scholarly journals A New Slope Index for Solving NxM Flow Shop Sequencing Problems with Minimum Makespan

2021 ◽  
Vol 45 (4) ◽  
Author(s):  
Reda M.S. Abdulaal ◽  
Omer A. Bafail
Keyword(s):  
Flow Shop ◽  
Minimum Makespan ◽  
Sequencing Problems

scholarly journals Sistem Penjadwalan Mesin Produksi Menggunakan Algoritma Johnson dan Campbell

2015 ◽  
Vol 6 (3) ◽  
Author(s):  
Fifin Sonata
Keyword(s):  
Production System ◽  
Essential Role ◽  
Production Efficiency ◽  
Flow Shop ◽  
Machine Scheduling ◽  
Minimum Makespan ◽  
Computerized System

Abstract. Scheduling production machine has an essential role in minimizing the makespan in order to raise the production efficiency. Makespan generated from more than one production machines, can still wait for jobs in the processsince the job is still in the process on another machine, or as a job waiting to be processed by a machine because the machine is still processing another job. The methode applied for flow shop and static production system is Integrated Algorithm which combines Johnson and Campbell Algorithm. By applying these two methods to the company, the company can see the schedule of the production machine that gives the minimum makespan in a computerized system. The presentation is in the form of tables mapping to facilitate the search makespan.Keywords: Production Machine Scheduling, Flow Shop, Static, Makespan, Johnson and Campbell Algorithm. Abstrak. Penjadwalan mesin produksi memiliki peran yang sangat penting dalam meminimalkan makespan  sehingga efisiensi produksi dapat tercapai. Makespan yang dihasilkan untuk jumlah mesin produksi lebih dari satu mesin, masih mungkin mengandung waktu tunggu karena sebuah mesin kemungkinan menunggu  job yang harus diprosesnya, sementara job tersebut masih diproses pada mesin yang lain, atau karena sebuah job menunggu untuk diproses oleh sebuah mesin karena mesin tersebut masih memproses job yang lain. Proses N Jobs M Mesin terdapat lebih dari 1 makespan tetapi yang dipilih adalah makespan terkecil. Metode yang dapat digunakan pada sistem produksi yang flow shop dan statis adalah algoritma kombinasi dari Algoritma Johnson dan Campbell. Dengan menerapkan dua metode tersebut pada perusahaan maka perusahaan dapat mengetahui  jadwal mesin produksi yang memberikan makespan paling minimum secara terkomputerisasi. Penyajian dalam bentuk pemetaan tabel mempermudah dalam pencarian makespan.Kata kunci: Penjadwalan Mesin Produksi, Flow shop, Statis, Makespan, Algoritma Johnson dan Campbell.

An Integrated Heuristic for Machine Sequencing With Specific Reference to the Permutation Flow-Shop Scheduling Problem

2019 ◽  
Vol 2 (2) ◽  
pp. 1-8
Author(s):  
Kaveh Sheibani
Keyword(s):  
Operations Management ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Specific Reference ◽  
Scheduling Problem ◽  
Shop Scheduling ◽  
Permutation Flow Shop Scheduling ◽  
Wide Range ◽  
Permutation Flow Shop ◽  
Sequencing Problems

This article presents an integrated heuristic for the permutation flow-shop scheduling problem with the makespan criterion as one of the most widely studied classical sequencing problems in operations management. The proposed method consists of two phases: arranging the jobs in a priority order and then constructing a sequence by a job insertion principle. A fuzzy greedy evaluation function is employed to prioritize the jobs for incorporating into the construction phase of the proposed heuristic. It is shown that the developed method gives a significantly improved performance for a wide range of standard problems of varying sizes.

scholarly journals A Machine-Learning-Assisted Simulation Approach for Incorporating Predictive Maintenance in Dynamic Flow-Shop Scheduling

2021 ◽  
Vol 11 (24) ◽  
pp. 11725
Author(s):  
Eman Azab ◽  
Mohamed Nafea ◽  
Lamia A. Shihata ◽  
Maggie Mashaly
Keyword(s):  
Machine Learning ◽  
Discrete Event Simulation ◽  
Flow Shop ◽  
Discrete Event ◽  
Machine Learning Algorithms ◽  
Predictive Maintenance ◽  
Simulation Approach ◽  
Dynamic Flow ◽  
Event Simulation ◽  
Minimum Makespan

In this paper, a machine-learning-assisted simulation approach for dynamic flow-shop production scheduling is proposed. This is achieved by introducing a novel framework to include predictive maintenance constraints in the scheduling process while a discrete event simulation tool is used to generate the dynamic schedule. A case study for a pharmaceutical company by the name of Factory X is investigated to validate the proposed framework while taking into consideration the change in forecast demand. The proposed approach uses Microsoft Azure to calculate the predictive maintenance slots and include it in the scheduling process to simplify the process of applying machine-learning techniques with no need for hard coding. Several machine-learning algorithms are tested and compared to see which one provides the highest accuracy. To gather the required dataset, multiple sensors were designed and deployed across machines to collect their vitals that allow the prediction of whether and when they require maintenance. The proposed framework with discrete event simulation generates optimized schedule with minimum makespan while taking into consideration predictive maintenance parameters. Boosted Decision Tree and Neural Network algorithms showed the best results in estimating the predictive maintenance slots. Furthermore, the Earliest Due Date (EDD) model produced the minimum makespan with 76.82 h while scheduling 25 products using 18 machines.

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