Hospital reconversion in response to the COVID-19 pandemic using simulation and multi-objective genetic algorithms.

With the outbreak of the novel coronavirus SARS-CoV2, many countries have faced problems because of their available hospital capacity. Health systems must be prepared to restructure their facilities and meet the requirements of the pandemic while keeping their services and specialties active. This process, known as hospital reconversion, contributes to minimizing the risk of contagion between hospital staff and patients and optimizing the efficient treatment and disposal of healthcare wastes that represent a risk of nosocomial infection contagion. A methodology based upon simulation and mathematical optimization with genetic algorithms is proposed to address the hospital reconversion problem. Firstly, a discrete event simulation model is developed to study the flow of patients within the hospital system. Subsequently, the hospital reconversion problem is formulated through a mathematical model seeking to maximize the proximity relationships between departments and minimize the costs due to the flow of agents within the system. Finally, the results obtained from the optimization process are evaluated through the simulation model. The proposed framework is validated by assessing the hospital reconversion process in a COVID-19 Hospital in Mexico. The results show the mathematical model's effectiveness by incorporating the medical personnel's expertise in decisions regarding the use of elevators, departments' location, structural dimensions, use of corridors, and the floors to which the departments are assigned when facing a pandemic. The contribution of this approach can be replicated during the hospital reconversion process in other hospitals with different characteristics.

Multi-period optimal design of an aerospace CFRP waste management supply chain: Data set, variables and criteria for the development of a multi-objective MILP model.

This paper presents the data set, variables and criteria for the development of a multi-objective and multi-period Mixed Integer Linear Programming (MILP) model for the deployment and design of an aerospace CFRP (Carbon Fibre Reinforced Polymer) waste supply chain. It involves ε-constraint, lexicographic techniques and Multiple Criteria Decision Making (MCDM) tools. In this model, the deployment of new recycling sites (Grinding, Pyrolysis, Supercritical Water, Microwave) is established. The system is optimised by bi-criteria optimisation including an economic objective based on cost minimisation or Net Present Value (NPV) maximisation and an environmental one (minimisation of Global Warming Potential). The presentation of the global strategy, the results and their discussion have been presented in a companion paper (Vo Dong, P.A., Azzaro-Pantel, C., Boix, A multi-period optimisation approach for deployment and optimal design of an aerospace CFRP waste management supply chain, Waste Management, Volume 95, 2019, Pages 201-216 [1]). The data were acquired by literature analysis, by use of Simapro v7.3 software tool and EcoInvent database, by use of institutional sources (Eurostat for energy prices) or from Airbus and Boeing websites for aircraft deliveries and calculation of CFRP content. The model was created by the authors within the framework of SEARRCH (Sustainability Engineering Assessment Research for Recycling Composite with High value) project supported by ANR (Agence Nationale de la Recherche Scientifique). The case study of CFRP waste supply chain in France has supported the deployment analysis.

A multi-period optimisation approach for deployment and optimal design of an aerospace CFRP waste management supply chain.

This paper presents a modelling framework for the deployment and design of aerospace CFRP (Carbon Fibre Reinforced Polymer) waste supply chain. The problem involves a multi-period Mixed Integer Linear Programming (MILP) formulation considering ε-constraint, lexicographic techniques and Multiple Criteria Decision Making (MCDM) tools. The methodology has been applied to a case study of France. In this model, the deployment of new recycling sites (Grinding, Pyrolysis, Supercritical Water, Microwave) is established. The system is optimised by bi-criteria optimisation including an economic objective based on cost minimisation or Net Present Value (NPV) maximisation and an environmental one (minimisation of Global Warming Potential). The potential for economic acceptability of recycled carbon fibres is assessed through a levelized cost derived from the supply chain total cost and the profitability via NPV with a range of various CFRP prices. The results show that the compromise strategy for both economic and environmental objectives leads to centralised configurations at the regions close to significant waste sources. The cooperation in the recovery system is needed to minimise cost and maximise profit. The improvement of recycling technology permits to achieve the compromise solution for both economic and environmental objectives. The results also highlight that a mix of technologies will be involved in deployment phase and that the answer is not straightforward due to the complexity of the system. The methodology is yet generic enough to be replicated in other contexts considering the upgrade of process database.

Extending the scope of eco-labelling in the food industry to drive change beyond sustainable agriculture practices.

New consumer awareness is shifting industry towards more sustainable practices, creating a virtuous cycle between producers and consumers enabled by eco-labelling. Eco-labelling informs consumers of specific characteristics of products and has been used to market greener products. Eco-labelling in the food industry has yet been mostly focused on promoting organic farming, limiting the scope to the agricultural stage of the supply chain, while carbon labelling informs on the carbon footprint throughout the life cycle of the product. These labelling strategies help value products in the eyes of the consumer. Because of this, decision makers are motivated to adopt more sustainable models. In the food industry, this has led to important environmental impact improvements at the agricultural stage, while most other stages in the Food Supply Chain (FSC) have continued to be designed inefficiently. The objective of this work is to define a framework showing how carbon labelling can be integrated into the design process of the FSC. For this purpose, the concept of Green Supply Chain Network Design (GSCND) focusing on the strategic decision making for location and allocation of resources and production capacity is developed considering operational, financial and environmental (CO2 emissions) issues along key stages in the product life cycle. A multi-objective optimization strategy implemented by use of a genetic algorithm is applied to a case study on orange juice production. The results show that the consideration of CO2 emission minimization as an objective function during the GSCND process together with techno-economic criteria produces improved FSC environmental performance compared to both organic and conventional orange juice production. Typical results thus highlight the importance that carbon emissions optimization and labelling may have to improve FSC beyond organic labelling. Finally, CO2 emission-oriented labelling could be an important tool to improve the effects eco-labelling has on food product environmental impact going forward.

A multiobjective optimization framework for multicontaminant industrial water network design.

The optimal design of multicontaminant industrial water networks according to several objectives is carried out in this paper. The general formulation of the water allocation problem (WAP) is given as a set of nonlinear equations with binary variables representing the presence of interconnections in the network. For optimization purposes, three antagonist objectives are considered: F(1), the freshwater flow-rate at the network entrance, F(2), the water flow-rate at inlet of regeneration units, and F(3), the number of interconnections in the network. The multiobjective problem is solved via a lexicographic strategy, where a mixed-integer nonlinear programming (MINLP) procedure is used at each step. The approach is illustrated by a numerical example taken from the literature involving five processes, one regeneration unit and three contaminants. The set of potential network solutions is provided in the form of a Pareto front. Finally, the strategy for choosing the best network solution among those given by Pareto fronts is presented. This Multiple Criteria Decision Making (MCDM) problem is tackled by means of two approaches: a classical TOPSIS analysis is first implemented and then an innovative strategy based on the global equivalent cost (GEC) in freshwater that turns out to be more efficient for choosing a good network according to a practical point of view.