Solving Level Scheduling in Mixed Model Assembly Line by Simulated Annealing Method

Simple genetic algorithm has shortcomings of poor local search ability and premature convergence. To overcome these disadvantages, simulated annealing algorithm which has good local search ability was combined with genetic algorithm to form simulated annealing genetic algorithm. The tests by two commonly used test functions of Shaffer’s F6 and Rosenbrock show that simulated annealing genetic algorithm outperforms the simple genetic algorithm both in convergence rate and convergence quality. Finally, the simulated annealing genetic algorithm was firstly applied in a practical problem of balancing and sequencing design of mixed-model assembly line, once again, the solution results show that simulated annealing genetic algorithm outperforms the simple genetic algorithm. Meanwhile, it provides a new algorithm for solving the design problem of mixed-model assembly line.

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Real-Time Scheduling of Mixed Model Assembly Line with Large Variety and Low Volume Based on Event-Triggered Simulated Annealing (ETSA)

Mathematical Problems in Engineering ◽

10.1155/2021/6657506 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Chunzhi Cai ◽

Shulin Kan

Keyword(s):

Simulated Annealing ◽

Assembly Line ◽

Mixed Model ◽

Scheduling Problem ◽

Real Time Scheduling ◽

Scheduling Scheme ◽

Time Scheduling ◽

Low Volume ◽

Mixed Model Assembly Line ◽

Mixed Model Assembly

In the contemporary industrial production, multiple resource constraints and uncertainty factors exist widely in the actual job shop. It is particularly important to make a reasonable scheduling scheme in workshop manufacturing. Traditional scheduling research focused on the one-time global optimization of production scheduling before the actual production. The dynamic scheduling problem of the workshop is getting more and more attention. This paper proposed a simulated annealing algorithm to solve the real-time scheduling problem of large variety and low-volume mixed model assembly line. This algorithm obtains three groups of optimal solutions and the optimal scheduling scheme of multiple products, with the shortest product completion time and the lowest cost. Finally, the feasibility and efficiency of the model are proved by the Matlab simulation.

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A versatile generalized simulated annealing using type-2 fuzzy controller for the mixed-model assembly line balancing problem

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2019.11.633 ◽

2019 ◽

Vol 52 (13) ◽

pp. 2804-2809 ◽

Cited By ~ 2

Author(s):

M. Lalaoui ◽

A. El Afia

Keyword(s):

Simulated Annealing ◽

Assembly Line ◽

Mixed Model ◽

Fuzzy Controller ◽

Assembly Line Balancing ◽

Line Balancing ◽

Assembly Line Balancing Problem ◽

Mixed Model Assembly Line ◽

Mixed Model Assembly

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An Approach for Level Scheduling Mixed Models on an Assembly Line in a JIT Production System

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.697.473 ◽

2014 ◽

Vol 697 ◽

pp. 473-477

Author(s):

Li Nie ◽

Yue Wei Bai ◽

Xin Jiang ◽

Chang Tao Pang

Keyword(s):

Production System ◽

Assembly Line ◽

Mixed Model ◽

Multiple Objectives ◽

Level Scheduling ◽

Jit Production ◽

Line Level ◽

Mixed Model Assembly Line ◽

Delivery Scheduling ◽

Mixed Model Assembly

A mixed-model assembly line (MMAL) is a type of production system that is capable of producing different models of a common base product simultaneously. Mixed-model assembly line level scheduling problem (MMALSP) is a challenge for Just-in-time (JIT) production systems. In the paper, a mixed-model assembly line level scheduling model is proposed which considers multiple objectives simultaneously. The considered objectives include the variation in parts consumption considering the batch part supply, inventory cost and maximum transportation load. An approach based on genetic algorithm is proposed to solve the multiple objectives problem. In order to translate individuals in the GA population into candidate scheduling schemes a delivery scheduling algorithm (DSA) is proposed. In addition, dimensionless processing technique is employed in the design of the fitness function in order to comprehensively evaluate different individual considering three objectives simultaneously. The approach’s performance is validated through comprehensive experiment.

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