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A substantial extent of harmful rubbish produced from manufacturing processes and health segments has posed a significant warning to the health of humans by affecting environmental concerns and the pollution of soil, air, and water resources. In this research, a multi-objective mixed-integer nonlinear programming (MINLP) model is presented for a sustainable hazardous waste location-routing problem. The position of the facilities and decisions on the routes for transferring hazardous waste as well as the waste remainder are considered to design a proper waste collection system. The proposed model tries to minimize the whole costs of the waste management system, the total hazards from the facilities and transportation, together with the CO2 emissions, simultaneously equipped with a real case study to show the applicability of the developed model. In order to show the sustainability importance, the outputs of the original model are compared with the model, not including sustainability. The outcomes illustrate that, under the lack of sustainability, total costs, transportation, and site risk along with the CO2 emissions increase, demonstrating the importance of sustainability. Besides, the extracted managerial insights support managers in making better decisions in the hazardous waste management system.

In this paper, we consider some problems that arise in connected and autonomous vehicle (CAV) systems. Their simplified variants can be formulated as scheduling problems. Therefore, scheduling solution algorithms can be used as a part of solution algorithms for real-world problems. For four variants of such problems, mathematical models and solution algorithms are presented. In particular, three polynomial algorithms and a branch and bound algorithm are developed. These CAV scheduling problems are considered in the literature for the first time. More complicated NP-hard scheduling problems related to CAVs can be considered in the future.

This Special Issue of the journal Mathematics is dedicated to new results on the topic of discrete optimization and scheduling [...]

This is the third edition of a Special Issue of Algorithms; it is of a rather different nature compared to other Special Issues in the journal, which are usually dedicated to a particular subject in the area of algorithms [...]

Heart disease is a global health concern of paramount importance, causing a significant number of fatalities and disabilities. Precise and timely diagnosis of heart disease is pivotal in preventing adverse outcomes and improving patient well-being, thereby creating a growing demand for intelligent approaches to predict heart disease effectively. This paper introduces an ensemble heuristic–metaheuristic feature fusion learning (EHMFFL) algorithm for heart disease diagnosis using tabular data. Within the EHMFFL algorithm, a diverse ensemble learning model is crafted, featuring different feature subsets for each heterogeneous base learner, including support vector machine, K-nearest neighbors, logistic regression, random forest, naive bayes, decision tree, and XGBoost techniques. The primary objective is to identify the most pertinent features for each base learner, leveraging a combined heuristic–metaheuristic approach that integrates the heuristic knowledge of the Pearson correlation coefficient with the metaheuristic-driven grey wolf optimizer. The second objective is to aggregate the decision outcomes of the various base learners through ensemble learning. The performance of the EHMFFL algorithm is rigorously assessed using the Cleveland and Statlog datasets, yielding remarkable results with an accuracy of 91.8% and 88.9%, respectively, surpassing state-of-the-art techniques in heart disease diagnosis. These findings underscore the potential of the EHMFFL algorithm in enhancing diagnostic accuracy for heart disease and providing valuable support to clinicians in making more informed decisions regarding patient care.

In this paper, we consider some problems that arise in connected and autonomous vehicle (CAV) systems. Their simplified variants can be formulated as scheduling problems. Therefore, scheduling solution algorithms can be used as a part of solution algorithms for real-world problems. For four variants of such problems, mathematical models and solution algorithms are presented. In particular, three polynomial algorithms and a branch and bound algorithm are developed. These CAV scheduling problems are considered in the literature for the first time. More complicated NP-hard scheduling problems related to CAVs can be considered in the future. [ABSTRACT FROM AUTHOR]

Scheduling and optimization have a central place in the research area of computing because it is increasingly important to achieve fully automated production processes to adjust manufacturing systems to the requirements of Industry 4.0. In this paper, we demonstrate how an automated wet-etch scheduling problem for the semiconductor industry can be solved by constraint answer set programming (CASP) and its solver called clingcon. A successful solution to this problem is achieved, and we found that for all tested problems, CASP is faster and obtains smaller makespan values for seven of the eight problems tested than the solutions based on mixed integer linear programming and constraint paradigms. The considered scheduling problem includes a robot for lot transfers between baths. CASP is a hybrid approach in automated reasoning that combines different research areas such as answer set programming, constraint processing, and Satisfiability Modulo Theories. For a long time, exact methods such as constraint programming have displayed difficulties in solving real large-scale problem instances. Currently, the performance of state-of-the-art constraint solvers is comparatively better than a decade ago, and some complex combinatorial problems can be better solved. These theoretical and technical achievements open new horizons for declarative programming applications such as logistics. [ABSTRACT FROM AUTHOR]

