Optimization of Re-handling and Load/Unload Operations in Small Container Terminal Operated by Reachstackers
|
|
- Adela Bryan
- 6 years ago
- Views:
Transcription
1 ptimization of Re-handling and Load/Unload perations in Small Container Terminal perated by Reachstackers ADAM GALUSZKA 1, KRZYSZTF DANIEC 2 Institute of Automatic Control Silesian University of Technology Akademicka16, Gliwice PLAND 1 Adam.Galuszka@polsl.pl, 2 Krzysztof.Daniec@polsl.pl Abstract :- In this paper efficient in time methods for support decision-making processes in small container terminal operated by reachstackers are proposed. Two main problems are considered: first, minimization the number of loading/unloading operations between container trains and bays, second, minimization the number of re-handing operations within container bays. in Gliwice, Poland to model the problem of loading and unloading containers in small container terminal in Gliwice, Poland. For the first problem it is assumed that containers are represented by blocks and reachstackers are represented by robot arms. Then so called STRIPS representaion is used to model and solve loading/unloading planning problem. For the second problem a heuristic algorithm that meets technical limitation of reachstackers is introduced. The problem data are assumed for small container terminal in Gliwice, Poland. Key-Words :- Container Terminal, STRIPS System, Computational efficiency, Heuristic Algorithm, WiMax 1 Introduction Decision problems at container terminals are very complex and divided into several groups: arrival of a ship (train), unloading and loading of a ship (train), transport of containers from and on a ship (train), stacking of containers (see e.g. Since arrival of a ship (train) and containers transport are usually treated as scheduling and allocation problems (e.g. Imai et al. 2001, Bish et al. 2001), problems of loading and unloading and container stacking can be treated as planning problems (e.g. Avriel et al. 1998). Exemplary heuristics that for re-handlings analysis at sea container terminals can be found in Kap and Kim (2002) and Kap and Hong (2006). In our paper we propose a heuristic that finds bays configuration minimizing number of re-handlings under some assumptions (see section heuristic). It should be noted that such approach can be applied in terminals that operate using reachstackers (not cranes). ur intention is also to propose efficient in time method for support loading/unloading processes in small container terminal in Gliwice, Poland ( In table 1 general technical data of this terminal are presented. Table 1. General data of the terminal Data Currently Project Area m m2 Capacity TEU TEU Track length 620m 620m Number of tracks 2 6 The loading-unloading situation is schematically shown in the fig.1 and the reachstacker technical data are presented in table 2. It should be noted that all operations are performed only by reachstackers. There is an operational difference between cranes that are usually considered in literature (see e.g. Kim and Kim 2002), and reachstackers. In case of cranes, when a driver of an outside truck requests an inbound container that has other containers on top of it, a crane must remove the containers on top of the target container (it is called re-handling operation). In case of reachstackers, also all containers between reachstacker and target container must be removed (compare fig.1). It implies that the number of re-handling operations increases. It is important to minimize re-handlings in order to increase performance of the terminal. We ISSN: ISBN:
2 propose to model this problem using STRIPS representation. Fig 1. Loading-unloading situation ( Table 2. Reachstacker technical data Quantity currently: 2 project: 4 Lifting capacity 1st rank - 45 tons 2st rank - 31 tons 3st rank - 15 tons Stocking height 5 levels The goal of the project is to efficient (in time) plan the reachstacter moves in the terminal, assuming that the target containers are reached and the number of re-handlings is minimalised. The size of the planning problem (the container bay) and rails placement is shown in the fig 2. To improve communication between reachstackers and planning module the WiMax communication technology can be used (Wang et al. 2008). Fig.2. Container bay and rails top view 2 Theoretical background In the paper loading/unloading problem has been modeled as Block World with STRIPS representation. This domain is often used to model planning problems (Nilson 1980, Boutilier and Brafman 2001, Kraus et al. 1998, Smith and Weld 1998, Slaney and Thiebaux 2001) because of complex operator interactions and simple physical interpretation. Starting from 1970s STRIPS formalism introduced by Nilson (1980) is popular for planning problems (Weld 1999). Planning problems are PSPACE-complete in general case (see e.g.: Bylander 1994, Baral et al. 2000), even in Block World environment are not easy (here the problem of optimal planning is NP-complete Gupta and Nau 1992). 3 STRIPS Representation for load/unload operations In general, STRIPS language is represented by four lists (C; ; I; G) (Bylander 1994, Nilson 1980): - a finite set of ground atomic formulas (C), called conditions; - a finite set of operators (); - a finite set of predicates that denotes initial state (I); - a finite set of predicates that denotes goal state (G). Initial state describes physical configuration of the blocks. Description should be complete i.e. should deal with every true predicate corresponding to the state. Goal state is a conjunction of predicates. Predicates in I and G are ground and function free. In multi-agent environment each agent defines own goal. This description does not need to be complete. The solution of the planning problem is an ordered set of operators (i.e. action sequence) that transforms an initial state into a goal situation. The optimal solution is the shortest action sequence. perators in STRIPS representation consist of three sublists: a precondition list (pre), an add list (add) and a delete list (del). Formally an operator o takes the form pre( add(, del(. The precondition list is a set of predicates that must be satisfied in world-state to perform this operator. The delete list is a set of predicates that become false after executing the operator and the add list is a set that become true. Two last lists show effects of the operator execution in the problem state. To model the operation of moving containers by reachstacker we assumed following set of conditions: C = { clear(x) there is no container on container x, ISSN: ISBN:
