Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Entropy Based Uncertain Multi Objective Multi-Item Fixed Charge 4D Transportation Problem Involving Breakable and Substitute Items. |
| Συγγραφείς: |
Bind, Awdhesh Kumar1 (AUTHOR) awdheshkb.ma.20@nitj.ac.in, Rani, Deepika1 (AUTHOR) ranid@nitj.ac.in |
| Πηγή: |
International Journal of Uncertainty, Fuzziness & Knowledge-Based Systems. Jun2026, Vol. 34 Issue 4, p395-437. 43p. |
| Θεματικοί όροι: |
*Entropy (Information theory), *Transportation problems (Programming), *Multi-objective optimization, *Fuzzy algorithms, *Brittle materials, *Sustainable transportation, *Stochastic models |
| Περίληψη: |
This study presents a novel entropy-based multi-objective 4D transportation model that integrates both breakable and substitute items to address challenges in sustainable logistics. The model aims to simultaneously maximize profit and minimize carbon emissions, considering multi-route delivery with vehicles of varying capacities and depot-specific pricing. A key innovation is the consideration of the breakability of the substitute items, which varies based on the transported amounts, conveyance type, and traversed distance. The substitutability of items is influenced by the supply side, guided by managements discretion, and the demand side, which is price-dependent. Various models are discussed, including those with and without substitution, with and without entropy, and considering manager or customer substitution in the expected value, optimistic, and pessimistic decision-making frameworks. These models are applied to the uncertain multi-objective 4D solid transportation problem, where uncertainty is managed through a chance-constrained programming approach that converts them into their corresponding deterministic forms. Intuitionistic fuzzy programming is used to derive robust solutions. A real-life based numerical example validates the proposed approach, the results are compared across different decision-making perspectives. The findings demonstrate the model's ability to offer efficient and sustainable transportation solutions under uncertainty. [ABSTRACT FROM AUTHOR] |
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