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This paper studies a variant of the Pollution Traveling Salesman Problem (PTSP) focused on fuel consumption and pollution emissions (PTSPC). The PTSPC generalizes the well-known Traveling Salesman Problem (TSP), classified as NP-Hard. In the PTSPC, a vehicle must deliver a load to each customer through a Hamiltonian cycle, minimizing an objective function that considers the speed of each edge, the mass of the truck, the mass of the load pending delivery, and the distance traveled. We have proposed a three-phase algorithm for the PTSPC. The first phase solves the Traveling Salesman Problem (TSP) exactly with a time limit and heuristically using a Nearest Neighborhood Search approach. This phase considers the constraints associated with the PTSPC by using commercial software. In the second phase, both the obtained solutions and their inverse sequences from the initial phase undergo enhancement utilizing metaheuristic algorithms tailored for the PTSPC. These algorithms include Variable Neighborhood Search (VNS), Tabu Search (TS), and Simulated Annealing (SA). Subsequently, for the third phase, the best solution identified in the second phase-determined by having the minimum value by the PTSPC objective function-is subjected to resolution by a mathematical model designed for the PTSPC, considering the heuristic emphasis of commercial software. The efficiency of the former algorithm has been validated through experimentation involving the adaptation of instances from the Pollution Routing Problem (PRP) to the PTSPC. This approach demonstrates the capacity to yield high-quality solutions within acceptable computing times.

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While foraging, animals have to find potential food sites, remember these sites, and plan the best navigation route. To deal with problems associated with foraging for multiple and patchy resources, primates may employ heuristic strategies to improve foraging success. Until now, no study has attempted to investigate experimentally the use of such strategies by a primate in a context involving foraging in large-scale space. Thus, we carried out an experimental field study that aimed to test if wild common marmosets (Callithrix jacchus) employ heuristic strategies to efficiently navigate through multiple feeding sites distributed in a large-scale space. In our experiment, we arranged four feeding platforms in a trapezoid configuration with up to 60 possible routes and observe marmosets' decisions under two experimental conditions. In experimental condition I, all platforms contained the same amount of food; in experimental condition II, the platforms had different amounts of food. According to the number and arrangement of the platforms, we tested two heuristic strategies: the Nearest Neighbor Rule and the Gravity Rule. Our results revealed that wild common marmosets prefer to use routes consistent with a heuristic strategy more than expected by chance, regardless of food distribution. The findings also demonstrate that common marmosets seem to integrate different factors such as distance and quantity of food across multiple sites distributed over a large-scale space, employing a combination of heuristic strategies to select the most efficient routes available. In summary, our findings confirm our expectations and provide important insights into the spatial cognition of these small neotropical primates.

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Introducción: La formación general de los profesionales, y para el nuevo paradigma social de la medicina, demanda la participación activa de los estudiantes en el aprendizaje de la filosofía marxista. Esta actividad les resulta difícil y poco significativa una vez que se inician en el nivel superior. La conversación heurística contribuye a la enseñanza de la filosofía marxista. Objetivo: Diseñar procedimientos didácticos que condicionan el desarrollo de la conversación heurística en la enseñanza aprendizaje de leyes y principios de la dialéctica materialista. Desarrollo: La psicología marxista de enfoque histórico cultural y la didáctica de la educación superior contemporánea fundamentan el aprendizaje desarrollador con la conversación heurística. Este tipo de conversación determina el aumento de capacidades para abordar el nivel superior en los estudiantes de primer año. La estrategia didáctica promueve procedimientos para que estos estudiantes descubran el significado de las leyes y los principios, y para que los momentos regresivos en el desarrollo social y su diferencia con el desarrollo biológico revelen las manifestaciones en el pensamiento y el diagnóstico médico hermenéutico. Conclusiones: Los procedimientos diseñados muestran la conversación heurística en la práctica pedagógica para un aprendizaje significativo de leyes y principios, la cual favorece el desarrollo de las capacidades para afrontar el nivel superior, e implica la apropiación de un método científico para la interpretación de la realidad y el fundamento de la práctica médica en su paradigma social(AU)

