scholarly journals A stochastic discrete optimization model for designing container terminal facilities

2017 ◽  
Author(s):  
Febri Zukhruf ◽  
Russ Bona Frazila ◽  
Jzolanda Tsavalista Burhani
Keyword(s):  
Discrete Optimization ◽  
Optimization Model ◽  
Container Terminal ◽  
Stochastic Discrete Optimization

scholarly journals A discrete optimization model for preserving biological diversity

1995 ◽  
Vol 19 (11) ◽  
pp. 696-701 ◽  
Author(s):  
Fred Glover ◽  
Kuo Ching-Chung ◽  
Krishna S. Dhir
Keyword(s):  
Discrete Optimization ◽  
Optimization Model ◽  
Biological Diversity

Discrete Multistage Optimization and Hierarchical Market

2006 ◽  
Vol 11 (2) ◽  
pp. 149-156
Author(s):  
V. J. Bistrickas ◽  
N. Šimelienė
Keyword(s):  
Discrete Optimization ◽  
Simple Form ◽  
Market Equilibrium ◽  
Continuous Optimization ◽  
Analytic Solutions ◽  
Continuous Processes ◽  
Mixed Solutions ◽  
Discrete And Continuous Optimization ◽  
Stochastic Discrete Optimization

New simple form of mixed solutions is described by bilinear continuous optimization processes. It enables investigate an analytic solutions and the connection between discrete and continuous optimization processes. Connection between discrete and continuous processes is stochastic. Discrete optimization processes are used for the control works in levels and groups of the hierarchical market. Equilibrium between local and global levels of works is investigated in hierarchical market.

A Multiobjective Discrete Optimization Model for Land Allocation

1985 ◽  
Vol 31 (12) ◽  
pp. 1509-1522 ◽  
Author(s):  
Kenneth C. Gilbert ◽  
David D. Holmes ◽  
Richard E. Rosenthal
Keyword(s):  
Discrete Optimization ◽  
Optimization Model ◽  
Land Allocation

Influence of Carbon Emission Constraint on Container Quay Crane Allocation

2013 ◽  
Vol 807-809 ◽  
pp. 936-940 ◽  
Author(s):  
Wen Yuan Wang ◽  
Yun Peng ◽  
Guo Lei Tang ◽  
Xiang Qun Song
Keyword(s):  
Optimization Model ◽  
Carbon Emission ◽  
Container Terminal ◽  
Total Loss ◽  
Constraint Condition ◽  
Container Crane ◽  
Classic Method ◽  
Original Plan ◽  
Economical Aspect ◽  
Optimization Allocation

As one of the important handling equipments in container terminal, the quayside container crane is a major energy consumer. Therefore, the optimization allocation of crane is studied in the paper considering the carbon emission. First, based on the classic method from the economical aspect, an optimization model is established with the goal of minimizing the total loss of vessel waiting for berth and idle berth. Second, a constraint condition for carbon emission of handling unit container by quayside crane is added. Finally, this paper analyses the influence of carbon emission constraint on quay crane allocation. The results show that the allocation optimization model will influence the original plan after considering the carbon emission constraint, and the goal of decreasing carbon emission can be achieved by rearranging quayside crane with the optimization model.

scholarly journals A Discrete Optimization Model to Minimize Organ Recovery Time Using Heuristic Algorithms

2013 ◽  
Vol 1 (1) ◽  
pp. 75-82
Author(s):  
Chanchal Saha ◽  
Sang Won Yoon
Keyword(s):  
Discrete Optimization ◽  
Optimization Model ◽  
Recovery Time ◽  
Initial Solution ◽  
Experimental Results ◽  
Elapse Time ◽  
Time Interval ◽  
Silhouette Index ◽  
Organ Recovery ◽  
Recovery Groups

This study proposes a discrete optimization model to minimize the organ recovery time in an Organ Procurement Organization (OPO) by grouping its associated hospitals and transplant centers into several clusters, based on their available organ recovery groups. Typically, the OPO covers a relatively large geographical area to recover organs from donors and deliver them to the recipients. Organs and/or tissues need to be transplanted within their viable time. Therefore, a discrete optimization model is proposed, based on the -median approach to identify optimal locations of the organ recovery groups to recover the organs within a desired time interval. Three heuristic solution approaches, such as Multi-start Fast Interchange (MFI), Simulated Annealing (SA), and Lagrangian Relaxation Algorithm (LRA), are applied to solve the -median clustering problems. Numerical examples are tested to identify a better solution approach in terms of a set of key performance indicators, such as elapse time, Silhouette index, and objective function value. The experimental results indicate that the MFI approach is effective finding an initial solution in the shortest possible time. To find a non-dominant optimal solution, the LRA outperformed the initial solution. In the future, the experimental results will be compared with real data to ensure the effectiveness of the proposed model.

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