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The paper discusses a German booklet entitled "Das Fürstliche Wurtzgertlein zu Arolsen", authored by Anna, Countess of Waldeck and Duchess of Jülich-Kleve-Berg (1495 - 1567). It contains the main articles of Luther's doctrine, which Countess Anna leaves as a spiritual legacy to her descendants. Published in 1589, twenty-two years after the author's death, its printing was overseen by the German Lutheran theologian Jeremias Homberger (1526 - 1595), who dedicated the work to Bishop Bernhard von Waldeck (1561 - 1591). The two surviving copies have the place of printing Güssing, and the printer Johannes Manlius listed in the impression. Never theless, the Hungarian scholars, Gedeon Borsa and Judit V. Ecsedy, have concurred that this is a false imprint. A third copy was discovered in the University Library in Bratislava, and is identical to the Güssing edition in dating, typeface, and typesetting. Its colophon, however, states Nuremberg as the place of printing, and Nikolaus Knorr as the printer, thus indicating that this is not a reprint. In determining who the actual printer of "Das Fürstliche Wurtzgertlein zu Arolsen" was, we examined the relationships between the publisher Homberger and the printers Manlius and Knorr. We have a proven relationship documented only between Homberger and Manlius. We verified that both printers Knorr and Manlius were experienced in concealing the Place of printing and both used false imprints. Our typographical analysis, based on V. Ecsedy's research, revealed that Manlius use the vignette from this printing only once, specifically in this edition with a false imprint. By contrast, we found the same vignette in four of Knorr's prints from 1586 - 1590. We thus conclude our initial research with the assertion that the sole and true place of printing, "Das Fürstliche Wurtzgertlein zu Arolsen" was Nuremberg and the printer was Nikolaus Knorr. How - ever, the reason why Knorr printed this work simultaneously with both a false and real imprint remains unclear. [ABSTRACT FROM AUTHOR]

The Vehicle Routing Problem belongs to graph optimization and its goal is to find shortest routes visiting a given set of customers with additional constraints present. The article presents the ant colony optimization metaheuristic which solves vehicle routing problems and its real-life application in transport logistics (finding routes for delivery companies). The metaheuristic generated high-quality solutions (superior to compared methods). Our tool is flexible and enables us to solve various variants of routing problems so it is well suited to specific needs of transportation companies. [ABSTRACT FROM AUTHOR]

This study delves into the critical need for generating real-world compatible data to support the application of deep reinforcement learning (DRL) in vehicle routing. Despite the advancements in DRL algorithms, their practical implementation in vehicle routing is hindered by the scarcity of appropriate real-world datasets. Existing methodologies often rely on simplistic distance metrics, failing to accurately capture the complexities inherent in real-world routing scenarios. To address this challenge, we present a novel approach for generating real-world compatible data tailored explicitly for DRL-based vehicle routing models. Our methodology centers on the development of a spatial data extraction and curation tool adept at extracting geocoded locations from diverse urban environments, encompassing both planned and unplanned areas. Leveraging advanced techniques, the tool refines location data, accounting for unique characteristics of urban environments. Furthermore, it integrates specialized distance metrics and location demands to construct vehicle routing graphs that represent real-world conditions. Through comprehensive experimentation on varied real-world testbeds, our approach showcases its efficacy in producing datasets closely aligned with the requirements of DRL-based vehicle routing models. It's worth mentioning that this dataset is structured as a graph containing location, distance, and demand information, with each graph stored independently to facilitate efficient access and manipulation. The findings underscore the adaptability and reliability of our methodology in tackling the intricacies of real-world routing challenges. This research marks a significant stride towards enabling the practical application of DRL techniques in addressing real-world vehicle routing problems. [ABSTRACT FROM AUTHOR]

