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"ABSTRAKMasalah penjadwalan adalah salah satu masalah klasik optimasi kombinatorial yang ada di berbagai segi seperti sistem manufaktur fleksibel, perencanaan produksi,industri penerbangan, dll. Baru-baru ini, beberapa algoritma yang efektif untukoptimasi global dan memecahkan masalah penjadwalan proyek telah diterapkan.Meta-heuristik adalah sistem cerdas, proses iteratif/perulangan yang menekankanpada proses pencarian dan dapat diterapkan terhadap masalah optimasi, sepertimasalah mesin tunggal. Algoritma Elektromagnetisme-Like Mechanism (EM)berbasis populasi meta-heuristik yang telah diusulkan untuk memecahkan masalahyang berkelanjutan secara efektif. Pendekatan baru ini mencoba untuk mencapai efekkonvergensi dan keragaman ketika iteratif diterapkan untuk memecahkan masalah.Algoritma ini diuji secara komputasi dan hasil perhitungan menunjukkan bahwaalgoritma ini melakukan lebih baik daripada aturan penjadwalan sederhana, sepertimetode penjadwalan EDD (Earliest Due Date), SPT (Shortest Processing Time) danLPT (Largest Processing Time).

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AbstractScheduling problem are one of the classical combinatorial optimisationproblems which exist in many diverse areas such as flexiblemanufacturing systems, production planning, air lane industry, etc. Recently, several effective algorithms for global optimization and solving the resource-constrained project scheduling problemhave been proposed. A Meta-heuristics is an intelligent, iterative process that guides a search and can be applied towards optimization problem, such as the single machine problem. An Electromagnetism-like Mechanism (EM) Algorithm is a populationbased meta-heuristic which has been proposed to solve continuous problems effectively. This new approach attempts to achieve the convergence and diversityeffects when it is iteratively applied to solve the problem. This algorithm is tested on the computational results show that this algorithm performs better than the simple scheduling rules, such as EDD (Earliest Due Date) scheduling method, SPT (ShortestProcessing Time) and LPT (Largest Processing Time) scheduling method."

"ABSTRAKPT X merupakan perusahaan yang bergerak dalam pemasaran alat tulis-menulis kantor. Sistem transportasi perusahaan hanya digunakan untuk keperluan administrasi barang. Belum ada sistem yang digunakan untuk menyelesaikan permasalahan rute kendaraan perusahaan. Heuristics klasik merupakan suatu pendekatan yang dapat digunakan untuk menyelesaikan masalah ini. Metode ini mampu menghasilkan solusi yang cukup baik dengan waktu perhitungan yang singkat. Dengan mengkombinasikan metode-metode yang ada danmembandingkan hasil yang diperoleh maka diketahui metode heuristics klasik terbaik bagi permasalahan rute kendaraan perusahaan, yaitu metode yang menghasilkan jarak tempuh terpendek. Dari hasil penelitian, solusi terbaik diperoleh dengan menggabungkan metode Savings dan Or-Opt.

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ABSTRACTPT X is a company that focusing its business on distributing stationeries. Its Transportation Management System is used only for administrative matters. There is not any system that can be used for managing company?s vehicle routing problem. Classical heuristics is an approach that can solve this problem. It can produce quite good solutions with modest computing time. By merging methods and comparing solutions produced, the best classical heuristics method will be known: it is a method that produces solutions with minimum total distance. From this research, the best solutions produced by merging Savings and Or-Opt Method."

"ABSTRACTThis paper considers an unrelated parallel machine scheduling problem with the objective of minimizing the number of tardy jobs. Each machine should stop periodically to perform maintenance activities. The problem, motivated from a wafer manufacturing company, considers the job scheduling and maintenance activities simultaneously under dirt constraint. That is, the dirt accumulation in the machine does not exceed the prespecified dirt limit. A mixed binary integer programming (MBIP) model is developed to find optimal solutions, and two three-phase heuristics are proposed. The heuristics assign each job to its most efficient machine first. Then, an intension of Moores algorithm is applied for each machine, and finally the solution is improved by the forward/backward insert mechanism. The experimental results showed that the proposed heuristics perform well. Furthermore, the efficiency of the MBIP model and the impact of the dirt accumulation as well as maintenance time are studied in detail."