3 clear_side(x) there is no container between reachstacker and container x, on(x,y) container x is placed on container y, before(x,y) container x is before container y, reachstacker_empty(index) reachstacker arm of index reachstacker is empty holding(x, index) reachstacker index is holding container x }, where x,y,z denotes containers or bay positions and index is an index of reachstacker. The operator that removes container x that is before z from y by index reachstacker can be defined using STRIPS representation as follow (Galuszka and Skrzypczyk 2008): Ustack(x,y,z,index): precondition list & delete list: clear(x), clear_side(x), on(x,y), before(x,z), reachstacker_empty(index) add list: clear(y), clear_side(z), holding(x,index). To rebuild the bay and load-unload the train three additional operators are needed: Stack to stack container on other container within the bay, Load_train to load the container on the train and, finally, Unload_train. Definitions are presented below: Stack(x,y,z,index): precondition list: clear(y), clear_side(z), holding(x,index), before(y,v), on(z,v) delete list: clear(y), clear_side(z), holding(x,index) add list: clear(x), clear_side(x), on(x,y), before(x,z), reachstacker_empty(index). Unload_train(x,index): precondition list & delete list: on_train(x), reachstacker_empty(index) add list: holding(x,index). Load_train(x,index): precondition list & delete list: holding(x,index) add list: on_train(x), reachstacker_empty(index). There are considered 2 planning problems in the terminal: to load containers from the bay to the train, or to unload containers from the train to the bay. The initial state of the first planning problem (I) is the initial configuration of the bay and the goal is: G = {on_train(x1) on_train(x2)... on_train(xn)}, (1) where xi, for i = 1,2...n denotes container, and ' ' denotes conjunction. The second planning problem is inverted situation: G_inv = I, I_inv = G. (2) The problem (C,, I, G) is called invertible (Koehler and Hoffmann 2000) if and only if s : P : P : Result ( Result ( s, P ), P ) = s (3) where Result( S, <>) = S, Result( S, <o>) = (S add() \ del( if pre( S S in the opposite case, Result( S, <o 1, o 2,..., o n >) = Result( Result( S, <o 1 >), <o 2,..., o n >), and P is called an inverted plan. Now an inverse operator is introduced following (Koehler and Hoffmann 2000). An operator o is called inverse if and only if it has the form pre ( add (, del ( and satisfies the conditions: 1. pre( pre( add( \ del( 2. add ( = del( (4) 3. del ( = add(. Under closed world assumption applying an inverse operator leads back to the previous state. It is proved by Koehler and Hoffmann (2000) that if for each operator there exist an inverse operator, then the problem is invertible. It is easy to see that planning problems in the terminal are invertible. This property will be used in next section. As it was mentioned above planning with STRIPS representation is a difficult computational problem. ISSN: ISBN:
4 It was shown (Bylander 1994) that computational complexity class of searching for the solution (optimal or non-optimal) depends of the number of predicates in operator definition (in presented case the problem is P-SPACE complete). Because of this complexity, to plan in the terminal efficiently subgoals serialization operation can be applied (Galuszka 2009). 4 Minimization of re-handling operations As it was mentioned earlier, there are some limitations when operating by reachstackers. In fig. 3 one can find an illustration of (partial order) plan diagram for reaching target container. Fig.4. The influence of a bay configuration to the number of re-handligs It should be noted that small changes in a bay configuration have significant influence on the number of re-handings necessary to reach the target container. It implies that is worth to consider different bay configurations for decreasing the number of re-handlings. In fig. 5 an exemplary configurations and corresponding average rehandling numbers are presented. Fig.3. Plan diagram for reaching a target container within a bay These limitations have been wider described in our work using so called Block World environment with STRIPS representation characteristic for artificial intelligence planning problems (Galuszka and Skrzypczyk 2008). In fig. 4 the influence of a bay configuration to the number of re-handligs is schematically shown. It should be noted that small changes in a bay configuration have significant influence on the number of re-handings necessary to reach the target container. It implies that is worth to consider different bay configurations for decreasing the number of re-handlings. In fig. 5 an exemplary configurations and corresponding average rehandling numbers are presented. a) b) c) Fig.5. An exemplary configurations and corresponding average re-handling numbers ISSN: ISBN:
5 Under the notions: k number of stacks in a bay, n number of stack (n = 1,2 k), h(n) height of the n-th stack (also the number of containers in n-th stack), N number of stack with target container, p place of the target container, r(n) number of not moved containers in n-th stack, g(n) number of moved containers in n-th stack, G sum of the re-handling operations for the bay with target container, the formula for calculating the number of not moved containers for requested target container is: then: So the formula for calculating the average number of re-handlings for the bay in the general case (when operating by reachstackers) takes the form: In table 3 the numbers of not moved containers for the target container indicated by (N,p) is presented. These numbers are direct implication of technical limitations of used reachstackers schematically shown in fig 6. Table 3. Number of not moved containers for the target container (N,p) h(n), r(n) r(n 1) r(n 2) r(n 3) 5 p p p p p For the situation presented in this figure the G = 10. This number can be also calculated basing using the general formula introduced earlier: Fig.6. Technical limitations of used reachstackers The analysis of possible configurations leads to conclusion that if the number of