Environmentally Concerned Logistics Operations in Fuzzy Environment: A Literature Survey

A sustainable green reverse logistics plan for plastic solid waste management using TOPSIS method

Recycling solid plastic waste is helpful for resource utilisation in a sustainable manner, and this can lead to reverse logistics to recover waste in supply chain management. In developing economies, it is very essential to recover waste and recycle it, as mismanagement of it could hasten the development of negative socioeconomic and environmental issues. This paper investigates plastic waste recycling by means of reverse logistics management. We feature Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), which is a multi-criteria decision-making approach to selecting a suitable warehouse that will assist the decision-maker and supply the product to the re-manufacturing cell by inspecting the waste. The paper also focuses on the carbon emissions that are emitted during re-manufacturing through a mathematical model that minimises the total cost and total carbon emissions. A case study of the plastic re-manufacturing industry in Tripura, India, is shown here to present the steps of the decision support system. The model is programmed with real-life data from the case study and yields a total cost of Rs. 2894.37 and total emissions of 414.15 gm/lt, when the disposal cost is considered; without it, the total cost is Rs. 2668.69 and the total emissions are 413.54 gm/lt. The results obtained are promising for reducing carbon emissions from the recycling process of plastic waste at a lower cost. This research will also help the environment cope with plastic waste, which has become a threat to nature, as well as carbon emissions that come from the industry. Finally, some managerial insights and suitable analyses with pictorial representation are proposed.

A Fuzzy Simheuristic for the Permutation Flow Shop Problem under Stochastic and Fuzzy Uncertainty

Stochastic, as well as fuzzy uncertainty, can be found in most real-world systems. Considering both types of uncertainties simultaneously makes optimization problems incredibly challenging. In this paper, we analyze the permutation flow shop problem (PFSP) with both stochastic and fuzzy processing times. The main goal is to find the solution (permutation of jobs) that minimizes the expected makespan. However, due to the existence of uncertainty, other characteristics of the solution are also taken into account. In particular, we illustrate how survival analysis can be employed to enrich the probabilistic information given to decision-makers. To solve the aforementioned optimization problem, we extend the concept of a simheuristic framework so it can also include fuzzy elements. Hence, both stochastic and fuzzy uncertainty are simultaneously incorporated in the PFSP. In order to test our approach, classical PFSP instances have been adapted and extended, so that processing times become either stochastic or fuzzy. The experimental results show the effectiveness of the proposed approach when compared with more traditional ones.

Evaluation of Waste Electronic Product Trade-in Strategies in Predictive Twin Disassembly Systems in the Era of Blockchain

Manufacturing and supply chain operations are on the cusp of an era with the emergence of groundbreaking technologies. Among these, the digital twin technology is characterized as a paradigm shift in managing production and supply networks since it facilitates a high degree of surveillance and a communication platform between humans, machines, and parts. Digital twins can play a critical role in facilitating faster decision making in product trade-ins by nearly eliminating the uncertainty in the conditions of returned end-of-life products. This paper demonstrates the potential effects of digital twins in trade-in policymaking through a simulated product-recovery system through blockchain technology. A discrete event simulation model is developed from the manufacturer’s viewpoint to obtain a data-driven trade-in pricing policy in a fully transparent platform. The model maps and mimics the behavior of the product-recovery activities based on predictive indicators. Following this, Taguchi’s Orthogonal Array design is implemented as a design-of-experiment study to test the system’s behavior under varying experimental conditions. A logistics regression model is applied to the simulated data to acquire optimal trade-in acquisition prices for returned end-of-life products based on the insights gained from the system.

Integrating Environmental and Social Sustainability Into Performance Evaluation: A Balanced Scorecard-Based Grey-DANP Approach for the Food Industry

In addition to retaining high levels of customer satisfaction, sustainability of businesses is also heavily reliant on the efficiency of their internal and external processes. Continuous performance evaluations using key performance metrics to leverage operations are essential in maintaining a sustainable business while achieving growth objectives for revenue and profitability. Traditionally, companies have considered various financial criteria, quality characteristics, and targeted levels of service as their primary factors for performance evaluation. However, increasing environmental and social awareness and accompanying governmental legislations are now requiring companies to integrate these two aspects into their performance evaluations. With this motivation, this study proposes a Balanced Scorecard (BSC)-based approach combining Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Analytic Network Process (ANP) methodologies for performance evaluation. The grey system theory has been utilized in order to capture the vagueness and the uncertainty in decision making. To demonstrate the functionality of the approach, a case study is conducted on a U.S.-based food franchise. The results of the algorithm and a discussion elaborating on the findings are provided.