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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 5 Documents
 1 
Search results for , issue "Vol. 1 No. 1 (2020)" : 5 Documents clear
Simulation of trends in the use of e-payment using agent based models Elanjati Worldailmi; Ismianti Ismianti
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.v1i1.1276

Abstract

Bank Indonesia (BI) as the central bank in Indonesia has launched a movement to use non-cash instruments in conducting transactions on economic activities. The majority of Indonesian people are increasingly ready to trade without cash or cashless society. The country's economic policy factors, the availability of various non-cash payments, and online sales and purchases, encourage the tendency to use non-cash transactions (e-payment). One way to find out these trends is to use a model. Models can help understand and explain real phenomena more easily and efficiently than directly observing. One model that can be used is Agent Based Modeling and Simulation (ABMS). By using ABMS, the development of models with complex behaviors, dependencies, and interactions can be developed more easily. ABMS is able to describe processes, phenomena, and situations. In this study, the factors that influence the tendency to use e-payment are obtained from various references. From these factors, then created a scenario as a sub-purpose of this model. In simulations using ABMS, detailed descriptions explained based on ODD Protocol elements can be more easily understood and complete. ODD systematically evaluates a model. The advantage is that ODD can improve the accuracy of model formulas and make the theoretical basis more visible.
Hybrid genetic-tabu search algorithm to optimize the route for capacitated vehicle routing problem with time window Mohammad Deni Akbar; Rio Aurachmana
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.v1i1.1421

Abstract

Optimization of transportation and distribution costs is one of the important issues in the supply chain management area. It is caused by their large contribution to the logistics costs that can reach up to 40%. Thus, choosing the right route is one of the efforts that can be done to resolve the issue. This study aims to optimize the capacitated vehicle routing problem with time windows (CVRPTW) for mineral water company distributor with pick-up and delivery problem. To achieve the aim, this study used hybrid algorithm, Genetic Algorithm (GA) and Tabu Search Algorithm (TS). The selection of this hybrid algorithm is due to its capability in minimizing travel distance. The result of this study shows that not only the algorithm has successfully reduced the existing route but also predicted the optimum number of homogenous fleet. By running the algorithm, this study concludes that the number of the optimum routes for this study can be reduced for up to 15.99% than the existing route.
The best route determination using nearest neighbor approach Nafia Rahma; Annie Purwani; Dwi Nita Febriyanto
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.v1i1.1423

Abstract

This research constitutes an application of heuristic optimization using the nearest neighbor (NN) method. It is a method used to design a route based on the next closest distance. The case here is the garbage freight of Yogyakarta City which becomes one of the Environmental Services Department duties. The sector of this research object is Malioboro-Kranggan because it has the highest number of TPS locations. There are 34 TPS locations, and 2 depots with an average volume of total garbage are 197 m3/day. Several alternative routes have resulted because the same distance was found when deciding the next distance (TPS 26 and TPS 31). The best alternative was determined based on the best scenario parameter of total mileage and operational time. The first scenario chose the garbage volume that is close to the remaining capacity, meanwhile, the second scenario chose the smallest garbage volume. At TPS 27, an alternative with the same closest distance appeared again (TPS 15 and TPS 18). Hence, the whole algorithm results in four alternative decisions. The first alternative results 13.59 hours as the total time and 40.092 km as the total distance, the second results 13.50 hours with 40.315 km, the third results 13.57 hours with 41.393 km, and the fourth results 13.803 hours with 40.41 km. The best alternative goes to the first alternative based on the parameter set before. It means that the scenario taken is by choosing the TPS with the closest remaining volume of the vehicle.
Optimizing shipping routes to minimize cost using particle swarm optimization Annur Rahman; Hayati Mukti Asih
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.v1i1.1605

Abstract

Product shipping is important in the economic process in the company. Efficient product shipping routes should provide low transportation costs. This study based on a case company of CV. Kayana, a distributor of “Sari Roti”, has 4 motorbikes and 2 cars. Each vehicle has their own shipping routes. Nowadays, high distance for each route results on high transportation cost. Therefore, the objective of this study to minimize the distance and cost of product shipping by developing shipping algorithm using Particle Swarm Optimization (PSO) for Traveling Salesman Problem (TSP). The MATLAB software was employed to solve this problem. The solution is obtained by varying the amount of particles and number of iterations. Experimental results proved that the developed PSO is enough effective and efficient to solve shipping routes problem. The results show the proposed model have lower distance and transportation cost. It helps the company in determining the routes for product shipping with minimum transportation cost.
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.

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