Perbandingan Metode Penyelesaian Permasalahan Optimasi Lintas Domain dengan Pendekatan Hyper-Heuristic Menggunakan Algoritma Reinforcement-Late Acceptance

<p>Sebuah organisasi terkadang membutuhkan solusi untuk permasalahan optimasi lintas domain. Permasalahan optimasi lintas domain merupakan permasalahan yang memiliki karakteristik berbeda, misalnya antar domain optimasi penjadwalan, rute kendaraan, bin packing, dan SAT. Optimasi tersebut digunakan untuk mendukung pengambilan keputusan sebuah organisasi. Dalam menyelesaikan permasalahan optimasi tersebut, dibutuhkan metode pencarian komputasi. Di literatur, hampir semua permasalahan optimasi dalam kelas NP-hard diselesaikan dengan pendekatan meta-heuristics. Akan tetapi meta-heuristic ini memiliki kekurangan, yaitu diperlukan <em>parameter tunning</em> untuk setiap problem domain yang berbeda. Sehingga pendekatan ini dirasa kurang efektif. Oleh karena itu diperlukan pendekatan baru, yaitu pendekatan hyper-heuristics. Metode hyper-heuristic merupakan metode pencarian komputasi approximate yang dapat menyelesaikan permasalahan optimasi lintas domain dengan waktu lebih cepat. Lintas domain permasalahan yang akan diselesaikan ada enam, yaitu satisfiability (SAT), one dimensional bin packing, permutation flow shop, personnel scheduling, travelling salesman problem (TSP), dan vehicle routing problem (VRP). Dalam meningkatkan kinerja, penelitian ini menguji pengaruh dari adaptasi algoritma Reinforcement Learning (RL) sebagai strategi seleksi LLH dikombinasikan dengan algoritma Late Acceptance sebagai move acceptance, selanjutnya disebut algoritma Reinforcement Learning-Late acceptance (RL-LA). Untuk mengetahui efektivitas performa dari algoritma RL-LA, performa algoritma RL-LA yang diusulkan dibandingkan dengan algoritma Simple Random-Late Acceptance (SR-LA). Hasil dari penelitian ini menunjukan bahwa algoritma yang diusulkan, i.e. RL-LA lebih unggul dari SR-LA pada 4 dari 6 domain permasalahan uji coba, yaitu SAT, personnel scheduling, TSP, dan VRP, sedangkan pada domain lainnya seperti bin packing dan flow shop mengalami penurunan. Secara lebih spesifik, RL-LA dapat meningkatkan peforma pencarian dalam menemukan solusi optimal pada 18 instance dari 30 instance atau sebesar 64%, dan jika dilihat dari nilai median dan minimum metode RL-LA lebih unggul 28% dari metode SR-LA. Kontribusi utama dari penelitian ini adalah studi performa algoritma hibrida reinforcement learning dan late acceptance dalam kerangka kerja hyper-heuristics untuk menyelesiakan permasalahan optimasi lintas domain.</p><p> </p><p><em><strong>Abstract</strong></em></p><p class="Abstract"><em>An organization sometimes needs solutions to cross domain optimization problems. The problem of cross domain optimization is a problem that has different characteristics, for example between domain optimization scheduling, vehicle routes, bin packing, and SAT. This optimization is used to support an organization's decision making. In solving these optimization problems, a computational search method is needed. In the literature, almost all optimization problems in NP-hard class are solved by meta-heuristics approach. However, this meta-heuristic has drawbacks, namely tuning parameters are needed for each different problem domain. So this approach is considered less effective. Therefore a new approach is needed, namely the hyper-heuristics approach. Hyper-heuristic method is an approximate computational search method that can solve cross domain optimization problems faster. In this final project there are six cross domain problems to be solved, namely satisfaction (SAT), one dimensional bin packing, permutation flow shop, personnel scheduling, traveling salesman problem (TSP), and vehicle routing problem (VRP). In improving performance, this study examines the effect of the adaptation of the Reinforcement Learning (RL) algorithm as LLH selection combined with the Late Acceptance algorithm as a move acceptance. The results of this study indicate that there are 4 out of six problem domains that have improved performance, namely the SAT, personnel scheduling, TSP, and VRP, while in other domains such as bin packing and flow shop has decreased.</em></p><p><em><strong><br /></strong></em></p>

A Systematic Literature Review for Personnel Scheduling Problems

Organizations need to focus on many parameters to reach their goals such as personnel satisfaction at the top level, profit maximization, increasing system efficiency and minimizing costs. By carefully examining the significant effect of personnel scheduling on the production of goods and services, achieving a fair distribution of work among the employees paves the way for higher motivation and performance of the employees, particularly, in production and service industries. In this paper, a systematic literature review (SLR) approach is used to demonstrate the necessity of scheduling studies in order to overcome the deficiencies in auxiliary activity groups. It sheds light on a new and very important areas such as examining the model structures of sector differences, and guiding researchers. New trends and approaches are presented for the personnel scheduling problems. Different classification perspectives are displayed.

A Hybrid Personnel Scheduling Model for Staff Rostering Problems

The problem of staff scheduling in the airline industry is extensively investigated in operational research studies because efficient staff employment can drastically reduce the operational costs of airline companies. Considering the flight schedule of an airline company, staff scheduling is the process of assigning all necessary staff members in such a way that the airline can operate all its flights and construct a roster line for each employee while minimizing the corresponding overall costs for the personnel. This research uses a rostering case study of the ground staff in the aviation industry as an example to illustrate the application of integrating monthly and daily schedules. The ground staff in the aviation industry case is a rostering problem that includes three different types of personnel scheduling results: fluctuation-centered, mobility-centered, and project-centered planning. This paper presents an integrated mixed integer programming (MIP) model for determining the manpower requirements and related personnel shift designs for the ground staff at the airline to minimize manpower costs. The aim of this study is to complete the planning of the monthly and daily schedules simultaneously. A case study based on real-life data shows that this model is useful for the manpower planning of ground services at the airline and that the integrated approach is superior to the traditional two-stage approach.