Applying heuristics in supply chain planning in the process industry
In this paper a mixed-integer linear programming (MILP) model is developed to be used as a decision support tool for the chemical company Perstorp Oxo AB. The intention with the mathematical model is to maximize the profit and the model can be used in the process of planning the supply chain for the...
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| Format: | Article |
| Language: | English |
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Growing Science
2020-06-01
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| Series: | International Journal of Industrial Engineering Computations |
| Subjects: | |
| Online Access: | http://www.growingscience.com/ijiec/Vol11/IJIEC_2020_10.pdf |
| _version_ | 1811251542628499456 |
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| author | Nils-Hassan Quttineh Helene Lidestam |
| author_facet | Nils-Hassan Quttineh Helene Lidestam |
| author_sort | Nils-Hassan Quttineh |
| collection | DOAJ |
| description | In this paper a mixed-integer linear programming (MILP) model is developed to be used as a decision support tool for the chemical company Perstorp Oxo AB. The intention with the mathematical model is to maximize the profit and the model can be used in the process of planning the supply chain for the company. Perstorp Oxo is classified as a global company in the process industry and is has production sites in Gent, Castellanza, Stenungsund and Perstorp. The site in Stenungsund is in focus in this paper. The company produces chemicals that later are used for example in textiles, plastic and glass production. Perstorp Oxo also uses inventories in other countries for enabling the selling abroad. It has two larger inventories in Antwerp and in Tees and two smaller in Philadelphia and in Aveiro. The larger facilities store five different products and the smaller take care of one type each. To be able to find feasible and profitable production plans for the company we have developed and implemented rolling horizon techniques for a time horizon of one year and used real sales data. The outcomes from the model show the transportation of products between different production sites, the different production rates, the levels of inventory, setups and purchases from external suppliers. The numerical results are promising and we conclude that a decision support tool based on an optimization model could improve the situation for the planners at Perstorp Oxo AB. |
| first_indexed | 2024-04-12T16:21:55Z |
| format | Article |
| id | doaj.art-8f57c96c043d43fcabea8acb935fee0d |
| institution | Directory Open Access Journal |
| issn | 1923-2926 1923-2934 |
| language | English |
| last_indexed | 2024-04-12T16:21:55Z |
| publishDate | 2020-06-01 |
| publisher | Growing Science |
| record_format | Article |
| series | International Journal of Industrial Engineering Computations |
| spelling | doaj.art-8f57c96c043d43fcabea8acb935fee0d2022-12-22T03:25:32ZengGrowing ScienceInternational Journal of Industrial Engineering Computations1923-29261923-29342020-06-0111458560610.5267/j.ijiec.2020.4.004Applying heuristics in supply chain planning in the process industryNils-Hassan QuttinehHelene LidestamIn this paper a mixed-integer linear programming (MILP) model is developed to be used as a decision support tool for the chemical company Perstorp Oxo AB. The intention with the mathematical model is to maximize the profit and the model can be used in the process of planning the supply chain for the company. Perstorp Oxo is classified as a global company in the process industry and is has production sites in Gent, Castellanza, Stenungsund and Perstorp. The site in Stenungsund is in focus in this paper. The company produces chemicals that later are used for example in textiles, plastic and glass production. Perstorp Oxo also uses inventories in other countries for enabling the selling abroad. It has two larger inventories in Antwerp and in Tees and two smaller in Philadelphia and in Aveiro. The larger facilities store five different products and the smaller take care of one type each. To be able to find feasible and profitable production plans for the company we have developed and implemented rolling horizon techniques for a time horizon of one year and used real sales data. The outcomes from the model show the transportation of products between different production sites, the different production rates, the levels of inventory, setups and purchases from external suppliers. The numerical results are promising and we conclude that a decision support tool based on an optimization model could improve the situation for the planners at Perstorp Oxo AB.http://www.growingscience.com/ijiec/Vol11/IJIEC_2020_10.pdfsupply chainprocess industryoptimizationmixed integer programmingheuristics |
| spellingShingle | Nils-Hassan Quttineh Helene Lidestam Applying heuristics in supply chain planning in the process industry International Journal of Industrial Engineering Computations supply chain process industry optimization mixed integer programming heuristics |
| title | Applying heuristics in supply chain planning in the process industry |
| title_full | Applying heuristics in supply chain planning in the process industry |
| title_fullStr | Applying heuristics in supply chain planning in the process industry |
| title_full_unstemmed | Applying heuristics in supply chain planning in the process industry |
| title_short | Applying heuristics in supply chain planning in the process industry |
| title_sort | applying heuristics in supply chain planning in the process industry |
| topic | supply chain process industry optimization mixed integer programming heuristics |
| url | http://www.growingscience.com/ijiec/Vol11/IJIEC_2020_10.pdf |
| work_keys_str_mv | AT nilshassanquttineh applyingheuristicsinsupplychainplanningintheprocessindustry AT helenelidestam applyingheuristicsinsupplychainplanningintheprocessindustry |