The vehicle routing problem with time windows (VRPTW) remains a formidable challenge, due to the intricate constraints of vehicle capacity and time windows. As a result, an algorithm tailored for this problem must demonstrate robust search capabilities and profound exploration abilities. Traditional methods often struggle to balance global search capabilities with computational efficiency, thus limiting their practical applicability. To address these limitations, this paper introduces a novel hybrid algorithm known as large neighborhood search with modified rat swarm optimization (LNS-MRSO). Modified rat swarm optimization (MRSO) is inspired by the foraging behavior of rat swarms and simulates the search process for optimization problems. Meanwhile, large neighborhood search (LNS) generates potential new solutions by removing and reinserting operators, incorporating a mechanism to embrace suboptimal solutions and strengthening the algorithm's prowess in global optimization. Initial solutions are greedily generated, and five operators are devised to mimic the position updates of the rat swarm, providing rich population feedback to LNS and further enhancing algorithm performance. To validate the effectiveness of LNS-MRSO, experiments were conducted using the Solomon VRPTW benchmark test set. The results unequivocally demonstrate that LNS-MRSO achieves optimal solutions for all 39 test instances, particularly excelling on the R2 and RC2 datasets with percentage deviations improved by 5.1% and 8.8%, respectively, when compared to the best-known solutions (BKSs). Furthermore, when compared to state-of-the-art algorithms, LNS-MRSO exhibits remarkable advantages in addressing VRPTW problems with high loading capacities and lenient time windows. Additionally, applying LNS-MRSO to an unmanned concrete-mixing station further validates its practical utility and scalability. [ABSTRACT FROM AUTHOR]

The rapid increase of aging population poses a great challenge on the public medical resources. Home health care (HHC), which provides the care services for the elderly and patients with mild disease, is an emerging approach to cope with such difficulty. This work formulates a stochastic HHC scheduling and routing problem with skill requirements to minimize total operation time. A stochastic programming model is given to mathematically define it. Then, a hybrid approach combining the genetic algorithm (GA) and simulation optimization with an approximated allocation (AA) rule is designed. The GA aims to search candidate solutions, while the simulation optimization method focuses on improving the efficiency of evaluating them. Numerical results show that the AA rule is more effective than equal allocation and proportional to variance rules in guiding the GA to find promising solutions. As a consequence, the designed approach acquires the better solutions for the problem under consideration. [ABSTRACT FROM AUTHOR]

The determination of functional residual capacity (FRC) would be extremely helpful for the controlled adjustment of mechanical ventilation in sick neonates and infants. However, these patients have small lung volumes and usually have been intubated by uncuffed endotracheal tubes (ETT). Therefore, the open-circuit nitrogen washout technique (N2wo) may give false FRC values if the inspired oxygen concentration (FIO2) is high and leakage around the ETT is present. We evaluated the N2wo as supplied by the Pediatric Pulmonary System 2600 (Sensor-Medics) in a small-sized lung model by 570 measurements using five different ventilator settings, an FIO2 increasing up to 0.9, different bypass flows between 0 and 12 L/min, and various patterns of leakage, either during inspiration or exhalation, or both. We found the most reliable results (error, 0.6%; CV, 0.7%) with a bypass flow of 6 L/min. Absolute N2 volumes as small as 14 mL could be measured using an FIO2 as high as 0.9 with only slight loss of accuracy (error, 4%; CV, 2.8%). During leakage, FRC had been underestimated with a very strong correlation to the total amount of leakage over the measurement period, which was irrespective of the ventilatory parameters (r = 0.9, P < 0.001). The regression equation could, therefore, be used for FRC correction in the lung model. However, most of the miscalculation was due to N2 loss during expiratory leakage, which quite simply and reliably can be excluded by an end-inspiratory occlusion test.(ABSTRACT TRUNCATED AT 250 WORDS)

Existing process-based models for simulating coastal foredune evolution largely use the same analytical approach for estimating wind induced surface shear stress distributions over spatially variable topography. Originally developed for smooth, low-sloping hills, these analytical models face significant limitations when the topography of interest exhibits large height-to-length ratios and/or steep, localized features. In this work, we utilize computational fluid dynamics (CFD) to examine the error trends of a commonly used analytical shear stress model for a series of idealized two-dimensional dune profiles. It is observed that the prediction error of the analytical model increases as compared to the CFD simulations for increasing height-to-length ratio and localized slope values. Furthermore, we explore two data-driven methodologies for generating alternative shear stress prediction models, namely, symbolic regression and linear, projection-based, non-intrusive reduced order modeling. These alternative modeling strategies demonstrate reduced overall error, but still suffer in their generalizability to broader sets of dune profiles outside of the training data. Finally, the impact of these improvements to aeolian sediment transport fluxes is examined to demonstrate that even modest improvements to the shear stress prediction can have significant impacts to dune evolution simulations over engineering-relevant timescales. [ABSTRACT FROM AUTHOR]