stored containers does not reach the capacity of the terminal bays then it is worth to search for the bay configurations that minimize the average number of re-handlings. Such approach will lead to minimization of real number of re-handlings if the probability that a container is a target container is the same for all containers within a bay. Such assumption (and simplification) is justified since the complete container data are often unknown when the decision of stacking must be done (Steenken et al. 2004). Tests for the efficiency of the heuristic have been performed for 3 capacities of the terminal: 1000, 2000 and 4000 containers and corresponding number of bays: 50, 100, Conclusion In the paper the problem of moving containers by reachstackers in small terminal is modeled as block world environment with STRIPS representation. Two planning problems are defined. It is also shown that two planning problems in the terminal are invertible. This property is useful in searching for the plan. The problem of moving containers by reachstackers in small terminal is presented and analyzed. The heuristic that finds the optimal bay configurations in such terminal is presented. Acknowledgement This work is a result of educational and research co-operation between PTK Holding SA Container Terminal, Poland and Institute of Automatic Control of Silesian University of Technology, Poland. The ISSN: ISBN:
6 research presented here were done as a part of research and development project no. R and this work has been supported by Polish Ministry of Science and Higher Education funds in the years References: [1] Avriel, M., Penn, M., Shpirer, N., Witteboon, S Stowage planning for container ships to reduce the number of shifts, Annals of perations Research 76, [2] Baral Ch., Kreinovich V., Trejo R Computational complexity of planning and approximate planning in the presence of incompleteness. Artificial Intelligence 122, [3] Bish, E.K., Leong, T.Y., Li, C.L., Ng, J.W.C., Simchi-Levi, D Analysis of a new vehicle scheduling and location problem, Naval Research Logistics 48, [4] Bylander T The Computational Complexity of Propositional STRIPS Planning. Artificial Intelligence 69, [5] Gałuszka A., Skrzypczyk K Moving containers in small terminal as STRIPS planning problem preliminary results. Proc. Int. North American Simulation Technology Conference. Montreal, Canada, August , str [6] Galuszka A Moving Containers in Small Terminal as STRIPS Planning Problem. [in] Simulation, Modelling and ptimization. Mathematics and Computers in Science and Engineering. A series of Reference Books and Textbooks (ed. Rudas I. et al.), ISBN , pp [7] Gupta N., Nau D.S n the complexity of Blocks-World Planning. Artificial Intelligence 56(2-3), [8] Howe A.E., Dahlman E A Critical Assessment of Benchmark Comparison in Planning. Journal of Artificial Intelligence Research 17, [9] Imai, A., Nishimura, E., Papadimitriou, S The dynamic berth allocation problem for a container port, Transportation Research B 35, [10] Joslin, D. and J. Roach A Theoretical Analysis of Conjunctive-Goal Problems. Artificial Intelligence, 41: [11] Kim K.H and H.B. Kim The optimal sizing of the storage space and handling facilities for import containers, Transportation Research B 36, [12] Klir G.J., T.A. Folger Fuzzy sets, uncertainty, and information, Prentice-Hall International, Inc. USA [13] Koehler J., Hoffmann J n Reasonable and Forced Goal rderings and their Use in an Agenda-Driven Planning Algorithm. Journal of Artificial Intelligence Research 12, [14] Kraus S., Sycara K., Evenchik A Reaching agreements through argumentation: a logical model and implementation. Artificial Intelligence 104, [15] Nilson N.J Principles of Artificial Intelligence. Toga Publishing Company, Palo Alto [16] Slaney J., Thiebaux S Block World revisited. Artificial Intelligence 125, [17] Slavin T Virtual port of call. New Scientist (June 1996) [18] Swierniak A., A. Galuszka Betweenness and indistinguishability in modelling and control of uncertain systems, Proc. of 18th IASTED Conference Modelling, Identification and Control, (Innsbruck 1999), [19] Wang F., A. Ghosh, C. Sankaran, P. J. Fleming, F. Hsieh, S. J. Benes. (2008). Mobile WiMAX systems: performance and evolution. Communications Magazine, IEEE, Vol. 46, No. 10. (2008), pp [20] Weld D.S., Anderson C.R., Smith D.E Extending Graphplan to Handle Uncertainty and Sensing Actions. Proc. 15th National Conf. on AI, [21] Weld D.S Recent Advantages in AI Planning. Technical Report UW-CSE , AI Magazine ISSN: ISBN:
A PETRI NET MODEL FOR SIMULATION OF CONTAINER TERMINALS OPERATIONS
Advanced OR and AI Methods in Transportation A PETRI NET MODEL FOR SIMULATION OF CONTAINER TERMINALS OPERATIONS Guido MAIONE 1, Michele OTTOMANELLI 1 Abstract. In this paper a model to simulate the operation
More informationStorage Allocation and Yard Trucks Scheduling in Container Terminals Using a Genetic Algorithm Approach
Storage Allocation and Yard Trucks Scheduling in Container Terminals Using a Genetic Algorithm Approach Z.X. Wang, Felix T.S. Chan, and S.H. Chung Abstract Storage allocation and yard trucks scheduling
More informationDispatching Policy Selection and Orbit Design in the Low Viaduct Rail Transportation System
Page 1 of 14 ANZAM 2009 Dispatching Policy Selection and Orbit Design in the Low Viaduct Rail Transportation System Dr.DING Yizhong * and Dr.HAN Xiaolong Logistics Research Center, Shanghai Maritime University,
More informationOptimizing a Containership Stowage Plan. using a modified Differential Evolution algorithm
Optimizing a Containership Stowage Plan using a modified Differential Evolution algorithm Speaker: Dr. Yun Dong ydong@tli.neu.edu.cn Supervisor: Pro. Lixin Tang Lixintang@mail.neu.edu.com The Logistics
More informationAn Intelligent Decision Support System for Crane Scheduling in a Container Terminal
An Intelligent Decision Support System for Crane Scheduling in a Container Terminal Guohua WAN Faculty of Business Administration, University of Macau, Taipa, Macao SAR, China E-mail: ghwan@umac.mo; Telephone:
More informationBerth allocation planning in Seville inland port by simulation and optimisation
Berth allocation planning in Seville inland port by simulation and optimisation Carlos Arango 1, Pablo Cortés 1, Jesús Muñuzuri 1, Luis Onieva 1 1 Ingeniería de Organización. Engineering School of Seville.
