Collective Operations Research Articles

Optimizing and evaluating the performance of integrated supply production centers: A hybrid heuristic-simulation applied to olive oil waste circular supply chains

This study examines the potential of an integrated bio-commodity supply-production center. Previous research has demonstrated the financial viability of circular bio-economies through Mixed Integer Linear Programming (MILP) or simulation. However, these models 1) do not incorporate the necessary logistics operations for biomass collection and storage, 2) do not integrate and optimize those with the requirements of multi-products line lot sizes. A Geographic Information System (GIS) based model is proposed, integrating insertion and genetic algorithms with a hybrid (ABM-DES) simulation model. A case study in Greece's Fthiotida region, a hub for olive cultivation, investigates the production of pellets, olive cakes, and pomace oil from prunings and Two-Phase Olive Mill Waste (TPOMW). The findings suggest that the model allows for a more accurate estimation of impacts when considering the supply and production operations. In particular, it shows how the frequency and lot sizes of replenishment cycles, influenced by the varying numbers of mechanized harvesters employed, significantly impacts the fluctuation observed in the return on investments. Running the model, it is found that the optimized system with two harvesters can reach a breakeven point, within just 36 months, and corresponding emissions of about 622,041 kgCo2eq. This provides a benchmark for other olive industries to harness unexploited resources.

Age and length characteristics of female and male Walleye broodstock collected at a spawning operation

AbstractObjectiveThe need to augment of Walleye Sander vitreus populations may increase as these populations face challenges of changing habitats and climatic conditions. Since little has been published outside of agency reports on wild broodstocks, the intent of this study was to describe sex‐specific Walleye spawning participation from a Great Plains broodstock operation. Specific objectives will assess standardization to collect target number of broodstock effort, catch per unit effort, and length and age participation of female and male broodstock.MethodsWalleye were collected at Sherman Reservoir, Nebraska, during April spawn collection operations from 2015 to 2019. Gill nets were used to collect female Walleye, and boat electrofishing was used to collect male Walleye. Catch per effort was calculated for each sex, fish were measured to nearest 1‐cm length‐group, and a dorsal spine was taken from a subset of fish per length‐group. Mean lengths and ages were calculated for each year of the study. Data were combined for all years of the project to display percent contribution by length and age.ResultThe amount of effort to capture 100 female and male Walleye varied by 4× and 2.5×, respectively, over the 5 years of sampling. Mean lengths and ages (±SE) of female (65 ± 1 cm; 10.4 ± 0.1 years) and male (43 ± 1 cm; 6.3 ± 0.1 years) Walleye collected were consistent during this study. The female Walleye collected represented 14 different ages, but ages 9–12 comprised 66% of all captured broodstock. The collected male Walleye also demonstrated age‐compressed spawning tendencies, with ages 4–7 accounting for 74% of collected individuals that represented 13 different ages.ConclusionThese results can define the anticipated variability in catch that can be expected at broodstock collection operations along with connection to specific lengths and ages of Walleye. Additionally, recognition that female and male Walleye broodstock contributions are consistently coming from four specific ages that are not the same year‐classes may allow spawning operation staff to anticipate brood availability and ultimately improve the planning efficiency of these operations.

Dynamics of Perceptions among Self-Employed Business Owners on Tax Collection Models in Tanzania.

Tax compliance issues among self-employed business owners in Tanzania arise from complex and inequitable tax models, exacerbated by inconsistent enforcement and poor policy communication. This study investigates the dynamics of tax collection models in Tanzania, focusing on the perceptions of self-employed business owners across key regions, including Tanga, Dodoma, Arusha, and Mbeya. Drawing on findings from the TAPACO research project, the research employs a mixed-methods approach grounded in pragmatism a philosophy that emphasizes practical solutions. The sample size consists of 150 participants, with data gathered through structured questionnaires and semi-structured interviews representing various sectors within these regions. Entrepreneurial theories such as the Entrepreneurial Orientation (EO) Theory, Resource-Based View (RBV), and Theory of Entrepreneurial Alertness are utilized to frame the analysis of how self-employed business owners perceive tax estimates, bylaws, policies, the regulatory framework, and the tax collection environment. EO Theory explores how entrepreneurs’ risk-taking behaviors and reactivity to changes in the tax environment influence compliance decisions, The RBV examines how entrepreneurs strategically utilize their resources, particularly in making decisions about tax payments and investments in compliance technologies like Electronic Fiscal Devices (EFDs). The findings indicate that self-employed business owners in Tanzania face significant challenges with tax compliance, driven by inaccuracies in tax assessments, corruption within tax enforcement agencies, and a lack of training and infrastructure for adopting Electronic Fiscal Devices (EFDs). These factors, combined with poor communication of tax policies and regulations, have resulted in widespread non-compliance. The study concludes that effective tax compliance strategies must consider the entrepreneurial behaviors influenced by resource availability, risk tolerance, and alertness to regulatory changes. The study recommends strengthening anti-corruption frameworks within tax enforcement agencies, including regular audits, ethical training for task force members, and protections for whistleblowers. Transparency in tax collection operations is essential for rebuilding trust and encouraging voluntary compliance.

