Coffee capsule impacts and recovery techniques: A literature review

The recently developing coffee market has been characterized by profound changes caused by new solutions and technologies for coffee preparation. The polylaminate materials that compose most popular capsules make them a type of waste that is difficult to manage and recycle. This paper analyses the scientific references that deal with studying and improving the management processes of waste coffee capsules, as well as the studies that have analysed their environmental impact. Through a bibliographic review, some encouraging aspects emerged in the recovery of materials that can be adequately recycled (plastics and metals), as well as their possible use for the production of biogas and energy recovery. The need to manually separate the components that make up the capsule still represents one of the main challenges. Many efforts are still needed to favour the environmental sustainability of this waste from a strategic, technological and consumer empowerment point of view

Evaporation Through a Dry Soil Layer: Column Experiments

Modeling of water vapor transport through a dry soil layer (DSL), typically formed in the top soil during dry seasons in arid and semi-arid areas, is still problematic. Previous laboratory experiments in controlled environments showed that the only vapor transport process through the DSL is by Fick's law of diffusion. However, field experiments exhibited consistently higher evaporation rates than predicted by diffusion flow only. Some proposed reasons for the mismatch were: (a) daily cycles of condensation and evaporation in the DSL due to changes in solar radiation; (b) wind effects on air movement in the DSL; (c) atmospheric pressure fluctuations; (d) nonlinear influence of the DSL thickness on the evaporation process. To link the laboratory experiments with field observations, we performed soil column experiments in the laboratory with thick (>50 cm) DSL, and with different wind speeds, two radiative lamp schedules (continuous and 12 h daily cycles) and different thicknesses of DSL. Atmospheric pressure, air temperature and humidity were measured continuously. The results show that the evaporation rates observed are larger than those predicted by diffusion flow only. We found that it was possible to model the evaporation rates as a function of atmospheric pressure fluctuations. In conclusion, atmospheric pressure fluctuations can induce evaporation rates in DSL larger than estimated by diffusion flow only, possibly explaining the discrepancy between laboratory and field evaporation rates

Indoor Environmental Quality (IEQ): A Comparison between TOPSIS- and PROMETHEE-Based Approaches for Indirect Eliciting of Category Weights

Indoor Environmental Quality (IEQ) has received a great deal of attention in recent years due to the relationship between worker comfort and productivity. Many academics have studied IEQ from both a building design and an IEQ assessment perspective. This latter line of research has mostly used direct eliciting to obtain weights assigned to IEQ categories such as thermal comfort, visual comfort, acoustic comfort, and indoor air quality. We found only one application of indirect eliciting in the literature. Such indirect eliciting operates without the need for imprecise direct weighing and requires only comfort evaluations, which is in line with the Industry 5.0 paradigm of individual, dynamic, and integrated IEQ evaluation. In this paper, we use a case study to compare the only indirect eliciting model already applied to IEQ, based on TOPSIS, to an indirect eliciting method based on PROMETHEE and to a classical direct eliciting method (AHP). The results demonstrate the superiority of indirect eliciting in reconstructing individual preferences related to perceived global comfort

Complexity measurement in two supply chains with different competitive priorities

Complexity measurement based on the Shannon information entropy is widely used to evaluate variety and uncertainty in supply chains. However, how to use a complexity measurement to support control actions is still an open issue. This article presents a method to calculate the relative complexity, i.e., the relationship between the current and the maximum possible complexity in a Supply Chain. The method relies on unexpected information requirements to mitigate uncertainty. The article studies two real-world Supply Chains of the footwear industry, one competing by cost and quality, the other by flexibility, dependability, and innovation. The second is twice as complex as the first, showing that competitive priorities influence the complexity of the system and that lower complexity does not ensure competitivity

Order Picking Problem: A Model for the Joint Optimisation of Order Batching, Batch Assignment Sequencing, and Picking Routing

