Sustainable planning and decision-making model for sugarcane mills considering environmental issues

Technology adaptation in sugarcane supply chain based on a novel p, q Quasirung Orthopair Fuzzy decision making framework

The present paper contributes to the literature in two ways. First, it develops a novel p, q Quasirung Orthopair Fuzzy (p, q QOF) based group decision making framework to modify a recently developed multi-criteria decision making (MCDM) model such as Comparisons between Ranked Criteria (COBRAC). Second, the paper ruminates on the Strength-Weakness-Opportunity-Threat (SWOT) of the sugarcane supply chain (SSC) in India vis-à-vis adaptation of the advanced technologies featuring Industry 4.0. To set the sub-factors of various dimensions of SWOT, the theoretical ground of Technology-Organization-Environment (TOE) framework has been used. The sub-factors of SWOT have been derived through an informal in-depth discussion with the experts of the sugar industry. Then using a Likert five-point linguistic scale the experts rated the sub-factors based on their relative importance. To determine the weights the modified COBRAC method has been applied. In subsequent stages the reliability of the model has been tested and sensitivity analysis has been carried out to check the stability of the result. The analysis reveals that while experience, by-product utilization and high demand provides strength and create opportunities for SSC, the areas of concern are lack of variety, fragmented nature of supply chains, shortage of next-gen talent and inadequate infrastructure. However, there are enough promises for SSC. The paper shall provide impetus to strategic decision makers for the sugar industry and puts forth a new decision-making framework for the analysts.

Biological responses to imazapic and methyl parathion pesticides in bioinspired lipid membranes and Tilapia fish

Pesticide misuse has well-documented detrimental effects on ecosystems, with Nile tilapia (Oreochromis niloticus) being particularly vulnerable. The current study focuses on the impact of widely used sugarcane crop pesticides, Imazapic (IMZ) and Methyl Parathion (MP), on tilapia gill tissues and their lipid membranes. This investigation was motivated by the specific role of the lipid membrane in transport regulation. Bioinspired cell membrane models, including Langmuir monolayers and liposomes (LUVs and GUVs), were utilized to explore the interaction of IMZ and MP. The results revealed electrostatic interactions between IMZ and MP and the polar head groups of lipids, inducing morphological alterations in the lipid bilayer. Tilapia gill tissue exposed to the pesticides exhibited hypertrophic increases in primary and secondary lamellae, total lamellar fusion, vasodilation, and lifting of the secondary lamellar epithelium. These alterations can lead to compromised oxygen absorption by fish and subsequent mortality. This study not only highlights the harmful effects of the pesticides IMZ and MP, but also emphasizes the crucial role of water quality in ecosystem well-being, even at minimal pesticide concentrations. Understanding these impacts can better inform management practices to safeguard aquatic organisms and preserve ecosystem health in pesticide-affected environments.

The IoT-enabled sustainable reverse supply chain for COVID-19 Pandemic Wastes (CPW)

Supply chains have been impacted by the COVID-19 pandemic, which is the most recent worldwide disaster. After the world health organization recognized the latest phenomena as a pandemic, nations became incapacitated to provide the required medical supplies. In the current situation, the world seeks an essential solution for COVID-19 Pandemic Wastes (CPWs) by pushing the pandemic to a stable condition. In this study, the development of a supply chain network is contrived for CPWs utilizing optimization modeling tools. Also, an IoT platform is devised to enable the proposed model to retrieve real-time data from IoT devices and set them as the model's inputs. Moreover, sustainability aspects are appended to the proposed IoT-enabled model considering its triplet pillars as objective functions. A real case of Puebla city and 15 experiments are used to validate the model. Furthermore, a combination of metaheuristic algorithms utilized to solve the model and also seven evaluation indicators endorse the selection of efficient solution approaches. The evaluation indicators are appointed as the inputs of statistical and multicriteria decision-making hybridization to prioritize the algorithms. The result of the Entropy Weights method and Combined Compromise Solution approach confirms that MOGWO has better performance for the medium-sizes, case study and an overall view. Also, NSHHO outclasses the small-size and large-size experiments.

