Heuristic algorithm for multistage scheduling in food processing industry

A multistage production system consists of a number of production stages that are interrelated, that is the output from one stage forms input to the next stage. There are constraints associated with each stage as well as constraints imposed by the overall system. Besides, there are multiple objectives that need to be satisfied, and in numerous cases, these objectives conflict with each other. What is required is an efficient technique to allocate and schedule resources so as to provide a balance between the conflicting objectives within the system constraints. This study is concerned with the problem of scheduling multistage production systems in food processing industry. The system and products have complex structure and relationships. This makes the system difficult to be solved analytically. Therefore, the problem is solved by developing a heuristic algorithm that considers most of the constraints. The output generated by the algorithm includes a production schedule which specifies the starting and completion times of the products in each stage and the machines where the products are to be processed. In addition, a summary of system performances including throughput times, resources' utilizations, and tardy products is reported

Penerapan Algoritma Consultant-Guided Search Dalam Masalah Penjadwalan Job Shop Untuk Meminimasi Makespan

This research uses the Consultant-Guided Search (CGS) algorithm to solve job shop schedulingproblems minimizing makespan. CGS is a metaheuristics inspired by people making decisionsbased on consultant's recommendations. A number of cases from literatures is developed to evaluatethe optimality of this algorithm. CGS is also tested against other metaheuristics, namely GeneticAlgorithms (GA) and Artificial Immune Systems (AIS) for the same cases. Performance evaluationsare conducted using the best makespan obtained by these algorithms. From computational results,it is shown that CGS is able to find 3 optimal solutions out of 10 cases. Overall, CGS performs bettercompared to the other algorithms where its solution lies within 0 - 6,77% from the optimal solution,averaging only 2,15%. Futhermore, CGS outperforms GA in 7 cases and performs equally well inthe other 3 cases. CGS is also better than AIS in 8 cases and is equally well in only 2 cases

Implementasi Metode Six Sigma DMAIC Untuk Mengurangi Paint Bucket Cacat Di PT X

PT X merupakan Perusahaan yang memproduksi paint bucket (ember cat) yang terdiri dari tiga jenis paint bucket, yaitu bucket polos, lid (tutup bucket) dan bucket berlabel. Persentase bucket polos cacat sebesar 1,95%, persentase lid cacat sebesar 0,65% dan persentase bucket berlabel cacat sebesar 6,28%. Peningkatan kualitas paint bucket dilakukan dengan menggunakan metode Six Sigma DMAIC. Pada tahap D (Define) dilakukan pembuatan deskripsi proses produksi, pembuatandiagram SIPOC dan penentuan critical to quality (CTQ). CTQ untuk bucket polos dan lid diperoleh sebanyak dua buah, sedangkan CTQ untuk bucket berlambel sebanyak delapan buah. Pada tahap M (Measure) dilakukan pengukuran performansi sebelum perbaikan berupa rata-rata DPMO.Rata-rata DPMO bucket polos, lid dan bucket berlabel berturut-turut sebesar 7.591,88, 3.420,77 dan 8.109,44. Pada tahap A (Analyze) dilakukan penentuan prioritas perbaikan CTQ dengan membuat diagram Pareto dan mencari penyebab terjadinya cacat pada bucket polos, lid dan bucket berlabel. Berdasarkan diagram Pareto, penelitian fokus memperbaiki 1 jenis cacat pada bucket polos dan lid, yaitu cacat susut dan 5 cacat pada bucket berlabel, yaitu perbedaan tinggi pada pertemuan foil, foil terkelupas, foil hanya menempel sebagian, penempelan tidak menghasilkan pertemuan foil dan bintik putih. Setelah diketahui penyebab terjadinya jenis cacat, dilakukan tahap I (Improve).Tindakan perbaikan yang dilakukan adalah penggunaan infrared thermometer, pembuatan alat bantu, penggunaan microfiber gloves, pembersihan jalur keluar bucket polos, dan lain-lain. Setelahdilakukan perbaikan, dilakukan tahap C (Control). Tindakan perbaikan mengakibatkan terjadinya penurunan nilai rata-rata DPMO pada bucket polos, lid dan bucket berlabel, yaitu berturut-turut sebesar 2.621,54, 1.169, dan 713,69

Penerapan Algoritma Consultant-Guided Search dalam Masalah Penjadwalan Job Shop untuk Meminimasi Makespan

This research uses the Consultant-Guided Search (CGS) algorithm to solve job shop schedulingproblems minimizing makespan. CGS is a metaheuristics inspired by people making decisionsbased on consultant’s recommendations. A number of cases from literatures is developed to evaluatethe optimality of this algorithm. CGS is also tested against other metaheuristics, namely GeneticAlgorithms (GA) and Artificial Immune Systems (AIS) for the same cases. Performance evaluationsare conducted using the best makespan obtained by these algorithms. From computational results,it is shown that CGS is able to find 3 optimal solutions out of 10 cases. Overall, CGS performs bettercompared to the other algorithms where its solution lies within 0 - 6,77% from the optimal solution,averaging only 2,15%. Futhermore, CGS outperforms GA in 7 cases and performs equally well inthe other 3 cases. CGS is also better than AIS in 8 cases and is equally well in only 2 cases

Penerapan Algoritma Consultant-Guided Search dalam Masalah Penjadwalan Job Shop untuk Meminimasi Makespan

This research uses the Consultant-Guided Search (CGS) algorithm to solve job shop scheduling problems minimizing makespan. CGS is a metaheuristics inspired by people making decisions based on consultant’s recommendations. A number of cases from literatures is developed to evaluate the optimality of this algorithm. CGS is also tested against other metaheuristics, namely Genetic Algorithms (GA) and Artificial Immune Systems (AIS) for the same cases. Performance evaluations are conducted using the best makespan obtained by these algorithms. From computational results, it is shown that CGS is able to find 3 optimal solutions out of 10 cases. Overall, CGS performs better compared to the other algorithms where its solution lies within 0 - 6,77% from the optimal solution, averaging only 2,15%. Futhermore, CGS outperforms GA in 7 cases and performs equally well in the other 3 cases. CGS is also better than AIS in 8 cases and is equally well in only 2 cases