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

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

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.

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

Decision Support Tools for Managing Unpredictability in the Supply Chain

2000 ◽  
Author(s):  
Carol Vesier
Keyword(s):  
Supply Chain ◽  
Decision Support ◽  
Inventory Management ◽  
Production Scheduling ◽  
Capital Investment ◽  
Investment Strategy ◽  
Decision Support Tools ◽  
Business Outcomes ◽  
Support Tools

Abstract Effectively managing unpredictability requires decision support tools that can predict the financial and business outcomes of various supply chain strategies. This paper will discuss the role of these decision support tools and their characteristics as well as review a case study. In the case study, decision support tools facilitated development of strategies that increased after tax profit by $140 Million. These strategies included: • Reliability improvement strategy: Identifying the reliability improvements that offered the biggest profitability impact. • Supply chain strategy: Defining inventory management and production scheduling rules that ensured order shipment within two days. • Capital investment strategy: Defining when new capacity should come on line as well as the minimum capital investment.

A Hyper-Heuristic Using GRASP with Path-Relinking

2011 ◽  
Vol 4 (2) ◽  
pp. 31-42 ◽  
Author(s):  
He Jiang ◽  
Junying Qiu ◽  
Jifeng Xuan
Keyword(s):  
Simulated Annealing ◽  
Local Search ◽  
Iterative Procedure ◽  
Experimental Results ◽  
Path Relinking ◽  
Nurse Rostering ◽  
Primary Motivation ◽  
Running Time ◽  
Nurse Rostering Problem

The goal of hyper-heuristics is to design and choose heuristics to solve complex problems. The primary motivation behind the hyper-heuristics is to generalize the solving ability of the heuristics. In this paper, the authors propose a Hyper-heuristic using GRASP with Path-Relinking (HyGrasPr). HyGrasPr generates heuristic sequences to produce solutions within an iterative procedure. The procedure of HyGrasPr consists of three phases, namely the construction phase, the local search phase, and the path-relinking phase. To show the performance of the HyGrasPr, the authors use the nurse rostering problem as a case study. The authors use an existing simulated annealing based hyper-heuristic as a baseline. The experimental results indicate that HyGrasPr can achieve better solutions than SAHH within the same running time and the path-relinking phase is effective for the framework of HyGrasPr.

Sign in / Sign up

Export Citation Format

Share Document