Investigation of a multi-objective fixed charge transportation problem with quantity dependent transportation cost and discount policy <i>via</i> metaheuristics
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Notice bibliographique
Résumé
Efficient transportation of goods is a primary economic concern for any business organization. For this reason, research on transportation-related issues is becoming increasingly important. It should be noted that the shipping amount is determined by taking the unit transportation cost into account in a traditional transportation problem (TP). However, in reality, there are many situations where the transported quantity is used to determine the unit cost. Regarding this, in this work, a TP has been addressed in which a manufacturing company has agreements with a few suppliers to deliver the products to the retailers. For this contract, the suppliers receive a commission from the company. Here, two types of transportation costs (actual and demanded) per unit are taken into account. The unit charge that suppliers impose on retailers is known as the ‘demanded unit transportation cost’, while the ‘actual unit transportation cost’ is the cost that suppliers bear during the transportation. In order to establish a business relationship with the retailers, suppliers offer a discount on the demanded charge based on the amount they receive. Here, two types of products are considered: products with lower rate of deterioration and products with higher rate of deterioration. The cost of transportation is relatively higher for later items with a larger quantity of deteriorating items. Based on these two categories, two deterministic models have been developed here. Subsequently, we have looked at the models in an uncertain setting while taking the commission and cost parameters into account as interval numbers. The objective of this study is to minimize the retailers’ overall cost and maximize the suppliers’ total profit simultaneously. To demonstrate the models, four numerical examples have been considered. Then we have used the artificial bee colony (ABC) algorithm in conjunction with four multi-objective optimization techniques that are currently in use to solve the multi-objective transportation models: the global criterion method (GCM), the Tchebycheff method, the weighted Tchebycheff method and the weighted sum method. Finally, a few more metaheuristic algorithms are used to compare the results.
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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 | 0,000 | 0,000 |
| 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)
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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 découle