<p><em>Abstrak</em> - <strong>Pemerintah Kota Semarang melakukan pembentukan bank sampah untuk menekan jumlah sampah di tempat pembuangan akhir. Masih bayak masyarakat di Kota Semarang yang belum mengikuti program tersebut. Pada penelitian ini dilakukan pemodelan untuk meningkatkan partisipasi masyarakat dalam mengelola sampah. Keikutsertaan masyarakat dipengaruhi oleh beberapa faktor yaitu </strong><strong><em>intention</em></strong><strong>, <em>sosial norm</em>, jarak menuju <em>recyclingsiteagent </em>dan <em>outcome</em>. <em>Intention</em> seseorang dalam melakukan pengelolaan sampah secara signifikan dipengaruhi oleh <em>awareness of consequences</em>, <em>ascription of responsibility</em> dan <em>personal norm</em>. Pemodelan dan skenario menggunakan metode <em>agent based modeling,</em> menghasilkan usulan kebijakan yaitu dengan mendirikan empat bank sampah. Melalui keputusan tersebut mampu menghasilkan 93% partisipasi rumah tangga dalam mengelola sampah dan 2,4 ton sampah yang dikumpulkan hingga periode ke 60 minggu.</strong></p><p><em>Abstract - </em><strong>Semarang government has established a Waste Bank to reduce the amount of waste in landfills. There are still many people in Semarang who have not participated in this program. This research modeling aims to increase public participation in managing waste. Public participation is influenced by intention, social norm, and distance to recycling site agents as well as the outcome. An intention to of managing waste is significantly influenced by awareness of consequences, the ascription of responsibility and personal norm. In this study using agent-based modeling. The results obtained from this model and scenarios are the intervention to establishing four Waste Bank. It produced 93% of households participating in managing waste and 2.4 tons of garbage collected in the 60 weeks.</strong></p><strong><em>Keywords</em></strong><em> – Agent-based modeling, Norm Activation Model, Waste Separation Behavior, Bank Sampah, </em>
Preventive maintenance is a planned and scheduled maintenance method that is carried out before a machine failure occurs. The maintenance schedule can be determined based on experience, historical data, or recommendations. Selecting the maintenance schedule greatly affects the production system. The Clin machine in cement manufacturing has an important role in the cement production process. During treatment, the client machine cannot produce clinker, so it is necessary to plan a production system to meet the demand. This paper aims to design an optimization model for determining the preventive maintenance schedule for cement manufacturing by considering the production process and inventory control. Mathematical models with binary options are used to model that system. The model supports showing the optimal preventive maintenance schedule for the ciln machines with a binary decision each period. This mathematical model describes the interaction of production planning, inventory control, and scheduling of total maintenance on a kiln machine. The goal of this system is to determine the optimal preventive maintenance schedule with minimum costs. In addition, the system's output is the optimal production and inventory decision rule for each period. Based on the analysis and simulation of the model with the deterministic and dynamic demand, the optimal preventive maintenance schedule is in the 9th and 21st periods. The kiln machines are maintained every July with minimal costs. The model scenario shows the interaction of the variables and the sensitivity of the production capacity and demand to the decision rule of the variable.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.