Looking-Forward Scheduling Approach Applied in Pre-erection Area of a Shipyard

2007 ◽  
Vol 23 (01) ◽  
pp. 30-35
Author(s):  
Duck Young Yoon ◽  
Ranjan Varghese
Keyword(s):  
Genetic Algorithm ◽  
Knapsack Problem ◽  
Material Handling ◽  
Building Blocks ◽  
Spatial Arrangement ◽  
Solution Procedure ◽  
Sequence Diagram ◽  
Forward Algorithm ◽  
Under Construction ◽  
Block Arrangement

The major problem arises at a shipyard because of scarcity of space for arranging the building blocks of a ship under construction. A standardized erection sequence diagram is generally available to provide the prioritized erection sequence, and it serves as the framework. In order to make a timely erection of the blocks, a post plan has to be developed so that the blocks lie in the nearest possible vicinity of the material-handling devices while keeping the priority of erection, and the blocks are arranged in the pre-erection area. The genetic algorithm based solution procedure is developed to produce the optimal spatial arrangement. An innovative algorithm nicknamed ISBAS (Intelligent Spatial Block Arrangement Scheduler) has been developed and implemented using genetic algorithm, and a workable computer program has been developed using VC++. The detailed genetic operation is performed to run to meet the objectives of the critically complicated problem. Also, hand-in-hand algorithms such as knapsack problem, traveling salesmen problem, and weight assignment algorithms are used to segregate the better options. An indigenously developed looking-forward algorithm is also in place.

Recycling Lethal Chromosomes Based on Immune Operation in Genetic Algorithm for Multi-Knapsack Problem

2014 ◽  
Vol 1 ◽  
pp. 219-222
Author(s):  
Jing Guo ◽  
Jousuke Kuroiwa ◽  
Hisakazu Ogura ◽  
Izumi Suwa ◽  
Haruhiko Shirai ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Knapsack Problem

scholarly journals A Building-Block-Based Genetic Algorithm for Solving the Robots Allocation Problem in a Robotic Mobile Fulfilment System

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Jingtian Zhang ◽  
Fuxing Yang ◽  
Xun Weng
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Scheduling ◽  
Building Blocks ◽  
Allocation Problem ◽  
Accurate Analysis ◽  
Allocation Problems ◽  
Scheduling Method ◽  
Project Scheduling Problem ◽  
Block Based ◽  
Better Than

Robotic mobile fulfilment system (RMFS) is an efficient and flexible order picking system where robots ship the movable shelves with items to the picking stations. This innovative parts-to-picker system, known as Kiva system, is especially suited for e-commerce fulfilment centres and has been widely used in practice. However, there are lots of resource allocation problems in RMFS. The robots allocation problem of deciding which robot will be allocated to a delivery task has a significant impact on the productivity of the whole system. We model this problem as a resource-constrained project scheduling problem with transfer times (RCPSPTT) based on the accurate analysis of driving and delivering behaviour of robots. A dedicated serial schedule generation scheme and a genetic algorithm using building-blocks-based crossover (BBX) operator are proposed to solve this problem. The designed algorithm can be combined into a dynamic scheduling structure or used as the basis of calculation for other allocation problems. Experiment instances are generated based on the characteristics of RMFS, and the computation results show that the proposed algorithm outperforms the traditional rule-based scheduling method. The BBX operator is rapid and efficient which performs better than several classic and competitive crossover operators.

Solving the 0/1 knapsack problem using an adaptive genetic algorithm

2002 ◽  
Vol 16 (1) ◽  
pp. 23-30 ◽  
Author(s):  
ZOHEIR EZZIANE
Keyword(s):  
Genetic Algorithm ◽  
Knapsack Problem ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Adaptive Genetic Algorithm ◽  
Stochastic Algorithms ◽  
Initial Population ◽  
Genetic Operators ◽  
Adaptive Penalty ◽  
Genetic Algorithm Approach

Probabilistic and stochastic algorithms have been used to solve many hard optimization problems since they can provide solutions to problems where often standard algorithms have failed. These algorithms basically search through a space of potential solutions using randomness as a major factor to make decisions. In this research, the knapsack problem (optimization problem) is solved using a genetic algorithm approach. Subsequently, comparisons are made with a greedy method and a heuristic algorithm. The knapsack problem is recognized to be NP-hard. Genetic algorithms are among search procedures based on natural selection and natural genetics. They randomly create an initial population of individuals. Then, they use genetic operators to yield new offspring. In this research, a genetic algorithm is used to solve the 0/1 knapsack problem. Special consideration is given to the penalty function where constant and self-adaptive penalty functions are adopted.

A Genetic Algorithm for Developing Modular Product Architectures

2003 ◽  
Author(s):  
Tian-Li Yu ◽  
Ali A. Yassine ◽  
David E. Goldberg
Keyword(s):  
Genetic Algorithm ◽  
Minimum Description Length ◽  
Building Blocks ◽  
Product Architecture ◽  
Modular Product ◽  
Modular Products ◽  
Design Structure ◽  
Development Literature ◽  
Industrial Gas Turbine ◽  
Optimal Set

The architecture of a product is determined by both the elements that compose the product and the way in which they interact with each other. In this paper, we use the design structure matrix (DSM) as a tool to capture this architecture. Designing modular products can result in many benefits to both consumers and manufacturers. The development of modular products requires the identification of highly interactive groups of elements and arranging (i.e. clustering) them into modules. However, no rigorous DSM clustering technique can be found in product development literature. This paper presets a review of the basic DSM building blocks used in the identification of product modules. The DSM representation and building blocks are used to develop a new DSM clustering tool based on a genetic algorithm (GA) and the minimum description length (MDL) principle. The new tool is capable of partitioning the product architecture into an “optimal” set of modules or sub-systems. We demonstrate this new clustering method using an example of a complex product architecture for an industrial gas turbine.

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