Combined Rough Sets and Rule-Based Expert System to Support Environmentally Oriented Sandwich Pallet Loading Problem
A sandwich pallet loading problem represents a significant challenge in the logistics of fast-moving consumer goods (FMCG), requiring optimisation of load units (LUs) arrangements to minimise their number in transportation and warehousing processes, leading to an environmental responsibility of orga...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/2/268 |
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| author | Piotr Sawicki Hanna Sawicka Marek Karkula Krzysztof Zajda |
| author_facet | Piotr Sawicki Hanna Sawicka Marek Karkula Krzysztof Zajda |
| author_sort | Piotr Sawicki |
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| description | A sandwich pallet loading problem represents a significant challenge in the logistics of fast-moving consumer goods (FMCG), requiring optimisation of load units (LUs) arrangements to minimise their number in transportation and warehousing processes, leading to an environmental responsibility of organisations. This study introduces an innovative approach combining Dominance-Based Rough Set Theory (DRST) with a rule-based expert system to improve the efficiency of the pallet loading and to provide sustainable development. Key criteria and attributes for the LU assessment, such as weight, height, and fragility, are defined. DRST is utilised to classify these units, leveraging its capability to handle imprecise and vague information. The rule-based system ensures an optimal arrangement of LUs by considering critical control parameters, thereby reducing LU numbers and mitigating the environmental impact of logistics operations, as measured by energy consumption. The proposed approach is validated using real-world data from the FMCG distribution company. Results demonstrate that integrating DRST with an expert system improves decision-making consistency and significantly reduces the number of LUs. This study shows a way to increase the level of environmental responsibility of the organisation by cutting energy consumption and delivering economic and social benefits through fewer shipments. For example, the approach reduces energy consumption for a customer order delivery by 40%, from 0.60 to 0.36 (kWh/pskm). |
| format | Article |
| id | doaj-art-b877688e67ed473692b99f11077298c3 |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-b877688e67ed473692b99f11077298c32025-01-24T13:30:50ZengMDPI AGEnergies1996-10732025-01-0118226810.3390/en18020268Combined Rough Sets and Rule-Based Expert System to Support Environmentally Oriented Sandwich Pallet Loading ProblemPiotr Sawicki0Hanna Sawicka1Marek Karkula2Krzysztof Zajda3Institute of Transport, Poznan University of Technology, 61-138 Poznań, PolandInstitute of Transport, Poznan University of Technology, 61-138 Poznań, PolandDepartment of Business Informatics and Management Engineering, AGH University of Krakow, 30-067 Kraków, PolandGrupa Maspex, 34-100 Wadowice, PolandA sandwich pallet loading problem represents a significant challenge in the logistics of fast-moving consumer goods (FMCG), requiring optimisation of load units (LUs) arrangements to minimise their number in transportation and warehousing processes, leading to an environmental responsibility of organisations. This study introduces an innovative approach combining Dominance-Based Rough Set Theory (DRST) with a rule-based expert system to improve the efficiency of the pallet loading and to provide sustainable development. Key criteria and attributes for the LU assessment, such as weight, height, and fragility, are defined. DRST is utilised to classify these units, leveraging its capability to handle imprecise and vague information. The rule-based system ensures an optimal arrangement of LUs by considering critical control parameters, thereby reducing LU numbers and mitigating the environmental impact of logistics operations, as measured by energy consumption. The proposed approach is validated using real-world data from the FMCG distribution company. Results demonstrate that integrating DRST with an expert system improves decision-making consistency and significantly reduces the number of LUs. This study shows a way to increase the level of environmental responsibility of the organisation by cutting energy consumption and delivering economic and social benefits through fewer shipments. For example, the approach reduces energy consumption for a customer order delivery by 40%, from 0.60 to 0.36 (kWh/pskm).https://www.mdpi.com/1996-1073/18/2/268sandwich pallet loading problemrough setsmachine learningdominance-based rough setslearning to optimiseenvironmental responsibility |
| spellingShingle | Piotr Sawicki Hanna Sawicka Marek Karkula Krzysztof Zajda Combined Rough Sets and Rule-Based Expert System to Support Environmentally Oriented Sandwich Pallet Loading Problem Energies sandwich pallet loading problem rough sets machine learning dominance-based rough sets learning to optimise environmental responsibility |
| title | Combined Rough Sets and Rule-Based Expert System to Support Environmentally Oriented Sandwich Pallet Loading Problem |
| title_full | Combined Rough Sets and Rule-Based Expert System to Support Environmentally Oriented Sandwich Pallet Loading Problem |
| title_fullStr | Combined Rough Sets and Rule-Based Expert System to Support Environmentally Oriented Sandwich Pallet Loading Problem |
| title_full_unstemmed | Combined Rough Sets and Rule-Based Expert System to Support Environmentally Oriented Sandwich Pallet Loading Problem |
| title_short | Combined Rough Sets and Rule-Based Expert System to Support Environmentally Oriented Sandwich Pallet Loading Problem |
| title_sort | combined rough sets and rule based expert system to support environmentally oriented sandwich pallet loading problem |
| topic | sandwich pallet loading problem rough sets machine learning dominance-based rough sets learning to optimise environmental responsibility |
| url | https://www.mdpi.com/1996-1073/18/2/268 |
| work_keys_str_mv | AT piotrsawicki combinedroughsetsandrulebasedexpertsystemtosupportenvironmentallyorientedsandwichpalletloadingproblem AT hannasawicka combinedroughsetsandrulebasedexpertsystemtosupportenvironmentallyorientedsandwichpalletloadingproblem AT marekkarkula combinedroughsetsandrulebasedexpertsystemtosupportenvironmentallyorientedsandwichpalletloadingproblem AT krzysztofzajda combinedroughsetsandrulebasedexpertsystemtosupportenvironmentallyorientedsandwichpalletloadingproblem |