OPTIMISING THE DESIGN OF SHORT SEA SHIPPING (SSS) OPERATION SYSTEM TO IMPROVE THE PERFORMANCE MULTIMODAL TRANSPORTATION NETWORK

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1 International Journal of Civil Engineering and Technology (IJCIET) Volume 8, Issue 10, October 2017, pp , Article ID: IJCIET_08_10_075 Available online at ISSN Print: and ISSN Online: IAEME Publication Scopus Indexed OPTIMISING THE DESIGN OF SHORT SEA SHIPPING (SSS) OPERATION SYSTEM TO IMPROVE THE PERFORMANCE MULTIMODAL TRANSPORTATION NETWORK J. E. Simangunsong Doctoral Student at Civil Engineering Department, Institute of Technology Bandung (ITB), Bandung, Indonesia A. Sjafruddin Professor at Civil Engineering Department, Institute of Technology Bandung (ITB), Bandung, Indonesia H. A. S. Lubis Associate Professor at Civil Engineering Department, Institute of Technology Bandung (ITB), Bandung, Indonesia R. B. Frazila Associate Professor at Civil Engineering Department, Institute of Technology Bandung (ITB), Bandung, Indonesia ABSTRACT This paper presents the design of the Short Sea Shipping (SSS) operating system particularly in selecting the most feasible combination of SSS operation system. The problem of choosing the best combination in this model is a combinatorial optimization problems. The developed mathematical model is the problem of bi-level programming, which the lower level problem is related to network assignment due to SSS operation, whereas the upper level problem is related to the selection of the optimum combination of SSS operating system. The optimization technique that will be used to choose the best combination is the Discrete Binary Particle Swarm Optimization (DBPSO) algorithm Keywords: Short Sea Shipping (SSS) Operating System, Bilevel Programming, Combinatorial Optimization, Discrete Binary Particle Swarm Optimization (DBPSO) Algorithm editor@iaeme.com

2 J. E. Simangunsong, A. Sjafruddin, H. A. S. Lubis and R. B. Frazila Cite this Article: J. E. Simangunsong, A. Sjafruddin, H. A. S. Lubis and R. B. Frazila, Optimising The Design of Short Sea Shipping (SSS) Operation System To Improve The Performance Multimodal Transportation Network, International Journal of Civil Engineering and Technology, 8(10), 2017, pp INTRODUCTION As the increasing of global trade and environmental issues due to transport and logistics systems, land transport is no longer a feasible solution, other modes of transport and its combination are required. Multimodal freight transport network offers more efficient, reliable, flexible, and sustainables transport. ITB Logistics and Supply Chain Study Center (2013) stated that the Logistics Performance Index (LPI) in 2010 is about 26.03% of Gross Domestic Product. Based on that total logistics cost, transportation cost component is the highest (12.04%) followed by the inventory cost (9.47%) and the administrative cost (4.52%) [1}. The dominance of the movement of goods using the land transport is one of the reasons of high transportation costs. Lubis et al. (2005) stated that around 90% of the total movement of goods occurring in Indonesia is done by land transport modes, 7% for sea transport modes, and the rest by other transport modes (railway, airplane and river) [2]. One of the efforts to solve the problems related to the high logistics cost and the dominance of land transportation mode is concerning the multimodal freight transport system by operating the freight transport line through the sea modes transport. In National Logiistic System (2012) stated that one of the programs planned to improve the flow of goods in support of the efficiency and effectiveness of the performance of national logistics system is the development of local, inter-island and national connectivity in an integrated system by developing scheduled shipping lines and operational Short Sea Shipping (SSS), and providing incentives to the actors and logistic service providers engaged in SSS [3]. This paper deals with the design of the SSS operating system model, selecting a set of SSS operating system scenario actions with the objective function to maximize the difference in total transportation costs due to the operation of the SSS. The best combination of the SSS operating system is a combination of combinatorial optimization that is very difficult to solve (Prado et al. 2014) [4]. This paper also presents a method for solving the problems above through a bilevel programming framework. 2. GENERAL FRAMEWORK This research tries to solve the weaknesses of SSS operation by making the model design operation of SSS. The model design is the Ro-Ro design of shipping operation which will consider the tariff, ship type, and selection of port as the decision variables. These decision variables are the acts or intervention carried out by the stakeholders, and will be implemented on the SSS operating system thus the SSS mode may be an alternative choice in freight transportation. The optimization approach that will be used in this model design is bilevel programming. This approach is used because there are two decisions describing the behavior of users and decision makers, and will be searched simultaneously. The first decision is a lower level problem. This decision is related to the assignment problem due to SSS operation, which will describe the behavior of freight carrier in choosing the route and the trasport modes, used to minimize transportation cost. The second decision is upper level problem. This decision is describing the behavior of stakeholders related to the selection of the optimum combination of editor@iaeme.com