The optimization of job shop scheduling is pivotal for improving overall production efficiency within a workshop. In demand-driven personalized production modes, achieving a balance between workshop resources and the diverse demands of customers presents a challenge in scheduling. Additionally, considering the self-interested behaviors of agents, this study focuses on tackling the problem of multi-agent job shop scheduling with private information. Multiple consumer agents and one job shop agent are considered, all of which are self-interested and have private information. To address this problem, a two-stage decentralized algorithm rooted in the genetic algorithm is developed to achieve a consensus schedule. The algorithm allows agents to evolve independently and concurrently, aiming to satisfy individual requirements. To prevent becoming trapped in a local optimum, the search space is broadened through crossover between agents and agent-based block insertion. Non-dominated sorting and grey relational analysis are applied to generate the final solution with high social welfare. The proposed algorithm is compared using a centralized approach and two state-of-the-art decentralized approaches in computational experiments involving 734 problem instances. The results validate that the proposed algorithm generates non-dominated solutions with strong convergence and uniformity. Moreover, the final solution produced by the developed algorithm outperforms those of the decentralized approaches. These advantages are more pronounced in larger-scale problem instances with more agents. [ABSTRACT FROM AUTHOR]

The impact of projects is vital, from business operations to research to the national economy. Therefore, management science and operation research have extensively studied project scheduling and resource allocation for over six decades. Project databases were proposed to test algorithms, including simulated or real, single or multiprojects, and single-mode or multi-mode projects. However, the dozens of project databases are extremely heterogeneous regarding the file structure and the features of the modeled projects. Furthermore, the efficiency and performance of project scheduling and resource allocation algorithms are susceptible to the characteristics of projects. Therefore, the proposed Compound Matrix-Based Project Database (CMPD) collects and consolidates the most frequently used project databases. The proposed Unified Matrix-Based Project-Planning Model (UMP) sparse matrix-based model enables the addition of new features to existing project structures, such as completion priorities, structural flexibility, and quality parameters, to broaden the scope of considered projects and to take account of flexible approaches, such as agile, extreme, and hybrid projects. [ABSTRACT FROM AUTHOR]

This paper presents a simulation approach to enhance the performance of heuristics for multiproject scheduling. Unlike other heuristics available in the literature that use only one priority criterion for resource allocation, this paper proposes a structured way to sequentially apply more than one priority criterion for this purpose. By means of simulation, different feasible schedules are obtained to; therefore, increase the probability of finding the schedule with the shortest duration. The performance of this simulation approach was validated with the MPSPLib library, one of the most prominent libraries for resource-constrained multiproject scheduling. These results highlight the proposed method as a useful option for addressing limited time and resources in portfolio management. [ABSTRACT FROM AUTHOR]

This paper focuses on the multi-agent parallel machines scheduling problem with consumer agents and resource agents. Within the context, all the agents are self-interested aiming at maximising their profits, and have private information, precluding the use of the centralised scheduling approaches that require complete information of all the consumer agents. Therefore, an iterative combinatorial auction mechanism based on a decentralised decision procedure is proposed to generate a collaborative scheduling scheme without violating information privacy. The developed approach adopts flexible bidding strategies to reduce the conflict in resource allocation, and a hybrid auction termination condition is developed to ensure the convergence of the approach while guaranteeing sufficient competition among agents. Experimental results show the developed approach generates high-quality solutions with a small price of anarchy compared with centralised approaches and outperforms the state-of-the-art decentralised scheduling approach in improving social welfare, especially for problems with a large number of consumer agents. [ABSTRACT FROM AUTHOR]

Dung beetles impressively coordinate their 6 legs to effectively roll large dung balls. They can also roll dung balls varying in the weight on different terrains. The mechanisms underlying how their motor commands are adapted to walk and simultaneously roll balls (multitasking behavior) under different conditions remain unknown. This study unravels the mechanisms of how dung beetles roll dung balls and adapt their leg movements to stably roll balls over different terrains for multitasking robots. A modular neural-based loco-manipulation control inspired by and based on ethological observations of the ball-rolling behavior of dung beetles is synthesized. The proposed neural-based control contains a central pattern generator (CPG) module, a pattern formation network (PFN) module, and a robot orientation control (ROC) module. The integrated control mechanisms can control a dung beetle-like robot (ALPHA) with biomechanical feet to perform adaptive (multitasking) loco-manipulation (walking and ball-rolling) on various terrains (flat and uneven). It can deal with different ball weights (2.0 and 4.6 kg) and ball types (soft and rigid). The control mechanisms can serve as guiding principles for solving sensory-motor coordination for multitasking robots. Furthermore, this study contributes to biological research by enhancing the understanding of sensory-motor coordination for adaptive (multitasking) loco-manipulation behavior in animals., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

