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Modern Heuristics of MCDM for the Operation Optimization in Container Terminals

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Multi-Criteria Decision Making in Maritime Studies and Logistics

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 260))

Abstract

This chapter systematically applies modern heuristics to solve multi-criteria decision making problems in the fields of container terminal, which consists of three geographically interrelated core areas: container terminal, anchorage ground on its sea side, and gateway on its land side. For the container terminal, the container loading sequence problem is considered and a hybrid dynamic programming approach is proposed. The considered problem aims at obtaining an optimized container loading sequence for a crane to retrieve all the containers from the yard to the ship. The proposed dynamic algorithms consist of two phases. A heuristic algorithm is developed to retrieve the containers subset which needs no relocation and may be loaded directly onto the ship at the first phase, and a dynamic programming with heuristic rules is applied to solve the loading sequence problem for the rest of the containers at the second phase. For the anchorage ground on the sea side of a container terminal, the tugboat scheduling problem is formulated as a multiprocessor tasks scheduling problem after analyzing the characteristics of tugboat operation. The model considers factors of multi-anchorage bases and three stages of operations (berthing/shifting-berth/unberthing). The objective is to minimize the total operation times for all tugboats and the waste of the tugboats horsepower in use at the same time. A hybrid simulated annealing algorithm is proposed to solve the addressed problem. For the gateway on the land side of a container terminal, resource deployment for truck appointment system on container terminals is solved as an optimization problem. A bi-objective model is set up to minimize resource input and balance workloads. Modern heuristics method based on non-dominated genetic algorithmII is proposed to solve difficulties of simultaneous optimization of resource input and appointment quotas. Three chromosomes representing quotas, yard cranes and gate lanes are set up, some of which are two dimensional. Numerical experiments are untaken to evaluate the effectiveness of the proposed algorithms and show the efficiency of the proposed algorithm. The three parts analyzed above cover all the core elements of modern heuristics of MCDM for the operation optimization in a container terminal from a container terminal to both its land side and its sea side.

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References

  • Alessandri A, Cervellera C, Cuneo M, Gaggero M, Soncin G (2009) Management of logistics operations in intermodal terminals by using dynamic modelling and nonlinear programming. Marit Econo Logist 11(1):58–76

    Article  Google Scholar 

  • Bellman R (1952) On the theory of dynamic programming. Proc Natl Acad Sci U S A 38(8):716–719

    Article  Google Scholar 

  • Bellman R (1953) Bottleneck problems and dynamic programming. Proc Natl Acad Sci U S A 39(9):947–951

    Article  Google Scholar 

  • Bellman R (1955) Functional equations in the theory of dynamic programming V. positivity and quasi-linearity. Proc Natl Acad Sci U S A 41(10):743–746

    Article  Google Scholar 

  • Bellman R (1965) ON the application of dynamic programming to the determination of optimal play in chess and checkers. Proc Natl Acad Sci U S A 53(2):244–247

    Article  Google Scholar 

  • Caserta M, Voß S, Sniedovich M (2011) Applying the corridor method to a blocks relocation problem. Eur J Oper Res 33(4):915–929

    Google Scholar 

  • Chen G, Govindan K, Golias MM (2013) Reducing truck emissions at container terminals in a low carbon economy: proposal of a queueing-based bi-objective model for optimizing truck arrival pattern. Transp Res E Log 55:3–22

    Article  Google Scholar 

  • Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6:182–197

    Article  Google Scholar 

  • Dong LC, Xu ZQ, Mi WJ (2012) The dynamic tugboat schedule based on particle swarm algorithm combined with genetic operators. Math Pract Theory 42(6):122–133

    Google Scholar 

  • Feldmann EG (1967) Dynamic program for drug quality. J Am Pharm Assoc 7(6):301–302

    Google Scholar 

  • Giuliano G, O’Brien T (2007) Reducing port-related truck emissions: the terminal gate appointment system at the Ports of Los Angeles and Long Beach. Transp Res D Transp Environ 12(7):460–473

    Article  Google Scholar 

  • Guan C, Liu R (2009) Container terminal gate appointment system optimization. Marit Econo Log 11(4):378–398

    Article  Google Scholar 

  • Guo X, Huang SY, Hsu WJ, Low MYH (2011) Dynamic yard crane dispatching in container terminals with predicted vehicle arrival information. Adv Eng Inform 25(3):472–484