Introduction: The general training of professionals, and for the new social paradigm of medicine, demands the active participation of students in learning Marxist philosophy. This activity is difficult and not very meaningful to them once they start at the higher level. Heuristic conversation contributes to the teaching of Marxist philosophy. Objective: To design didactic procedures that condition the development of heuristic conversation in the teaching-learning of laws and principles of materialist dialectics. Development: Marxist psychology of a cultural-historical approach and contemporary higher education didactics make up the foundations of developmental learning through heuristic conversation. This type of conversation determines the increase of skills to approach the higher level in first-academic-year students. The didactic strategy promotes procedures for these students to discover the meaning of laws and principles, as well as for regressive moments in social development and their difference with respect to biological development to reveal manifestations in hermeneutic medical thinking and diagnosis. Conclusions: The designed procedures show heuristic conversation in the pedagogical practice for a meaningful learning of laws and principles, favoring the development of skills to face the higher level, and implies the appropriation of a scientific method for the interpretation of reality and the foundation of medical practice in its social paradigm(AU)

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Cutting problems consist of cutting a set of objects available in stock in order to produce the desired items in specified quantities and sizes. The cutting process can generate leftovers (which can be reused in the case of new demand) or losses (which are discarded). This paper presents a tree-based heuristic method for minimizing the number of cut bars in the one-dimensional cutting process, satisfying the item demand in an unlimited bar quantity of just one type. The results of simulations are compared with the RGRL1 algorithm and with the limiting values for this considered type of problem. The results show that the proposed heuristic reduces processing time and the number of bars needed in the cutting process, while it provides a larger leftover (by grouping losses) for the one-dimensional cutting stock problem. The heuristic contributes to reduction in raw materials or manufacturing costs in industrial processes.

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LoRaWAN is a long range and low power protocol devised for connecting devices under the Internet of Things (IoT) paradigm. This protocol was not conceived to support real-time message delivery; therefore, it is not always feasible using it to support IoT solutions involving large wireless sensors networks and time constraint messaging, e.g., in early warning systems for natural hazards, remote monitoring of industrial machinery or autonomous control of transportation systems. This paper presents a model that provides certainty, at the design time of IoT systems, about the real-time communication capability of their supporting network. It allows solution designers: (1) to decide if developing or not a real-time IoT solution based on the feasibility of its communication infrastructure, and (2) to improve the communication infrastructure to try making real-time communication feasible using LoRaWAN.

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Biorefinery feasibility is highly influenced by the early design of the best feedstock transformation pathway to obtain value-added products. Pretreatment has been identified as the critical stage in biorefinery design since proper pretreatment influences subsequent reaction, separation, and purification processes. However, many pretreatment analyses have focused on preserving and valorizing six-carbon sugars for future use in bioconversion processes, leaving aside fractions such as hemicellulose and lignin. To date, there has been no pretreatment systematization for the removal of lignocellulosic fractions. This work defines pretreatment efficacy through operational, economic, environmental, and social indicators. Thus, using the data reported in the literature, as well as the results of the simulation schemes, a multi-criteria weighting of the best-performing schemes for the isolation or removal of cellulose, hemicellulose, and lignin was carried out. As a main result, it was concluded that dilute acid is the most effective for cellulose isolation and hemicellulose removal for producing platform products based on six- and five-carbon sugars, respectively. Additionally, the kraft process is the best methodology for lignin removal and its future use in biorefineries. The results of this work help to elucidate a methodological systematization of the pretreatment efficacy in the design of biorefineries as an early feasibility stage considering sustainability aspects.

