Inventory management in sustainable closed-loop supply chains with continuous improvement

拟议的研究计划将受益于以前的研究项目中创建的知识，并建立在所获得的知识，以开发更能代表闭环供应链（CLSC）中环境问题的数学模型。它还将研究学习曲线所捕获的学习如何能够改善这些链的可持续性表现。两种方法，经典和非经典，将被用来研究CLSC。经典方法侧重于最小化CLSC的（经典）成本之和，而不考虑环境因素，而非经典方法通过使用其定律来减少系统的熵（无序）并提高其可持续性能，从而将库存和物流系统与热力学系统进行比较。 这项研究计划将分为几个阶段。第一阶段和第二阶段将侧重于通过包括环境因素来扩展先前项目的经典和非经典模型，以尝试更忠实地模拟真实的场景。第一阶段将把以前开发的经典CLSC模型整合到一个模型中，该模型具有以下特征：回收物品在回收之前被拆卸，回收次数有限，回收率可变，生产和回收过程不完善等。逐渐地，将在引入温室气体排放，能源使用，固体废物，第二阶段将建立在熵的关系上，这浪费了有用功（火用），并使用扩展火用会计作为实现CLSC环境，社会和经济可持续性的工具。它还将制定一个有效能的可持续性指数，以更好地反映所消耗资源的真实数量。稍后，该项目的第3阶段将研究一些CLSC功能的学习如何影响第1阶段和第2阶段开发的模型的性能。这是一个重要的步骤，因为一个组织在未来拥有的唯一可持续的优势是它比竞争对手更快地学习的能力。第四阶段将分析和比较前几个阶段的结果，以确定相似之处和差异，以及CLSC系统是否可以从整合两种方法中受益。拟议的研究将以瑞尔森作为专业教育、培训和当代研究中心的专业知识为基础。这项研究的结果将为管理人员提供关于如何提高其公司的环境、经济和社会可持续性的建议。

The proposed research program will benefit from the knowledge created in previous research projects and build on that acquired knowledge to develop mathematical models that are more representative of environmental issues in closed-loop supply chains (CLSC). It will also investigate how learning, captured by learning curves,  can improve the sustainability performance of such chains. Two approaches, classical and non-classical, will be used to study CLSCs. The classical approach focuses on minimizing the sum of (classical) costs of a CLSC with no consideration of environmental factors, while the non-classical approach draws a parallel between an inventory and logistics system and a thermodynamic system by using its laws to reduce the system’s entropy (disorder) and improve its sustainable performance. This research program will be divided into phases. Phases 1 and 2 will focus on extending the classical and non-classical models from previous projects by including environmental factors in an attempt to have models that resemble real scenarios more faithfully. Phase 1 will integrate the classical CLSC models developed before into a model that captures the following features: returned items are disassembled prior to recovery, the number of recovery times is limited, the return rate is variable, production and recovery processes are imperfect, etc. Gradually, the mathematical models will be built upon the introduction of environmental factors such as greenhouse gas emissions, energy usage, solid waste, biodegradability, landfill, fuel consumption, etc. Phase 2 will build on the relationship of entropy, which wastes useful work (exergy), and use Extended Exergy Accounting as a tool to attain environmental, social and economic sustainability in CLSC. It will also develop an exergetic sustainability index that may better reflect the true amount of consumed resources. Later, Phases 3 of the project will investigate how learning in some CLSC functions affects the performance of the models developed in Phases 1 and 2. This is an important step since the only sustainable advantage an organization will have in the future is its ability to learn faster than its competitors. Phase 4 will analyse and compare the results of the previous phases to identify similarities and differences, and whether a CLSC system can benefit from integrating the two indicated approaches. The proposed research will build on Ryerson’s expertise as a centre of professional education, training and contemporary research. The results from this research will provide managers with recommendations on how to improve the environmental, economic and social sustainability of their firms.