A berth allocation planning problem with direct transshipment consideration

2011 ◽  
Vol 23 (6) ◽  
pp. 2207-2214 ◽  
Author(s):  
Chengji Liang ◽  
Hark Hwang ◽  
Mitsuo Gen
Keyword(s):  
Planning Problem ◽  
Berth Allocation ◽  
Berth Allocation Planning ◽  
Allocation Planning

Novel wharf-based genetic algorithm for berth allocation planning

2016 ◽  
Vol 21 (11) ◽  
pp. 2897-2910 ◽  
Author(s):  
An-Hsiou Tsai ◽  
Chung-Nan Lee ◽  
Jain-Shing Wu ◽  
Fu-Sheng Chang
Keyword(s):  
Genetic Algorithm ◽  
Berth Allocation ◽  
Berth Allocation Planning ◽  
Allocation Planning

scholarly journals Berth allocation planning in Seville inland port by simulation and optimisation

2011 ◽  
Vol 25 (3) ◽  
pp. 452-461 ◽  
Author(s):  
Carlos Arango ◽  
Pablo Cortés ◽  
Jesús Muñuzuri ◽  
Luis Onieva
Keyword(s):  
Berth Allocation ◽  
Berth Allocation Planning ◽  
Allocation Planning

scholarly journals A Dynamic Programming-Based Sustainable Inventory-Allocation Planning Problem with Carbon Emissions and Defective Item Disposal under a Fuzzy Random Environment

2018 ◽  
Vol 2018 ◽  
pp. 1-18
Author(s):  
Kai Kang ◽  
Wei Pu ◽  
Yanfang Ma
Keyword(s):  
Dynamic Programming ◽  
Random Environment ◽  
Carbon Emissions ◽  
Poor Quality ◽  
Planning Problem ◽  
Defective Item ◽  
Inventory Allocation ◽  
The Impact ◽  
Allocation Planning ◽  
Fuzzy Random

There is a growing concern that business enterprises focus primarily on their economic activities and ignore the impact of these activities on the environment and the society. This paper investigates a novel sustainable inventory-allocation planning model with carbon emissions and defective item disposal over multiple periods under a fuzzy random environment. In this paper, a carbon credit price and a carbon cap are proposed to demonstrate the effect of carbon emissions’ costs on the inventory-allocation network costs. The percentage of poor quality products from manufacturers that need to be rejected is assumed to be fuzzy random. Because of the complexity of the model, dynamic programming-based particle swarm optimization with multiple social learning structures, a DP-based GLNPSO, and a fuzzy random simulation are proposed to solve the model. A case is then given to demonstrate the efficiency and effectiveness of the proposed model and the DP-based GLNPSO algorithm. The results found that total costs across the inventory-allocation network varied with changes in the carbon cap and that carbon emissions’ reductions could be utilized to gain greater profits.

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