In order to realize the efficient collection and low-carbon transport of municipal garbage and accelerate the realize the "dual-carbon" goal for urban transport system, based on the modeling and solving method of vehicle routing problem, the municipal solid waste (MSW) collection and transport routing optimization of an Improved Ant Colony-Shuffled Frog Leaping Algorithm (IAC-SFLA) is proposed. In this study, IAC-SFLA routing Optimization model with the goal of optimization collection distance, average loading rate, number of collections, and average number of stations is constructed. Based on the example data of garbage collection and transport in southern Baohe District, the comparative analysis with single-vehicle models, multiple-vehicle models, and basic ant colony algorithms. The multi-vehicle model of collection and transportation is superior to the single-vehicle model and the improved ant colony algorithm yields a total collection distance that is 19.76 km shorter and an average loading rate that rises by 4.15% from 93.95% to 98.1%. Finally, the improved ant colony algorithm solves for the domestic waste collection and transportation path planning problem in the north district of Baohe. Thus, the effectiveness and application of the proposed algorithm is verified. The research result can provide reference for vehicle routing in the actual collection and transport process, improve collection and transport efficiency, and achieve the goal of energy conservation and emission reduction [ABSTRACT FROM AUTHOR]

The study of permutation patterns has recently seen several surprising results, and the purpose of this mini-workshop was to bring together researchers from across the field to focus on four hot topics related to these recent developments. The topics covered the nature of generating functions that enumerate permutation classes, the structure of permutation classes and the impact this has on their growth rates, and the study of permutons, which lies at the interface of permutation patterns and discrete probability. The workshop offered an opportunity for knowledge exchange, but also time and space to initiate group collaborations on open problems related to these topics. [ABSTRACT FROM AUTHOR]

We propose a vehicle routing problem with time windows (VRPTW) with compatibility-matching constraints and total completion time as the objective function, with applications in home healthcare routing and scheduling. Mixed integer linear programming is provided with total completion time minimization as the objective function. The solution approach has two objectives, total completion time (primary objective) and total distance (secondary objective). A heuristic is proposed comprising three phases: initializing to find an initial feasible routing (inserting the procedure with a modified K-means algorithm), swapping and moving the procedure to find a local optimal routing, and shooting the procedure to move away from the local optimum. Proof of feasibility for the inserting procedure is provided to prevent unnecessary insertions. Phases 2 and 3 will be repeated as needed to ensure solution quality. Solving our model with the proposed heuristic algorithm increases the total distance by 90.00% but reduces the total completion time by 25.86%. To test our model and heuristic, we examined a system with 400 home-healthcare cases in Chiang Mai. The heuristic quickly solved the problem. When total completion time is minimized, some caretakers serve up to twice as many patients as their coworkers; when total distance is minimized, workload discrepancies can increase up to seven-fold. [ABSTRACT FROM AUTHOR]

Crowd-shipping is an innovative delivery model, based on the sharing economy concept. In this framework, delivery operations are carried out by using existing underused resources, i.e., ordinary people who usually travel on the roads with their own vehicles and have empty space to share, in addition to the company's conventional vehicles. We refer to these non-professional couriers as "occasional drivers". Occasional drivers are not company's employees: they are common people who may decide to perform a delivery service during their free time, for a small compensation. Usually, this process is possible thanks to a crowd-shipping platform, which connects the company, the occasional drivers, and the customers. In this paper, we tackle the crowd-shipping model, by developing an approach inspired to variable neighborhood search (VNS) approach, where several machine learning techniques are used to explore the most promising areas of the search space. VNS is a well-known meta-heuristic already used in crowd-shipping applications. In this paper, the learning strategies embedded into the framework have shown to improve the effectiveness of the basic framework. [ABSTRACT FROM AUTHOR]

Cutting and packing problems have been widely studied in the last decades, mainly due to the variety of industrial applications where the problems emerge. This paper presents an overview of the solution approaches that have been proposed for solving two‐dimensional rectangular cutting and packing problems. The main emphasis of this work is on two distinct problems that belong to the cutting and packing problem family. The first problem aims to place onto an object the maximum‐profit subset of items, that is, output maximization, while the second one aims to place all the items using as few identical objects as possible, that is, input minimization. The objective of this paper is not to be exhaustive but to provide a solid grasp on two‐dimensional rectangular cutting and packing problems by describing their most important solution approaches. [ABSTRACT FROM AUTHOR]

