Perencanaan Sumber Daya Pada Proyek Menggunakan Algoritma Ant Colony

Savitri, Niken A. (2008) Perencanaan Sumber Daya Pada Proyek Menggunakan Algoritma Ant Colony. Masters thesis, Institut Teknologi Sepuluh November.

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Abstract

Alokasi sumber daya sering kali menjadi salah satu masalah penting dalam penjadwalan proyek. Masalah ini sering kali timbul karena sebagian besar Project Manager mengestimasi jadwal pekerjaan dan waktu jaringan antaraktivitas tanpa mempertimbangkan ketersediaan sumber daya. Resource-Constrained Project Scheduling Problem (RCPSP) berkaitan dengan pengalokasian suatu sumber daya atau sekumpulan sumber daya yang memiliki kapasitas terbatas kepada aktivitas-aktivitas tertentu untuk mencapai tujuan yang telah ditetapkan. Beberapa pendekatan telah dikembangkan untuk mengatasi RCPSP. Untuk proyek dengan skala yang relatif kecil, penggunaan konsep matematis murni, seperti linear programming, dapat digunakan untuk menemukan solusi optimal. Namun, untuk proyek dengan kombinasi permasalahan yang cukup kompleks, konsep matematis murni tidak lagi mampu mengakomodasi proses pencarian solusi secara efektif. Hal tersebut mendorong penggunaan pendekatan alternatif untuk mengatasi permasalahan resource-constrained project scheduling, salah satunya adalah pendekatan heuristik. Pada penelitian ini, algoritma Ant Colony Optimization (ACO) diterapkan untuk menyelesaikan RCPSP dengan melakukan analisis sensitivitas terhadap parameter-parameter input algoritma ACO. Berdasarkan penerapan algoritma Ant Colony terhadap 11 contoh proyek dengan jumlah aktivitas antara 4 hingga 10 aktivitas, diperoleh persentase solusi mendekati optimal (optimal) sebesar 63,64%. Sementara itu, dari seluruh proyek yang diujicobakan, diperoleh persentase durasi yang lebih tinggi dibandingkan penjadwalan manual sebesar 4,29%. Artinya, persentase kelebihan durasi yang dihasilkan oleh Ant Colony dibandingkan dengan penjadwalan manual hanya sebesar 4,29%. Berdasarkan analisis sensitivitas, diketahui bahwa parameter-parameter ACO tidak memberikan pengaruh yang signifikan terhadap durasi proyek yang diperoleh.
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Resource allocation is often one of the major challenges in project scheduling. This problem generally arises because most Project Managers estimate activity schedules and network durations without considering resource availability. The Resource-Constrained Project Scheduling Problem (RCPSP) concerns the allocation of a resource or a set of resources with limited capacities to specific activities in order to achieve predetermined objectives. Several approaches have been developed to address the RCPSP. For relatively small-scale projects, pure mathematical approaches, such as linear programming, can be used to obtain optimal solutions. However, for projects involving complex combinatorial problems, pure mathematical approaches are no longer able to effectively accommodate the solution-search process. This limitation has encouraged the use of alternative approaches to address resource-constrained project scheduling problems, including heuristic approaches. In this research, the Ant Colony Optimization (ACO) algorithm is applied to solve the RCPSP, with a sensitivity analysis conducted on the input parameters of the ACO algorithm. Based on the application of the Ant Colony algorithm to 11 project instances with between 4 and 10 activities, a near-optimal (optimal) solution was obtained in 63.64% of the cases. Furthermore, across all tested project instances, the percentage of project durations exceeding those obtained through manual scheduling was 4.29%. This means that the percentage of additional duration produced by the Ant Colony algorithm compared with manual scheduling was only 4.29%. Based on the sensitivity analysis, it was found that the ACO parameters did not have a significant effect on the resulting project duration.

Item Type:	Thesis (Masters)
Additional Information:	RTI 658.53 Sav p
Uncontrolled Keywords:	Resource Constraint Project Scheduling Problem, Ant Colony Algorithm.
Subjects:	T Technology > TS Manufactures > TS157.5 Production scheduling
Divisions:	Faculty of Industrial Technology > Industrial Engineering > 26101-(S2) Master Thesis
Depositing User:	magang .
Date Deposited:	29 Sep 2026 06:28
Last Modified:	29 Sep 2026 06:28
URI:	http://repository.its.ac.id/id/eprint/145015

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