<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This developed modified ant colony algorithm includes an additional improvement with local optimization methods, which reduces the time required to find a solution to the problem of optimization of combinatorial order sequence planning in a food enterprise. The planning problem requires consideration of a number of partial criteria, constraints, and an evaluation function to determine the effectiveness of the established version of the order fulfillment plan. The partial criteria used are: terms of storage of raw materials and finished products, possibilities of occurrence and processing of substandard products, terms of manufacturing orders, peculiarities of fulfillment of each individual order, peculiarities of use of technological equipment, expenses for storage and transportation of manufactured products to the end consumer, etc. The solution of such a problem is impossible using traditional methods. The proposed algorithm allows users to build and reconfigure plans, while reducing the time to find the optimum by almost 20% compared to other versions of algorithms.</p>
</abstract>

This developed modified ant colony algorithm includes an additional improvement with local optimization methods, which reduces the time required to find a solution to the problem of optimization of combinatorial order sequence planning in a food enterprise. The planning problem requires consideration of a number of partial criteria, constraints, and an evaluation function to determine the effectiveness of the established version of the order fulfillment plan. The partial criteria used are: terms of storage of raw materials and finished products, possibilities of occurrence and processing of substandard products, terms of manufacturing orders, peculiarities of fulfillment of each individual order, peculiarities of use of technological equipment, expenses for storage and transportation of manufactured products to the end consumer, etc. The solution of such a problem is impossible using traditional methods. The proposed algorithm allows users to build and reconfigure plans, while reducing the time to find the optimum by almost 20% compared to other versions of algorithms.

Development of a Modified Ant Colony Algorithm for Order Scheduling in Food Processing Plants

Journal of Automation, Mobile Robotics and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

This developed modified ant colony algorithm includes an additional improvement with local optimization methods, which reduces the time required to find a...

Development of a Modified Ant Colony Algorithm for Order Scheduling in Food Processing Plants

Published Online: Apr 04, 2023

Page range: 53 - 61

Received: Sep 14, 2021

Accepted: Feb 24, 2022

DOI: https://doi.org/10.14313/jamris/1-2022/6

Keywords
order fulfillment planning, modified ant colony algorithm, efficiency of the algorithms, optimization, food industry

© 2022 Igor Korobiichuk et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Development of a Modified Ant Colony Algorithm for Order Scheduling in Food Processing Plants

Published Online: Apr 04, 2023

Page range: 53 - 61

Received: Sep 14, 2021

Accepted: Feb 24, 2022

DOI: https://doi.org/10.14313/jamris/1-2022/6

Keywordsorder fulfillment planning, modified ant colony algorithm, efficiency of the algorithms, optimization, food industry

© 2022 Igor Korobiichuk et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Keywords
order fulfillment planning, modified ant colony algorithm, efficiency of the algorithms, optimization, food industry