Using Four Metaheuristic Algorithms to Reduce Supplier Disruption Risk in a Mathematical Inventory Model for Supplying Spare Parts
Due to the unexpected breakdowns that can happen in various components of a production system, failure to reach production targets and interruptions in the process of production are not surprising. Since this issue remains for manufactured products, this halting results in the loss of profitability...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/1/42 |
| _version_ | 1797625360993484800 |
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| author | Komeyl Baghizadeh Nafiseh Ebadi Dominik Zimon Luay Jum’a |
| author_facet | Komeyl Baghizadeh Nafiseh Ebadi Dominik Zimon Luay Jum’a |
| author_sort | Komeyl Baghizadeh |
| collection | DOAJ |
| description | Due to the unexpected breakdowns that can happen in various components of a production system, failure to reach production targets and interruptions in the process of production are not surprising. Since this issue remains for manufactured products, this halting results in the loss of profitability or demand. In this study, to address a number of challenges associated with the management of crucial spare parts inventory, a mathematical model is suggested for the determination of the optimal quantity of orders, in the case of an unpredicted supplier failure. Hence, a production system that has various types of equipment with crucial components is assumed, in which the crucial components are substituted with spare parts in the event of a breakdown. This study’s inventory model was developed for crucial spare parts based on the Markov chain process model for the case of supplier disruption. Moreover, for optimum ordering policies, re-ordering points, and cost values of the system, four metaheuristic algorithms were utilized that include Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), Moth–Flame Optimization (MFO) Algorithm, and Differential Evolution (DE) Algorithm. Based on the results, reliable suppliers cannot meet all of the demands; therefore, we should sometimes count on unreliable suppliers to reduce unmet demand. |
| first_indexed | 2024-03-11T09:55:28Z |
| format | Article |
| id | doaj.art-e2179b98dd474f98affbd9f8f7648039 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T09:55:28Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-e2179b98dd474f98affbd9f8f76480392023-11-16T15:52:37ZengMDPI AGMathematics2227-73902022-12-011114210.3390/math11010042Using Four Metaheuristic Algorithms to Reduce Supplier Disruption Risk in a Mathematical Inventory Model for Supplying Spare PartsKomeyl Baghizadeh0Nafiseh Ebadi1Dominik Zimon2Luay Jum’a3Innovation and Technology Institute, University of Southern Denmark, 5230 Odense, DenmarkDepartment of Industrial Engineering, Iran University of Science and Technology, Tehran 46899, IranDepartment of Management Systems and Logistics, Rzeszow University of Technology, 35-959 Rzeszow, PolandDepartment of Logistic Sciences, School of Management and Logistic Sciences, German Jordanian University, Amman 11180, JordanDue to the unexpected breakdowns that can happen in various components of a production system, failure to reach production targets and interruptions in the process of production are not surprising. Since this issue remains for manufactured products, this halting results in the loss of profitability or demand. In this study, to address a number of challenges associated with the management of crucial spare parts inventory, a mathematical model is suggested for the determination of the optimal quantity of orders, in the case of an unpredicted supplier failure. Hence, a production system that has various types of equipment with crucial components is assumed, in which the crucial components are substituted with spare parts in the event of a breakdown. This study’s inventory model was developed for crucial spare parts based on the Markov chain process model for the case of supplier disruption. Moreover, for optimum ordering policies, re-ordering points, and cost values of the system, four metaheuristic algorithms were utilized that include Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), Moth–Flame Optimization (MFO) Algorithm, and Differential Evolution (DE) Algorithm. Based on the results, reliable suppliers cannot meet all of the demands; therefore, we should sometimes count on unreliable suppliers to reduce unmet demand.https://www.mdpi.com/2227-7390/11/1/42inventory policymathematical modelingdecision makingMarkov chain |
| spellingShingle | Komeyl Baghizadeh Nafiseh Ebadi Dominik Zimon Luay Jum’a Using Four Metaheuristic Algorithms to Reduce Supplier Disruption Risk in a Mathematical Inventory Model for Supplying Spare Parts Mathematics inventory policy mathematical modeling decision making Markov chain |
| title | Using Four Metaheuristic Algorithms to Reduce Supplier Disruption Risk in a Mathematical Inventory Model for Supplying Spare Parts |
| title_full | Using Four Metaheuristic Algorithms to Reduce Supplier Disruption Risk in a Mathematical Inventory Model for Supplying Spare Parts |
| title_fullStr | Using Four Metaheuristic Algorithms to Reduce Supplier Disruption Risk in a Mathematical Inventory Model for Supplying Spare Parts |
| title_full_unstemmed | Using Four Metaheuristic Algorithms to Reduce Supplier Disruption Risk in a Mathematical Inventory Model for Supplying Spare Parts |
| title_short | Using Four Metaheuristic Algorithms to Reduce Supplier Disruption Risk in a Mathematical Inventory Model for Supplying Spare Parts |
| title_sort | using four metaheuristic algorithms to reduce supplier disruption risk in a mathematical inventory model for supplying spare parts |
| topic | inventory policy mathematical modeling decision making Markov chain |
| url | https://www.mdpi.com/2227-7390/11/1/42 |
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