This document is an editorial for a special issue of the journal AIMS Mathematics on the topic of discrete optimization. The issue includes 21 papers covering a range of subjects, including molecular trees, network systems, variational inequality problems, scheduling, image restoration, spectral clustering, integral equations, convex functions, graph products, optimization algorithms, air quality prediction, humanitarian planning, inertial methods, neural networks, transportation problems, emotion identification, fixed-point problems, structural engineering design, single machine scheduling, and ensemble learning. The papers present new theoretical results, algorithms, and applications in these areas. The guest editor expresses gratitude to the journal staff and reviewers and hopes that readers will find inspiration for their own research. [Extracted from the article]

This document is a special issue of the journal Algorithms, featuring selected papers from the journal's editorial board members from 2022 and 2023. The issue includes 16 research papers covering a range of topics such as game theory, fault detection in cellular networks, optimization algorithms, machine learning, cryptocurrency trading, and more. Each paper presents its own unique research findings and methodologies. The issue aims to showcase the diverse research interests and expertise of the journal's editorial board members. [Extracted from the article]

The use of wood-based panels such as Oriented Strand Board has grown in civil construction. This follows the contemporary trend towards low environmental impact materials. However, there is a lack of relevant information about their life cycle assessment, appearing as a current and relevant research topic. Experimental panels made with Eucalyptus wood and castor oil-based polyurethane adhesive already demonstrated great physical-mechanical performance. Therefore, this study aimed to continue the evaluation of this innovative product, estimating their potential environmental impacts using life cycle assessment from a cradle-to-gate perspective and comparing the results with traditional panels and literature data. System boundaries, environmental impacts and environmental hotspots were identified using the ReCiPe H method in terms of ten impact categories. Comparing experimental (heat-treated) and traditional panels, the experimental versions performed better in most categories and showed safer behavior in categories related to human health in addition to not using paraffin, termiticide, and other organic chemicals presented in the traditional panels. Though made of different types of adhesives, the adhesive was the main environmental hotspot for both types. [ABSTRACT FROM AUTHOR]

Heart disease is a global health concern of paramount importance, causing a significant number of fatalities and disabilities. Precise and timely diagnosis of heart disease is pivotal in preventing adverse outcomes and improving patient well-being, thereby creating a growing demand for intelligent approaches to predict heart disease effectively. This paper introduces an ensemble heuristic–metaheuristic feature fusion learning (EHMFFL) algorithm for heart disease diagnosis using tabular data. Within the EHMFFL algorithm, a diverse ensemble learning model is crafted, featuring different feature subsets for each heterogeneous base learner, including support vector machine, K-nearest neighbors, logistic regression, random forest, naive bayes, decision tree, and XGBoost techniques. The primary objective is to identify the most pertinent features for each base learner, leveraging a combined heuristic–metaheuristic approach that integrates the heuristic knowledge of the Pearson correlation coefficient with the metaheuristic-driven grey wolf optimizer. The second objective is to aggregate the decision outcomes of the various base learners through ensemble learning. The performance of the EHMFFL algorithm is rigorously assessed using the Cleveland and Statlog datasets, yielding remarkable results with an accuracy of 91.8% and 88.9%, respectively, surpassing state-of-the-art techniques in heart disease diagnosis. These findings underscore the potential of the EHMFFL algorithm in enhancing diagnostic accuracy for heart disease and providing valuable support to clinicians in making more informed decisions regarding patient care. [ABSTRACT FROM AUTHOR]

This document is a special issue of the journal Mathematics dedicated to recent advances in discrete optimization and scheduling. The issue includes 10 papers covering a range of topics such as batch loading and scheduling, optimization algorithms, scheduling surgeries, routing problems, and mathematical models for simplification. The papers present various approaches and algorithms for solving these problems, and they have been reviewed by experts in the field. The authors express their gratitude to the contributors, reviewers, and editorial staff, and hope that the readers will find stimulating ideas for further research in this area. [Extracted from the article]