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The one-dimensional cutting-stock problem (1D-CSP) consists of obtaining a set of items of different lengths from stocks of one or different lengths, where the minimization of waste is one of the main objectives to be achieved. This problem arises in several industries like wood, glass, and paper, among others similar. Different approaches have been designed to deal with this problem ranging from exact algorithms to hybrid methods of heuristics or metaheuristics. The African Buffalo Optimization (ABO) algorithm is used in this work to address the 1D-CSP. This algorithm has been recently introduced to solve combinatorial problems such as travel salesman and bin packing problems. A procedure was designed to improve the search by taking advantage of the location of the buffaloes just before it is needed to restart the herd, with the aim of not to losing the advance reached in the search. Different instances from the literature were used to test the algorithm. The results show that the developed method is competitive in waste minimization against other heuristics, metaheuristics, and hybrid approaches.

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Video tracking involves detecting previously designated objects of interest within a sequence of image frames. It can be applied in robotics, unmanned vehicles, and automation, among other fields of interest. Video tracking is still regarded as an open problem due to a number of obstacles that still need to be overcome, including the need for high precision and real-time results, as well as portability and low-power demands. This work presents the design, implementation and assessment of a low-power embedded system based on an SoC-FPGA platform and the honeybee search algorithm (HSA) for real-time video tracking. HSA is a meta-heuristic that combines evolutionary computing and swarm intelligence techniques. Our findings demonstrated that the combination of SoC-FPGA and HSA reduced the consumption of computational resources, allowing real-time multiprocessing without a reduction in precision, and with the advantage of lower power consumption, which enabled portability. A starker difference was observed when measuring the power consumption. The proposed SoC-FPGA system consumed about 5 Watts, whereas the CPU-GPU system required more than 200 Watts. A general recommendation obtained from this research is to use SoC-FPGA over CPU-GPU to work with meta-heuristics in computer vision applications when an embedded solution is required.

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In the last decades, cities have increased the number of activities and services that depends on an efficient and reliable electricity service. In particular, households have had a sustained increase of electricity consumption to perform many residential activities. Thus, providing efficient methods to enhance the decision making processes in demand-side management is crucial for achieving a more sustainable usage of the available resources. In this line of work, this article presents an optimization model to schedule deferrable appliances in households, which simultaneously optimize two conflicting objectives: the minimization of the cost of electricity bill and the maximization of users satisfaction with the consumed energy. Since users satisfaction is based on human preferences, it is subjected to a great variability and, thus, stochastic resolution methods have to be applied to solve the proposed model. In turn, a maximum allowable power consumption value is included as constraint, to account for the maximum power contracted for each household or building. Two different algorithms are proposed: a simulation-optimization approach and a greedy heuristic. Both methods are evaluated over problem instances based on real-world data, accounting for different household types. The obtained results show the competitiveness of the proposed approach, which are able to compute different compromising solutions accounting for the trade-off between these two conflicting optimization criteria in reasonable computing times. The simulation-optimization obtains better solutions, outperforming and dominating the greedy heuristic in all considered scenarios.

