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Adaptive Large Neighborhood Search for a Production Planning Problem Arising in Pig Farming

Faculty of Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand
Research Unit on System Modeling for Industry, Deparment of Industrial Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand
Faculty of Informatics, Mahasarakham University, Maha Sarakham 44150, Thailand
Faculty of Engineering Management, Poznan University of Technology, 60-965 Poznan, Poland
Author to whom correspondence should be addressed.
J. Open Innov. Technol. Mark. Complex. 2019, 5(2), 26;
Received: 9 April 2019 / Revised: 1 May 2019 / Accepted: 2 May 2019 / Published: 7 May 2019
PDF [600 KB, uploaded 7 May 2019]


. This article aims to resolve a particular production planning and workforce assignment problem. Many production lines may have different production capacities while producing the same product. Each production line is composed of three production stages, and each stage requires different periods of times and numbers of workers. Moreover, the workers will have different skill levels which can affect the number of workers required for production line. The number of workers required in each farm also depends on the amount of pigs that it is producing. Production planning must fulfill all the demands and can only make use of the workers available. A production plan aims to generate maximal profit for the company. A mathematical model has been developed to solve the proposed problem, when the size of problem increases, the model is unable to resolve large issues within a reasonable timeframe. A metaheuristic method called adaptive large-scale neighborhood search (ALNS) has been developed to solve the case study. Eight destroy and four repair operators (including ant colony optimization based destroy and repair methods) have been presented. Moreover, three formulas which are used to make decisions for acceptance of the newly generated solution have been proposed. The present study tested 16 data sets, including the case study. From the computational results of the small size of test instances, ALNS should be able to find optimal solutions for all the random data sets in much less computational time compared to commercial optimization software. For medium and larger test instance sizes, the findings of the heuristics were 0.48% to 0.92% away from the upper bound and generated within 480–620 h, in comparison to the 1 h required for the proposed method. The Ant Colony Optimization-based destroy and repair method found solutions that were 0.98 to 1.03% better than the original ALNS.
Keywords: ant colony optimization; adaptive large neighborhood search; assignment problems; scheduling problems ant colony optimization; adaptive large neighborhood search; assignment problems; scheduling problems
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Praseeratasang, N.; Pitakaso, R.; Sethanan, K.; Kaewman, S.; Golinska-Dawson, P. Adaptive Large Neighborhood Search for a Production Planning Problem Arising in Pig Farming. J. Open Innov. Technol. Mark. Complex. 2019, 5, 26.

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J. Open Innov. Technol. Mark. Complex. EISSN 2199-8531 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
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