可持续供应链下考虑碳税政策、不完全生产和部分积压的逆向物流库存模型

IF 2.1 Q3 ENERGY & FUELS

Process Integration and Optimization for Sustainability Pub Date : 2023-11-10 DOI:10.1007/s41660-023-00381-4

Nidhi Handa, S. R. Singh, Chandni Katariya

引用次数: 0

摘要

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A reverse Logistics Inventory Model with Consideration of Carbon Tax Policy, Imperfect Production, and Partial Backlogging Under a Sustainable Supply Chain

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来源期刊

Process Integration and Optimization for Sustainability Multiple-

CiteScore

4.30

自引率

8.30%

发文量

期刊介绍： This journal is an interdisciplinary platform for information on innovation, research, development and demonstration in the areas of resource conservation, optimal use of various resources, analysis and optimization of various systems and processes, reduction and mitigation of environmental pollutions, and overall sustainable developments. It provides an international forum to researchers, policy makers, decision makers, managers, consultants, and planners to publish, learn, and discuss about various aspects related to Process Integration and Optimization. The breadth of coverage ranges from various optimization and system analysis methods (such as mathematical programming, pinch analysis, P-graphs, heuristics based approaches, decision analysis, fuzzy optimization, etc.) to applications, innovations, and methodological developments for resource conservation. This journal publishes the following types of contributions:Original research papers that propose novel methodologyReview articles providing surveys and critical appraisal of developments in a specific topic of interestShort communications describing initial research findings of potential significance, or substantive comments on previously published papersShort technical notes that focus on the implementation of established methodology (e.g., industrial case studies/success stories; software tools)PIOS does not accept experimental optimization papers based on statistical techniques, unless such results are integrated from a process system perspective.