We consider a novel scheme for same-day delivery with a strong potential to reduce transportation costs. A delivery company has two distinct fleets for last-mile delivery: trucks with known (i.e., fixed) delivery routes leaving early in the day carrying one or more-day delivery packages, and shuttles leaving later in the day carrying same-day delivery packages. By allowing shuttles to intercept trucks and hand off packages for truck delivery it may be possible to leverage the unfinished portion of truck routes to shorten the delivery routes of the shuttles. We refer to this as the Rendezvous Vehicle Routing Problem. We present a mathematical formulation of the problem, as well as a column generation algorithm that can quickly find optimal solutions for instances with up to 200 nodes. We also develop and demonstrate the effectiveness of a specialized heuristic for use in larger instances with up to 1000 nodes. Our computational study validates the efficacy of truck-shuttle synchronization in this scheme, demonstrating an average savings of 20% across the test instances. [ABSTRACT FROM AUTHOR]

In recent years, integrated production and distribution scheduling (IPDS) has become an important subject in supply chain management. However, IPDS considering distributed manufacturing environments is rarely researched. Moreover, reinforcement learning is seldom combined with metaheuristics to deal with IPDS problems. In this work, an integrated distributed flow shop and distribution scheduling problem is studied, and a mathematical model is provided. Owing to the problem's NP-hard nature, a multi-objective Q-learning-based brain storm optimization is designed to minimize makespan and total weighted earliness and tardiness. In the presented approach, a double-string representation method is utilized, and a dynamic clustering method is developed in the clustering phase. In the generating phase, a global search strategy, a local search strategy, and a simulated annealing strategy are introduced. A Q-learning process is performed to dynamically choose the generation strategy. It consists of four actions defined as the combinations of these strategies, four states described by convergence and uniformity metrics, a reward function, and an improved ε-greedy method. In the selecting phase, a newly defined selection method is adopted. To assess the effectiveness of the proposed approach, a comparison pool consisting of four prevalent metaheuristics and a CPLEX optimizer is applied to conduct numerical experiments and statistical tests. The results suggest that the designed approach outperforms its competitors in acquiring promising solutions when handling the considered problem. [ABSTRACT FROM AUTHOR]

In response to the COVID-19 pandemic, communities utilize unmanned vehicles to minimize person-to-person contact and lower the risk of infection. This paper addresses the critical considerations of these unmanned vehicles' maximum load capacity and service time, formulating them as constraints within a multi-traveling salesman problem (MTSP). We propose a comprehensive optimization approach that combines a genetic simulated annealing algorithm with clustering techniques and an improved Hopfield neural network (IHNN). First, the MTSP is decomposed into multiple independent TSPs using the fuzzy C-means clustering algorithm based on a genetic simulated annealing algorithm (SA-GA-FCM). Subsequently, the HNN is employed to introduce the data transformation technique and dynamic step factor to prepare more suitable inputs for the HNN training process to avoid the energy function from falling into local solutions, and the simulated annealing algorithm is introduced to solve multiple TSP separately. Finally, the effectiveness of the proposed algorithm is verified by small-scale and large-scale instances, and the results clearly demonstrate that each unmanned vehicle can meet the specified constraints and successfully complete all delivery tasks. Furthermore, to gauge the performance of our algorithm, we conducted ten simulation comparisons with other combinatorial optimization and heuristic algorithms. These comparisons indicate that IHNN outperforms the algorithms mentioned above regarding solution quality and efficiency and exhibits robustness against falling into local solutions. As presented in this paper, the solution to the unmanned vehicle traveling salesman problem facilitates contactless material distribution, reducing time and resource wastage while enhancing the efficiency of unmanned vehicle operations, which has profound implications for promoting low-carbon sustainable development, optimizing logistics efficiency, and mitigating the risk of pandemic spread. [ABSTRACT FROM AUTHOR]

Notwithstanding its disruptive potential, which has been the object of considerable debate, Industry4.0 (I4.0) operationalisation still needs significant study. Specifically, scheduling is a key process that should be explored from this perspective. The purpose of this study is to shed light on the issues regarding scheduling that need to be considered in the new I4.0 framework. To achieve this, a two-stage cascade literature review is performed. The review begins with an analysis regarding the opportunities and challenges brought by I4.0 to the scheduling field, outputting a set of critical scheduling areas (CSA) in which development is essential. The second-stage literature review is performed to understand which steps have been taken so far by previous research in the scheduling field to address those challenges. Thus, a first contribution of this work is to provide insight on the influence and expected changes brought by I4.0 to scheduling, while showcasing relevant research. Another contribution is to identify the most promising future lines of research in this field, in which relevant challenges such as holistic scheduling, or increased flexibility requirements are highlighted. Concurrently, CSA such as decentralised decision-making, and human–robot collaboration display large gaps between current practice and the required technological level of development. [ABSTRACT FROM AUTHOR]

