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All Journal International Journal of Electrical and Computer Engineering TELKOMNIKA (Telecommunication Computing Electronics and Control) Journal of Environmental Engineering and Sustainable Technology Indonesian Journal of Electrical Engineering and Computer Science
Yusuf Priyo Anggodo
Brawijaya University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Environmental Science Mechanical Engineering

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Hybrid Genetic Algorithms and Simulated Annealing for Multi-trip Vehicle Routing Problem with Time Windows Amalia Kartika Ariyani; Wayan Firdaus Mahmudy; Yusuf Priyo Anggodo
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 6: December 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (85.504 KB) | DOI: 10.11591/ijece.v8i6.pp4713-4723

Abstract

Vehicle routing problem with time windows (VRPTW) is one of NP-hard problem. Multi-trip is approach to solve the VRPTW that looking trip scheduling for gets best result. Even though there are various algorithms for the problem, there is opportunity to improve the existing algorithms in order gaining a better result. In this research, genetic algoritm is hybridized with simulated annealing algoritm to solve the problem. Genetic algoritm is employed to explore global search area and simulated annealing is employed to exploit local search area. Four combination types of genetic algorithm and simulated annealing (GA-SA) are tested to get the best solution. The computational experiment shows that GA-SA1 and GA-SA4 can produced the most optimal fitness average values with each value was 1.0888 and 1.0887. However GA-SA4 can found the best fitness chromosome faster than GA-SA1.
Improve Interval Optimization of FLR using Auto-speed Acceleration Algorithm Yusuf Priyo Anggodo; Imam Cholissodin
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 4: August 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i4.6668

Abstract

Inflation is a benchmark of a country's economic development. Inflation is very influential on various things, so forecasting inflation to know on upcoming inflation will impact positively. There are various methods used to perform forecasting, one of which is the fuzzy time series forecasting with maximum results. Fuzzy logical relationships (FLR) model is a very good in doing forecasting. However, there are some parameters that the value needs to be optimised. Interval is a parameter which is highly influence toward forecasting result. The utilizing optimization with hybrid automatic clustering and particle swarm optimization (ACPSO). Automatic clustering can do interval formation with just the right amount. While the PSO can optimise the value of each interval and it is providing maximum results. This study proposes the improvement in find the solution using auto-speed acceleration algorithm. Auto-speed acceleration algorithm can find a global solution which is hard to reach by the PSO and time of computation is faster. The results of the acquired solutions can provide the right interval so that the value of the FLR can perform forecasting with maximum results.
A Novel Forecasting Based on Automatic-optimized Fuzzy Time Series Yusuf Priyo Anggodo; Wayan Firdaus Mahmudy
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 4: August 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i4.8430

Abstract

In this paper, we propose a new method for forecasting based on automatic-optimized fuzzy time series to forecast Indonesia Inflation Rate (IIR). First, we propose the forecasting model of two-factor high-order fuzzy-trend logical relationships groups (THFLGs) for predicting the IIR. Second, we propose the interval optimization using automatic clustering and particle swarm optimization (ACPSO) to optimize the interval of main factor IIR and secondary factor SF, where SF = {Customer Price Index (CPI), the Bank of Indonesia (BI) Rate, Rupiah Indonesia /US Dollar (IDR/USD) Exchange rate, Money Supply}. The proposed method gets lower root mean square error (RMSE) than previous methods.
AUTOMATIC CLUSTERING AND OPTIMIZED FUZZY LOGICAL RELATIONSHIPS FOR MINIMUM LIVING NEEDS FORECASTING Yusuf Priyo Anggodo; Wayan Firdaus Mahmudy
Journal of Environmental Engineering and Sustainable Technology Vol 4, No 1 (2017)
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (777.074 KB) | DOI: 10.21776/ub.jeest.2017.004.01.1

Abstract

Forecasting of minimum living needs is useful for companies in financial planning next year. In this study, the firescasting is done using automatic clustering and optimized fuzzy logical relationships. Automatic clustering is used to form a sub-interval time series data. Particle swarm optimization is used to set and optimze interval values in fuzzy logical relationships. The data used as many as 11 years of historical data from 2005-2015. The optimal value of the test results obtained by the p = 4, the number of iterations = 100, the number of particles = 45, a combination of Vmin and Vmax = [-0.6, 0.6], as well as combinations Wmax and Wmin = [0, 4, 0 , 8]. These parameters values produce good forecasting results.Keywords: minimum living needs, automatic clustering, particle swarm optimization, fuzzy logical relationships
K-Means Clustering and Genetic Algorithm to Solve Vehicle Routing Problem with Time Windows Problem Adyan Nur Alfiyatin; Wayan Firdaus Mahmudy; Yusuf Priyo Anggodo
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 2: August 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v11.i2.pp462-468

Abstract

Distribution is an important aspect of industrial activity to serve customers on time with minimal operational cost. Therefore, it is necessary to design a quick and accurate distribution route. One of them can be design travel distribution route using the k-means method and genetic algorithms. This research will combine k-means method and genetic algorithm to solve VRPTW problem. K-means can do clustering properly and genetic algorithms can optimize the route. The proposed genetic algorithm employs initialize chromosome from the result of k-means and using replacement method of selection. Based on the comparison between genetic algorithm and hybrid k-means genetic algorithm proves that k-means genetic algorithm is a suitable combination method with relative low computation time, are the comparison between 2700 and 3900 seconds.
Co-Authors Adyan Nur Alfiyatin Amalia Kartika Ariyani Imam Cholissodin Wayan Firdaus Mahmudy