Flow Shop Scheduling with No-wait Flexible Lot Streaming using Adaptive Genetic Algorithm

2007 ◽  
Author(s):  
KwanWoo Kim ◽  
In-Jae Jeong
Keyword(s):  
Genetic Algorithm ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Adaptive Genetic Algorithm ◽  
Lot Streaming ◽  
Shop Scheduling ◽  
No Wait

scholarly journals Integration of Lot Sizing and Flow Shop Scheduling with Lot Streaming

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Navid Mortezaei ◽  
Norzima Zulkifli
Keyword(s):  
Flow Shop ◽  
Lot Sizing ◽  
Flow Shop Scheduling ◽  
Mixed Integer ◽  
Lot Streaming ◽  
Optimal Sequence ◽  
Shop Scheduling ◽  
Proposed Model ◽  
Integer Linear Model ◽  
No Wait

We will develop a mathematical model for the integration of lot sizing and flow shop scheduling with lot streaming. We will develop a mixed-integer linear model for multiple products lot sizing and lot streaming problems. Mixed-integer programming formulation is presented which will enable the user to find optimal production quantities, optimal inventory levels, optimal sublot sizes, and optimal sequence simultaneously. We will use numerical example to show practicality of the proposed model. We test eight different lot streaming problems: (1) consistent sublots with intermingling, (2) consistent sublots and no intermingling between sublots of the products (without intermingling), (3) equal sublots with intermingling, (4) equal sublots without intermingling, (5) no-wait consistent sublots with intermingling, (6) no-wait equal sublots with intermingling, (7) no-wait consistent sublots without intermingling, and (8) no-wait equal sublots without intermingling. We showed that the best makespan can be achieved through the consistent sublots with intermingling case.

Adaptive Genetic Algorithm for Hybrid Flow-Shop Scheduling

2013 ◽  
Vol 753-755 ◽  
pp. 2925-2929
Author(s):  
Xiao Chun Zhu ◽  
Jian Feng Zhao ◽  
Mu Lan Wang
Keyword(s):  
Genetic Algorithm ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Adaptive Genetic Algorithm ◽  
Shop Scheduling ◽  
Mutation Operators ◽  
Hybrid Flow Shop Scheduling ◽  
Encoding Method ◽  
Crossover And Mutation

This paper studies the scheduling problem of Hybrid Flow Shop (HFS) under the objective of minimizing makespan. The corresponding scheduling simulation system is developed in details, which employed a new encoding method so as to guarantee the validity of chromosomes and the convenience of calculation. The corresponding crossover and mutation operators are proposed for optimum sequencing. The simulation results show that the adaptive Genetic Algorithm (GA) is an effective and efficient method for solving HFS Problems.

Discrete Particle Swarm Optimization Algorithm for Lot-Streaming No-Wait Flow Shop Scheduling Problem

2012 ◽  
Vol 538-541 ◽  
pp. 863-868 ◽  
Author(s):  
Lin Yang ◽  
Yu Xia Pan
Keyword(s):  
Particle Swarm Optimization ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Discrete Particle Swarm Optimization ◽  
Lot Streaming ◽  
Swarm Optimization ◽  
Discrete Particle ◽  
Shop Scheduling ◽  
No Wait

This paper proposed discrete particle swarm optimization(DPSO) algorithm to solve lot-streaming no-wait flow shop scheduling problem(LNFSP) with the objective of the maximum completion time. The natural encoding scheme based on job permutation and newly-designed methods were adopted to produce new individuals . After the DPSO-based exploration, a efficient fast local search based on swap neighborhood structure is used to enhance the exploitation capability. Simulation results show the effectiveness of the proposed algorithms.

scholarly journals Minimising Total Flowtime in a No-Wait Flow Shop (NWFS) using Genetic Algorithms

2018 ◽  
Vol 38 (3) ◽  
pp. 68-79 ◽  
Author(s):  
Imran Ali Chaudhry ◽  
Isam AbdulQader Elbadawi ◽  
Muhammad Usman ◽  
Muhammad Tajammal Chughtai
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Flow Shop ◽  
General Purpose ◽  
Flow Shop Scheduling ◽  
Benchmark Problems ◽  
Shop Scheduling ◽  
Total Flowtime ◽  
Spreadsheet Model ◽  
No Wait

This paper considers a no-wait flow shop scheduling (NWFS) problem, where the objective is to minimise the total flowtime. We propose a genetic algorithm (GA) that is implemented in a spreadsheet environment. The GA functions as an add-in in the spreadsheet. It is demonstrated that with proposed approach any criteria can be optimised without modifying the GA routine or spreadsheet model. Furthermore, the proposed method for solving this class of problem is general purpose, as it can be easily customised by adding or removing jobs and machines. Several benchmark problems already published in the literature are used to demonstrate the problem-solving capability of the proposed approach. Benchmark problems set ranges from small (7-jobs, 7 machines) to large (100-jobs, 10-machines). The performance of the GA is compared with different meta-heuristic techniques used in earlier literature. Experimental analysis demonstrate that solutions obtained in this research offer equal quality as compared to algorithms already developed for NWFS problems.

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