A genetic algorithm for order promising
The aim of this Master’s Thesis is to propose a solution to the Order Promising problem, which involves optimally allocating limited inventory from distribution centres to customer sales orders. This is a broad challenge where the optimal solution depends on the specific characteristics and constrai...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/424624 |
| Acceso en línea: | https://hdl.handle.net/2117/424624 |
| Access Level: | acceso embargado |
| Palabra clave: | Combinatorial optimization Business logistics Programming (Mathematics) Supply Chain Management Order Promising Stock Allocation Optimization Genetic Algorithm MILP Optimització combinatòria Logística (Indústria) Programació (Matemàtica) Classificació AMS::90 Operations research, mathematical programming::90B Operations research and management science Classificació AMS::90 Operations research, mathematical programming::90C Mathematical programming Classificació AMS::68 Computer science::68T Artificial intelligence Àrees temàtiques de la UPC::Matemàtiques i estadística |
| Sumario: | The aim of this Master’s Thesis is to propose a solution to the Order Promising problem, which involves optimally allocating limited inventory from distribution centres to customer sales orders. This is a broad challenge where the optimal solution depends on the specific characteristics and constraints of the problem. In this thesis, we present an end-to-end solution accounting for factors such as delays, order priorities, and costs arising from alternative sourcing or penalties associated with customer contracts. The proposed solution is implemented using both exact methods (MILP formulation) and metaheuristics (genetic algorithms). Additionally, a PowerBI dashboard is developed to visualize the outcomes of the allocations, offering practical insights into the quality of the solution and enabling scenario comparisons. |
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