Mixed-Model Assembly Line Balancing with Considering Reliability

Authors

Abstract:

This paper presents a multi-objective simulated annealing algorithm for the mixed-model assembly line balancing with stochastic processing times. Since, the stochastic task times may have effects on the bottlenecks of a system, maximizing the weighted line efficiency (equivalent to the minimizing the number of station), minimizing the weighted smoothness index and maximizing the system reliability are considered. After solving an example in detail, the performance of the proposed algorithm is examined on a set of test problems. The experimental results show the new approach performs well.

Upgrade to premium to download articles

Sign up to access the full text

Already have an account?login

similar resources

‘BALANCING AND SEQUENCING’ VERSUS ‘ONLY BALANCING’ IN MIXED MODEL U-LINE ASSEMBLY SYSTEMS: AN ECONOMIC ANALYSIS

With the growth in customers’ demand diversification, mixed-model U-lines (MMUL) have acquired increasing importance in the area of assembly systems. There are generally two different approaches in the literature for balancing such systems. Some researchers believe that since the types of models can be very diverse, a balancing approach without simultaneously sequencing of models will not yield...

full text

A Multi-Objective Particle Swarm Optimization for Mixed-Model Assembly Line Balancing with Different Skilled Workers

This paper presents a multi-objective Particle Swarm Optimization (PSO) algorithm for worker assignment and mixed-model assembly line balancing problem when task times depend on the worker’s skill level. The objectives of this model are minimization of the number of stations (equivalent to the maximization of the weighted line efficiency), minimization of the weighted smoothness index and minim...

full text

An algorithm for integrated worker assignment, mixed-model two-sided assembly line balancing and bottleneck analysis

This paper addresses a multi-objective mixed-model two-sided assembly line balancing and worker assignment with bottleneck analysis when the task times are dependent on the worker’s skill. This problem is known as NP-hard class, thus, a hybrid cyclic-hierarchical algorithm is presented for solving it. The algorithm is based on Particle Swarm Optimization (PSO) and Theory of Constraints (TOC) an...

full text

‘balancing and sequencing’ versus ‘only balancing’ in mixed model u-line assembly systems: an economic analysis

with the growth in customers’ demand diversification, mixed-model u-lines (mmul) have acquired increasing importance in the area of assembly systems. there are generally two different approaches in the literature for balancing such systems. some researchers believe that since the types of models can be very diverse, a balancing approach without simultaneously sequencing of models will not yield...

full text

Solving a multi-objective mixed-model assembly line balancing and sequencing problem

This research addresses the mixed-model assembly line (MMAL) by considering various constraints. In MMALs, several types of products which their similarity is so high are made on an assembly line. As a consequence, it is possible to assemble and make several types of products simultaneously without spending any additional time. The proposed multi-objective model considers the balancing and sequ...

full text

Simultaneous Multi-Skilled Worker Assignment and Mixed-Model Two-Sided Assembly Line Balancing

This paper addresses a multi-objective mathematical model for the mixed-model two-sided assembly line balancing and worker assignment with different skills. In this problem, the operation time of each task is dependent on the skill of the worker. The following objective functions are considered in the mathematical model: (1) minimizing the number of mated-stations (2), minimizing the number of ...

full text

My Resources

Save resource for easier access later

Save to my library Already added to my library

{@ msg_add @}


Journal title

volume 30  issue 3

pages  411- 423

publication date 2017-03-01

By following a journal you will be notified via email when a new issue of this journal is published.

Hosted on Doprax cloud platform doprax.com

copyright © 2015-2023