A Genetic Algorithm for Rack Sequencing Problem in Robotized Warehouse

2021 ◽  
Vol 31 (3) ◽  
pp. 139-147
Author(s):  
Yun-Hong Min ◽  
In-Taek Gong
Keyword(s):  
Genetic Algorithm ◽  
Sequencing Problem

A Heuristic Approach for Disassembly Sequencing Problem for Robotic Disassembly Operations

2012 ◽  
pp. 438-447
Author(s):  
Ahmed ElSayed ◽  
Elif A. Kongar ◽  
Surendra M. Gupta
Keyword(s):  
Genetic Algorithm ◽  
Value Added ◽  
Recovery Process ◽  
Electronic Products ◽  
Sequencing Problem ◽  
Material Recovery ◽  
Disassembly Sequence ◽  
Robotic Disassembly ◽  
Efficient Recovery ◽  
Disassembly Sequencing

Electronic products enter the waste stream rapidly due to technological enhancements. Their parts and material recovery involve significant economic and environmental gain. To regain the value added to such products a certain level of disassembly may be required. Disassembly operations are often expensive and the complexity of determining the best disassembly sequence increases as the number of parts in a product grows. Therefore, it is necessary to develop methodologies for obtaining optimal or near optimal disassembly sequences to ensure efficient recovery process. To that end, this chapter introduces a Genetic Algorithm based methodology to develop disassembly sequencing for end-of-life products. A numerical example is presented to provide and demonstrate better understating and functionality of the algorithm.

scholarly journals An autonomous vehicle sequencing problem at intersections: A genetic algorithm approach

2013 ◽  
Vol 23 (1) ◽  
pp. 183-200 ◽  
Author(s):  
Fei Yan ◽  
Mahjoub Dridi ◽  
Abdellah El Moudni
Keyword(s):  
Genetic Algorithm ◽  
Traffic Control ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Control Methods ◽  
Traffic Condition ◽  
Sequencing Problem ◽  
Genetic Algorithm Approach ◽  
Short Time ◽  
Intersection Management

This paper addresses a vehicle sequencing problem for adjacent intersections under the framework of Autonomous Intersection Management (AIM). In the context of AIM, autonomous vehicles are considered to be independent individuals and the traffic control aims at deciding on an efficient vehicle passing sequence. Since there are considerable vehicle passing combinations, how to find an efficient vehicle passing sequence in a short time becomes a big challenge, especially for more than one intersection. In this paper, we present a technique for combining certain vehicles into some basic groups with reference to some properties discussed in our earlier works. A genetic algorithm based on these basic groups is designed to find an optimal or a near-optimal vehicle passing sequence for each intersection. Computational experiments verify that the proposed genetic algorithms can response quickly for several intersections. Simulations with continuous vehicles are carried out with application of the proposed algorithm or existing traffic control methods. The results show that the traffic condition can be significantly improved by our algorithm.

A Modified Genetic Algorithm for Sequencing Problem in Mixed Model Assembly Lines

2013 ◽  
Vol 655-657 ◽  
pp. 1675-1681
Author(s):  
Shu Xu ◽  
Fu Ming Li
Keyword(s):  
Genetic Algorithm ◽  
Mixed Model ◽  
Assembly Lines ◽  
Sequencing Problem ◽  
Mixed Model Assembly Lines ◽  
Modified Genetic Algorithm ◽  
Good Ability ◽  
Sequencing Problems ◽  
Dependent Setup Times ◽  
Mixed Model Assembly

On the base of summarizing and contrasting the objectives of sequencing problem in mixed model assembly lines (MMAL) , and in consideration of the influence sequence-dependent setup times , a objective is proposed to minimize the total unfinished works and idle times over all jobs and stations . And the corresponding model is presented. To solve this model, a modified genetic algorithm is proposed to determine suitable sequences. Comparing with the Lingo 9 software, the proposed GA turns out to have a good ability to solve the sequencing problems.

Genetic Algorithms, Path Relinking, and the Flowshop Sequencing Problem

1998 ◽  
Vol 6 (1) ◽  
pp. 45-60 ◽  
Author(s):  
Colin R. Reeves ◽  
Takeshi Yamada
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Simulated Annealing Algorithm ◽  
Search Method ◽  
Neighborhood Search ◽  
Search Technique ◽  
Permutation Flowshop ◽  
Sequencing Problem ◽  
Annealing Algorithm ◽  
Tabu Search Method

In a previous paper, a simple genetic algorithm (GA) was developed for finding (approximately) the minimum makespan of the n-job, m-machine permutation flowshop sequencing problem (PFSP). The performance of the algorithm was comparable to that of a naive neighborhood search technique and a proven simulated annealing algorithm. However, recent results have demonstrated the superiority of a tabu search method in solving the PFSP. In this paper, we reconsider the implementation of a GA for this problem and show that by taking into account the features of the landscape generated by the operators used, we are able to improve its performance significantly.

scholarly journals Solving the order batching and sequencing problem with multiple pickers: A grouped genetic algorithm

2021 ◽  
Vol 11 (3) ◽  
pp. 2516
Author(s):  
Jose Alejandro Cano ◽  
Pablo Cortés ◽  
Emiro Antonio Campo ◽  
Alexander Alberto Correa-Espinal
Keyword(s):  
Genetic Algorithm ◽  
Customer Service ◽  
Load Capacity ◽  
Operating Time ◽  
Distribution Centers ◽  
Operations Planning ◽  
Sequencing Problem ◽  
Order Batching ◽  
Feasible Solutions ◽  
First Time

This paper introduces a grouped genetic algorithm (GGA) to solve the order batching and sequencing problem with multiple pickers (OBSPMP) with the objective of minimizing total completion time. To the best of our knowledge, for the first time, an OBSPMP is solved by means of GGA considering picking devices with heterogeneous load capacity. For this, an encoding scheme is proposed to represent in a chromosome the orders assigned to batches, and batches assigned to picking devices. Likewise, the operators of the proposed algorithm are adapted to the specific requirements of the OBSPMP. Computational experiments show that the GGA performs much better than six order batching and sequencing heuristics, leading to function objective savings of 18.3% on average. As a conclusion, the proposed algorithm provides feasible solutions for the operations planning in warehouses and distribution centers, improving margins by reducing operating time for order pickers, and improving customer service by reducing picking service times.

The Genetic Algorithm with Two Heuristic Rules for TSP

2013 ◽  
Vol 694-697 ◽  
pp. 2787-2793
Author(s):  
Yong Wang
Keyword(s):  
Genetic Algorithm ◽  
Optimal Solutions ◽  
Improved Genetic Algorithm ◽  
Heuristic Rules ◽  
Heuristic Rule ◽  
Hamiltonian Paths ◽  
Sequencing Problem ◽  
Discrete Manufacturing ◽  
Np Complete ◽  
Manufacturing Problems

Many complex discrete manufacturing problems, such as manufacturing sequencing problem or machine scheduling problem etc, can be converted into a general traveling salesman problem (TSP). TSP has been proven to be NP-complete. The genetic algorithm is improved with two heuristic rules for TSP. The first heuristic rule is the four vertices and three lines inequality. It is applied to the local Hamiltonian paths to generate the better solutions. The second heuristic rule is executed to reverse the local Hamiltonian paths, which generates new better solutions. The two heuristic rules coordinate with each other and they are merged into the optimization process of genetic algorithm to improve its performance. The computation results show that the improved genetic algorithm can find the near optimal solutions for most of the TSP instances.

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