3 Optimising The Design of Short Sea Shipping (SSS) Operation System To Improve The Performance Multimodal Transportation Network SSS operating system to improve the operation of the SSS shipping line. The bi-level programming approach used to formulate the problems and the process is shown in Figure 1. Figure 1 Modelling and computational framework (Russ et al. 2005) [5} 3. THE UPPER LEVEL PROBLEM 3.1. Formulation Model The objective function at the upper level is to maximize the deviation total transportation cost between without operation and operation the SSS. The total transportation cost due to the operation of the SSS is obtained from the total link flows (ton-km) shifting to the SSS multiplied by the transportation tariff per ton-km. The formulation of the objective function at the upper level to select the best set of actions is formulated as follows: = +, where To shows total transportation costs on the network without SSS operation: = Xoa is the equilibrium link flow at the time SSS not operated. where x * a: link flows that are the solution for the user equilibrium problem with the set of implemented combination actions ca(x * a,ya) : transportation costs on link a at equilibrium flow condition and set of implemented combination actions or not (ya, is indicator of combination action which has binary value 1 if it is implemented and 0 if it is not) A1 : set of existed link without implemented-action A2 : set of existed link which allow the implemented-action editor@iaeme.com

4 J. E. Simangunsong, A. Sjafruddin, H. A. S. Lubis and R. B. Frazila 3.2. Solution Techniques To solve the upper level problem, the optimization solution technique that will be used is Discrete Binary Particle Swarm Optimization (DBPSO). DBPSO is relatively the new metaheuristic techniques inspired by the behavior off birds or fish. It is similar to the Genetic Algorithm (GA) which is a population-based search method. This method is used because of the problem of the adopted SSS operating system is a combinatorial optimization problem where a set of randomly generated actions representing particles consisting of port location selection, tariff, and ship size. Their value is 1 if implemented and 0 if not implemented. DBPSO procedures can be explained with the following steps (Kennedy & Eberhart 1997, Menhas et al. 2012) [6, 7]: 1. Initialization a. Set the value of inertia weight (ω), learning factor θ1 and θ2 number of particle N in swarm and number of iteration. b. Set the upper-lower limit of the particle velocity (velmas and velmin) c. Set the initial velocity of particle (initial velocity is zero). Z 0 m = Z 0 m1, Z 0 m2,,z 0 mn Z 0 m = [0.00, ,0.00} d. the results of initial position of particle will be obtained with the following equation :! $ # % 1 ' ()*+ -./ $ # 0 +1h(3- Where: -. / $ # =1/ : Evaluate the position of each particle by using a predetermined objective function (fit (u n m) 3. Set the initial postion of particles as the best personal (pbest n m) as the result of objective function evaluation. 4. Choose the best posisition of particle in the group/swarm to keep as the best global position (gbest n ). 5. Update the velocity of the particle ( / # $ ) using this function : 6. Determine the new of particle population based on step 1.d 7. Re-evaluate position each new particle obtained by using determined objective function (fit (u n m). 8. Renew note of each particle by comparing the value from evaluation (fitness value) with the best personal position (pbest n m) 9. Renew the note of the best global position (g best n ). 10. Change n = n + 1. If the algorithm has reached the criteria to stop, stop the iteration. If it is not, back to step 4. Where Z n mo : Particle velocity m at the iteration n in the dimension o Z n mo : particle velocity m at the iteration n+1, n in the dimension o U n mo : particle position m at iteration n at dimension o (binary number 0-1) U n mo : particle position m at iteration n=1 at dimension o (binary number 0-1) pbest n mo: The best position of particle m at iteration n at dimension o (binary 1-0) gbest n mo: The global best position of particle m at iteration n+1 at dimension o (binary 1-0) editor@iaeme.com