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Esta pesquisa teve o objetivo de identificar se há falta de difusão do conhecimento e possíveis vieses cognitivos que estejam sendo limitadores para que o que se conhece em ciência sobre a Remoção Seletiva do Tecido Cariado (RSTC) não seja transmitido para a prática clínica. Ela foi composta por duas séries de perguntas de elaboração própria, online (via 'Formulários Google'). A série de perguntas 1 foi aplicada a cirurgiões-dentistas e estudantes do último ano de Odontologia, e buscou investigar seus conhecimentos sobre a técnica de Remoção Seletiva, se eram a favor e estavam usando na prática clínica, e possíveis fatores relacionados à tomada de decisão sobre qual técnica utilizar. A série de perguntas 2 foi aplicada a coordenadores e/ou colaboradores de disciplinas relacionadas a cariologia, dentística ou materiais dentários; esta buscou investigar se os respondentes eram a favor e estavam usando a técnica, pesquisou possíveis fatores relacionados à tomada de decisão sobre qual técnica utilizar, e se estavam transmitindo conhecimentos sobre a mesma durante suas aulas. Para o recrutamento de voluntários, foram usadas estratégias de comunicação através de Entidades de Classe de Odontologia, de Secretarias Municipais de Saúde, de Universidades públicas e privadas, de redes sociais e por meio de comunicação presencial. Ao final da coleta dos dados, estes foram descritos por porcentagem de frequência e analisados por testes de associação (5%). De um total de 568 dentistas, 319 afirmaram sentirem-se inseguros quanto a seus conhecimentos sobre a RSTC. Dos 568, 406 erraram sobre quanto deve-se remover de tecido cariado em paredes pulpares de cavidades muito profundas, enquanto 410 acertaram o principal critério clínico para decidir até onde remover. Dos 568, 89 afirmaram que não usavam a RSTC. Destes 89, 54 não estudaram a técnica, 59 eram de especialidades não relacionadas à cariologia e apenas 7 eram especialistas em áreas relacionadas. 07 dos 53 professores afirmaram desacreditar na RSTC como melhor técnica. Concluiuse que há falta de difusão do conhecimento a respeito da RSTC e que há vieses cognitivos relacionados, os quais diminuem a adesão de cirurgiões-dentistas à RSTC (AU)

This research aimed to identify if there is diffusion failure of the knowledge and possible cognitive biases that are been limiters to what is known in science about Selective Removal of Carious Tissue (SRCT) is not being transmitted to clinical practice. It was based on two question series elaborated by the authors, online (by 'Google Forms'). Question series 1 was applied to dentists and Dentistry students of last year of graduation, and aimed to search their knowledges about SRCT, if They were in favor of and were using the technique on their clinical practice, and possible factors related to their decision-making of what technique to use. Question series 2 was Applied to coordinators and/or collaborators of disciplines related to cariology, dentistry or dental materials; it tried to investigate if the respondents were in favor of and if They were using the technique, searched possible factors related to decisionmaking of what technique to use, and if They were transmitting the knowledges about SRCT in their classes. To voluntary recruitment, communication strategies were used through Entities of Dentistry Class, Municipal Health Departments, public and private Universities, social media and face communication. At the end of data collection, date was described by frequency percentage and analyzed by association tests (5%). From 568 dentists, 319 said They feel insecure about their understanding regarding SRCT. Of these 568, 406 were wrong about how many the carious tissue must be removed from Pulp walls of very deep cavities, while 410 were right about the main clinical parameter to decide how far to remove the carious tissue. Of the same 568, 89 said They did not use the SRCT. From these 89, 54 did not study the technique, 59 were specialized in areas not related to cariology and only 7 dentists were specialized in cariology areas. 07 of 53 professors said they not believe SRCT as the best approach. It was concluded there is failure on SRCT knowledge and there are cognitive biases related, which decrease the dentist adherence to SRCT (AU)

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This work is focused on the performance analysis and optimal routing of wireless technology for intelligent energy metering, considering the inclusion of micro grids. For the study, a geo-referenced scenario has been taken into account, which will form the structure of a graph to be solved using heuristic-based algorithms. In the first instance, the candidate site of the world geography to perform the case study is established, followed by deploying infrastructure devices and determining variables and parameters. Then, the model configuration is programmed, taking into account that a set of nodes and vertices is established for proper routing, resulting in a preliminary wireless network topology. Finally, from a set of restrictions, a determination of users connected to the concentrator and optimal routing is performed. This procedure is treated as a coverage set problem. Consequently, to establish the network parameters, two restrictions are specifically considered, capacity and range; thus, can be determined the best technology to adapt to the location. Finally, a verification of the resulting network topologies and the performance of the infrastructure is done by simulating the wireless network. With the model created, scenarios are tested, and it is verified that the optimization model demonstrates its effectiveness.

