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A Cognitive Paradigm to Investigate Interference in Working Memory by Distractions and Interruptions

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Disruption management in a constrained multi-product imperfect production system.

Asif Iqbal Malik¹, Biswajit Sarkar²

¹Department of Industrial and Management Engineering, Incheon National University, 119, Academy-ro, Yeonsu-gu, 22012 Incheon, South Korea.

Journal of Manufacturing Systems

|August 25, 2020

Summary

This study presents a production-disruption model to optimize manufacturing batch sizes for multi-item systems. It aims to maximize profit during recovery periods following unforeseen disruptions.

Keywords:

Constrained optimization Disruption recovery Genetic algorithm Imperfect production M90B50 Multi-item production Production disruption

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Published on: October 1, 2019

Area of Science:

Operations Research
Industrial Engineering
Supply Chain Management

Background:

Production and inventory systems are extensively studied, yet production disruption remains under-researched.
Unforeseen disturbances can significantly disrupt manufacturing plans and inventory levels.

Purpose of the Study:

To develop a mathematical model for a multi-product, single-stage production-inventory system facing disruptions.
To determine optimal manufacturing batch sizes within a recovery time window to maximize total profit.

Main Methods:

A mathematical model was formulated to maximize profit under disruption.
Genetic algorithms and pattern search techniques were utilized for optimization.
Budget and space constraints were incorporated for multi-product scenarios.

Main Results:

The model provides an optimal manufacturing batch size for multi-item production during recovery.
Numerical examples and sensitivity analysis demonstrate the model's effectiveness.
Comparison with randomly generated test results validates the proposed approach.

Conclusions:

The developed model offers a practical recovery plan for production disruptions.
It enables managers to make informed, real-time decisions to mitigate disruption impacts.
The research enhances decision-making in dynamic manufacturing environments.