More informationAN EFFICIENT BLOCK-BASED HEURISTIC METHOD FOR STOWAGE PLANNING OF LARGE CONTAINERSHIPS WITH CRANE SPLIT CONSIDERATION
AN EFFICIENT BLOCK-BASED HEURISTIC METHOD FOR STOWAGE PLANNING OF LARGE CONTAINERSHIPS WITH CRANE SPLIT CONSIDERATION Xiantao Xiao (a), Malcolm Yoke Hean Low (b), Fan Liu (c), Shell Ying Huang (d), Wen
More informationContainer Terminal Modelling in Simul8 Environment
Acta Technica Jaurinensis Series Logistica Vol. 6. No. 4. 2013 Container Terminal Modelling in Simul8 Environment G. Bohács, B. Kulcsár, D. Gáspár Budapest University of Technology and Economics 1111 Budapest,
More informationSegregating space allocation models for container inventories in port container terminals
Int. J. Production Economics 59 (1999) 415 423 Segregating space allocation models for container inventories in port container terminals Kap Hwan Kim*, Hong Bae Kim Department of Industrial Engineering,
More informationSIMULATION AND FORECASTING IN INTERMODAL CONTAINER TERMINAL
SIMULATION AND FORECASTING IN INTERMODAL CONTAINER TERMINAL Luca Maria Gambardella 1, Gianluca Bontempi 1, Eric Taillard 1, Davide Romanengo 2, Guido Raso *2, Pietro Piermari 3 1 IDSIA, Istituto Dalle
More informationOptimization and Simulation Approach for Empty Containers Handling
Optimization and Simulation Approach for Empty Containers Handling Chafik Razouk / PhD Student at ENSIAS Cedoc ST2I Operations Research & Logistics UM5S Université de Rabat Rabat, Morocco Youssef Benadada
More informationScheduling Quay Crane and Yard Equipment A case study of terminal in Central region
Thousand TEU Scheduling Quay Crane and Yard Equipment A case study of terminal in Central region Phong Ho Thanh Department of Industrial and Systems Engineering International University Vietnam National
More informationSimulation of Container Queues for Port Investment Decisions
The Sixth International Symposium on Operations Research and Its Applications (ISORA 06) Xinjiang, China, August 8 12, 2006 Copyright 2006 ORSC & APORC pp. 155 167 Simulation of Container Queues for Port
More informationBerth Allocation Planning for Improving Container Terminal Performances
Proceedings of the 2012 International Conference on Industrial Engineering and Operations Management Istanbul, Turkey, July 3 6, 2012 Berth Allocation Planning for Improving Container Terminal Performances
More informationAnalysis of the job shop system with transport and setup times in deadlock-free operating conditions
Archives of Control Sciences Volume 22(LVIII), 2012 No. 4, pages 417 425 Analysis of the job shop system with transport and setup times in deadlock-free operating conditions JOLANTA KRYSTEK and MAREK KOZIK
More informationDISPATCHING TRANSPORT VEHICLES IN MARITIME CONTAINER TERMINALS
DISPATCHING TRANSPORT VEHICLES IN MARITIME CONTAINER TERMINALS by Pyung-Hoi Koo Department of Systems Management and Engineering, Pukyong National University, Busan, Korea Yongsoro 45, Namgu, Busan, South
More informationModeling and analyzing of railway container hub scheduling system based on multi-agent system
ISSN 1816-6075 (Print), 1818-0523 (Online) Journal of System and Management Sciences Vol. 2 (2012) No. 1, pp. 31-39 Modeling and analyzing of railway container hub scheduling system based on multi-agent
More informationHybrid search method for integrated scheduling problem of container-handling systems
Hybrid search method for integrated scheduling problem of container-handling systems Feifei Cui School of Computer Science and Engineering, Southeast University, Nanjing, P. R. China Jatinder N. D. Gupta
More informationDEVELOPMENT OF OPERATION STRATEGY TO IMPROVE EFFICIENCY FOR TWIN AUTOMATED TRANSFER CRANE IN AN AUTOMATED CONTAINER TERMINAL
DEVELOPMENT OF OPERATION STRATEGY TO IMPROVE EFFICIENCY FOR TWIN AUTOMATED TRANSFER CRANE IN AN AUTOMATED CONTAINER TERMINAL Byung Joo PARK 1, Hyung Rim CHOI 2 1 Research Professor, Department of MIS,
More informationA Framework for Integrating Planning Activities in Container Terminals
A Framework for Integrating Planning Activities in Container Terminals August 30th, 2007 S. H. Won and K. H. Kim Dept. of Industrial Engineering, Pusan National University, South Korea Contents 1 Introduction
More informationRandomized Algorithm with Tabu Search for Multi-Objective Optimization of Large Containership Stowage Plans
Randomized Algorithm with Tabu Search for Multi-Objective Optimization of Large Containership Stowage Plans Fan Liu, Malcolm Yoke Hean Low, Wen Jing Hsu, Shell Ying Huang, Min Zeng, Cho Aye Win School
More informationDetermination of the number of AGVs required at a semi-automated container terminal
Determination of the number of AGVs required at a semi-automated container terminal Iris F.A. Vis, René de Koster, Kees Jan Roodbergen, Leon W.P. Peeters Rotterdam School of Management, Erasmus University
More informationSimulation-Based Dynamic Partitioning of Yard Crane Workload for Container Terminal Operations
Simulation-Based Dynamic Partitioning of Yard Crane Workload for Container Terminal Operations Xi Guo, Shell Ying Huang, Wen Jing Hsu and Malcolm Yoke Hean Low School of Computer Engineering Nanyang Technological
More informationSpace-Sharing Strategy for Building Dynamic Container Yard Storage Considering Uncertainty on Number of Incoming Containers
Jurnal Teknik Industri, Vol. 19, No. 2, December 2017, 67-74 ISSN 1411-2485 print / ISSN 2087-7439 online DOI: 10.9744/jti.19.2.67-74 Space-Sharing Strategy for Building Dynamic Container Yard Storage
More informationStowage Planning and Storage Space Assignment of Containers in Port Yards
Stowage Planning and Storage Space Assignment of Containers in Port Yards Catarina Junqueira*, Aníbal Tavares de Azevedo, Takaaki Ohishi Abstract The efficiency of a port terminal is essential to allow
More informationDetermination of Routing and Sequencing in a Flexible Manufacturing System Based on Fuzzy Logic
Determination of Routing and Sequencing in a Flexible Manufacturing System Based on Fuzzy Logic Pramot Srinoi 1* and Somkiat Thermsuk 2 Abstract This paper is concerned with scheduling in Flexible Manufacturing
More informationA Dynamic Truck Dispatching Problem in Marine Container Terminal
A Dynamic Truck Dispatching Problem in Marine Container Terminal Jianjun Chen, Ruibin Bai (IEEE senior member), Haibo Dong, Rong Qu and Graham Kendall Division of Computer Science, University of Nottingham
More informationProceedings of the 2010 Winter Simulation Conference B. Johansson, S. Jain, J. Montoya-Torres, J. Hugan, and E. Yücesan, eds.