    Article  Google Scholar 

  • Huynh N, Walton CM (2008) Robust scheduling of truck arrivals at marine container terminals. J Transp Eng 134(8):347–353

    Article  Google Scholar 

  • Huynh N, Walton CM (2011) Improving efficiency of Drayage operations at seaport container terminals through the use of an appointment system. In: Böse JW (ed) Handbook of terminal planning, vol 49. Springer, New York, pp 323–344

    Chapter  Google Scholar 

  • Huynh N, Walton C, Davis J (2004) Finding the number of yard cranes needed to achieve desired truck turn time at marine container terminals. Transp Res Rec (J Transp Res Board) 1873:99–108

    Article  Google Scholar 

  • Jin C, Gao P (2006) Container berth expansion planning with dynamic programming and fuzzy set theory. 2006 I.E. international conference on service operations and logistics, and informatics (SOLI 2006), Proceedings, pp 260–265

    Google Scholar 

  • Jin ZH, Lan H, Bian Z, Ji MJ (2011a) Optimization on containership loading scheduling based on actual constraints. J Dalian Marit Univ 37(1):71–74

    Google Scholar 

  • Jin ZH, Mao J, Li N (2011b) Scheduling of relocating containers within a bay in container yard based on hybrid dynamic programming. J Transp Syst Eng Inf Technol 11(6):131–136

    Google Scholar 

  • Kim KH (1997) Evaluation of the number of re-handles in container yards. Comput Ind Eng 32(4):701–711

    Article  Google Scholar 

  • Kim KH, Hong GP (2006) A heuristic rule for relocating blocks. Comput Oper Res 33(4):940–954

    Article  Google Scholar 

  • Kim KH, Kim HB (1999) Segregating space allocation models for container inventories in port container terminals. Int J Prod Econ 59(1–3):415–423

    Google Scholar 

  • Kim KH, Park YM, Ryu KR (2000) Deriving decision rules to locate export containers in container yard. Eur J Oper Res 124(1):89–101

    Article  Google Scholar 

  • Kim KH, Lee KM, Hwang H (2003) Sequencing delivery and receiving operations for yard cranes in port container terminals. Int J Prod Econ 84(3):283–292

    Article  Google Scholar 

  • Lam SW, Lee LH, Tang LC (2007) An approximate dynamic programming approach for the empty container allocation problem. Transp Res C Emerg Technol 15(4):265–277

    Article  Google Scholar 

  • Lee YS, Lee YJ (2010) A heuristic for retrieving containers from a yard. Comput Oper Res 37(6):1139–1147

    Article  Google Scholar 

  • Li D, Glazebrook KD (2010) An approximate dynamic programming approach to the development of heuristics for the scheduling of impatient jobs in a clearing system. Nav Res Logist 57(3):225–236

    Google Scholar 

  • Li N, Chen G, Govindan K, Jin Z (2015) Disruption management for truck appointment system at a container terminal: a green initiative. Transportation Research Part D

    Google Scholar 

  • Liu ZX (2009) Hybrid evolutionary strategy optimization for port tugboat operation scheduling. The third international symposium on intelligent information technology application, pp 511–515

    Google Scholar 

  • Liu ZX (2011) Port tugboat operation scheduling optimization considering the minimum operation distance. J Southwest Jiaotong Univ 46(5):875–881

    Google Scholar 

  • Liu ZX, Wang SM (2005) Research on bi-objectives parallel machines scheduling problem with special process constraint. Comput Integr Manuf Syst 11(11):1616–1620

    Google Scholar 

  • Liu ZX, Wang SM (2006) Research on parallel machines scheduling problem based on particle optimization algorithm. Comput Integr Manuf Syst 12(2):183–187+296

    Google Scholar 

  • Liu ZX, Li J, Shao ZY, He JJ (2016) Design of hybrid evolutionary strategy algorithm of dynamic tugboat scheduling problem. Comput Eng Des 37(2):519–524+529

    Google Scholar 

  • Meng Q, Wang TS (2011) A scenario-based dynamic programming model for multi-period liner ship fleet planning. Transp Res E-Log 47(4):401–413

    Article  Google Scholar 

  • Morais P, Lord E (2006) Terminal appointment system study

    Google Scholar 

  • Namboothiri R, Erera AL (2008) Planning local container drayage operations given a port access appointment system. Transp Res E-Log 44(2):185–202

