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GA+BUF best solutions (Experiment 1)

October 2015

Project: The Assembly Line Simulator Project

Authors:

Università degli Studi di Perugia

This file contains the best solutions related to Experiment 1 found by the GA and GA+BUF algorithms presented in the paper by the title "Simultaneous balancing and buffer allocation decisions for the design of mixed-model assembly lines with parallel workstations and stochastic task times"

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GA+BUF best solutions (Experiment

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Simultaneous balancing and buffer allocation decisions for the design of mixed-model assembly lines with parallel workstations and stochastic task times

Article

April 2015

Project

The Assembly Line Simulator Project

Rationale To evaluate performances of a mixed model assembly line may be complicated. This complication is a result of blockage and starvation caused by the arrival of different models to the line , having different assembly time requirements at each station. Considering the throughput as the main operational design objective, the effects of these phenomena on line throughput are very difficult to evaluate. Unfortunately its evaluation is fundamental in almost all procedures and algorithms developed to solve MALBP (Mixed-model Assebly Line Balancing Problem), since the estimation of objective functions that includes performance indicators is often required. The delicacy of this issue has been accurately outlined by [Bukchin 1998]. He argued that the only practical method to accurately evaluate throughput is a simulation study, which is very time consuming and hard to perform, and that for this reason, instead, various performance measures are usually used in order to evaluate and compare design alternatives. These performance measures are required to be highly correlated with the objective in question (i.e. throughput). Aim The aim of this project is to overcome the limit of using performance measures instead of simulated throughput, by developing a parametric simulator for mixed model lines that can quickly simulate different line configurations and can be used iteratively in algorithms and procedures. Bukchin, J., 1998, "A comparative study of performance measures for throughput of a mixed model assembly line in a JIT environment", International Journal of Production Research, 36, 2669-2685. ... [more]

Data

Full-text available

instances of Experiment 1

May 2014

Lorenzo Tiacci

This file contains the instances utilized in Experiment 1 described in the paper "Coupling a genetic algorithm approach and a discrete event simulator to design mixed-model un-paced assembly lines with parallel workstations and stochastic task times"

Data

Full-text available

GA+BUF best solutions (Experiment 2)

October 2015

Lorenzo Tiacci

This file contains the best solutions related to Experiment 2 found by the GA and GA+BUF algorithms presented in the paper by the title "Simultaneous balancing and buffer allocation decisions for the design of mixed-model assembly lines with parallel workstations and stochastic task times"

Data

Full-text available

instances of Experiment 2

May 2014

Lorenzo Tiacci

This file contains the instances utilized in Experiment 2 described in the paper "Coupling a genetic algorithm approach and a discrete event simulator to design mixed-model un-paced assembly lines with parallel workstations and stochastic task times"

Data

Full-text available

GA+ALS best solutions (Experiment 2)

October 2015

Lorenzo Tiacci

This file contains the best solutions related to Experiment 2 found by the Genetic Algorithm presented in the paper "Coupling a genetic algorithm approach and a discrete event simulator to design mixed-model un-paced assembly lines with parallel workstations and stochastic task times"

Data

Full-text available

GA+ALS best solutions (Experiment 1)

May 2014

Lorenzo Tiacci

This file contains the best solutions related to Experiment 1 found by the Genetic Algorithm presented in the paper "Coupling a genetic algorithm approach and a discrete event simulator to design mixed-model un-paced assembly lines with parallel workstations and stochastic task times"