Aligning supply chain decisions of separate entities with independent objectives can be considered to be one of the difficulties of supply chain management. This difficulty becomes worse if the supply chains are characterised by an asymmetrical distribution of information. Although considerable research has recently been devoted to supply chain coordination, less attention has been paid to different information asymmetry settings to the mechanisms underlying it. This research attempts to help fill this gap by reviewing and classifying the literature based on supply chain features, applied methodology, coordination mechanisms, and types of information asymmetry. The proposed classification is used to highlight the ongoing issues in the area and identify the direction for future research. [ABSTRACT FROM AUTHOR]

Flexibility is one of the most significant advantages of legged robots in unstructured environments. However, quadruped robots cannot interact with environments to complete some manipulation tasks. One effective way is to load a manipulation arm. In this paper, we exhibit a quadruped locomotion manipulation system (LMS) named HITPhanT. This system comprises a quadruped locomotion platform and a six-degree-of-freedom manipulation arm. Besides, when the LMS moves to a designated position for operation, it is necessary to constrain the foot contact points to avoid sliding. Therefore, the foot contact point is regarded as a spherical hinge. So the locomotion platform can be considered as a parallel mechanism. A hybrid kinematics model is established by considering the serial robotic arms connecting this parallel mechanism. Besides, the trajectory planning method, which improves the system's manipulability in evaluating the system balance, is also proposed. Finally, corresponding experiments verify the overall system's stabilization and algorithm's effectiveness. [ABSTRACT FROM AUTHOR]

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This paper deals with the school timetabling problem. The problem was formulated as encountered in a typical Greek high school. A local version of the particle swarm optimization algorithm was developed and applied to the problem at hand. Results on well-established benchmark instances showed that the proposed algorithm achieved the proven optima provided from an integer programming method presented in earlier research. In almost all cases, the current algorithm beat the integer programming method, either concerning the lower bound yielded or the execution time needed. [ABSTRACT FROM AUTHOR]

Natural disasters usually occur unexpectedly, causing loss of life and property. It is essential to quickly and effectively distribute aid materials to minimize the damage in the aftermath of a disaster. Aid organizations require decision-making mechanisms that provide hard data to make quick and accurate decisions during the distribution of aid materials. In this study, the delivery of aid materials to the victims of disasters is investigated as a vehicle routing problem. For this purpose, a new method is developed by integrating the interval type-2 fuzzy TOPSIS method with the Clarke and Wright savings algorithm. In this way, while determining the routes, different criteria specific to the problem could also be analyzed with the distance criterion. The proposed method is presented with a numerical example to show how it can be implemented in the humanitarian aid distribution problem. As a result of the numerical example, it is determined that the proposed method completed the delivery with 826 distance units in four rounds, and the classical Clarke and Wright savings algorithm completed the delivery at 820 distance units in four rounds. Although the proposed method provides a longer distance solution than the classical Clarke and Wright savings algorithm, it has the advantage of determining safer routes by taking into account the different risks that may arise during a disaster. Finally, well-known benchmark problems are solved using the proposed method. [ABSTRACT FROM AUTHOR]

With the continuous development of urban distribution services, customers have increasingly strict requirements for the delivery time windows. Therefore it is necessary to study the vehicle routing problem with simultaneous pickup-delivery and time windows (VRPSPDTW) in urban distribution. However, as one of the most important classification of the much anticipated reverse logistics, the VRPSPDTW problem has not received much attention, and lacks an efficient and simple implementation. Our method combines the enhanced Late Acceptance Hill Climbing algorithm (enhanced LAHC) to ensure the diversity of solutions and the Multi-armed Bandit Algorithm (MAB) to choose a neighborhood structure which has the best performance. In order to enhance its search ability at a later stage, a perturbation strategy is designed to effectively prevent premature convergence. To our best knowledge, the proposed h_LAHC (hybrid LAHC) algorithm is applied to the VRPSPDTW for the first time. We provide abundant experiments were conducted on 93 benchmark instances, and the results demonstrated that our algorithm can achieve better, totally equal or approximately equal results in 97.85%, 56.99% and 73.12% instances compared with the latest three mainstream algorithms respectively. In particular, the proposed algorithm has a prominent performance in scenarios with relatively narrow time windows. Moreover, we conduct an empirical analysis on critical components of the algorithm to highlight their impact on the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The world is currently undergoing water scarcity problems, even if it seems to be the most abundant resource on earth. However, the freshwater account is really in small amounts, while agriculture production consumes 70% of the majority of water withdrawals than any other source. Therefore, in order to preserve it, the irrigation operation has to be optimized by controlling efficiently the water used for irrigation. For that purpose, several technologies can be applied, such as the internet of things (IoT) technology which can perform as decision support in the irrigation process. The precise irrigation systems based on IoT involve several intricacies such as huge amounts of data and integration of large system components, which makes it difficult to be optimized analytically or with deterministic methods. For this reason, it was necessary to develop stochastic multi-objective optimization methods such as the evolutionary algorithms (EAs), which can solve complicated problems with a large number of parameters in relation. The EAs may be of relevant use except that they introduce processing time constraints. In this article, we aim at making a state of the art about the use of EAs combined with IoT and applied to precise irrigation. We will focus particularly on their uses classifications as well as the manner in which they have been implemented to reduce their computing times in distributed computing architectures, particularly those using the cloud, as well as in hardware accelerators forms. [ABSTRACT FROM AUTHOR]

