A multi-objective production scheduling case study solved by simulated annealing

2007 ◽  
Vol 179 (3) ◽  
pp. 709-722 ◽  
Author(s):  
Taïcir Loukil ◽  
Jacques Teghem ◽  
Philippe Fortemps
Keyword(s):  
Simulated Annealing ◽  
Production Scheduling ◽  
Multi Objective

Efficiency of Interactive Multi-Objective Simulated Annealing Through a Case Study

1998 ◽  
Vol 49 (10) ◽  
pp. 1044 ◽  
Author(s):  
E. L. Ulungu ◽  
J. Teghem ◽  
Ch. Ost
Keyword(s):  
Simulated Annealing ◽  
Multi Objective

Simulated Annealing for Production Scheduling: A Case Study

2015 ◽  
pp. 107-114
Author(s):  
António Santos Marques ◽  
Nelson Chibeles-Martins ◽  
Tânia Pinto-Varela
Keyword(s):  
Simulated Annealing ◽  
Production Scheduling

Efficiency of interactive multi-objective simulated annealing through a case study

1998 ◽  
Vol 49 (10) ◽  
pp. 1044-1050 ◽  
Author(s):  
E L Ulungu ◽  
J Teghem ◽  
Ch Ost
Keyword(s):  
Simulated Annealing ◽  
Multi Objective

Complex Housing: Modelling and Optimization Using an Improved Multi-Objective Simulated Annealing Algorithm

2016 ◽  
Author(s):  
Pei Cao ◽  
Zhaoyan Fan ◽  
Robert Gao ◽  
Jiong Tang
Keyword(s):  
Simulated Annealing ◽  
Optimization Problem ◽  
Simulated Annealing Algorithm ◽  
Line Length ◽  
Multi Objective ◽  
Hard Constraints ◽  
Improved Performance ◽  
The Mathematical Model ◽  
Complex Housing

This research concerns the complex housing optimization problem encountered in engineering design, where the volume in space occupied by components need to be minimized along with other objectives and constraints. Since in real applications the constraints are usually complex, the formulation of computationally tractable optimization becomes challenging. In this research, we first develop the mathematical model of such optimization problem, then propose two versions of an improved multi-objective simulated annealing (MOSA) approach towards the design optimization, i.e., optimizing the placement of cylinders under prescribed constraints to minimize the volume occupied, and the estimated plumbing line length. Our case study indicates that the new MOSA algorithm has improved performance towards complex housing with hard constraints and the design can indeed be automated. The outcome of this research may benefit both existing manufacturing practice and future additive manufacturing.

Efficiency of interactive multi-objective simulated annealing through a case study

1998 ◽  
Vol 49 (10) ◽  
pp. 1044-1050 ◽  
Author(s):  
E L Ulungu ◽  
J Teghem ◽  
Ch Ost
Keyword(s):  
Simulated Annealing ◽  
Multi Objective

Penjadwalan Produksi Menggunakan Metode Simulated Annealing pada Unit Produksi Daun Pintu di PT. ML

2019 ◽  
Vol 2 (2) ◽  
Author(s):  
Rosnani Ginting ◽  
Chairul Rahmadsyah Manik
Keyword(s):  
Simulated Annealing ◽  
Problem Solving ◽  
Production Scheduling ◽  
Production Control ◽  
Delivery Time ◽  
Production Schedule ◽  
A Company ◽  
Predetermined Time ◽  
Factory Floor

Penjadwalan merupakan aspek yang sangat penting karena didalamnya terdapat elemen perencanaan dan pengendalian produksi bagi suatu perusahaan yang dapat mengirim barang sesuai dengan waktu yang telah ditentukan, untuk memperoleh waktu total penyelesaian yang minimum. Masalah utama yang dihadapi oleh PT. ML adalah keterlambatan penyelesaian order yang mempengaruhi delivery time ke tangan costumer karena pelaksanaan penjadwalan produksi dilantai pabrik belum menghasilkan makespan yang sesuai dengan order yang ada. Oleh kaena itu dituntut untuk mencari solusi pemecahan masalah optimal dalam penentuan jadwal produksi untuk meminimisasi total waktu penyelessaian (makespan) semua order. Dalam penelitian ini, penjadwalan menggunakan metode Simulated Annealing (SA) diharapkan dapat menghasilkan waktu total penyelesaian lebih cepat dari penjadwalan yang ada pada perusahaan.   Scheduling is a very important aspect because in it there are elements of planning and production control for a company that can send goods in accordance with a predetermined time, to obtain a minimum total time of completion. The main problem faced by PT. ML is the delay in completing orders that affect delivery time to customer because the implementation of production scheduling on the factory floor has not produced the makespan that matches the existing order. Therefore, it is required to find optimal problem solving solutions in determining the production schedule to minimize the total time of elimination (makespan) of all orders. In this study, scheduling using the Simulated Annealing (SA) method is expected to produce a total time of completion faster than the existing scheduling in the company.

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