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Cracking The Truck Dispatch Problem in Open-Pit Mining to Enhance Competitiveness Coal Market in Indonesia
is a country which is rich in energy and mineral resources. With an area of 1,910,931 km2, Indonesia has some of the pre-eminent resources such as petroleum, natural gas, nickel, coal and others. Indonesia is listed as one of the largest coal producer in the world in 2014. As one of the highest coal producing countries, Indonesia has a reliance on the use of coal for generating electricity or power plant. This is in line with the Indonesian government program in the long-term energy mix of coal has increased the portion of the year 2013 by 24% to 2025 by 33%. But in fact it actually happened inversely where increased production of domestic coal is not followed by a competitive price.
The existence of weakening coal prices, which decreased from year to year, causing the mining sector, especially companies engaged in coal experienced significant negative impacts. One of the most essential where companies are not experiencing growth on corporate profits, but declined.
Based on these phenomena is the only way for the company to continue running the business activities through efficiency measures on many things to be able to cover operational costs. One of the efficiencies that can be done such efficiency on the operational side. As mentioned by Alarie & Gamache (2002) and Ercelebi & Bascetin (2009) the material transportation represents 50 per cent of the operating costs for an open pit mine. Therefore, one of the efficiencies that can be done and has a substantial impact through efficient on truck dispatching problem in the mine area
SEMS was proposing to overcome this problem using simulation and optimization approach to analyze the complexity of truck dispatch problem in open pit mining. This approach uses the look-ahead algorithm and multi-stage algorithm. Solving the issue focuses on the development of look-ahead methods that will be applied to the truck dispatch problem. Simulation and optimization approach on this issue is run simultaneously with the help of software ProModel as the media to solve the problem. Look-ahead algorithm is an approach to solve the problems of the assignment of the fleet to look ahead what will happen near future with based on previous information system gathered as a reference to decide the scheduling for entities that exist in back of the queue. This research basically to answer the question about where this truck should go now and when it leave this place in order to maximaze amount of coal production. The output of the research is to obtain the best assignment rules for truck dispatch problem with four indicators are, amount of coal production, productivity of loader, productivity truck with block and without block.
Using the model, a significant improvement on amount of production has been achieved to enhance competitiveness coal market.
This research is conducted by Achmad Yusaq Faiz Fadin, Komarudin, and Armand Omar Moeis.
It’s inevitable that the price of commodities in the eastern part of Indonesia is much higher than in the west. The unbalance economic growth between these two parts of Indonesia, might be one of the reason. Hence, the main trade activities happen mostly in the west, especially in Java island which could be called as the center of this country.
According to Meeuws and Bahagia, Indonesia as an archipelago country is dependent to maritime logistics on transporting goods and transportation. But unfortunately the performance of logistic in Indonesia is still poor. Also, the cost of logistics in Indonesia is still high. Until 2011, the cost of logistics is equal to 24.64% of Indonesia’s GDP.
Liner shipping company, as the provider of maritime logistics services, is looking for technology for optimizing their cost planning in operating and enhancing their fleets. The purpose of this plan is to make the capacity of their fleets matches the demand of container. The main goal for every company is of course to gain maximum profit. But the high of logistic cost and the unbalance trade activities in Indonesia might be the problem for liner shipping company to achieve that.
In liner shipping there are three different time-horizon levels. There are strategic level, tactical level, and operational level. The strategic level has the longest horizon and it involves determining the optimal fleet. The tactical planning level is done once in the several months and it involves constructing ship schedules. The shortest term decision level is the operational level. It involves determining the optimal allocation of cargo.
Previously there has been a research on Indonesia’s maritime logistic, especially in liner shipping made by Meijer and Van Rijn from Netherland. The purpose of their research is to gain maximum profit by optimizing liner shipping network design in Indonesia. But their research did not consider demand in the future (multi-period planning). Multi-period planning is crucial for liner shipping company in planning and developing the maritime logistics business.
SEMS researchers see this as an opportunity for our research. Therefore we make a research on multi-period liner shipping network design in Indonesia. The purpose is for the liner shipping company to gain maximum profit by optimizing their network design. In doing so, there are given conditions and scenarios in order to acknowledge which scenario suits best for multi-period planning. The outcome of this research is also to design the optimum logistics network which involves, which routes to be used, how many ships have to be allocated, and the cargo allocation.
This Research is conducted by Mellianna Fiannnita Purba, Komarudin and Armand Omar Moeis.
“A game could makes you forget that you are actually learning. Packaging a very complicated and
complex reality in to a simple learning tool is what Serious Simulation Game designed for.”
Have you ever thought about how your soft drinks, or your lotion, could arrive in the shelf even before you need it? That effort to make sure you get what you want when you step into the store is called Supply Chain Management (SCM). SCM is indeed important not only for customers, but also for the company. Every company tries to manage its supply chain efficiently and effectively, as SCM alone costs 30% – 70% of the total of production cost. However, managing a supply chain is not an easy job to do.