Proceedings of the 2010 Winter Simulation Conference B. Johansson, S. Jain, J. Montoya-Torres, J. Hugan, and E. Yücesan, eds. AUTOMATED STOWAGE PLANNING FOR LARGE CONTAINERSHIPS WITH IMPROVED SAFETY AND
More informationDesign of an AGV Transportation System by Considering Management Model in an ACT
Intelligent Autonomous Systems 9 Book Editors IOS Press, 2006 1 Design of an AGV Transportation System by Considering Management Model in an ACT Satoshi Hoshino a,1,junota a, Akiko Shinozaki b, and Hideki
More informationRehandling Strategies for Container Retrieval
Rehandling Strategies for Container Retrieval Tonguç Ünlüyurt and Cenk Aydin Sabanci University, Faculty of Engineering and Natural Sciences e-mail: tonguc@sabanciuniv.edu 1 Introduction In this work,
More informationA SIMULATION MODEL FOR INTEGRATING QUAY TRANSPORT AND STACKING POLICIES ON AUTOMATED CONTAINER TERMINALS
A SIMULATION MODEL FOR INTEGRATING QUAY TRANSPORT AND STACKING POLICIES ON AUTOMATED CONTAINER TERMINALS Mark B. Duinkerken, Joseph J.M. Evers and Jaap A. Ottjes Faculty of OCP, department of Mechanical
More informationEfficient Container Security Operations at Transshipment Seaports
1 Efficient Container Security Operations at Transshipment Seaports Brian M. Lewis, Alan L. Erera, and Chelsea C. White III Abstract This paper describes an approach for aiding the management of a container
More informationStorage and stacking logistics problems in container terminals
Original Article Storage and stacking logistics problems in container terminals Jiabin Luo a,yuewu a, *, Arni Halldorsson b and Xiang Song c a School of Management, University of Southampton, University
More informationPlanning. Characterizing Planning Problems. Some Actual Planning Applications. Scheduling. Also used for Rovers. CPS 570 Ron Parr
Some Actual Planning Applications Planning CPS 570 Ron Parr Used to fulfill mission objectives in Nasa s Deep Space One (Remote Agent) Particularl important for space operations due to latenc Also used
More informationA Method of Container Terminal Resources Scheduling Simulation Research Li Mingqi, Zhang Peng*, Du Yuyue
Joint International Mechanical, Electronic and Information Technology Conference (JIMET 2015) A Method of Container Terminal Resources Scheduling Simulation Research Li Mingqi, Zhang Peng*, Du Yuyue College
More informationWorld Academy of Science, Engineering and Technology International Journal of Industrial and Manufacturing Engineering Vol:7, No:10, 2013
An Integrated Operational Research and System Dynamics Approach for Planning Decisions in Container Terminals A. K. Abdel-Fattah, A. B. El-Tawil, N. A. Harraz Abstract This paper focuses on the operational
More informationContainer Transfer Logistics at Multimodal Container Terminals
Container Transfer Logistics at Multimodal Container Terminals Erhan Kozan School of Mathematical Sciences, Queensland University of Technology Brisbane Qld 4001 Australia e.kozan@qut.edu.au Abstract:
More informationPLANNING SYSTEM BASED ON PROBLEM SOLVERS. Tadashi Nagata, Masato Yamazaki and Michiharu Tsukamoto Electrotechnical Laboratory Tokyo, Japan
Session 15 Robot Problem Solving ROBOT PLANNING SYSTEM BASED ON PROBLEM SOLVERS Tadashi Nagata, Masato Yamazaki and Michiharu Tsukamoto Electrotechnical Laboratory Tokyo, Japan ABSTRACT This paper is concerned
More informationSPACE ALLOCATION AND LOCATION MATCHING IN CONTAINER TERMINALS
Advanced OR and AI Methods in Transportation SPACE ALLOCATION AND LOCATION MATCHING IN CONTAINER TERMINALS Tonguç ÜNLÜYURT, Hacı Murat ÖZDEMIR Abstract. Operational efficiency at container terminals has
More informationBottleneck Detection of Manufacturing Systems Using Data Driven Method
Proceedings of the 2007 IEEE International Symposium on Assembly and Manufacturing Ann Arbor, Michigan, USA, July 22-25, 2007 MoB2.1 Bottleneck Detection of Manufacturing Systems Using Data Driven Method
More informationAn Agent-Based Solution for the Berth Allocation Problem
INT J COMPUT COMMUN, ISSN 1841-9836 8(3):384-394, June, 2013. An Agent-Based Solution for the Berth Allocation Problem C. Cubillos, R. Díaz, E. Urra, D. Cabrera-Paniagua, G. Cabrera, G. Lefranc Claudio
More informationDesign of Multithreaded Simulation Software through UML for a Fully Automated Robotic Parking Structure
10 Int'l Conf. Modeling, Sim. and Vis. Methods MSV'16 Design of Multithreaded Simulation Software through UML for a Fully Automated Robotic arking Structure J. K. Debnath and G. Serpen Electrical Engineering
More informationChance Constrained Multi-objective Programming for Supplier Selection and Order Allocation under Uncertainty
Chance Constrained Multi-objective Programming for Supplier Selection and Order Allocation under Uncertainty Xi Li 1, Tomohiro Murata 2 Abstract This paper proposes a chance-constrained multi-objective
More informationA SIMULATION MODEL FOR DETERMINING THROUGHPUT CAPACITY OF CONTAINER TERMINALS
A SIMULATION MODEL FOR DETERMINING THROUGHPUT CAPACITY OF CONTAINER TERMINALS Zijian Guo (a), Xuhui Yu (b), Guolei Tang (c), Wenyuan Wang (d) (a),(b),(c),(d) Faculty of Infrastructure Engineering, Dalian
More informationThe Challenges of Container Terminal Development. Ashebir Jacob, PE September 10, 2013
The Challenges of Container Terminal Development Ashebir Jacob, PE September 10, 2013 State of Containerization Since its inception, the container shipping industry has done their best to increase the
More informationLOAD SHUFFLING AND TRAVEL TIME ANALYSIS OF A MINILOAD AUTOMATED STORAGE AND RETRIEVAL SYSTEM WITH AN OPEN-RACK STRUCTURE
LOAD SHUFFLING AND TRAVEL TIME ANALYSIS OF A MINILOAD AUTOMATED STORAGE AND RETRIEVAL SYSTEM WITH AN OPEN-RACK STRUCTURE Mohammadreza Vasili *, Seyed Mahdi Homayouni * * Department of Industrial Engineering,
More informationThe master bay plan problem: a solution method based on its connection to the three-dimensional bin packing problem
IMA Journal of Management Mathematics (2003) 14, 251 269 The master bay plan problem: a solution method based on its connection to the three-dimensional bin packing problem ANNA SCIOMACHEN AND ELENA TANFANI
More informationAGV APPLICATIONS FOR SHORT SEA CONTAINER TERMINALS
AGV APPLICATIONS FOR SHORT SEA CONTAINER TERMINALS Osman Kulak 1, Mustafa Egemen Taner 2, Olcay Polat 3, Mehmet Ulaş Koyuncuoğlu 4 Abstract Container terminal authorities have been forced to find more
More informationProceedings of the 5th WSEAS International Conference on Applied Computer Science, Hangzhou, China, April 16-18, 2006 (pp )
Application of an automatic fuzzy logic based pattern recognition method for DNA Microarray Reader M.Sc. Wei Wei M.Sc. Xiaodong Wang Prof. Dr.-Ing Werner Neddermeyer Prof. Dr.-Ing Wolfgang Winkler Prof.