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Healthcare service centers must be sited in strategic locations that meet the immediate needs of patients. The current situation due to the COVID-19 pandemic makes this problem particularly relevant. Assume that each center corresponds to an assigned place for vaccination and that each center uses one or more vaccine brands/laboratories. Then, each patient could choose a center instead of another, because she/he may prefer the vaccine from a more reliable laboratory. This defines an order of preference that might depend on each patient who may not want to be vaccinated in a center where there are only her/his non-preferred vaccine brands. In countries where the vaccination process is considered successful, the order assigned by each patient to the vaccination centers is defined by incentives that local governments give to their population. These same incentives for foreign citizens are seen as a strategic decision to generate income from tourism. The simple plant/center location problem (SPLP) is a combinatorial approach that has been extensively studied. However, a less-known natural extension of it with order (SPLPO) has not been explored in the same depth. In this case, the size of the instances that can be solved is limited. The SPLPO considers an order of preference that patients have over a set of facilities to meet their demands. This order adds a new set of constraints in its formulation that increases the complexity of the problem to obtain an optimal solution. In this paper, we propose a new two-stage stochastic formulation for the SPLPO (2S-SPLPO) that mimics the mentioned pandemic situation, where the order of preference is treated as a random vector. We carry out computational experiments on simulated 2S-SPLPO instances to evaluate the performance of the new proposal. We apply an algorithm based on Lagrangian relaxation that has been shown to be efficient for large instances of the SPLPO. A potential application of this new algorithm to COVID-19 vaccination is discussed and explored based on sensor-related data. Two further algorithms are proposed to store the patient's records in a data warehouse and generate 2S-SPLPO instances using sensors.

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Considering that there are many alternatives in the literature for composing groups in collaborative learning contexts, we present a proposal that exhibits several features. First, and from the operational point of view, our proposal is highly flexible because i) it allows for several group sizes and an arbitrary array of grouping attributes, and ii) it may be easily adapted to consider several homogeneity/heterogeneity criteria. Second, and from the algorithmic point of view, it combines the best of two apparently opposite worlds: it uses a local brute-force search within an iterative process guided by a randomized heuristic criterion. Thus, this approach is still Non-Polynomic (NP) but in terms of the size of the groups, whereas is Polynomic (P) in terms of the number of students. Third, the experiments with several datasets, with student numbers varying from 20 to 3500, demonstrate reasonable performance and running times for this approach. We contrasted these times with those reported in 19 related works and, first taking into account certain considerations, we found that ours were lower in most cases. Nevertheless, and as the fourth feature, we make available both the datasets and the source code to allow for more objective comparisons of approaches, including our own.

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Resumen Este artículo presenta los resultados de una evaluación heurística de usabilidad aplicada a 17 sitios web de bibliotecas de universidades públicas chilenas, a través del método SIRIUS, "Sistema de Evaluación de la Usabilidad Web Orientado al Usuario y Basado en la Determinación de Tareas Críticas". Para la recolección de datos se utilizó la pauta fija de SIRIUS que contiene 10 heurísticas subdivididas en un total de 83 sub-heurísticas evaluadas por cinco personas expertas con conocimiento en sitios web de bibliotecas y usabilidad. Los resultados indican que las bibliotecas universitarias mejor evaluadas presentan buenos mecanismos de rotulado, layout (diseño) y facilidad de interacción, mientras que, en su conjunto, los sitios web carecen de ayudas de navegación para las personas usuarias. Finalmente, se concluye que la evaluación heurística es un método válido, rápido, fácil y aplicable en sitios web de bibliotecas universitarias, siempre y cuando se definan adecuadamente las personas expertas y las heurísticas. Asimismo, SIRIUS resulta una herramienta adecuada al contar con una pauta fija que permite obtener evaluaciones con rapidez y facilitar la comparación de sus resultados.