5 Optimising The Design of Short Sea Shipping (SSS) Operation System To Improve The Performance Multimodal Transportation Network ω : inertia weight θ1, θ2 : leraning factor -. / # : transformation function sigmoid to particle i rand 1,rand 2 : random numbers between 0 and 1 The flow diagram of the DBPSO algorithm can be seen in figure 2 below Initialization velocity of particle Initialization position of particle Evaluate position of each particle by using a objective function Set particles as the best personal position (pbest) Set particles as the best global position (gbest) Update the velocity of the particle Stoping criteria No Yes Output 4. THE LOWER LEVEL PROBLEM Figure 2 Flow diagram of the DBPSO algorithm 4.1. Formulation Model The mechanism of assignment flow of goods in network is formulated in the mathematical model, in example let p as the path and Pω as the set of all paths in the network connecting origin-destination (OD) pair ω, where all (OD) pairs belong to the set of Ω. Furthermore, fpω can be defined as the flow on path p connecting ω, and xa is defined as the flow at link a. The following is links flow formulation (Russ et al. 2005, Yamada et al. 2009) [5, 8]: editor@iaeme.com

6 J. E. Simangunsong, A. Sjafruddin, H. A. S. Lubis and R. B. Frazila x < = f >? δ <>?, a A Where:?єD> A B 1,if path p connecting ω uses link a δ <>I =% 0,otherwise Furthermore, the conservation of flow between (OD) pair ω in the non-negative path flow can be expressed as: q? = f >? > A? f >? 0, ω Ω Where qω as the travel demand related to pair OD ω The assignment problem of user equilibrium (EU) flow above will be solved by using the equation below: Determine xa so every path has the equal and minimum transportation costs. with constraints: b c Minimasi Z x < =` C < x < < e dx < x < = f >? δ <>?, a A?єD> A B q? = f >? > A?, ω Ω f >? Cost Fuction In this study the generalized cost model is described as a function of cost on link, link transfer and time value of the commodity that is explained using the following equation (Sjafruddin et al. 2010) [9]: Where Cp = unit generalized cost (Rp/ton) αlp = unit link cost (Rp/ton) αtp = unit transfer cost (Rp/ton) βp = value of time (Rp/hr/ton) ll = travel time in link (hr) lt = transfer time at node f g =h ig +h jg +k g l i +l j editor@iaeme.com

