PENGEPAKAN LINGKARAN DALAM PERSEGI PANJANG DENGAN METODE ALGORITMA GENETIKA

Makalah ini bertujuan untuk menyelesaikan masalah pengepakan lingkaran dalam persegi panjang dengan metode algoritma genetika. Masalah pengepakan lingkaran digambarkan dengan N item berbentuk lingkaran yang akan dimasukkan ke dalam satu objek persegi panjang (yang lebih besar) dengan tujuan untuk mendapatkan urutan masuk dan koordinat dari item yang dapat meminimalkan panjang objek terpakai.Secara umum, proses algoritma genetika adalah membangkitkan populasi awal, mengevaluasi kromosom, seleksi, crossover, dan mutasi. Proses seleksi yang digunakan adalah seleksi roulette wheel, proses crossover yang digunakan adalah partial mappedcrossover, dan proses mutasiyang digunakan adalah mutasi respirocal exchange.Data yang digunakan berupa 3 jenis data yaitu data 10, 50, dan 110 unit item. Penyelesaian dengan bahasa pemrograman C++ menggunakan software Borland C++ diperoleh kesimpulan bahwa semakin besar nilai parameter popsize dan maxgenyang diberikan, maka solusi yang diperoleh semakin baik.Begitu juga nilai probabilitas crossoveryang rendah dan nilai probabilitas mutasi yang tinggi menghasilkan solusi yang lebih bai

A deterministic algorithm for generating optimal three- stage layouts of homogenous strip pieces

Purpose: The time required by the algorithms for general layouts to solve the large-scale two-dimensional cutting problems may become unaffordable. So this paper presents an exact algorithm to solve above problems. Design/methodology/approach: The algorithm uses the dynamic programming algorithm to generate the optimal homogenous strips, solves the knapsack problem to determine the optimal layout of the homogenous strip in the composite strip and the composite strip in the segment, and optimally selects the enumerated segments to compose the three-stage layout. Findings: The algorithm not only meets the shearing and punching process need, but also achieves good results within reasonable time. Originality/value: The algorithm is tested through 43 large-scale benchmark problems. The number of optimal solutions is 39 for this paper’s algorithm; the rate of the rest 4 problem’s solution value and the optimal solution is 99. 9%, and the average consumed time is only 2. 18seconds. This paper’s pattern is used to simplify the cutting process. Compared with the classic three-stage, the two-segment and the T-shape algorithms, the solutions of the algorithm are better than that of the above three algorithms. Experimental results show that the algorithm to solve a large-scale piece packing quickly and efficiency.Peer Reviewe

Packing non-identical circles within a rectangle with open length

10.1007/s10898-012-9948-6Journal of Global Optimization5631187-1215JGOP

Evolutionary algorithms for scheduling operations

While business process automation is proliferating through industries and processes, operations such as job and crew scheduling are still performed manually in the majority of workplaces. The linear programming techniques are not capable of automated production of a job or crew schedule within a reasonable computation time due to the massive sizes of real-life scheduling problems. For this reason, AI solutions are becoming increasingly popular, specifically Evolutionary Algorithms (EAs). However, there are three key limitations of previous studies researching application of EAs for the solution of the scheduling problems. First of all, there is no justification for the selection of a particular genetic operator and conclusion about their effectiveness. Secondly, the practical efficiency of such algorithms is unknown due to the lack of comparison with manually produced schedules. Finally, the implications of real-life implementation of the algorithm are rarely considered. This research aims at addressing all three limitations. Collaborations with DBSchenker,the rail freight carrier, and Garnett-Dickinson, the printing company,have been established. Multi-disciplinary research methods including document analysis, focus group evaluations, and interviews with managers from different levels have been carried out. A standard EA has been enhanced with developed within research intelligent operators to efficiently solve the problems. Assessment of the developed algorithm in the context of real life crew scheduling problem showed that the automated schedule outperformed the manual one by 3.7% in terms of its operating efficiency. In addition, the automatically produced schedule required less staff to complete all the jobs and might provide an additional revenue opportunity of £500 000. The research has also revealed a positive attitude expressed by the operational and IT managers towards the developed system. Investment analysis demonstrated a 41% return rate on investment in the automated scheduling system, while the strategic analysis suggests that this system can enable attainment of strategic priorities. The end users of the system, on the other hand, expressed some degree of scepticism and would prefer manual methods