"Masalah pemilihan rute optimal dari sejumlah kendaraan untuk melayani sejumlah pelanggan pada jasa penjemputan (pickup service) barang dengan jumlah pelanggan yang berubah terhadap waktu (Dynamic Vehicle Routing Problem/ DVRP) terjadi pada perusahaan-perusahaan layanan angkutan barang. Untuk mengatasi masalah DVRP, optimasi rute dilakukan secara berkala berdasarkan pembagian waktu yang proporsional pada jam operasional perusahaan. Jumlah pelanggan yang bertambah banyak mengakibatkan kemungkinan rute bertambah banyak, dimana setiap rute bersesuaian dengan setiap variabel/ kolom pada model matematis DVRP yang berupa pemrograman linier. Salah satu metode yang digunakan untuk menyelesaikan masalah pemrograman linier dengan jumlah kolom yang sangat besar adalah metode column generation. Pada skripsi ini akan dibahas penerapan metode dynamic column generation yang merupakan perluasan dari metode column generation (statis) untuk menyelesaikan masalah pemilihan rute kendaraan yang optimal pada jasa penjemputan barang (pickup service) yang mempertimbangkan jumlah pelanggan yang berubah terhadap waktu (Dynamic Vehicle Routing Problem/ DVRP). Berdasarkan hasil implementasi menggunakan program komputer, rute yang diterapkan metode dynamic column generation lebih baik dibandingkan dengan rute yang tidak diterapkan metode dynamic column generation.

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The choice of the best routes used a set of vehicle to serve a set of customer on pickup of goods services with the number of customers changing (dynamic) over time (Dynamic Vehicle Routing Problem/ DVRP) occur on a freight services company. To overcome the problem, a route optimization is done periodically based on the proportional time partition to the company operational hours. Growing number of customer cause growing number of possibilities route, which every route corresponding to every decision variable/ column in the DVRP linear programming model. One of the methods used to solve the problem of linear programming with a very large number of columns is column generation method. In this undergraduate thesis will be discussed about the application of dynamic column generation method which is an extension of the column generation (static) to solve the problem of selecting an optimal vehicle route on pickup of goods services which consider the number of customers changing (dynamic) over time (Dynamic Vehicle Routing Problem/ DVRP). Based on the result of implementation using the computer program, the route applied using the dynamic column generation method better than the route not applied using the dynamic column generation method."

"Indonesia adalah salah satu negara yang paling rentan terhadap bencana di dunia. DKI Jakarta sebagai ibukota Indonesia tidak luput dari kerentananan ini, terutama dalam hal bencana banjir. Oleh karena itu, DKI Jakarta perlu menerapkan suatu strategi untuk meminimalisasi dampak bencana. Salah satu yang dapat dilakukan dalam adalah minimalisasi total waktu evakuasi korban bencana. Permasalahan tersebut dapat dimodelkan sebagai Rescue Units Assignment and Scheduling Problem (RUASP). Penelitian ini menggunakan algoritma SCHED heuristics serta column generation dalam mengurangi waktu evakuasi melalui 3 parameter evaluasi: average, makespan, dan weighted sum of completion time. Algoritma tersebut diterapkan pada 25 skenario yang dikembangkan berdasarkan 4 variabel: (1) Jumlah tim evakuasi; (2) Jumlah titik bencana; (3) Jenis fungsi objektif; dan (4) Kondisi jalanan. Secara rata-rata, column generation dapat mengurangi waktu average sebesar 38%, waktu makespan sebesar 1%, dan weighted sum of completion time sebesar 57%. Setiap variabel memiliki pengaruh yang berbeda terhadap ketiga parameter waktu evakuasi. Jumlah titik bencana adalah variabel yang paling berpengaruh sedangkan kondisi jalanan adalah variabel yang memiliki pengaruh paling kecil.