7 Optimising The Design of Short Sea Shipping (SSS) Operation System To Improve The Performance Multimodal Transportation Network Assuming that nodes ot transfer point can be considered as a link or transfer link, the cost functions at a transfer link are as follow: Where f g =h g l +k g 1 cap = generalized cost at link a (Rp/ton) αp = tariff at a transfer link (Rp/ton/km) βp = value of time (Rp/ton/hr) ta = travel time at link a (hr) la = length of link a (km) for transfer link la= 1 Transfer time at node describes loading-unloading process it s depend on the number of berths (port) or peron (railway) and delay during the process. The equation used to estimate it is: t=t + r 1 b m.o where t = time at port or railway station to = delay at port or railway station (hr) r = loading-unloading time (hr) x = total loading and unloading flow (ton/hr) k = capacity of loading and unloading (ton/hr/berths or peron) n = number of berth or peron (unit) The equations required for travel time at links are: where: ta = travel time at link a (hr) la = distance at link a (km) Sa = average speed at link a (km/hr) 1 = i p q p 4.3 Solution Techniques The lower level condition concerns the network assignment problem. The network assignment problem will be solved by using Incremental Assignment Method. This technique is used in connection with the searching space that gives optimum results. The lower level condition problem is a single user multimodal, moreover no need the algorithm to convergen the Wardrop's Equilibrium solution, so the process of searching the optimum solution can be faster 5. CASE STUDY WITH HYPOTHETICAL DATA The test of model optimization of SSS operating system on hypothetical model of simple freight transportation network is to examine the effectiveness of the optimization process. Figure 3 shows a model of simple multimodal freight transportation network without SSS operations whereas figure 4 shows a a model of simple multimodal freight transportation network with SSS operations. Based on figures 3 and 4 the A, B, C, and D describe the centroid zone indicating the starting and ending points of the goods flows. Node with number 1, 2, and 3 show the truck terminal; 4, 5, and 6 show the railway station; 7, 8, and 9 show the port. The red lines of editor@iaeme.com

8 J. E. Simangunsong, A. Sjafruddin, H. A. S. Lubis and R. B. Frazila and 2-3 indicate links for truck modes, the black lines of 4-5 and 5-6 indicate links for railway mode, and the green lines of 7-8 and 8-9 lines indicate links for SSS modes. The discontinuous lines show the point of transfer between the modes (transfer link), i.e. links 2-5 and 2-8 showing the mode transfer from truck to railway and SSS, links 5-8 and 5-2 indicate mode displacements from railway mode to SSS and truck mode, and links 8-2 and 8-5 show the shifted-mode from SSS mode to truck and train mode. Figure 3 Model of simple multimodal freight transportation network without SSS operations Figure 4 Model of simple multimodal freight transportation network with SSS operations Several scenarios which are decision variables that will be applied on the example of hypothetical Model of simple multimodal freight transportation network with the operation of SSS (figure 4) are: There are 3 ports (Node 7, 8, and 9) that will be selected to operate the SSS shipping line There are two types tariff (Rp. 40/ton/km and Rp. 30/ton/km) that will be selected on SSS links i.e. links 7-8, 8-9, and 7-9. There are two types average speed of SSS ships (30 km/hr and 25 km/hr) that will be used to illustrate the improvement on decreasing travel time costs in SSS links i.e. links 7-8, 8-9, and 7-9. The hypothetical data required to examine this optimization model consist of the origindestination matrix and distance between the links shown in tables 1 and 2, as well as the tariff modes in links, transfer links tariff, speed of modes, value of time, delay time at the port and railway station, time for loading and unloading, loading-unloading capacity, and number of berths and peron shown in table editor@iaeme.com

9 Optimising The Design of Short Sea Shipping (SSS) Operation System To Improve The Performance Multimodal Transportation Network Table 1 Origin-destination matrix (ton) A B C D A B C D Table 2 Distance between links (km) Link Distance (km) Link Distance (km) Table 3 Parameters value 1 Tariff at link 2 3 Tariff at transfer link Average speed 4 Delay time 5 6 Loadingunloading time Loadingunloading capacity Truck Rail SSS Truck-SSS Truck-Rail Rail-SSS Truck Rail SSS Truck-SSS Truck-Rail Rail-SSS Truck-SSS Truck-Rail Rail-SSS Truck-SSS Truck-Rail Rail-SSS Rp. 60/ton/km Rp. 50/ton/km Rp. 40/ton/km Rp. 30/ton/km Rp. 10/ton/km Rp. 15/ton/km Rp. 20/ton/km 20 km/hr 40 km/hr 30 km/hr 25 km/hr 2 hr 3 hr 4 hr 2 hr 4 hr 5 hr 50 ton/hr 40 ton/hr 40 ton/hr 7 Value of time Rp. 8 ton/hr The optimization results from the case above using optimization program and prepared by Mathlab resulted in the optimum SSS operating system, there are: 3 ports (Node number 7, 8, and 9) selected for SSS shipping line, tariff on SSS link Rp. 30/ton/km, and average speed of SSS ship 30 km/hr. The optimum SSS operating system from the case above gives the objective function value Rp , editor@iaeme.com