More informationDesign of an Automated Transportation System in a Seaport Container Terminal for the Reliability of Operating Robots
Design of an Automated Transportation System in a Seaport Container Terminal for the Reliability of Operating Robots Satoshi Hoshino and Jun Ota Abstract For the design of an automated transportation system
More informationOn double cycling for container port productivity improvement
DOI 10.1007/s10479-014-1645-z On double cycling for container port productivity improvement Dusan Ku Tiru S. Arthanari Springer Science+Business Media New York 2014 Abstract How quay cranes (QC) are scheduled
More informationManagement Science Letters
Management Science Letters 2 (202) 7 80 Contents lists available at GrowingScience Management Science Letters homepage: www.growingscience.com/msl Investigating transportation system in container terminals
More informationMission Planning Systems for Earth Observation Missions
Mission Planning Systems for Earth Observation Missions Marc Niezette Anite Systems GmbH Robert Bosch StraJ3e 7 Darmstadt, Germany Marc.Niezette@AniteSystems.de Abstract This paper describes two different
More informationSIMULATION AND OPTIMIZATION OF SELECTED CLASSIFICATION NUMBERS AT A CONTAINER TERMINAL: TECON - RIO GRANDE, BRAZIL
SIMULATION AND OPTIMIZATION OF SELECTED CLASSIFICATION NUMBERS AT A CONTAINER TERMINAL: TECON - RIO GRANDE, BRAZIL Leif Hendrik Meier Andreas Lackner Helge Fischer Jörg Biethahn University of Göttingen,
More informationA STUDY OF YARD TRUCK DYNAMIC PLANNING AT A CONTAINER TERMINAL
A STUDY OF YARD TRUCK DYNAMIC PLANNING AT A CONTAINER TERMINAL DRAGOVIĆ, Branislav, Associate Professor, Maritime Faculty, University of Montenegro, Dobrota 36, 85330, Kotor, Montenegro, branod@ac.me PARK,
More informationSIMULATION A WAY HOW TO QUANTIFY FREIGHT VILLAGE PERFORMANCE
ALS Advanced Logistic Systems SIMULATION A WAY HOW TO QUANTIFY FREIGHT VILLAGE PERFORMANCE Michal Dorda, Jaromír Široký VŠB - TU Ostrava, Czech Republic Abstract: In Moravian - Silesian country a freight
More informationResearch into container reshuffling and stacking problems in container terminal yards
IIE Transactions (2015) 47, 751 766 Copyright C IIE ISSN: 0740-817X print / 1545-8830 online DOI: 10.1080/0740817X.2014.971201 Research into container reshuffling and stacking problems in container terminal
More informationHighly Efficient AGV Transportation System Management Using Agent Cooperation and Container Storage Planning
Highly Efficient AGV Transportation System Management Using Agent Cooperation and Container Storage Planning Satoshi Hoshino and Jun Ota Dept. of Precision Engineering, School of Engineering The University
More informationOptimal Design Methodology for an AGV Transportation System by Using the Queuing Network Theory
Optimal Design Methodology for an AGV Transportation System by Using the Queuing Network Theory Satoshi Hoshino 1, Jun Ota 1, Akiko Shinozaki 2, and Hideki Hashimoto 2 1 Dept. of Precision Engineering,
More informationSimulation of a Four-Car Elevator Operation Using MATLAB
Vol. 2, No. 6 Modern Applied Science Simulation of a Four-Car Elevator Operation Using MATLAB Saw Soon King & Omrane Bouketir School of Electrical and Electronic Engineering University of Nottingham, Malaysia
More informationTopic 5 Rule-based expert systems
Topic 5 Rule-based expert systems Introduction, or what is knowledge? Rules as a knowledge representation technique The main players in the development team Structure of a rule-based expert system haracteristics
More informationThis document is downloaded from DR-NTU, Nanyang Technological University Library, Singapore.
