reference for applying the automation model in the clinker production process.
Designing project schedule using crashing method to compress the fiber to the home project schedule Zha Sha Putri Anugerah; Devi Pratami; Mohammad Deni Akbar
International Journal of Industrial Optimization Vol. 2 No. 1 (2021)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v2i1.3025

Abstract

ABC Company is an agent of network construction, operation, and maintenance. ABC Company is currently implementing the STTF (Shit to the Front) project, which is the project to add FTTH (Fiber to the Home) networks in areas that can have high customer demand. One of the STTF project construction sites is the Indra Prahasta II housing location. However, the project is currently experiencing work delays due to the Covid-19 disaster in Indonesia. Delays in project execution can result in potential prospects choosing another company that provides similar services. The project schedule can be accelerated using the crashing method and TCTO (Time Cost Trade-Off) analysis to solve this problem. This research's acceleration will be carried out with alternatives for adding 3 hours, 2 hours, 1 hour, and an alternative to increasing workers' number. This project has an average duration of 55 working days with a total cost of Rp 604,124,460. The results obtained from data processing, on the alternative of adding 1 hour of overtime work, the total duration becomes 54 working days with total project cost is Rp 605,734,138. In addition to 2 hours of overtime work, the project's total duration can be reduced to 54 days with a total project cost Rp 606,803,619. And for the addition of 3 hours overtime, the total duration can be shortened to 54 days with a total cost of Rp 606,803,619. As for increasing the number of workers, project work duration can be shortened to 54 working days with a total project cost Rp 604,556,748
A dai-liao hybrid hestenes-stiefel and fletcher-revees methods for unconstrained optimization Nasiru Salihu; Mathew Remilekun Odekunle; Also Mohammed Saleh; Suraj Salihu
International Journal of Industrial Optimization Vol. 2 No. 1 (2021)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v2i1.3054

Abstract

Some problems have no analytical solution or too difficult to solve by scientists, engineers, and mathematicians, so the development of numerical methods to obtain approximate solutions became necessary. Gradient methods are more efficient when the function to be minimized continuously in its first derivative. Therefore, this article presents a new hybrid Conjugate Gradient (CG) method to solve unconstrained optimization problems. The method requires the first-order derivatives but overcomes the steepest descent method’s shortcoming of slow convergence and needs not to save or compute the second-order derivatives needed by the Newton method. The CG update parameter is suggested from the Dai-Liao conjugacy condition as a convex combination of Hestenes-Stiefel and Fletcher-Revees algorithms by employing an optimal modulating choice parameterto avoid matrix storage. Numerical computation adopts an inexact line search to obtain the step-size that generates a decent property, showing that the algorithm is robust and efficient. The scheme converges globally under Wolfe line search, and it’s like is suitable in compressive sensing problems and M-tensor systems.
Implementation of lean thinking through A3 report in plastic injection company Sartika Rini
International Journal of Industrial Optimization Vol. 2 No. 1 (2021)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v2i1.3055

Abstract

Lean thinking, or lean production, which has long been introduced by Toyota, is a process improvement concept that is carried out by eliminating waste and focusing more on things that create values. Its emergence was inspired by the fact on the production floor, where only a small fraction of the total times and efforts contributed to creating added value to customers’ final product. Lots of prior studies have shown various benefits of implementing lean production, especially in manufacturing industries. However, many companies still find difficulties trying to implement a lean approach for the first time. Furthermore, they do not have a clear and concise picture of each component of the lean approach they want to apply. This company is based on case study which has many rejected products so that it makes higher production cost. Therefore, this study proposed an implementation of lean thinking to reduce the number of rejected products through A3 report. This result show the defects can be reduced and the standard operational procedure has been developed. 
Optimization of the cyclone separator performance using taguchi method and multi-response pcr-topsis Arifin Zulkarnain; Abbas Hammada; Fauzan Fauzan
International Journal of Industrial Optimization Vol. 3 No. 1 (2022)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v3i1.4272

Abstract

Pollutant control uses cyclone separators as pre-cleaners and is widely used in manufacturing and mining industries. Research on cyclone performance is carried out with changes in various variations that affect it, the problem that occurs is that multi-response can give results of different factors and levels as a result of equipment design cannot provide optimal results and research topics on inlet scroll types have not been widely carried out, this study aims to improve cyclone performance inlet scroll type separator with helical angle, experimental and development methods to get optimal performance where pressure drop and efficiency are indications of cyclone separator performance, to get optimal performance the use of Taguchi experimental design produces different factors and levels so that multi-response methods such as PCR and TOPSIS was used to produce the best combination of factors and levels, confirmation experiments and computational fluid dynamics (CFD) methods were carried out to ensure the validity of the study, the results showed that the scroll inlet prototype cyclone separator with a helical angle of 150, inlet velocity of 10m/s, outlet diameter of 72 mm provides empirical values ​​for pressure drop and the best particle separation efficiency for multi-parameter responses, further research can be done by modifying the shape and dimensions of the bottom outlet.

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