Wildfires are a natural phenomenon that regularly occurs in many terrestrial ecosystems. Due to global warming, the rate and the span of wildfires have remarkably increased during the last years, causing important economic losses and human casualties. Several initiatives have been undertaken in the last years in order to apply operations research tools to help firefighting teams schedule and optimize their protection activities when dealing with wildfires. In this context, a recent variant of the Team Orienteering Problem, referred to as the Asset Protection Problem, was proposed in van der Merwe et al. (2015). In this problem, firefighting teams provide a protective service to a set of assets endangered by wildfires. These activities can be performed by a heterogeneous fleet of vehicles and occur within specific time intervals estimated on the basis of fire fronts progression. This problem incorporates three additional constraints: time windows, synchronized visits, and compatibility constraints between vehicles and assets. In this paper, we propose a hybrid approach that combines a Greedy Randomized Adaptive Search Procedure coupled with an Iterated Local Search (GRASP×ILS) and a post-optimization phase based on a set covering formulation. Interestingly, GRASP×ILS incorporates an adaptive candidate list-based insertion heuristic and a Variable Neighborhood Descent search procedure. Detailed computational tests were carried out on benchmark instances from the literature. The results show that our method outperforms the other methods in the literature, since it improves all the best-known solutions on medium- and large-size instances, while maintaining shorter computational times. [ABSTRACT FROM AUTHOR]

Emissivity as a function of wavelength, direction, and temperature correlates to a material's efficiency in radiating thermal energy. Knowledge of emissivity is essential for designing and developing radiation‐cooled thermal protective systems for hypersonic applications. It is desirable to achieve a high emissivity (with a value close to 1) to maximize heat radiation from a hot surface of a hypersonic vehicle's leading edge during atmospheric re‐entry. With the goal of providing the hypersonic materials community with this necessary knowledge, this article offers a basic understanding of thermal radiation, methods for measuring emissivity at high temperatures, and a comprehensive overview of the emissivity of ultra‐high temperature ceramics. [ABSTRACT FROM AUTHOR]

In this note, we study the mean length of the longest increasing subsequence of a uniformly sampled involution that avoids the pattern 3412 and another pattern. [ABSTRACT FROM AUTHOR]

This study considers the transfer of shared resources among multiple geographically dispersed projects. To formulate this problem, we establish a two-stage decision-making model including the local decision-making stage and the global coordination decision-making stage and develop a two-stage approach (TA) to solve this model. In the local decision-making stage, each project agent (PA) uses a beetle antenna search algorithm (BASA) to generate an initial local schedule to minimize the completion time of each individual project. In the global coordination decision-making stage, a sealed bid auction-based approach with minimizing idle times scheme is developed to transfer the shared resources and to minimize the average delay of multiple projects. The performance of the proposed method is tested on a standard set of 140 problem instances. Computational experiments show that, compared with the branch and bound algorithm and two meta-heuristic algorithms, BASA can obtain high-quality solutions in all project instances. Compared to the existing algorithm for solving the decentralized multiproject scheduling problem with resource transfers, our proposed TA method can obtain lower average project delays and total project makespans on most problem subsets. These new, best results can be used as a benchmark for other methods for solving the same problem. [ABSTRACT FROM AUTHOR]

