Integration of Machine Learning and Optimization Models to Solve Supplier Selection and Order Allocation Problems
Notice bibliographique
Résumé
<p dir="ltr">Supplier Selection (SS) and Order Allocation (OA) are two major decision-making processes in supply chain management. Integration of SS and OA is crucial to have effective supply chain designs and efficient operations. In the real world, decision-making processes incorporate complex evaluations of potential suppliers which can be erroneous due to judgemental errors. Moreover, uncertainty in demand can make the decision-making processes difficult. To mitigate these barriers, this dissertation presents hybrid solution approaches that use machine learning techniques and proposes integrating Supplier Selection and Order Allocation (SS&OA) planning frameworks. </p><p dir="ltr">Chapter 2 of this dissertation introduces a two-stage solution approach for SS&OA planning that integrates forecasting techniques and optimization models. Demand forecasting in Stage 1 of this approach addresses the demand ambiguity. Also, Stage 1 considers the demand interdependencies between products that may affect the accuracy of the forecast. Stage 2 develops a Multi-Objective Programming (MOP) model that includes quantitative criteria and the forecasted demands from Stage 1. A Canadian food supply network dataset is considered in this chapter. The results suggest that by integration of inter-product correlations into the forecasting model, businesses can more accurately predict the demand and make better-informed decisions on SS&OA planning when the products are correlated. </p><p dir="ltr">Unprecedented events can disrupt the market dynamics, leading to changes in the balance of supply and demand, and cause fluctuations in historical data. The fluctuations in the data can adversely impact forecasting accuracy. To overcome this challenge during SS&OA planning, Chapter 3 examines a three-stage framework where the effects of data fluctuations are minimized during demand prediction. The framework involves a deep learning technique based on a multistep Long Short Term Memory (LSTM) network for demand prediction. To consider qualitative criteria, a fuzzy Strengths, Weaknesses, Opportunities, and Threats (SWOT) model is developed in Stage 2 of this solution framework. Stage 3 offers the development of a MOP model by retrieving the forecasting model's results from Stage 1 and the fuzzy model's results from Stage 2. The proposed framework's application is also discussed in this chapter using a realistic dataset from the Canadian juice industries. The results show that the fluctuation minimization using machine learning methods may affect the SS&OA planning. It has been also observed that the planning process might be affected by the number of internal and external supplier selection criteria. </p><p dir="ltr">Chapter 4 examines the effect of correlational data components during SS&OA planning. A three stage planning framework is presented in this chapter. Demand forecasting by examining the correlation among data components such as trend, seasonality, and fluctuations involves analyzing the historical data to determine how these components are interrelated. By understanding the relationships between these data components, more precise predictions can be made regarding future demand. In Stage 1 of this planning framework, a modified relational deep learning forecasting method is designed to predict the future demand where the correlations among the data components of different products are considered. To confirm the forecasting accuracy, the proposed deep learning model is compared to a Light-Gradient Boosting Machine and a standalone LSTM. A new fuzzy Principal Component Analysis (PCA) technique is used to calculate the suppliers’ weight in Stage 2. Then, Stage 3 develops a MOP model using Stages 1 and 2 outcomes. Canadian meat sector data is used to discuss the planning framework. The experimental results show that the inter-product correlation functions can change the suppliers and the order quantities during the SSOA planning.</p>
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».