A hybrid model for robust design of sustainable closed-loop supply chain in lead-acid battery industry

A novel multi-objective robust possibilistic flexible programming to design a sustainable apparel closed-loop supply chain network

Designing a sustainable Closed-Loop Supply Chain (CLSC) network is imperative for the apparel industry, given its escalating adverse effects on economic, environmental, and social dimensions. In this study, a novel tri-objective location-allocation optimization model is specifically developed for designing a sustainable apparel CLSC, incorporating the industry's unique facilities. The aim of the model is to simultaneously minimize the costs and negative environmental impacts while maximizing social benefits under demands and returns uncertainty. A notable research contribution lies in addressing the unique challenges of treating different types of returns, including commercial, End Of Use (EOU) and End Of Life (EOL) returns due to their uncertain quality and quantity. Additionally, the model optimizes the environmental performance levels of production facilities, a novel aspect in the apparel CLSC research. Moreover, the flexibility of constraints related to the demand fulfilment is considered. To cope with such flexibility and uncertainties, a new hybrid robust possibilistic flexible programming model is developed, by extending the previous methodologies. A core innovation of this solution approach lies in the pioneering utilization of hexagonal fuzzy numbers for uncertain epistemic parameters, making a significant advancement in the field of CLSC. Comparative analysis with the similar studies demonstrates the superiority of the proposed model, incorporating hexagonal fuzzy numbers over the method using triangular fuzzy numbers. Furthermore, AUGMECON method using lexicographic optimization is applied to handle the multi-objective model. The application of the proposed model is shown focusing on Southwestern Ontario in Canada. The results reveal that commercial and EOU returns have a more detrimental impact on economic, environmental, and social sustainability aspects compared to EOL returns.

Microbial and potentially toxic elements risk assessment in high Andean river water based on Monte Carlo simulation, Peru

The study evaluated microbial and Potentially Toxic Elements-PTEs risks in high Andean river water in Peru using Monte Carlo simulation. A total of 144 water samples were collected from four rivers and evaluated for physicochemical parameters, PTEs and bacterial pathogens. The microbial risk analysis for exposure to pathogens present in the water was based on the probability of occurrence of diseases associated with Escherichia coli, Pseudomonas aeruginosa and enterococci. PTEs risk analysis was performed using a Monte Carlo simulation approach. The results showed that the highest microbial risk due to exposure to water contaminated by E. coli, P. aeruginosa and enterococci was recorded in the Miraflores and Chia rivers. Meanwhile, the analysis of carcinogenic and non-carcinogenic risk by PTEs in adults and children revealed that the Chia river presents a high risk of contamination by PTEs, especially the carcinogenic risk for children. The Monte Carlo simulation indicated a 56.16% and 94.85% probability of exceeding the limit value of 0.0001 for carcinogenic risk in adults and children, respectively. It can be concluded that children consuming the waters of the Chia river are potentially at risk of As toxicity.

Robust design of a logistics system using FePIA procedure and analysis of trade-offs between CO2 emissions and net present value

The problems of flexible planning of the design of logistics systems for the collection of food products such as raw milk can result in a decrease in the performance of critical indicators for their performance. This paper proposes a new efficient methodology for robustly designing a first-mile logistics system for storing and refrigerating milk as a perishable product considering decisions related to open facilities and the flow of products, including sustainability indices. The proposed approach is modeled as a bi-objective problem by considering the minimization of greenhouse gas emissions (CO2) produced by milk transportation canteens and the maximization of the system configuration's net present value (NPV). We have analyzed and determined the most robust configuration for the first time and explained the robustness-NPV and robustness-CO2 relationships. The proposed mathematical model is solved by the Epsilon constraints method, and the robustness is calculated considering an extension of the FePIA methodology for multiobjective problems. A novel contribution is a balance in the possible future values generated by the company related to its cash flows and the generation of CO2 emissions when using a motorized transport frequently used in the shipment of raw milk considering a new important aspect such as the volume of product transported and the slope of the path between the production farm and the storage cooling tanks.