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Indonesia is one of the most vulnerable countries to disasters in the world. DKI Jakarta, the capital city of Indonesia, is also as vulnerable, especially in case of flood disaster. Therefore, DKI Jakarta needs to implement strategies to minimize disaster impacts when it happens. One of the ways to do so is to minimize the evacuation time needed to rescue flood casualties. This can be modeled as Rescue Units Assignment and Scheduling Problem (RUASP). This study utilizes SCHED heuristics and column generation algorithm to reduce evacuation time measured in three parameters: average, makespan, and weighted sum of completion time. These algorithms are implemented on 25 scenarios that are developed based on 4 variables: (1) Number of rescue units; (2) Number of incidents; (3) Type of objective function; and (4) Road condition. On average, column generation is able to reduce average completion time by 38%, makespan of completion time by 1%, and weighted sum of completion time by 57%. Each variable has yields different sensitivity on each of the three parameters. Number of incidents is the most sensitive variable while road condition is the least sensitive variable.

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"This book presents a wide-ranging review of the latest research and development directions in thermal systems optimization using population-based metaheuristic methods. It helps readers to identify the best methods for their own systems, providing details of mathematical models and algorithms suitable for implementation.To reduce mathematical complexity, the authors focus on optimization of individual components rather than taking on systems as a whole. They employ numerous case studies: heat exchangers; cooling towers; power generators; refrigeration systems; and others. The importance of these subsystems to real-world situations from internal combustion to air-conditioning is made clear.The thermal systems under discussion are analysed using various metaheuristic techniques, with comparative results for different systems. The inclusion of detailed MATLAB® codes in the text will assist readers-researchers, practitioners or students-to assess these techniques for different real-world systems."

"Penelitian ini menyelidiki optimasi rute pengiriman untuk CJ Logistics di Wilayah X, Area A, Korea Selatan, dengan mengatasi Capacitated Vehicle Routing Problem (CVRP) menggunakan algoritma hybrid heuristik. Studi ini mengintegrasikan Pendekatan Tetangga Terdekat, Pencarian Lokal 2-Opt, dan Simulated Annealing untuk mengurangi jarak perjalanan dengan tetap mempertimbangkan kendala kapasitas kendaraan. Dalam kondisi penambahan dan penghapusan hingga 30% node untuk menilai kinerja algoritme di bawah permintaan yang berfluktuasi, pendekatan heuristik hibrid mencapai peningkatan kinerja keseluruhan sebesar 1,08%. Simulated Annealing dikombinasikan dengan Local Search 2-Opt Inter-route mengungguli Simulated Annealing dikombinasikan dengan Local Search 2-Opt Intra-route dengan rata-rata sebesar 3,73%, menunjukkan kemampuannya untuk mencari tempat pencarian yang lebih luas. Temuan ini menekankan manfaat praktis heuristik hibrid, termasuk pengurangan jarak tempuh dan peningkatan ketepatan waktu pengiriman, sehingga menawarkan wawasan berharga bagi CJ Logistics dan sektor logistik e-commerce.

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This research investigates delivery route optimization for CJ Logistics in Region X, Area A, South Korea, by addressing the Capacitated Vehicle Routing Problem (CVRP) using hybrid heuristic algorithms. The study integrates Nearest-Neighbor Approach, Local Search 2-Opt, and Simulated Annealing to reduce travel distance while considering vehicle capacity constraints. Under the conditions of adding and removing up to 30% of nodes to assess the performance of the algorithm under fluctuating demand, hybrid - heuristics approach achieved overall performance improvement of 1.08%. Simulated Annealing combined with the Local Search 2-Opt Inter-route outperformed Simulated Annealing combined with the Local Search 2-Opt Intra-route by an average of 3.73%, demonstrating its ability to search broader search place. These findings emphasizes the practical benefits of hybrid heuristics, including reduced mileage and improved delivery timeliness, offering valuable insights for CJ Logistics and the e-commerce logistics sector. "

"In the chapters in Part I of this textbook the author introduces the fundamental ideas of artificial intelligence and computational intelligence. In Part II he explains key AI methods such as search, evolutionary computing, logic-based reasoning, knowledge representation, rule-based systems, pattern recognition, neural networks, and cognitive architectures. Finally, in Part III, he expands the context to discuss theories of intelligence in philosophy and psychology, key applications of AI systems, and the likely future of artificial intelligence. A key feature of the author's approach is historical and biographical footnotes, stressing the multidisciplinary character of the field and its pioneers.The book is appropriate for advanced undergraduate and graduate courses in computer science, engineering, and other applied sciences, and the appendices offer short formal, mathematical models and notes to support the reader."