April  2009, 5(2): 285-302. doi: 10.3934/jimo.2009.5.285

Optimizing container movements using one and two automated stacking cranes

1. 

Operations Research Department, Naval Postgraduate School, Monterey, CA 93943, United States, United States

2. 

Command and General Staff College, Hellenic Army, Thessalonik, Greece

Received  April 2007 Revised  February 2009 Published  April 2009

Productivity of a sea port depends, in part, on stacking cranes working in blocks of its storage yard. Each container leaving a block must be moved by a storage-yard crane to a buffer zone during a specific time window so it can reach its destination on time. Containers entering a block for storage must be moved out of the buffer zone sufficiently soon to avoid overflow. In this paper, we formulate integer linear programs to prescribe movements to transport and stack containers in storage yards using one and two equally-sized Automated Stacking Cranes (ASCs) working with straddle carriers. Using real world data, we construct test problems varying both the number of container bays and fullness of the block. We find that one ASC working alone requires up to 70% more time than two ASCs working together to accomplish the same container movements. Optimal solutions of the integer linear programs are typically obtained in only a few seconds.
Citation: Robert F. Dell, Johannes O. Royset, Ioannis Zyngiridis. Optimizing container movements using one and two automated stacking cranes. Journal of Industrial & Management Optimization, 2009, 5 (2) : 285-302. doi: 10.3934/jimo.2009.5.285
[1]

Elham Mardaneh, Ryan Loxton, Qun Lin, Phil Schmidli. A mixed-integer linear programming model for optimal vessel scheduling in offshore oil and gas operations. Journal of Industrial & Management Optimization, 2017, 13 (4) : 1601-1623. doi: 10.3934/jimo.2017009

[2]

Ali Tebbi, Terence Chan, Chi Wan Sung. Linear programming bounds for distributed storage codes. Advances in Mathematics of Communications, 2020, 14 (2) : 333-357. doi: 10.3934/amc.2020024

[3]

Louis Caccetta, Syarifah Z. Nordin. Mixed integer programming model for scheduling in unrelated parallel processor system with priority consideration. Numerical Algebra, Control & Optimization, 2014, 4 (2) : 115-132. doi: 10.3934/naco.2014.4.115

[4]

Zhiguo Feng, Ka-Fai Cedric Yiu. Manifold relaxations for integer programming. Journal of Industrial & Management Optimization, 2014, 10 (2) : 557-566. doi: 10.3934/jimo.2014.10.557

[5]

Wan Nor Ashikin Wan Ahmad Fatthi, Adibah Shuib, Rosma Mohd Dom. A mixed integer programming model for solving real-time truck-to-door assignment and scheduling problem at cross docking warehouse. Journal of Industrial & Management Optimization, 2016, 12 (2) : 431-447. doi: 10.3934/jimo.2016.12.431

[6]

Mahmoud Ameri, Armin Jarrahi. An executive model for network-level pavement maintenance and rehabilitation planning based on linear integer programming. Journal of Industrial & Management Optimization, 2020, 16 (2) : 795-811. doi: 10.3934/jimo.2018179

[7]

Edward S. Canepa, Alexandre M. Bayen, Christian G. Claudel. Spoofing cyber attack detection in probe-based traffic monitoring systems using mixed integer linear programming. Networks & Heterogeneous Media, 2013, 8 (3) : 783-802. doi: 10.3934/nhm.2013.8.783

[8]

Mahdi Roozbeh, Saman Babaie–Kafaki, Zohre Aminifard. Two penalized mixed–integer nonlinear programming approaches to tackle multicollinearity and outliers effects in linear regression models. Journal of Industrial & Management Optimization, 2020  doi: 10.3934/jimo.2020128

[9]

Yongjian Yang, Zhiyou Wu, Fusheng Bai. A filled function method for constrained nonlinear integer programming. Journal of Industrial & Management Optimization, 2008, 4 (2) : 353-362. doi: 10.3934/jimo.2008.4.353

[10]

Wei Huang, Ka-Fai Cedric Yiu, Henry Y. K. Lau. Semi-definite programming based approaches for real-time tractor localization in port container terminals. Numerical Algebra, Control & Optimization, 2013, 3 (4) : 665-680. doi: 10.3934/naco.2013.3.665

[11]

Ye Tian, Cheng Lu. Nonconvex quadratic reformulations and solvable conditions for mixed integer quadratic programming problems. Journal of Industrial & Management Optimization, 2011, 7 (4) : 1027-1039. doi: 10.3934/jimo.2011.7.1027

[12]

Zhenbo Wang, Shu-Cherng Fang, David Y. Gao, Wenxun Xing. Global extremal conditions for multi-integer quadratic programming. Journal of Industrial & Management Optimization, 2008, 4 (2) : 213-225. doi: 10.3934/jimo.2008.4.213

[13]

Jing Quan, Zhiyou Wu, Guoquan Li. Global optimality conditions for some classes of polynomial integer programming problems. Journal of Industrial & Management Optimization, 2011, 7 (1) : 67-78. doi: 10.3934/jimo.2011.7.67

[14]

Yasmine Cherfaoui, Mustapha Moulaï. Biobjective optimization over the efficient set of multiobjective integer programming problem. Journal of Industrial & Management Optimization, 2019  doi: 10.3934/jimo.2019102

[15]

Mohamed A. Tawhid, Ahmed F. Ali. A simplex grey wolf optimizer for solving integer programming and minimax problems. Numerical Algebra, Control & Optimization, 2017, 7 (3) : 301-323. doi: 10.3934/naco.2017020

[16]

Charles Fefferman. Interpolation by linear programming I. Discrete & Continuous Dynamical Systems - A, 2011, 30 (2) : 477-492. doi: 10.3934/dcds.2011.30.477

[17]

Jiayu Shen, Yuanguo Zhu. An uncertain programming model for single machine scheduling problem with batch delivery. Journal of Industrial & Management Optimization, 2019, 15 (2) : 577-593. doi: 10.3934/jimo.2018058

[18]

Min-Fan He, Li-Ning Xing, Wen Li, Shang Xiang, Xu Tan. Double layer programming model to the scheduling of remote sensing data processing tasks. Discrete & Continuous Dynamical Systems - S, 2019, 12 (4&5) : 1515-1526. doi: 10.3934/dcdss.2019104

[19]

Jean Creignou, Hervé Diet. Linear programming bounds for unitary codes. Advances in Mathematics of Communications, 2010, 4 (3) : 323-344. doi: 10.3934/amc.2010.4.323

[20]

Jinling Wei, Jinming Zhang, Meishuang Dong, Fan Zhang, Yunmo Chen, Sha Jin, Zhike Han. Applications of mathematics to maritime search. Discrete & Continuous Dynamical Systems - S, 2019, 12 (4&5) : 957-968. doi: 10.3934/dcdss.2019064

2019 Impact Factor: 1.366

Metrics

  • PDF downloads (49)
  • HTML views (0)
  • Cited by (7)

[Back to Top]