10 J. E. Simangunsong, A. Sjafruddin, H. A. S. Lubis and R. B. Frazila 6. CONCLUSION This paper proposes a model that can be used as a tool to select the best combination of SSS operating systems, and to improve the performance of the multimodal transport network. With hypothetical data, the parameter model (decision variables) for SSS operating system has been developed and implemented to obtain a combination of optimum SSS operating systems. It is indicated by the maximize objective function value. A novel formulation of the optimization process in this model proposes solve by Discrete Binary Particle Swarm Optimization (DBPSO) algorithm. The method which is proposed is supposed to offer the contribution to the decision maker providing the tools for increasing the attractiveness of the Short Sea Shipping (SSS) operating system The optimization methodology has been proved useful for improving the model results and thus it could be employed in real cases in which more complete data were available. ACKNOWLEDGEMENTS The authors acknowledge the support from Doctoral Research Grant (Penelitian Hibah Doktor) 2017 of Ministry of Research, Technology and Higher Education of the Republic Indonesia. REFERENCES [1] Center of Logistics and Supply Chain Study Institute of Technology Bandung. State of Logistic Indonesia. Indonesia : INDF Project, 2013 [2] Lubis, H.A.S., Sjafruddin, A., Isnaeni, M. and Dharmowijoyo, D. B. Multimodal Transportn Indonesia: Recent Profile and Strategy Development. Proceedings of Eastern Asia Society for Transportation Studies, Vol. 5, 2005, pp [3] Indonesian Government Regulation No. 26 of 2012 [4] Prado, R.R., Pereira, D.C., Vilas, D.R., Monteil, N.R. and Valle, A.G. A parameterized model of multimodal freight transportation for maritime services optimization. Int. J. Simulation and Process Modelling, Vol. 9, 2014, pp [5] Russ, B.F, Castro, J., Yamada, T. and Yasukawa, H. Optimising The Design of Multimodal Freight Transport Network in Indonesia. Journal of Eastern Asia Society for Transportation Studies, Vol. 6, 2005, pp [6] Kennedy, J and Eberhart, R.C. A discrete binary version of the particle swarm algorithm. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics 5, 1997, pp [7] Menhas, M.L., Wang, L., Fei, M. and Pan, H. Comparative performance analysis of various binary coded PSO algorithms in multivariable PID controller design. Expert Systems with Applications, Vol. 39, No. 4, 2012, pp [8] S. Mohanaselvi, K. Ganesan, A New Approach for Solving Linear Fuzzy Fractional Transportation Problem. International Journal of Civil Engineering and Technology, 8(8), 2017, pp [9] Dr.S.Ramachandran and S.Aravindan An Analysis of Traffic, Transportation And Operations of Nargolport, India A Case Study. International Journal of Civil Engineering and Technology, 8(6), 2017, pp [10] Yamada, T., Frazila, R.B., Castro, J. and Taniguchi, E. Designing Multimodal Freight Transport Networks: A heuristic Approach and Applications. Transportation Science Journal, Vol. 43, 2009, pp [11] Sjafruddin, A., Lubis, H.A.S., Frazila, R.B. and Dharmowijoyo, D.B. Policy Evaluation of Multimodal Transportation Network: The Case of Inter-island Freight Transportation in Indonesia. Asian Transport Studies, Vol. 1, 2010, Issue 1, editor@iaeme.com