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PERBANDINGAN MODEL LOT SIZING BERBASIS MATERIAL REQUIREMENT PLANNING UNTUK MENGOPTIMALKAN BIAYA PERSEDIAN Widodo, Dian Setiya
Heuristic Vol 15 No 02 (2018)
Publisher : Fakultas Teknik Universitas 17 Agustus 1945 Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30996/he.v15i02.2138

Persediaan menjadi sangat penting untuk melancarkan aktivitas proses produksi. Artikel ini bertujuan untuk menentukan lot sizing optimal dengan meminimalkan biaya persediaan. Untuk mendapatkan lot sizing optimal dengan membandingkan model lot sizing menggunakan Lot For Lot, Economic Order Quantity, Period Order QuantityÂ dan Algorithm Wagner Within berbasis Material Requirement Planning untuk mendapatkan biaya persediaan minimal. Dari hasil perhitungan dengan membandingkan model-model lot sizing Lot For Lot, Economic Order Quantity, Period Order Quantity dan Algorithm Wagner Within, ditunjukkan bahwa Algorithm Wagner Within memberikan Â total Â biaya persediaan Â lebih minimal dengan Â biaya perediaannyaÂ sebesar Â Rp. 927,600. dibandingkan teknik lot sizing lainnya.Kata kunci: Biaya Persediaan, Lot sizing, MRP

Analisis Model Sustainable Economic Order Quantity Dengan Mempertimbangkan Emisi Karbon Dan Batasan Kapasitas Gudang Untuk Menekan Total Biaya Persediaan Widodo, Dian Setiya; Utama, Dana Marsetiya
TEKNIK Vol 40, No. 3 (2019): Desember 2019
Publisher : Diponegoro University

Model Economic Order Quantity (EOQ) merupakan salah satu model persediaan dasar dalam rantai pasok. Model EOQ menawarkan pendekatan matematis untuk menentukan jumlah optimal produk yang harus dipesan oleh perusahaan ke supplier. Penelitian ini menganalisa model sutainable EOQ dengan mempertimbangkan emisi karbon dan batasan kapasitas gudang untuk meminimalkan total biaya persediaan. Percobaan numerik dan analisa sensitivitas dilakukan terhadap model EOQ usulan menggunakan metode lagrange. Terdapat dua model yang diusulkan untuk menyelesaikan permasalahan. Model pertama adalah model EOQ dengan mempertimbangkan karbon emisi dan model kedua adalah model EOQ dengan mempertimbangkan karbon emisiÂ dan batasan gudang. Hasil penelitian menunjukkan bahwa model sustainable EOQ dengan mempertimbangkan emisi karbon dan batasan kapasitas gudang adalah efektif untuk menyelesaikan permasalahan penentuan jumlah optimal yang harus dipesan oleh perusahaan sehingga menekan total biaya persediaan

ENERGY-EFFICIENT FLOW SHOP SCHEDULING USING HYBRID GRASSHOPPER ALGORITHM OPTIMIZATION Utama, Dana Marsetiya; Baroto, Teguh; Widodo, Dian Setiya
Jurnal Ilmiah Teknik Industri Vol. 19, No. 1, June 2020
Publisher : Muhammadiyah University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/jiti.v19i1.10079

Manufacturing companies have a significant impact on environmental damage, and energy consumption in manufacturing companies is a widespread issue because the energy used is derived from fossil fuels. This research aims to minimize energy consumption using develop Hybrid Grasshopper Algorithm Optimization (HGAO). The focus of the issue in this article is the Permutation Flow Shop Scheduling Problem (PFSSP). A case study was conducted in offset printing firms. The results showed that the HGAO algorithm is capable of reducing energy consumption in offset printing firms. The higher the population of search agents and iterations produces less energy consumption. The HGAO algorithm is also compared with the genetic algorithm (GA). The results show that HGAO is more efficient in reducing energy consumption than GA.

PENJADWALAN FLOW SHOP DENGAN PENDEKATAN CROSS ENTROPY-GENETIC ALGORITHM UNTUK MENURUNKAN MAKESPAN PADA PEMBUATAN RODA GIGI Widodo, Dian Setiya; Ellianto, Mario Sarisky Dwi
Heuristic Vol 12 No 02 (2015)
Publisher : Fakultas Teknik Universitas 17 Agustus 1945 Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30996/he.v12i02.629

Flow shop is the process of determining the sequence of jobs that have the same product path. While the flow-shop scheduling takes assumption that a number of jobs that each has the same machine work sequence. The problems faced by com-panies, namely the high demand and yet the existence of a good scheduling planning resulted in the company should be able to optimize sche-duling job. One of the ways to solve these problems is by minimizing the Makespan. This research will solve the problem regarding to the flow shop scheduling by using cross entropy-genetic algorithm (CEGA) method to minimize Makespan. The technique that is used to solve the problem is by comparing the result of the application of existing methods in the company with the proposed method (CEGA). To support the application of CEGA used MATLAB software. Finally known that the results CEGA can give optimal solution, Makespan values obtained for 10829 seconds. So far, it was more effective than the method that used at the firm with Makespan efficiency by 10.06%.Keywords: Flow Shop, Scheduling, Cross Entropy-Genetic Algorithm, Makespan.