Abstract This article presents the results of a heuristic usability evaluation applied to 17 websites of libraries of public chilean universities, using the SIRIUS method "System of Web Usability Evaluation, User Oriented and Based on the Determination of Critical Tasks" For data collection, SIRIUS fixed guideline was used, which contains 10 heuristics that are subdivided into a total of 83 sub-heuristics evaluated by 5 experts with knowledge about libraries and usability websites. The results indicate that the best evaluated university libraries have good labeling mechanisms, layout and ease of interaction, while, as a whole, the websites lack navigation aids for their users. Finally, it is concluded that heuristic evaluation is a valid, fast, easy and applicable method in university library websites, as long as experts and heuristics are properly defined. Likewise, SIRIUS is an adequate tool since it has a fixed guideline that allows evaluations to be obtained quickly and to facilitate the comparison of its results.

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Given humans' limited ability to recall past experiences for evaluation, scholars have proposed the peak-end rule stating that if perceived discomfort at the end of an aversive experience is lower than the peak discomfort experienced, the aversive experience will be remembered more positively. The purpose of this study was to evaluate the peak-end rule as applied to high-intensity interval exercise (HIIE). Participants were 30 inactive men (M age = 27.9, SD = 5.2 years). In the first session they performed a graded exercise test on cycle-ergometer to determine their maximal aerobic power (MAP) (M = 233, SD = 35W); and, in the second and third sessions, they performed two HIIE protocols in randomized order: (a) Short trial - 20-minutes of HIIE, composed of 30-second efforts at 100% of MAP interspersed by 30-seconds of passive recovery; and (b) Long trial - 20-minutes of the short trial, plus 10-minutes more of HIIE, decreasing 3% of MAP in each additional bout, resulting in 70% of MAP in the last bout. During exercise, we recorded the participants' rating of perceived exertion (RPE) and affect, using the Feeling Scale (FS). At 30-minutes post-exercise, we again recorded the participants' affect, using the Global Affect Evaluation (GAE) and their session-RPE, and we recorded their enjoyment, using the Physical Activity Enjoyment Scale (PACES). In the last session, the participants chose a favorite protocol to repeat. All sessions were interspersed by at least 72 hours. The 10-minutes extra HIIE in the Long-trial condition resulted decreased heart rate values (M = 157, SD = 13bpm to M = 144, SD = 14bpm; p < 0.001), but psychological responses during and after exercise did not differ, nor did participants' preferred HIIE protocol. As the load drop for the Long-trial was not enough to change the psychological responses during exercise, there was no difference in the retrospective evaluation as the peak-end rule would have suggested.

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This work proposes a new approach to improve swarm intelligence algorithms for dynamic optimization problems by promoting a balance between the transfer of knowledge and the diversity of particles. The proposed method was designed to be applied to the problem of video tracking targets in environments with almost constant lighting. This approach also delimits the solution space for a more efficient search. A robust version to outliers of the double exponential smoothing (DES) model is used to predict the target position in the frame delimiting the solution space in a more promising region for target tracking. To assess the quality of the proposed approach, an appropriate tracker for a discrete solution space was implemented using the meta-heuristic Shuffled Frog Leaping Algorithm (SFLA) adapted to dynamic optimization problems, named the Dynamic Shuffled Frog Leaping Algorithm (DSFLA). The DSFLA was compared with other classic and current trackers whose algorithms are based on swarm intelligence. The trackers were compared in terms of the average processing time per frame and the area under curve of the success rate per Pascal metric. For the experiment, we used a random sample of videos obtained from the public Hanyang visual tracker benchmark. The experimental results suggest that the DSFLA has an efficient processing time and higher quality of tracking compared with the other competing trackers analyzed in this work. The success rate of the DSFLA tracker is about 7.2 to 76.6% higher on average when comparing the success rate of its competitors. The average processing time per frame is about at least 10% faster than competing trackers, except one that was about 26% faster than the DSFLA tracker. The results also show that the predictions of the robust DES model are quite accurate.

