DYNAMIC SCHEDULING MODELLING OF JETTY AT FUEL OIL TERMINAL PONTIANAK USING DISCRETE-EVENT SYSTEMS AND MODEL PREDICTIVE CONTROL

Terminal BBM (TBBM) are units that receive large volumes of BBM products and then distribute them to various service areas, both industrial and retail. The location of TBBM Pontianak is in the middle of residential areas and close to the Sungai Kapuas. There are 2 types of jetties at TBBM, namely je...

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Main Author: Ramanda, Adrian
Format: Final Project
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/68654
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Institution: Institut Teknologi Bandung
Language: Indonesia
id id-itb.:68654
spelling id-itb.:686542022-09-19T08:02:05ZDYNAMIC SCHEDULING MODELLING OF JETTY AT FUEL OIL TERMINAL PONTIANAK USING DISCRETE-EVENT SYSTEMS AND MODEL PREDICTIVE CONTROL Ramanda, Adrian Indonesia Final Project Terminal BBM, berth allocation, total waiting time, discrete-event systems, and model predictive control INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/68654 Terminal BBM (TBBM) are units that receive large volumes of BBM products and then distribute them to various service areas, both industrial and retail. The location of TBBM Pontianak is in the middle of residential areas and close to the Sungai Kapuas. There are 2 types of jetties at TBBM, namely jetties for supply (jetties 1 and 2) and a jetty for consignment (jetty 3). The berth allocation is not working efficiently. This is indicated by the average waiting time for ships at jetty 1, jetty 2, and jetty 3, respectively, 17.69 hours, 18.26 hours, and 2.11 hours. The UNCTAD 2021 standard states that the average waiting time for ships is 14 hours. The root cause of the problem raised in this research is the inefficient berth allocation. The purpose of this study is to minimize the total waiting time of the ship as well as minimize berth occupancy ratio (BOR) and the cost of chartering the ship (charter fee). Approaches in this research are modelling with discrete-event systems (DES). Modelling with the DES method is chosen due to the characteristics of the berth allocation status that change from time to time. The berth allocation involves several status variables, namely starting time of berthing, remaining operating time, and time of completion of berthing. The optimization of the DES model is carried out using model predictive control (MPC). There are two scenarios, namely scenario 1 that consider the tides, and scenario 2 that does not consider the tides. Compared to the existing berth allocation, from the model output, the average waiting time for ships at the jetty for supply (jetty 1 and 2) becomes 8.15 hours and 0.24 hours, respectively for scenarios 1 and 2. Average waiting time for ships at the jetty for consignment (jetty 3) to 1.83 hours and 1.43 hours respectively for scenarios 1 and 2. text
institution Institut Teknologi Bandung
building Institut Teknologi Bandung Library
continent Asia
country Indonesia
Indonesia
content_provider Institut Teknologi Bandung
collection Digital ITB
language Indonesia
description Terminal BBM (TBBM) are units that receive large volumes of BBM products and then distribute them to various service areas, both industrial and retail. The location of TBBM Pontianak is in the middle of residential areas and close to the Sungai Kapuas. There are 2 types of jetties at TBBM, namely jetties for supply (jetties 1 and 2) and a jetty for consignment (jetty 3). The berth allocation is not working efficiently. This is indicated by the average waiting time for ships at jetty 1, jetty 2, and jetty 3, respectively, 17.69 hours, 18.26 hours, and 2.11 hours. The UNCTAD 2021 standard states that the average waiting time for ships is 14 hours. The root cause of the problem raised in this research is the inefficient berth allocation. The purpose of this study is to minimize the total waiting time of the ship as well as minimize berth occupancy ratio (BOR) and the cost of chartering the ship (charter fee). Approaches in this research are modelling with discrete-event systems (DES). Modelling with the DES method is chosen due to the characteristics of the berth allocation status that change from time to time. The berth allocation involves several status variables, namely starting time of berthing, remaining operating time, and time of completion of berthing. The optimization of the DES model is carried out using model predictive control (MPC). There are two scenarios, namely scenario 1 that consider the tides, and scenario 2 that does not consider the tides. Compared to the existing berth allocation, from the model output, the average waiting time for ships at the jetty for supply (jetty 1 and 2) becomes 8.15 hours and 0.24 hours, respectively for scenarios 1 and 2. Average waiting time for ships at the jetty for consignment (jetty 3) to 1.83 hours and 1.43 hours respectively for scenarios 1 and 2.
format Final Project
author Ramanda, Adrian
spellingShingle Ramanda, Adrian
DYNAMIC SCHEDULING MODELLING OF JETTY AT FUEL OIL TERMINAL PONTIANAK USING DISCRETE-EVENT SYSTEMS AND MODEL PREDICTIVE CONTROL
author_facet Ramanda, Adrian
author_sort Ramanda, Adrian
title DYNAMIC SCHEDULING MODELLING OF JETTY AT FUEL OIL TERMINAL PONTIANAK USING DISCRETE-EVENT SYSTEMS AND MODEL PREDICTIVE CONTROL
title_short DYNAMIC SCHEDULING MODELLING OF JETTY AT FUEL OIL TERMINAL PONTIANAK USING DISCRETE-EVENT SYSTEMS AND MODEL PREDICTIVE CONTROL
title_full DYNAMIC SCHEDULING MODELLING OF JETTY AT FUEL OIL TERMINAL PONTIANAK USING DISCRETE-EVENT SYSTEMS AND MODEL PREDICTIVE CONTROL
title_fullStr DYNAMIC SCHEDULING MODELLING OF JETTY AT FUEL OIL TERMINAL PONTIANAK USING DISCRETE-EVENT SYSTEMS AND MODEL PREDICTIVE CONTROL
title_full_unstemmed DYNAMIC SCHEDULING MODELLING OF JETTY AT FUEL OIL TERMINAL PONTIANAK USING DISCRETE-EVENT SYSTEMS AND MODEL PREDICTIVE CONTROL
title_sort dynamic scheduling modelling of jetty at fuel oil terminal pontianak using discrete-event systems and model predictive control
url https://digilib.itb.ac.id/gdl/view/68654
_version_ 1822278274966880256