Tactical level supply chain planning with multi-echelon safety stock optimization
Notice bibliographique
Résumé
S chain (SC) is a system with multi-layer entities, multi-layer processes, to convert raw material into products. There are three levels of planning issues in a supply chain, i.e., strategic, tactical, and operational levels. They differ from each other based on their considerations and time effects. The strategic level planning deals with issues in the design stage of a supply chain, tactical level considers utilizing the SC resources, and the operational level deals with daily or weekly scheduling issues. SC optimization and its responsiveness are greatly influenced by inventory and as such inventory and amount of safety stock are important issues in a supply chain to manage demand uncertainties and maintain customer service level at lowest possible cost and shortest responsive time. In this paper, the multi-echelon safety stock optimization (MESSO) for tactical supply chain planning (SCP) in manufacturing systems is addressed. The problem is formulated as a multi-objective mixed integer non-linear programming (MINLP) model. Unlike other works that consider serial, assembly, or distribution system only, our model considers general supply chain topology. In a competitive global market, different objectives should be optimized simultaneously to avoid conflicting decisions. In the SC planning context, two methods are often used to optimize multi-objective models. The first one is to convert the multi-objective problem into a single-objective using a weighted sum or weighted goal programming method. The main drawback of this method is the subjectivity and bias in weight setting. The other method is known as e-constraint method in which one objective is optimized while the others are used as un-equality constraints. The same approach is applied to every objective leading to a set of solutions called Pareto optimal solutions or nondominated points which form the Pareto-Optimal (Pareto-Efficient) Frontier. However, this method again involves subjectivity in determining which frontier point is selected eventually. For this reason, the modified Chebyshev programming (MFC) method is adopted to solve the multi-objective model. Unlike the conventional weighted sum method that subjectively assigns weight to each objective, the weights in the MCP method are automatically selected based on the importance order of the objectives, the model and input data. Application of the proposed model and solution method are illustrated by solving an example problem and compared with the traditional weighted-goal programming method.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| 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.
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