Background: Order picking is a critical activity in end-product warehouses, particularly using the picker-to-part system, entail substantial manual labor, representing approximately 60% of warehouse work. Methods: This study develops a new linear model to perform batching, which allows for defining, assigning, and sequencing batches and determining the best routing strategy. Its goal is to minimise the completion time and the weighted sum of tardiness and earliness of orders. We developed a second linear model without the constraints related to the picking routing to reduce complexity. This model searches for the best routing using the closest neighbour approach. As both models were too complex to test, the earliest due date constructive heuristic algorithm was developed. To improve the solution, we implemented various algorithms, from multi-start with random ordering to more complex like iterated local search. Results: The proposed models were tested on a real case study where the picking time was reduced by 57% compared to single-order strategy. Conclusions: The results showed that the iterated local search multiple perturbation algorithms could successfully identify the minimum solution and significantly improve the solution initially obtained with the heuristic earliest due date algorithm

A human-machine learning curve for stochastic assembly line balancing problems

The Assembly Line Balancing Problem (ALBP) represents one of the most explored research topics in manufacturing. However, only a few contributions have investigated the effect of the combined abilities of humans and machines in order to reach a balancing solution. It is well-recognized that human beings learn to perform assembly tasks over time, with the effect of reducing the time needed for unitary tasks. This implies a need to re-balance assembly lines periodically, in accordance with the increased level of human experience. However, given an assembly task that is partially performed by automatic equipment, it could be argued that some subtasks are not subject to learning effects. Breaking up assembly tasks into human and automatic subtasks represents the first step towards more sophisticated approaches for ALBP. In this paper, a learning curve is introduced that captures this disaggregation, which is then applied to a stochastic ALBP. Finally, a numerical example is proposed to show how this learning curve affects balancing solutions

The Long-Term Experiment Platform for the Study of Agronomical and Environmental Effects of the Biochar: Methodological Framework

In this communication, a wide overview of historical Long-Term Experimental Platforms (LTEP) regarding changes in soil organic matter is presented for the purpose of networking, data sharing, experience sharing and the coordinated design of experiments in the area of Earth system science. This serves to introduce a specific platform of experiments regarding biochar application to soil (LTEP-BIOCHAR) and its use for agronomic and environmental purposes (e.g., carbon sequestration, soil erosion, soil biodiversity) in real conditions and over a significative timeframe for pedosphere dynamics. The methodological framework, including the goals, geographical scope and eligibility rules of such a new platform, is discussed. Currently, the LTEP-BIOCHAR is the first of its kind, a community-driven resource dedicated to biochar, and displays around 20 long-term experiments from Europe, the Middle East and Africa. The selected field experiments take place under dynamically, meteorologically and biologically different conditions. The purposes of the platform are (1) listing the field experiments that are currently active, (2) uncovering methodological gaps in the current experiments and allowing specific metadata analysis, (3) suggesting the testing of new hypotheses without unnecessary duplications while establishing a minimum standard of analysis and methods to make experiments comparable, (4) creating a network of expert researchers working on the agronomical and environmental effects of biochar, (5) supporting the design of coordinated experiments and (6) promoting the platform at a wider international level

Machine learning for multi-criteria inventory classification applied to intermittent demand

Multi-criteria inventory classification groups inventory items into classes, each of which is managed by a specific re-order policy according to its priority. However, the tasks of inventory classification and control are not carried out jointly if the classification criteria and the classification approach are not robustly established from an inventory-cost perspective. Exhaustive simulations at the single item level of the inventory system would directly solve this issue by searching for the best re-order policy per item, thus achieving the subsequent optimal classification without resorting to any multi-criteria classification method. However, this would be very time-consuming in real settings, where a large number of items need to be managed simultaneously. In this article, a reduction in simulation effort is achieved by extracting from the population of items a sample on which to perform an exhaustive search of best re-order policies per item; the lowest cost classification of in-sample items is, therefore, achieved. Then, in line with the increasing need for ICT tools in the production management of Industry 4.0 systems, supervised classifiers from the machine learning research field (i.e. support vector machines with a Gaussian kernel and deep neural networks) are trained on these in-sample items to learn to classify the out-of-sample items solely based on the values they show on the features (i.e. classification criteria). The inventory system adopted here is suitable for intermittent demands, but it may also suit non-intermittent demands, thus providing great flexibility. The experimental analysis of two large datasets showed an excellent accuracy, which suggests that machine learning classifiers could be implemented in advanced inventory classification systems