    Article  Google Scholar 

  • Phan M-H, Kim KH (2015) Negotiating truck arrival times among trucking companies and a container terminal. Transp Res E-Log 75:132–144

    Article  Google Scholar 

  • Phan M-H, Kim KH (2016) Collaborative truck scheduling and appointments for trucking companies and container terminals. Transp Res B Methodol 86:37–50

    Article  Google Scholar 

  • Sanaye S, Mahmoudimehr J (2012) Minimization of fuel consumption in cyclic and non-cyclic natural gas transmission networks: assessment of genetic algorithm optimization method as an alternative to non-sequential dynamic programming. J Taiwan Inst Chem Eng 43(6):904–917

    Article  Google Scholar 

  • Van Asperen E, Borgman B, Dekker R (2011) Evaluating impact of truck announcements on container stacking efficiency. Flex Serv Manuf J 25(4):543–556

    Article  Google Scholar 

  • Wang S, Meng B (2007) Resource allocation and scheduling problem based on genetic algorithm and ant colony optimization. Lect Notes Artif Intell 4426:879–886

    Google Scholar 

  • Wang X, Chen HY, Wang C, Liu D, Lv C (2005) The algorithm of getting the reasonable harbor’s shipping order. Math Econ 22(3):284–290

    Google Scholar 

  • Wang S, Kaku I, Chen GY, Zhu M (2012) Research on the modeling of tugboat assignment problem in container terminal. Adv Mat Res 433-440:1957–1961

    Article  Google Scholar 

  • Xu Y, Chen QH, Long L, Yang LZ, Liu LY (2008) Heuristics for container relocation problem. J Syst Simul 20(14):3666–3669

    Google Scholar 

  • Xu Q, Mao J, Jin ZH (2012) Simulated annealing–based ant colony algorithm for tugboat scheduling optimization. Math Probl Eng 246978

    Google Scholar 

  • Xu Q, Bian Z, Chen Y, Jin ZH (2014a) Scheduling optimization of port tugboat operation considering multi-anchorage. J Shanghai Jiaotong Univ 48(1):132–139

    Google Scholar 

  • Xu Q, Shao QQ, Jin ZH (2014b) Optimization on tugboat operation scheduling based upon the hybrid flow shop arrangement. Syst Eng Theory Pract 34(2):485–493

    Google Scholar 

  • Yang JH, Kim KH (2006) A grouped storage method for minimizing relocations in block stacking systems. J Intell Manuf 17(4):453–463

    Article  Google Scholar 

  • Yi ZJ, Li BS, Li XQ (2010) Game heuristic optimization algorithm for reshuffle in container yards. J Shanghai Marit Univ 31(3):47–51

    Google Scholar 

  • Zhao W, Goodchild AV (2010) The impact of truck arrival information on container terminal rehandling. Transport Res E-Log 46(3):327–343

    Article  Google Scholar 

  • Zhao W, Goodchild AV (2013) Using the truck appointment system to improve yard efficiencyin container terminals. Marit Econ Log 15(1):101–119

    Article  Google Scholar 

  • Zhu MH, Fan XM, Cheng HC, He Q (2010) Heuristics for export container loading sequence problem. China Mech Eng 21(9):1066–1070

    Google Scholar 

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Author information

Authors and Affiliations

  1. College of Transportation Management, Dalian Maritime University, Dalian, China

    Zhihong Jin, Na Li & Qi Xu

  2. College of Business Administration, Capital University of Economics and Business, Beijing, China

    Zhan Bian

Authors
  1. Zhihong Jin
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  2. Na Li
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  3. Qi Xu
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  4. Zhan Bian
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Corresponding author

Correspondence to Zhihong Jin .

Editor information

Editors and Affiliations

  1. Institute of Maritime Logistics, Ocean College, Zhejiang University, Zhoushan, Zhejiang Province, China

    Paul Tae-Woo Lee

  2. School of Maritime and Mechanical Engineering, Liverpool John Moores University, Liverpool, United Kingdom

    Zaili Yang

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Jin, Z., Li, N., Xu, Q., Bian, Z. (2018). Modern Heuristics of MCDM for the Operation Optimization in Container Terminals. In: Lee, PW., Yang, Z. (eds) Multi-Criteria Decision Making in Maritime Studies and Logistics. International Series in Operations Research & Management Science, vol 260. Springer, Cham. https://doi.org/10.1007/978-3-319-62338-2_11

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