This document is downloaded from DR-NTU, Nanyang Technological University Library, Singapore. Title Deciding on planning windows by partitioning time for yard crane management in container terminals Author(s)
More informationA Simulation-based Multi-level Redundancy Allocation for a Multi-level System
International Journal of Performability Engineering Vol., No. 4, July 205, pp. 357-367. RAMS Consultants Printed in India A Simulation-based Multi-level Redundancy Allocation for a Multi-level System YOUNG
More informationImprovement and Simulation of Rear Axle Assembly Line Based on Plant Simulation Platform
2017 International Conference on Mechanical Engineering and Control Automation (ICMECA 2017) ISBN: 978-1-60595-449-3 Improvement and Simulation of Rear Axle Assembly Line Based on Plant Simulation Platform
More informationOptimization of Container Operations at Inland Intermodal Terminals
Optimization of Container Operations at Inland Intermodal Terminals Chiara Colombaroni, Gaetano Fusco, Natalia Isaenko Dipartimento di Ingegneria Civile Edile e Ambientale University of Rome Sapienza Rome,
More informationIntermodal Handling TOP PERFORMANCE WITH SMART SOLUTIONS
Intermodal Handling TOP PERFORMANCE WITH SMART SOLUTIONS Intelligent intermodal solutions ensure safe and sustainable transport of goods International trade is constantly changing its ways and requirements
More informationImproving Productivity of Yard Trucks in Port Container Terminal Using Computer Simulation
The 31st International Symposium on Automation and Robotics in Construction and Mining (ISARC 2014) Improving Productivity of Yard Trucks in Port Container Terminal Using Computer Simulation Essmeil Ahmed
More informationPusan National University, Busandaehak-ro, Geumjeong-gu, Busan, , Korea
A GENETIC ALGORITHM-BASED HEURISTIC FOR NETWORK DESIGN OF SERVICE CENTERS WITH PICK-UP AND DELIVERY VISITS OF MANDATED VEHICLES IN EXPRESS DELIVERY SERVICE INDUSTRY by Friska Natalia Ferdinand 1, Hae Kyung
More informationDepth-Bound Heuristics and Iterative-Deepening Search Algorithms in Classical Planning
Depth-Bound Heuristics and Iterative-Deepening Search Algorithms in Classical Planning Bachelor s Thesis Presentation Florian Spiess, 13 June 2017 Departement of Mathematics and Computer Science Artificial
More informationSIMULATION MODEL FOR MARITIME CONTAINER TERMINAL
TRANSPORT PROBLEMS 2018 Volume 13 Issue 4 PROBLEMY TRANSPORTU DOI: 10.20858/tp.2018.13.4.5 Keywords: container terminal; simulation model; maritime transport Florin RUSCĂ*, Mihaela POPA, Eugen ROȘCA, Mircea
More informationAn optimization framework for modeling and simulation of dynamic systems based on AIS
Title An optimization framework for modeling and simulation of dynamic systems based on AIS Author(s) Leung, CSK; Lau, HYK Citation The 18th IFAC World Congress (IFAC 2011), Milano, Italy, 28 August-2
More informationIndustrial Engineering Applications to Optimize Container Terminal Operations
Industrial Engineering Applications to Optimize Container Terminal Operations Asela K. Kulatunga* & D.H. Haasis+ *glink Postdoctoral researcher, University of Bremen Germany Senior Lecturer, Faculty of
More informationEvolutionary Computation for Minimizing Makespan on Identical Machines with Mold Constraints
Evolutionary Computation for Minimizing Makespan on Identical Machines with Mold Constraints Tzung-Pei Hong 1, 2, Pei-Chen Sun 3, and Sheng-Shin Jou 3 1 Department of Computer Science and Information Engineering
More informationIntroduction to Information Systems Fifth Edition
Introduction to Information Systems Fifth Edition R. Kelly Rainer Brad Prince Casey Cegielski Appendix D Intelligent Systems Copyright 2014 John Wiley & Sons, Inc. All rights reserved. 1. Explain the potential
More informationIMPROVING MANUFACTURING PROCESSES USING SIMULATION METHODS
Applied Computer Science, vol. 12, no. 4, pp. 7 17 Submitted: 2016-07-27 Revised: 2016-09-05 Accepted: 2016-09-09 computer simulation, production line, buffer allocation, throughput Sławomir KŁOS *, Justyna
More informationFLEXIBLE APPOINTMENT BASED SYSTEM WITH ADAPTIVE RESPONSE TO TRAFFIC AND PROCESSING DELAYS
FLEXIBLE APPOINTMENT BASED SYSTEM WITH ADAPTIVE RESPONSE TO TRAFFIC AND PROCESSING DELAYS Amrinder Arora, DSc NTELX Research and Development, 1945 Old Gallows Rd, Suite 700, McLean VA 22182, USA +1 703
More informationA New Fuzzy Modeling Approach for Joint Manufacturing Scheduling and Shipping Decisions
A New Fuzzy Modeling Approach for Joint Manufacturing Scheduling and Shipping Decisions Can Celikbilek* (cc340609@ohio.edu), Sadegh Mirshekarian and Gursel A. Suer, PhD Department of Industrial & Systems
More informationResearch on Multi-Objective Co-operation Optimization of Shipyard Berth Allocation System
Research on Multi-Objective Co-operation Optimization of Shipyard Berth Allocation System Tang Weiwei 1*, Yang Kunrong 2 1 School of Civil Engineering and Transportation South China University of Technology
More informationD²CTS: A DYNAMIC AND DISTRIBUTED CONTAINER TERMINAL SIMULATOR
D²CTS: A DYNAMIC AND DISTRIBUTED CONTAINER TERMINAL SIMULATOR G. Lesauvage (a), S. Balev (b), F. Guinand (c) Université du Havre, LITIS EA 4108 BP 540, 76058 Le Havre, France (a) gaetan.lesauvage@litislab.eu,
More informationPEMA Student Challenge 2017
PEMA Student Challenge 2017 DIGITAL TRANSFORMATION FOR PORTS & TERMINALS Hamburg University of Technology Sören Braren Anna Hoffmann Thorben Klaß Burkhard Pfeiffer Torben Werner INTRODUCTION Port 4.0 Transparency
More informationA Genetic Algorithm on Inventory Routing Problem
A Genetic Algorithm on Inventory Routing Problem Artvin Çoruh University e-mail: nevin.aydin@gmail.com Volume 3 No 3 (2014) ISSN 2158-8708 (online) DOI 10.5195/emaj.2014.31 http://emaj.pitt.edu Abstract
More informationProceedings of the 2016 Winter Simulation Conference T. M. K. Roeder, P. I. Frazier, R. Szechtman, E. Zhou, T. Huschka, and S. E. Chick, eds.