Road freight transport is one of the sectors with the highest greenhouse gas emissions and fuel consumption in the logistics industry. In recent years, due to the increase in carbon dioxide emissions, several companies have considered reducing them in their daily logistics operations by means of better routing management. Green vehicle routing problems (GVRPs) constitute a growing problem direction within the interplay of vehicle routing problems and environmental sustainability that aims to provide effective routes while considering environmental concerns. These NP-hard problems are one of the most studied ones in green logistics, and due to their difficulty, there are many different heuristic and hybrid techniques to solve them under the need of having high-quality solutions within reasonable computational time. Given the role and importance of these methods, this review aims at providing a comprehensive overview of them while reviewing their defining strategies and components. In addition, we analyze characteristics and problem components related to how emissions are being considered. Lastly, we map and analyze the benchmarks proposed so far for the different GVRP variants considering emissions. [ABSTRACT FROM AUTHOR]

A current challenge in 3D bioprinting of skin equivalents is to recreate the distinct basal and suprabasal layers and promote their direct interactions. Such a structural arrangement is essential to establish 3D stratified epidermis disease models, such as for the autoimmune skin disease pemphigus vulgaris (PV), which targets the cell–cell junctions at the interface of the basal and suprabasal layers. Inspired by epithelial regeneration in wound healing, a method that combines 3D bioprinting and spatially guided self‐reorganization of keratinocytes is developed to recapture the fine structural hierarchy that lies in the deep layers of the epidermis. Herein, keratinocyte‐laden fibrin hydrogels are bioprinted to create geographical cues, guiding dynamic self‐reorganization of cells through collective migration, keratinocyte differentiation, and vertical expansion. This process results in a region of self‐organized multilayers (SOMs) that contain the basal‐to‐suprabasal transition, marked by the expressed levels of different types of keratins that indicate differentiation. Finally, the reconstructed skin tissue as an in vitro platform to study the pathogenic effects of PV is demonstrated, illuminating a significant difference in cell–cell junction dissociation induced by PV antibodies in different epidermis layers, which indicates their applications in the preclinical test of possible therapies. [ABSTRACT FROM AUTHOR]

Taxi crowdsourcing has gained great interest from the logistics industry and academe due to its significant economic and environmental impact. However, existing approaches have several limitations and focus solely on single objective optimization problem. In this paper, we propose a three-stage framework, namely MOOP4PD to improve the existing approaches. Firstly, we propose a DesCloser* pruning algorithm with no limitation on taxi capacity and use A* algorithm to further optimize the delivery routes. Then, a novel multi-objective pruning algorithm, named MDesCloser*, is presented to find the non-dominated set, which contains waiting time window MaxWT and taxi capacity MaxC constraints. Finally, we develop a constraint solving approach to obtain the ideal solution (i.e., MaxWT equals 11 and MaxC equals 6). We evaluate the performance using the data set generated by Brinkhoff road network generator in the city of Luoyang, China. Results show that our approach improve the objectives of success rate, average number of participating taxis, average delivery distance and average delivery time. Especially, MDesCloser* have best performance on the success rate with more than 0.88 and minimize the total waiting time of all packages to 14916.6 time slices if failure in delivering and maximize the average transshipping rate of interchange stations. [ABSTRACT FROM AUTHOR]

Sustainable development is embraced by many groups for economic growth, environmental preservation, and social equality. This work presents a Tabu Search approach for minimising Sustainable-cost in the Single Depot Vehicle Routing Problem with Time Windows (VRPTW) with a heterogeneous fleet of vehicles. Tabu Search was given five greedy heuristic algorithms such as SAVING, SWEEP, NNH-1, NNH-2, and CIH, a random algorithm, and its sustainable variations as initial input. The algorithms were tested on the benchmark datasets of Solomon, Gehring, and Homberger, as well as a randomly generated dataset. Various computational analyses performed using Relative Percentage Deviation and Kruskal Wallis-H test indicates that the Tabu search algorithm tries to minimize the sustainable cost obtained from these initial solutions. TS-S-SAVING relatively and consistently outperforms in terms of the different performance measures considered in this study by attaining an efficiency of 96.55% for the benchmark dataset and 100% for the instance generated datasets. [ABSTRACT FROM AUTHOR]

Home health services arise from the need for hospitals to care for patients and/or dependent persons who, due to special conditions, require hospitalisation and/or care at home. The organisation of this service impacts the quality and cost of health services, which implies the programming of medical and social staff and the design of their daily routes. This literature review presents a description of the problem and a taxonomy of its characteristics and restrictions. It summarises the state-of-the-art decision-making solutions to deal with the home health care routing and scheduling problem and studies related objectives and constraints. [ABSTRACT FROM AUTHOR]