Quantifying waste prevention with LCA to motivate holistic resource

With the Waste Framework Directive, waste prevention was promoted first priority for all EU member states in 2008 because it is superior to preparing for re-use, recycling and other waste management options in terms of resource efficiency. However, the actual implementation of waste prevention activities has so far been hesitant and the focus on end-of-pipe waste management instead of a holistic resource management prevails. Reasons include the limited measurability of waste prevention effects and the consequential lack of awareness, motivation and incentives. Our research aims to quantify waste prevention and its environmental impacts using Life Cycle Assessments and, thereby, to induce the efficient implementation of waste prevention concepts into municipalities’ strategies. Within a two-year-project, we developed a framework to support municipalities in establishing holistic resource management. The framework comprises three phases to set up waste prevention concepts and includes a detailed guideline as well as a methodology for the evaluation of potentials and measures. Since the impacts of waste prevention can only be identified with a holistic approach, LCAs were used to calculate the potentials for quantitative as well as qualitative waste prevention. The analyzed measures were selected based on an empirical study covering 111 municipalities and include 5 waste streams and several areas of responsibility. To better motivate communities to take action and to implement waste prevention concepts, tangible impact categories and their midpoint-values were used. The impact categories for this study emerge directly from the European Waste Framework Directive and cover Waste Generation (Waste), Global Warming Potential (GWP), Water Scarcity (WSI), Mineral Resource Depletion (MRD) and Human Toxicity (H.-Toxicity). Please click Additional Files below to see the full abstract

Environmental benefits of large‐scale second‐generation bioethanol production in the EU: an integrated supply chain network optimization and life cycle assessment approach

The use of agricultural residues for the generation of bioethanol has the potential to substitute fuels such as petrol or first‐generation bioethanol and thereby generate environmental benefits. Scientific research in this field typically confines the environmental dimension to global warming, disregarding other environmental impact and damage categories. By multi‐criteria mixed‐integer linear programming, this work examines environmental benefits and economic viability of optimal second‐generation bioethanol production network configurations to substitute petrol and/or first‐generation bioethanol in the EU. The results comprise environmentally optimal decisions for 18 impact and 3 damage categories, as well as economically optimal solutions for different excise and carbon tax scenarios. The impact categories global warming potential, particulate matter, and land use are affected the most. Optimal network decisions for different environmental objectives can be clustered into three groups of mutual congruencies, but opportunity costs between the different groups can be very high, indicating conflicting decisions. The decision to substitute petrol or first‐generation ethanol has the greatest influence. The results of the multi‐dimensional analysis suggest that the damage categories human health and ecosystem quality are suitable to unveil tradeoffs between conflicting environmental impacts, for example, global warming and land use. Taking human health and ecosystem quality as environmental decision criteria, second‐generation bioethanol should be used to concurrently substitute first‐generation bioethanol and petrol (100% and 18% of today's demand in the EU, respectively). However, economic optimization shows that with current taxation, bioethanol is hardly competitive with petrol, and that excise tax abatement or carbon taxes are needed to achieve these volumes. This article met the requirements for a gold‐gold JIE data openness badge described at http://jie.click/badges.Horizon 2020 Framework Programme http://dx.doi.org/10.13039/10001066

A machine learning enhanced multi-start heuristic to efficiently solve a serial-batch scheduling problem

Serial-batch scheduling problems are widespread in several industries (e.g., the metal processing industry or industrial 3D printing) and consist of two subproblems that must be solved simultaneously: the grouping of jobs into batches and the sequencing of the created batches. This problem’s NP-hard nature prevents optimally solving large-scale problems; therefore, heuristic solution methods are a common choice to effectively tackle the problem. One of the best-performing heuristics in the literature is the ATCS–BATCS(β) heuristic which has three control parameters. To achieve a good solution quality, most appropriate parameters must be determined a priori or within a multi-start approach. As multi-start approaches performing (full) grid searches on the parameters lack efficiency, we propose a machine learning enhanced grid search. To that, Artificial Neural Networks are used to predict the performance of the heuristic given a specific problem instance and specific heuristic parameters. Based on these predictions, we perform a grid search on a smaller set of most promising heuristic parameters. The comparison to the ATCS–BATCS(β) heuristics shows that our approach reaches a very competitive mean solution quality that is only 2.5% lower and that it is computationally much more efficient: computation times can be reduced by 89.2% on average