PENDEKATAN ALGORITMA CROSS ENTROPY-GENETIC ALGORITHM UNTUK MENURUNKAN MAKESPAN PADA PENJADWALAN FLOW SHOP Dian Setiya Widodo; Purnomo Budi Santoso; Eko Siswanto
Journal of Engineering and Management in Industrial System Vol 2, No 1 (2014)
Publisher : Badan Penerbit Jurnal, Faculty of Engineering, Universitas Brawijaya

In the flow shop scheduling each job will go through every machine in the same order. The goal of this research is to complete a series of jobs in order to obtain the optimal makespan for stick skewers rounding machine. It is become necessities because the company has no planning and scheduling, while the demand exceeds the capacity Thus, optimize the scheduling is needed to be done. One way to optimize the scheduling is to minimize makespan. Based on the results of the research Cross Entropy Algorithm-Genetic Algorithm (CEGA) can provide the optimal solution. This is evident by a comparison technique that enumeration techniques obtained similar results with makespan 1982 seconds, but when compared to the company, CEGA method is better with makespan efficiency of 12.18%. The use of CEGA with the MATLAB has more advantages that simplify and speed in performing the calculations to can optimal solution.

An effective hybrid ant lion algorithm to minimize mean tardiness on permutation flow shop scheduling problem Dana Marsetiya Utama; Dian Setiya Widodo; Muhammad Faisal Ibrahim; Shanty Kusuma Dewi
International Journal of Advances in Intelligent Informatics Vol 6, No 1 (2020): March 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v6i1.385

This article aimed to develop an improved Ant Lion algorithm. The objective function was to minimize the mean tardiness on the flow shop scheduling problem with a focus on the permutation flow shop problem (PFSP). The Hybrid Ant Lion Optimization Algorithm (HALO) with local strategy was proposed, and from the total search of the agent, the NEH-EDD algorithm was applied. Moreover, the diversity of the nominee schedule was improved through the use of swap mutation, flip, and slide to determine the best solution in each iteration. Finally, the HALO was compared with some algorithms, while some numerical experiments were used to show the performances of the proposed algorithms. It is important to note that comparative analysis has been previously conducted using the nine variations of the PFSSP problem, and the HALO obtained was compared to other algorithms based on numerical experiments.

Improve Algoritma Hodgson Untuk Meminimasi Jumlah Job Terlambat Pada Penjadwalan Flow shop Dian Setiya Widodo
Jurnal Teknik Industri Vol. 19 No. 1 (2018): Februari
Publisher : Department Industrial Engineering, University of Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/JTIUMM.Vol19.No1.73-81

Since Johnson published a paper in 1954, the problem of job scheduling has received the attention of hundreds of practitioners and researchers, as one of the most studied topics in Operation research. Several heuristic studies have discussed the problem of flow shop scheduling to minimize the completion time (makespan). In this paper, we consider the problem of pure flow shop scheduling to minimize the number of jobs of tardy. We have developed a Hudson algorithm for minimization solution the number jobs of tardy. Hodgson's improved Heuristic algorithm was tested and compared to the EDD priority rule. The Numerical experimental results show the new algorithm provides a better solution than priority EDD. The improving Hodgson algorithm gives a minimum number of tardy jobs.

A novel hybrid jellyfish algorithm for minimizing fuel consumption capacitated vehicle routing problem Dana Marsetiya Utama; Bahru Widjonarko; Dian Setiya Widodo
Bulletin of Electrical Engineering and Informatics Vol 11, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i3.3263

Distribution is a critical activity that must be carefully planned in order to minimize the company's fuel costs. The hybrid jellyfish (HJF) algorithm is proposed in this study to solve the fuel consumption capacity vehicle routing problem (FCCVRP). This problem's objective function is to minimize fuel consumption costs. The proposed HJF algorithm is used in this study to generate optimal fuel consumption through a population and iteration parameter experiment. The experiment results indicate that the HJF increasing parameter has an effect on reducing the total cost of fuel. Additionally, this study presents an algorithm comparison that demonstrates how effectively the proposed HJF algorithm solves FCCVRP.

An energy-efficient flow shop scheduling using hybrid Harris hawks optimization Dana Marsetiya Utama; Dian Setiya Widodo
Bulletin of Electrical Engineering and Informatics Vol 10, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i3.2958

The energy crisis has become an environmental problem, and this has received much attention from researchers. The manufacturing sector is the most significant contributor to energy consumption in the world. One of the significant efforts made in the manufacturing industry to reduce energy consumption is through proper scheduling. Energy-efficient scheduling (EES) is a problem in scheduling to reduce energy consumption. One of the EES problems is in a flow shop scheduling problem (FSSP). This article intends to develop a new approach to solving an EES in the FSSP problem. Hybrid Harris hawks optimization (hybrid HHO) algorithm is offered to resolve the EES issue on FSSP by considering the sequence-dependent setup. Swap and flip procedures are suggested to improve HHO performance. Furthermore, several procedures were used as a comparison to assess hybrid HHO performance. Ten tests were exercised to exhibit the hybrid HHO accomplishment. Based on numerical experimental results, hybrid HHO can solve EES problems. Furthermore, HHO was proven more competitive than other algorithms.

Energy-Efficient Flow Shop Scheduling Using Hybrid Grasshopper Algorithm Optimization Dana Marsetiya Utama; Teguh Baroto; Dian Setiya Widodo
Jurnal Ilmiah Teknik Industri Vol. 19, No. 1, June 2020
Publisher : Department of Industrial Engineering Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/jiti.v19i1.10079

Manufacturing companies have a significant impact on environmental damage, and energy consumption in manufacturing companies is a widespread issue because the energy used is derived from fossil fuels. This research aims to minimize energy consumption using develop Hybrid Grasshopper Algorithm Optimization (HGAO). The focus of the issue in this article is the Permutation Flow Shop Scheduling Problem (PFSSP). A case study was conducted in offset printing firms. The results showed that the HGAO algorithm is capable of reducing energy consumption in offset printing firms. The higher the population of search agents and iterations produces less energy consumption. The HGAO algorithm is also compared with the genetic algorithm (GA). The results show that HGAO is more efficient in reducing energy consumption than GA.

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