Andersson and Wemner, through their article in 2008, even stated that many SCM practitioners have difficulties in making the right decision. This fact triggers other urgency: to find talents who understand SCM and able to make the right decision. To gain better knowledge about making decision in SCM, some researches have proven that traditional classroom teaching method might not be the best. Experiential learning, just like its name, provides you a learning method by experiencing the reality, which is an effective way to learn how to make a decision. One of its most effective media is Serious Simulation Game (SSG). To simply put, SSG is an entertaining game with non-entertaining purpose. However, there are still a very limited number of researches on the SSG about the whole supply chain.
In order to enhance the knowledge of SCM, especially for students, SEMS researchers have created a SSG named H2! Supply Chain Management Game. This game helps players to improve their ability in decision making, and also their skills in analyzing report and teamwork. H2! Supply Chain Management Game is played by 4 players per team, where there will be 4 teams competing against each other. The SSG was designed using a Spreadsheet Applications as its basis. It was then tested two times, Prototype 1 and Prototype 2. To deliver the learning points of the game well, this SSG was played in 4 phases. It consists of Briefing, Playing, Debriefing, and Evaluation.
The result of the evaluation phase shows that H2! Supply Chain Management Game can be used to deepen the knowledge of the players. It also helps players to understand supply chain’s important elements which improve their ability in analyzing data and making right decisions.
This research is conducted by Rachel Giovanni Hasibuab, Maya Hashilah, Arry Rahmawan and Akhmad Hidayatno
An article by Bhagas & Ara
Academics? Yes. Researches? Of course, yes. Having fun? Extremely yes! That’s SEMS – in spite of its activities related to its functions as a laboratory, SEMS members will always find a great way, perhaps many ways, to meet excitement, freshen moods, while also strengthen the relationship between them all. Exceptional in the lab, but seriously, we can turn things real fun.
There are actually many informal events, from the small one to the extraordinary one, by SEMS members to have fun together. One of them is written here. This article will mainly spread you up about our latest day out – Paintball! On Saturday, 14th of May 2016, we set out from Depok to a paintball field in Cibubur. This event was attended by SEMS’ RAs and LAs of 2015-2016, one researcher, and three exchange students from French – although it was also open for the lecturers and alumni.
We’re divided into 3 matches with 2 teams in each match, red and blue team. The red team (rangga, gita, pierre, shilla, and randa) beats the blue team (adit, anggit, qualid, ayud, and sydney) in the first match. Randa went rouge and razed adit and anggit with bullets and they actually felt hurts from the paint bullets. In the end, it was shilla’s “hiding trick” that won it for the red team.
SEMS Research Highlights 2015: Enabling the Adoption of Alternative Fuel Vehicles – An Approach to Refueling Station Spatial Placements
Refueling station accessibility for more cleaner and greener energy is one of the most important factors in the adoption of alternative fuel vehicles.
If the refueling station is not strategically located, people will be hesitant to adopt the new alternative fuel (such as gas, hydrogen or other type of alternative fuel).
Due to the importance of locations, researcher in System Engineering, Modeling, and Simulation lab of Universitas Indonesia develop an operations research-based spatial-model to determine ideal refueling station locations. Early results has delivered a whopping gain up to 96% demand coverage, while at same time maintained profitability in each individual location.
The model was develop in three stages: demand mapping, spatial simulation and financial screening.
First stage is determining how many refueling stations are needed to cover potential demand within the scope. This is done through a series of calculations: total vehicles converted are multiplied by each vehicle’s fuel demand, subtracted by the amount of existing alternative fuel supply (if some refueling stations already exist), and then divided by an individual station’s capacity. Through these calculations, a number of stations needed to be built can be obtained.
In the second stage, multiple variables are used as input to reflect real-world conditions in a geographic information system-based spatial model. These variables include spatial data, such as the locations of distributed potential demand, already-existing alternative refueling stations, and candidate locations to build the new refueling stations, as well as non-spatial data, like the daily capacity of each refueling station and the maximum distance car owners are willing to travel to reach a station. The model then uses a location-allocation technique—the ‘maximize capacitated coverage’ approach—to determine the ideal locations for every refueling stations. These locations cover the most demand possible while subjecting to the capacity of individual stations.
The final stage is financial screening of the chosen locations. Three economic metrics are used to determine profitability: the NPV, IRR, and payback period. The demand in each location chosen, obtained through the spatial model, is entered into a simple financial model of a refueling station’s operations to reveal the three economic metrics. Afterwards, a final analysis is conducted to determine other alternatives to reach demand points not yet covered, or to replace unprofitable locations.
In this study, the researchers focused on the adoption of natural gas vehicles by public transportation fleets in DKI Jakarta, as the case study. There were four scenarios used, based primarily on the types of candidate locations (to simulate ease of implementation) and the simulated traffic conditions. The resulting locations show a range of 79-96% coverage, with the lower numbers found in traffic jam scenarios. To boost coverage and replace unprofitable locations, there were 2 possible alternatives: constructing stand-alone refueling stations (not constrained by candidate locations) and deploying mobile refueling units (MRUs).
This work has implications for various types of alternative fuel vehicles, not just limited to natural gas. Refueling stations are capital-heavy infrastructures regardless of fuel type, especially for new, still growing vehicle types. The approach used is replicate-able and adjustable for other situations to improve the adoption process.
This research was conducted by Aziiz Sutrisno, Akhmad Hidayatno, Dio Aufa Handoyo, Eka Nugraha Putra