Integrated Biorefinery and Life Cycle Assessment of Cassava Processing Residue-From Production to Sustainable Evaluation

Commonly known as a subsistence culture, cassava came to be considered a commodity and key to adding value. However, this tuber's processing for starch and flour production is responsible for generating a large amount of waste that causes serious environmental problems. This biomass of varied biochemical composition has excellent potential for producing fuels (biogas, bioethanol, butanol, biohydrogen) and non-energetic products (succinic acid, glucose syrup, lactic acid) via biorefinery. However, there are environmental challenges, leading to uncertainties related to the sustainability of biorefineries. Thus, the provision of information generated in life cycle assessment (LCA) can help reduce bottlenecks found in the productive stages, making production more competitive. Within that, this review concentrates information on the production of value-added products, the environmental impact generated, and the sustainability of biorefineries.

Blood plasma supply chain planning to respond COVID-19 pandemic: a case study

The COVID-19 pandemic causes a severe threat to human lives worldwide. Convalescent plasma as supportive care for COVID-19 is critical in reducing the death rate and staying in hospitals. Designing an efficient supply chain network capable of managing convalescent plasma in this situation seems necessary. Although many researchers investigated supply chains of blood products, no research was conducted on the planning of convalescent plasma in the supply chain framework with specific features of COVID-19. This gap is covered in the current work by simultaneous regular and convalescent plasma flow in a supply chain network. Besides, due to the growing importance of environmental problems, the resulting carbon emission from transportation activities is viewed to provide a green network. In other words, this study aims to plan the integrated green supply chain network of regular and convalescent plasma in the pandemic outbreak of COVID-19 for the first time. The presented mixed-integer multi-objective optimization model determines optimal network decisions while minimizing the total cost and total carbon emission. The Epsilon constraint method is used to handle the considered objectives. The model is applied to a real case study from the capital of Iran. Sensitivity analyses are carried out, and managerial insights are drawn. Based on the obtained results, product demand impacts the objective functions significantly. Moreover, the systems' total carbon emission is highly dependent on the flow of regular plasma. The results also reveal that changing transportation emission unit causes significant variation in the total emission while the total cost remains fixed.

A New Wooden Supply Chain Model for Inventory Management Considering Environmental Pollution: A Genetic algorithm

Nowadays, companies need to take responsibility for addressing growing markets and the growing expectations of their customers to survive in a highly competitive context that is progressing on a daily basis. Rapid economic changes and increasing competitive pressure in global markets have led companies to pay special attention to their supply chains. As a result, in this research, a mathematical model is proposed to minimize closed loop supply chain costs taking into account environmental effects. Thus, suppliers first send wood as raw materials from forests to factories. After processing the wood and turning it into products, the factories send the wood to retailers. The retailers then send the products to the customers. Finally, customers send returned products to recovery centers. After processing the products, the recovery centers send their products to the factories. The considered innovations include: designing a supply chain of wood products regarding environmental effects, customizing the genetic solution approach to solve the proposed model 3-Considering the flow of wood products and determining the amount of raw materials and products sent and received.

A review of recent advances in quantum-inspired metaheuristics

Quantum-inspired metaheuristics emerged by combining the quantum mechanics principles with the metaheuristic algorithms concepts. These algorithms extend the diversity of the population, which is a primary key to proper global search and is guaranteed using the quantum bits' probabilistic representation. In this work, we aim to review recent quantum-inspired metaheuristics and to cover the merits of linking the quantum mechanics notions with optimization techniques and its multiplicity of applications in real-world problems and industry. Moreover, we reported the improvements and modifications of proposed algorithms and identified the scope's challenges. We gathered proposed algorithms of this scope between 2017 and 2022 and classified them based on the sources of inspiration. The source of inspiration for most quantum-inspired metaheuristics are the Genetic and Evolutionary algorithms, followed by swarm-based algorithms, and applications range from image processing to computer networks and even multidisciplinary fields such as flight control and structural design. The promising results of quantum-inspired metaheuristics give hope that more conventional algorithms can be combined with quantum mechanics principles in the future to tackle optimization problems in numerous disciplines.