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Introduction: Usability is a quality attribute that can evaluate the ease of use of user interfaces, based on standards called usability heuristics. Objective: To evaluate the usability components of the Brazilian Immunization Information System (IIS), focusing on the users and their interaction and agility with the interfaces. Materials and Methods: It was a concomitant and convergent mixed-method study that used a cross-sectional design for the quantitative approach and the indirect method of heuristic evaluation for the qualitative approach. Participants were 137 nursing professionals working in vaccination rooms, who completed a structured questionnaire on standards of usability quality, and 4 specialists in information technology, who used a semistructured form to carry out a software inspection. Descriptive and inferential statistics and the heuristic inspection were used for the analyses. Results: The evaluation resulted in 10 violated heuristics and identified 14 usability problems on the 68 screens of the IIS. The system presented simple usability problems (grade 2 severity), which can be repaired, with a low correction priority. The heuristics best evaluated were error prevention (3.03 ± 0.54) and help and documentation (3.00 ± 0.68); and the worst evaluated was visibility of system status, with a mean of 2.62 ± 0.55. Professionals with a technical education level presented a higher score on the scales for the recognition rather than recall heuristic when compared with the nurses (2.77 ± 0.49 vs. 3.67 ± 0.66, p = 0.003). Conclusion: The system provides easy access for users, however, has weaknesses in its ability to allow the users to easily achieve their goals of interaction with the interface.

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Technology has the potential to facilitate the development of higher-order thinking skills in learning. There has been a rush towards online learning by education systems during COVID-19; this can therefore be seen as an opportunity to develop students' higher-order thinking skills. In this short report we show how critical thinking and creativity can be developed in an online context, as well as highlighting the importance of grit. We also suggest the importance of heuristic evaluation in the design of online systems to support twenty-first century learning.

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This study proposes a design of a household waste collection system based on a two-stage procedure. First, the bin location-allocation problem is solved by selecting collection sites from a set of potential sites, and determining the type and number of bins at each selected collection site. Second, bin-to-bin waste collection routes are obtained for a fleet of homogeneous vehicles that are restricted by either work shift duration or vehicle capacity. Mixed integer linear programming (MILP) models are proposed for both stages, considering the particular characteristics of the problem. The models are applied to a real-world instance in the commune of Renca in Santiago, Chile. The results of first stage indicate an important preference for small bins since they have a lower unitary cost. Due to the large size of the real instance, a Large Neighborhood Search (LNS) heuristic is used in the second stage to find good feasible vehicle routing solutions in a reasonable period of time. The results for the routing phase suggest a larger number of routes in the morning work shift since these routes have shorter distances. The LNS heuristic presents a satisfactory behavior when compared to the MILP model with small instances. The proposed bin-to-bin household waste collection vehicle routing presents a more efficient solution than the existing door-to-door waste collection in the commune of Renca with respect to the total daily traveled distance and the average work shift duration. Finally, a sensitivity analysis is presented and discussed for both models.

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The unpredictable increase in electrical demand affects the quality of the energy throughout the network. A solution to the problem is the increase of distributed generation units, which burn fossil fuels. While this is an immediate solution to the problem, the ecosystem is affected by the emission of CO2. A promising solution is the integration of Distributed Renewable Energy Sources (DRES) with the conventional electrical system, thus introducing the concept of Smart Microgrids (SMG). These SMGs require a safe, reliable and technically planned two-way communication system. This paper presents a heuristic based on planning capable of providing a bidirectional communication that is near optimal. The model follows the structure of a hybrid Fiber-Wireless (FiWi) network with the purpose of obtaining information of electrical parameters that help us to manage the use of energy by integrating conventional electrical system with SMG. The optimization model is based on clustering techniques, through the construction of balanced conglomerates. The method is used for the development of the clusters along with the Nearest-Neighbor Spanning Tree algorithm (N-NST). Additionally, the Optimal Delay Balancing (ODB) model will be used to minimize the end to end delay of each grouping. In addition, the heuristic observes real design parameters such as: capacity and coverage. Using the Dijkstra algorithm, the routes are built following the shortest path. Therefore, this paper presents a heuristic able to plan the deployment of Smart Meters (SMs) through a tree-like hierarchical topology for the integration of SMG at the lowest cost.