Proceedings of the 2016 Winter Simulation Conference T. M. K. Roeder, P. I. Frazier, R. Szechtman, E. Zhou, T. Huschka, and S. E. Chick, eds. MODULAR CONSTRUCTION SYSTEM SIMULATION INCORPORATING OFF-SHORE
More informationSimulation based Performance Analysis of an End-of-Aisle Automated Storage and Retrieval System
Simulation based Performance Analysis of an End-of-Aisle Automated Storage and Retrieval System Behnam Bahrami, El-Houssaine Aghezzaf and Veronique Limère Department of Industrial Management, Ghent University,
More informationChapter 1 Introduction
Lecture slides for Automated Planning: Theory and Practice Chapter 1 Introduction Dana S. Nau University of Maryland 1:39 PM February 1, 2012 1 Some Dictionary Definitions of Plan plan n. 1. A scheme,
More informationImprove Inter Terminal Transportation: Using Agent Technology. May 16, 2017
Improve Inter Terminal Transportation: Using Agent Technology May 16, 2017 Lawrence Henesey School of Computing (COM) Blekinge Institute of Technology Biblioteksgatan 3 Karlshamn, S-37424 Sweden lhe@bth.se
More informationOptimal Design, Evaluation, and Analysis of AGV Transportation Systems Based on Various Transportation Demands
Optimal Design, Evaluation, and Analysis of Systems Based on Various Demands Satoshi Hoshino and Jun Ota Dept. of Precision Engineering, School of Engineering The University of Tokyo Bunkyo-ku, Tokyo 113-8656,
More informationContainer Sharing in Seaport Hinterland Transportation
Container Sharing in Seaport Hinterland Transportation Herbert Kopfer, Sebastian Sterzik University of Bremen E-Mail: kopfer@uni-bremen.de Abstract In this contribution we optimize the transportation of
More informationFIS Based Speed Scheduling System of Autonomous Railway Vehicle
International Journal of Scientific & Engineering Research Volume 2, Issue 6, June-2011 1 FIS Based Speed Scheduling System of Autonomous Railway Vehicle Aiesha Ahmad, M.Saleem Khan, Khalil Ahmed, Nida
More informationDISCRETE-EVENT SIMULATION OF A COMPLEX INTERMODAL CONTAINER TERMINAL A Case-Study of Standard Unloading/Loading Processes of Vessel Ships
DISCRETE-EVENT SIMULATION OF A COMPLEX INTERMODAL CONTAINER TERMINAL A Case-Study of Standard Unloading/Loading Processes of Vessel Ships Guido Maione DEESD, Technical University of Bari, Viale del Turismo
More informationXiaobing Zhao Ameya Shendarkar and Young-Jun Son
BDI Agent-based Human Decision-Making Model and its Implementation in Agentin-the-loop, Human-in-the-loop, Hardware-in-the-loop, Distributed Simulation Xiaobing Zhao (xiaobing@email.arizona.edu), Ameya
More informationInter-Terminal Transport with uncertain connections
Inter-Terminal Transport with uncertain connections Bachelor thesis Sven van den Berg (343536) Erasmus University of Rotterdam, Faculty of Economics 26-6-2013 This thesis is about controlling the transport
More informationProceedings of the 2010 Winter Simulation Conference B. Johansson, S. Jain, J. Montoya-Torres, J. Hugan, and E. Yücesan, eds.
Proceedings of the 2010 Winter Simulation Conference B. Johansson, S. Jain, J. Montoya-Torres, J. Hugan, and E. Yücesan, eds. REUSABLE TEMPLATE FOR SIMULATION OF OVERHEAD CRANES INTERFERENCES ABSTRACT
More informationAGENTS MODELING EXPERIENCE APPLIED TO CONTROL OF SEMI-CONTINUOUS PRODUCTION PROCESS
Computer Science 15 (4) 2014 http://dx.doi.org/10.7494/csci.2014.15.4.411 Gabriel Rojek AGENTS MODELING EXPERIENCE APPLIED TO CONTROL OF SEMI-CONTINUOUS PRODUCTION PROCESS Abstract The lack of proper analytical
More informationGame Theory: Spring 2017
Game Theory: Spring 2017 Ulle Endriss Institute for Logic, Language and Computation University of Amsterdam Ulle Endriss 1 Plan for Today This and the next lecture are going to be about mechanism design,
More information27th Australasian Transport Research Forum, Adelaide, 29 September 1 October 2004
27th Australasian Transport Research Forum, Adelaide, 29 September 1 October 2004 Paper title: Author(s) name(s): Organisation(s): Location modelling: grouping genetic algorithm using a Geographic Information
More informationA Discrete Time Heuristic for Storage and Scheduling Unloading Operations in Container Terminal under Capacity and Non-Interference Constraints
A Discrete Time Heuristic for Storage and Scheduling Unloading Operations in Container Terminal under Capacity and Non-Interference Constraints Sanae Kouismi MOAD-SCM Team, Mohammadia School of Engineering,
More informationCSE 259: Logic in Computer Science (Spring 2017)
CSE 259: Logic in Computer Science (Spring 2017) Final Study Guide Final: In class, Monday, May 1, 2:30PM to 4:20PM (1 hour & 50min) Comprehensive but with a bit more emphasis on the materials after mid-term
More informationResearch Article Rail Mounted Gantry Crane Scheduling Optimization in Railway Container Terminal Based on Hybrid Handling Mode
Computational Intelligence and euroscience, Article ID 682486, 8 pages http://dx.doi.org/10.1155/2014/682486 Research Article Rail Mounted Gantry Crane Scheduling Optimization in Railway Container Terminal
More informationLogistics for Public Freight Planners: Theory and Practice. Bruce Wang, Ph.D. Zachry Department of Civil Engineering Texas A&M University March 2009
Logistics for Public Freight Planners: Theory and Practice Bruce Wang, Ph.D. Zachry Department of Civil Engineering Texas A&M University March 2009 Outline Background Introduction to Supply Chain and Logistics
More informationSecond Generation Model-based Testing
CyPhyAssure Spring School Second Generation Model-based Testing Provably Strong Testing Methods for the Certification of Autonomous Systems Part I of III Motivation and Challenges Jan Peleska University
More information