Machine learning based algorithm selection and genetic algorithms for serial-batch scheduling

Whenever combinatorial optimization problems cannot be solved by exact solution methods in reasonable time, tailor-made algorithms (heuristics, meta-heuristics) are developed. Often, these heuristics exploit structural properties and perform well on selected subsets of the problem space. For example, this is how the two best-known construction heuristics solve the scheduling problem investigated in this study (i.e., the scheduling of parallel serial-batch processing machines with incompatible job families, restricted batch capacities, arbitrary batch capacity demands, and sequence-dependent setup times). However, when the properties change, the performance of one algorithm might decrease, and another algorithm might have been the better choice. To resolve this issue, we propose using Machine Learning to exploit the strengths of different algorithms and to select the probably best-performing algorithm for each problem instance individually. To that, we investigate a variety of methods from the “learning-to-rank” literature and propose several adaptations. Furthermore, because there is no algorithm for the considered scheduling problem that is capable to explore the entire solution space, we developed two Genetic Algorithms for the improvement of initial solutions computed by the selected algorithms. Here, we put special emphasis on ensuring that the solution representation (encoding) reflects the entire solution space and that the operators (e.g., for recombination and mutation) are appropriate to explore and exploit this space completely. Our computational experiments show an average increase of 39.19% in solution quality

Review of metrics to assess resilience capacities and actions for supply chain resilience

Efficiency and profitability are the main drivers of globalization and have led to long and complex supply chains. Recent disturbances such as COVID-19 or the Suez Canal obstruction caused severe supply disruptions and thereby unveiled the vulnerability of global trade. Resilient supply chains are characterized by the capacity to absorb, adapt to, and restore after disruptions. Building upon the established concept of the ‘resilience curve’, this article explores the interplay between resilience capacities, metrics, and actions in the state-of-the-art literature. We first analyze and harmonize the terminology used to describe capacities as well as metrics for quantifying resilience. This results in a set of 17 resilience metrics that describe all characteristics of the resilience curve and can be used as a tool to assess the resilience of a supply chain. Subsequently, we propose how these metrics can be applied to quantify the effect of resilience actions. Finally, we analyze which actions are proposed in the literature and classify those actions according to their relation to traditional supply chain planning tasks. Practitioners such as supply chain decision-makers can implement these actions to strengthen the absorptive, adaptive, and restorative capacities and are provided with mathematical formulations to quantify the strengthening effect of actions. Academic research can, inter alia, integrate the metrics into multi-criteria optimization models for decision-making and explore the interplay between economic efficiency, environmental sustainability, and resilience

Indicator-based environmental and social sustainability assessment of hospitals: a literature review

The healthcare sector’s direct and indirect GHG emissions account for 4%−5% of global net emissions. Hospitals face the challenge of sustainable transformations and need to measure, monitor, and report on their sustainability performance. While indicator-based assessments of hospital sustainability have received increased attention over the last years, they are heterogenous in their terminologies, categories, and included indicators. This study reviews taxonomies and included indicators in hospital sustainability assessments, laying the foundation for future developments of consistent indicator-based assessments. The objective is to (1) critically review existing assessments of hospitals; (2) identify relevant sustainability topics in a hospital context and derive a best-practice categorization; (3) highlight thematical gaps. Based on the PRISMA method, we identify 88 relevant articles. First, 47 articles (comprehensive hospital sustainability assessments with extensive indicator sets) are reviewed, forming the basis for deriving a best-practice categorization. Second, considering an additional 41 articles (proposing indicators for specific hospital aspects), we collect all indicators and compile a consolidated indicator pool. We find substantial variations in the taxonomies and terminologies of the reviewed articles; most notably, there is a disagreement about what constitutes an indicator. 73% of all consolidated indicators are qualitative, and 78% are site-specific. Thematical gaps relate to sustainability along upstream and downstream value chains (esp. food and pharmaceuticals) and quantitative social indicators in general. The developed best-practice taxonomy and the compiled indicator pool serve as a comprehensive basis for future sustainability assessments of hospitals