scholarly journals Pembentukan Solusi Awal International Timetabling Competition 2021

2021 ◽  
Vol 8 (4) ◽  
pp. 1939-1944
Author(s):  
Rizal Risnanda Hutama
Keyword(s):  
Integer Programming ◽  
Tabu Search ◽  
Constraint Programming ◽  
Hill Climbing ◽  
Hard Constraint ◽  
Low Level ◽  
Late Acceptance Hill Climbing

Penjadawalan olahraga merupakan salah satu cabang dari optimasi di riset operasi. Penjadwalan olahraga memiliki berbagai macam batasan yang menantang para peneliti untuk menyelesaikannya. International Timetabling Competition (ITC) 2021 merupakan salah satu kompetisi optimasi yang menyediakan permasalahan penjadwalan olahraga. Permasalahan utama pada ITC 2021 yaitu menentukan jadwal waktu yang tepat untuk sebuah pertandingan. Sebuah jadwal dikatakan dapat digunakan (feasible) apabila tidak melanggar hard constraint yang ada. Pembentukan solusi awal yang feasible saat ini dapat dilakukan dengan algoritma constraint programming atau integer programming. Akan tetapi, kedua algoritma tersebut cukup rumit untuk diimplementasikan. Penelitian ini berfokus pada pembentukan solusi awal yang feasible dengan cara yang mudah untuk diimplementasikan. Cara yang digunakan yaitu dengan mengoptimasi pelanggaran hard constraint menggunakan algoritma Late Acceptance Hill Climbing (LAHC) dan Tabu Search dengan kerangka kerja Hyper-Heuristic yang melibatkan low level heuristic (LLH). Algoritma dijalankan maksimal dengan batasan waktu 6 jam untuk setiap data. Hasil dari optimasi pelanggaran hard constraint menggunakan algoritma LAHC dan tabu search dapat menghasilkan solusi awal yang feasible sebanyak 44.44% atau 24 dari 54 keseluruhan dataset.

scholarly journals A Tabu search method for the optimization of fluid power circuits

1998 ◽  
Vol 212 (5) ◽  
pp. 373-381 ◽  
Author(s):  
A M Connor ◽  
D G Tilley
Keyword(s):  
Tabu Search ◽  
Simulation Software ◽  
Search Method ◽  
Fluid Power ◽  
Hill Climbing ◽  
Test Functions ◽  
Local Optima ◽  
Power Circuits ◽  
Tabu Search Method ◽  
Optimum Solution

This paper describes the development of an efficient algorithm for the optimization of fluid power circuits. The algorithm is based around the concepts of Tabu search, where different time-scale memory cycles are used as a metaheuristic to guide a hill climbing search method out of local optima and locate the globally optimum solution. Results are presented which illustrate the effectiveness of the method on mathematical test functions. In addition to these test functions, some results are presented for real problems in hydraulic circuit design by linking the method to the Bath fp dynamic simulation software. In one such example the solutions obtained are compared to those found using simple steady state calculations.

Late acceptance hill-climbing for high school timetabling

2015 ◽  
Vol 19 (4) ◽  
pp. 453-465 ◽  
Author(s):  
George H. G. Fonseca ◽  
Haroldo G. Santos ◽  
Eduardo G. Carrano
Keyword(s):  
High School ◽  
Hill Climbing ◽  
High School Timetabling ◽  
School Timetabling ◽  
Late Acceptance Hill Climbing

scholarly journals Comparing Mixed-Integer Programming and Constraint Programming Models for the Hybrid Flow Shop Scheduling Problem with Dedicated Machines

2021 ◽  
Vol 54 (4) ◽  
pp. 591-597
Author(s):  
Asma Ouled Bedhief
Keyword(s):  
Integer Programming ◽  
Mixed Integer Programming ◽  
Constraint Programming ◽  
Flow Shop ◽  
Programming Model ◽  
Flow Shop Scheduling ◽  
Mixed Integer ◽  
Hybrid Flow Shop ◽  
Dedicated Machines ◽  
Hybrid Flow Shop Scheduling

The paper considers a two-stage hybrid flow shop scheduling problem with dedicated machines and release dates. Each job must be first processed on the single machine of stage 1, and then, the job is processed on one of the two dedicated machines of stage 2, depending on its type. Moreover, the jobs are available for processing at their respective release dates. Our goal is to obtain a schedule that minimizes the makespan. This problem is strongly NP-hard. In this paper, two mathematical models are developed for the problem: a mixed-integer programming model and a constraint programming model. The performance of these two models is compared on different problem configurations. And the results show that the constraint programming outperforms the mixed-integer programming in finding optimal solutions for large problem sizes (450 jobs) with very reasonable computing times.

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