Variable Neighborhood Search for Multi-Cycle Medical Waste Recycling Vehicle Routing Problem with Time Windows

BACKGROUND

Improper disposal of urban medical waste is likely to cause a series of neglective impacts. Therefore, we have to consider how to improve the efficiency of urban medical waste recycling and lowering carbon emissions when facing disposal.

METHODS

This paper considers the multi-cycle medical waste recycling vehicle routing problem with time windows for preventing and reducing the risk of medical waste transportation. First, a mixed-integer linear programming model is formulated to minimize the total cost consisting of the vehicle dispatch cost and the transportation costs. In addition, an improved neighborhood search algorithm is designed for handling large-sized problems. In the algorithm, the initial solution is constructed using the Clarke-Wright algorithm in the first stage, and the variable neighborhood search algorithm with a simulated annealing strategy is introduced for exploring a better solution in the second stage.

RESULTS

The computational results demonstrate the performance of the suggested algorithm. In addition, the total cost of recycling in the periodic strategy is lower than with the single-cycle strategy.

CONCLUSIONS

The proposed model and algorithm have the management improvement value of the studied medical waste recycling vehicle routing problem.

A public-private partnership based model for regulating out-of-pocket expenditures to strengthen primary care system

In developing countries like India, the cost of health care is largely borne by patient out-of-pocket payments. Recent studies have reported that patients skip public-funded clinics providing free consultation for distant private care providers. Some of the reasons identified for such behaviour include longer waiting times, perception regarding quality of care, etc. Therefore, optimal allocation of existing and new capacity is critical for a greater public interest. This article presents a decision-making framework towards this intent for strengthening the existing government primary healthcare network. In this article, a mixed-integer linear programing (MILP) model is developed for optimal reconfiguration of the existing government primary healthcare network to minimise patient out-of-pocket expenditures (OOPE). The model involves three types of facilities: Primary Health Centre (PHC), Community Health Centre (CHC), and Private OPD (outpatient department). Implementation of the proposed model can help in reducing out-of-pocket expenditures. The optimization model proposed in the article is unique as it incorporates for the first time, patient out-of-pocket expenditure, capacity reconfiguration, and public-private partnership decisions in the primary healthcare system. A solution algorithm is also proposed for the optimization model. The model would be useful for theory development and also in policy-making.

Impact of Socioeconomic Environment on Home Social Care Service Demand and Dependent Users

An aging population and rising life expectancy lead to an increased demand for social services to care for dependent users, among other factors. In Barcelona, home social care (HSC) services are a key agent in meeting this demand. However, demand is not evenly distributed among neighborhoods, and we hypothesized that this can be explained by the user’s social environment. In this work, we describe the user’s environment at a macroscopic level by the socioeconomic features of the neighborhood. This research aimed to gain a deeper understanding of the dependent user’s socioeconomic environment and service needs. We applied descriptive analytics techniques to explore possible patterns linking HSC demand and other features. These methods include principal components analysis (PCA) and hierarchical clustering. The main analysis was made from the obtained boxplots, after these techniques were applied. We found that economic and disability factors, through users’ mean net rent and degree of disability features, are related to the demand for home social care services. This relation is even clearer for the home-based social care services. These findings can be useful to distribute the services among areas by considering more features than the volume of users/population. Moreover, it can become helpful in future steps to develop a management tool to optimize HSC scheduling and staff assignment to improve the cost and quality of service. For future research, we believe that additional and more precise characteristics could provide deeper insights into HSC service demand.