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

拟议的研究计划将受益于先前研究项目中创造的知识，并以获得的知识为基础，开发更能代表闭环系统供应链(CLSC)环境问题的数学模型。它还将调查通过学习曲线捕获的学习如何提高此类链的可持续性能。两种方法，经典和非经典，将被用来研究CLSC。经典方法侧重于在不考虑环境因素的情况下最小化CLSC的(经典)成本之和，而非经典方法将库存和物流系统与热力学系统进行比较，利用其定律来减少系统的熵(无序)，提高系统的可持续性能。 这项研究计划将分阶段进行。第一阶段和第二阶段将重点扩展以前项目中的经典和非经典模型，包括环境因素，以尝试拥有更接近真实场景的模型。第一阶段将把以前开发的经典的清洁能源供应链模型整合成一个模型，该模型捕捉到以下特征：被退回的物品在回收之前被分解，回收次数有限，返回率可变，生产和回收过程不完善等。逐步地，数学模型将建立在引入温室气体排放、能源使用、固体废物、可生物降解性、垃圾填埋场、燃料消耗等环境因素的基础上。第二阶段将建立在浪费有用功(火用)的熵的关系上，并将扩展(火用)会计作为实现可持续的环境、社会和经济的工具。它还将制定一个可能更好地反映所消耗资源的真实数量的可持续发展指数。稍后，项目的第三阶段将调查在某些CLSC功能中的学习如何影响在第一阶段和第二阶段中开发的模型的性能。这是一个重要的步骤，因为一个组织未来唯一可持续的优势是它比竞争对手学习得更快的能力。第四阶段将对前几个阶段的结果进行分析和比较，以找出相似之处和不同之处，并确定CLSC系统是否能从这两个所述办法的结合中受益。拟议的研究将建立在瑞尔森作为专业教育、培训和当代研究中心的专业知识的基础上。这项研究的结果将为管理者提供关于如何改善其公司的环境、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.