Search

This paper proposed a mathematical model: Mixed Integer Non-Linear Programming to optimize the utilization of space in the container yard. The model developed was considering in two types of different blocks: exclusive block and sharing block. The exclusive block is a space in the container yard uniquely serving to the certain forwarder, otherwise it is classified as sharing block type. Base on this model, the maximum revenue is gained by reducing the loss of sales opportunity and considering fulfilment number of containers demand in each block in the container yard according to the capacity available. The mathematical model was applied to LINGO 11 software to obtain the optimal solution and its results are 14 sharing blocks and 9 exclusive blocks. The Total maximum profit is $ 29,816 per month. The proposed model was flexible enough to respond to changes in parameters related to future demand.

Carriers and freight forwarders (FFs) play several roles in ensuring the effective flow of goods delivery. They are tasked with accommodating the shippers’ needs in transporting goods via containers, following the carrier’s ship destination plan. In practice, FFs often experience overbook and underbook capacity toward the capacity limit for shipping goods. This has consequently increased FF costs. However, for the carrier, this will increase profits. The aim of this study is to develop strategies for carriers and FFs using a mathematical model approach to obtain the optimal quantity of booking shipping capacity; thus, overbooks or underbooks can be minimized. More broadly, this study also proposes several strategies to increase the profits of all parties, both for FFs through collaboration and for carriers by directly selling marketing shipping capacity to shippers. Optimum booking quantity for goods delivery from each FF is performed through the particle swarm optimization (PSO) approach. Using four FF collaboration scenarios, the model test results yield a profit of $121,270, 2.14% more than the non-collaboration scenarios with a profit of $119,169. The carrier generated an average profit of $39,926 when the FF did not collaborate. Conversely, when the FFs collaborated, the carrier experienced a decline of –1.88% on average profit, which was $39,175. However, if the carrier responds with direct selling, the profit will increase by 9.36%, which is $42,840. It is concluded that collaboration can increase the profits of FFs but reduce the profits of carriers, while direct selling can increase the carrier’s profits.

The Berth Allocation Problem (BAP) is a critical issue for the efficient operation of a container terminal. While there are many works on berth allocation, most of the BAP models have used the assumption of a deterministic situation where arrival time and number of containers brought by a vessel are known in advance. Such a deterministic assumption, however, never holds true in real life. The purpose of this paper is to examine how collaboration between berth terminals could affect the port performance when dealing with uncertainty. Given the complexity of the problem, we have used discrete event simulation to model the system. Two major scenarios were evaluated, namely non-collaborative-response and collaborative-response. Collaborative-response is implemented by sharing resources such as berth, quay cranes and container yard among two terminals. The port performance was evaluated based on ship waiting time, container handling time and total ship turnaround time. The results show that collaborative strategy can reduce overall waiting time, container-handling time and total ship turnaround time; however, the impacts on each terminal vary.

Every single country of this world wants to enlarge and optimize their territory. It causes every single country get competition to strengthen their country. In the other side they want some comfort condition, the condition is free from anxiety and the ability to predict threats, is a situation esired by all citizens. Not yet optimal in countermeasures and predicting the direction of the threat. The need for innovation in the method of determination according to the current state. Research based on the Decision Support System (DSS) trying will provide a solution in determining the predictor of the threat state. Using the Profile Matching method researchers try to map the profiles of countries located in the region. Providing problem solving by modifying the Profile Matching method is to start with delphi method in obtaining the determining criteria of research and weighting it with Borda technique. The criteria and weighting as the constituent criteria of the threat predictor country's strength profile, followed by the role so that the names of the threat predictor countries in Southeast Asia are obtained. The determination of the predictor state will facilitate in countermeasures or deal with it as well as provide actual information of where the threat comes from and the disertor criteria where the country can be weakened by Indonesia. Keywords: Strategic Position, threat, Southeast Asia Country, Profile Matching, Borda Method.

Indonesia as archipelagic country, needs strong maritime sector security. The Indonesian Navy in Law Number 34 Year 2004 has been given the mandate to safeguard the sovereignty of the Republic of Indonesia. The Armada II Command as Kotamaops annually carries out Military Operations Other Than War (OMSP) which is carried out under the command of Guspurla and Guskamla. Intelligence information regarding the estimated threat / contingency that will occur is very influential on decision makers in operational planning. With the contingency provided by the intelligence staff, it is necessary to have an appropriate marine operation modeling. In a multi-operation operation, it is necessary to have a supporting attribute, that is, a base ship. The purpose of this study is to formulate an operation modeling using the selection of a headquarter ship which is preceded by the determination of contingency priorities. This study uses MCDM which consists of MCDA and MCDO which uses the integration of the Delphi method, AHP, Fuzzy weighting, Goal programming and linear Integer-Zero one programming. Based on the processing of Delphi and AHP, in determining the threat of OMSP, it is influenced by the number / intensity of incidents of 20.4%; impact of Ipoleksosbudhankam 29.4%, Capabiity 38.2% and involvement factor of 12.1% where national jurisdiction marine security operations are selected as the first priority. Meanwhile, in the selection of the headquarters ship which functions to coordinate the KRI in carrying out sector patrols, using the FWH and IGP, the KRI was chosen as the Sigma class to be the headquarters ship. This modeling uses 22 of the 24 KRI that have been allocated by obtaining an increase in the optimization of the coverage area and fuel savings.. Keywords: OMSP, MCDM, Contigency, headquarters warship and Maritime Security Operations