Enhancing Laboratory Sample Collection Efficiency through Laboratory Information Systems: Insights into Optimal Despatch Rider Management

Effective administration of dispatch riders is crucial for delivering reliable diagnoses and research results by enabling prompt and precise sample collection. Laboratory Information Systems (LIS) offers a solution to enhance the efficiency and organization of dispatch riders in laboratory settings. Thus, the main objective of this study is to provide insights and knowledge on the potential benefits of implementing LIS to optimize the management of dispatch riders and enhance the efficiency of sample collection. An analysis of previous research findings illustrates the varied benefits of using LIS in improving key performance indicators such as turnaround time, mistake rates, and coordination of dispatch riders. The LIS platform serves as a centralized system for managing and allocating sample collection jobs, minimizing scheduling conflicts, and optimizing dispatch rider routes. Real-time tracking capabilities enable laboratory management to monitor dispatch rider locations and sample collection progress, facilitating enhanced coordination and resource allocation. Furthermore, LIS-generated data analytics provide valuable insights into sample collection patterns, enabling proactive management strategies to mitigate potential bottlenecks. Integration of LIS into payroll systems allows for automated compensation calculation based on dispatch rider performance metrics, ensuring fair and transparent pay rates. Studies consistently demonstrate that adequate compensation positively impacts dispatch rider motivation and sample collection effectiveness. This study highlights the vital importance of LIS in enhancing dispatch rider administration for efficient sample collection operations. It provides insights for laboratory managers and policymakers to optimize the potential of LIS to improve operational performance, facilitate patient care, and advance research outcomes.

Personalized Mood-Centric Book Recommendation Integrating Machine Learning with Content Based Filtering

This paper introduces a novel personalized book recommendation system aimed at enhancing subjective well-being (SWB). Despite the vast array of books available on the internet, people often struggle to find literature that aligns with their current emotional state. The system dynamically detects users' emotional states and recommends books tailored to their mood. It utilizes a content-based filtering algorithm to suggest top-rated books in real-time based on the user's current emotional state. For users with low mood, uplifting and inspirational books are recommended, while a mix of happy and sad books is suggested for happy users to acknowledge the nuanced interplay of emotions. Neutral-themed books are proposed to users with a neutral mood for balance. The system employs an emotion classification algorithm for accurate mood detection, analyzing book summaries to extract emotional tones. Users' emotional states are evaluated using a Likert scale, adjusting recommendations accordingly. The methodology involves data collection, preprocessing, emotion classification, and mood meter operation. Results demonstrate the system's effectiveness in providing tailored book recommendations, enriching users' reading experiences.

Between Public and Private: A Study of the Rural Fertilization System during the Collectivization Period (1949–1963) – With a Focus on Shanxi Province

Abstract Manure is a critical productive resource for agricultural development. With the advancement of the collectivization movement, household manure accumulation became a key source of fertilizer for collective agricultural operations. However, the process of farmers contributing fertilizer to the collective faced setbacks due to factors such as unreasonable compensation. In response, various localities gradually developed fertilization systems centered around tasks, compensation, and rewards and penalties. The essence of these systems was an economic balance between collective fertilizer use and increased farmer income, generally characterized by considerations for both public and private interests, but prioritizing the public over the private. In the dynamic restructuring of the relationship between labor input and income distribution, political norms and economic measures adjusted to one another. The economic rationality of the peasants interacted with the “egalitarianism” culture in rural areas, reflecting the complex interplay between public and private interests during the collectivization period in rural society.

Optimal Network Design for Municipal Waste Management: Application to the Metropolitan City of Rome

This work is part of the regional research project PIPER—Intelligent Platform for the Optimization of Recycling Operations, which aims to develop a network design model for waste collection in the metropolitan city of Rome, Italy. The goal is to enhance the strategic planning of treatment and disposal facilities by incorporating transshipment locations to improve the efficiency of waste collection. The motivation for this study stems from the stringent targets set by the European Green Deal to achieve near-zero emissions and the critical role of waste management in realizing circular economy practices. Methods: The problem is formulated as a mixed integer linear program (MILP) that includes constraints on vehicle allocation to shifts and driver requirements. An additional feature of the model is its ability to account for vehicle maintenance issues when deployed on consecutive shifts. Results: The model was tested against a real-world case study in Rome and demonstrated significant potential savings, with a reduction in total traveled distance exceeding 10%. The mathematical model was also used for evaluating strategic scenarios in a “what-if” analysis, allowing the municipal collection company to assess location options for depots, waste treatment facilities, and transshipment points, as well as to optimize the fleet composition and driver allocation during work shifts. Conclusions: This study presents a robust tool for strategic planning in waste collection operations, highlighting the benefits of using transshipment locations to improve efficiency. The findings indicate substantial potential for cost savings and operational improvements, making the model a valuable asset for municipal waste management planning.

Smartbin Track

This project presents a sophisticated waste management system that combines IoT technology with an Android application to optimize garbage collection processes. The core infrastructure includes IoT-enabled smart trash cans equipped with sensors to detect fill levels autonomously. Complementing this hardware, the Android app serves as a comprehensive platform for managing various aspects of waste collection operations. Divided into modules for administration, employees, and truck drivers, the app offers functionalities such as real-time monitoring of trash levels, route optimization for collection trucks, and automated alerts for full bins. Through seamless integration of IoT capabilities and mobile technology, the system facilitates efficient and environmentally responsible waste management practices, contributing to a cleaner and more sustainable urban environment. Furthermore, the system enhances coordination and transparency within the municipal waste management framework. Municipal administrators gain valuable insights into waste collection efficiency through the app's dashboard. Employees can input data and perform tasks seamlessly, while truck drivers receive real-time updates on bin fill levels and optimized routes, improving overall operational efficiency. By harnessing IoT technology and mobile platforms, this innovative solution not only streamlines waste collection processes.

Self-powered wireless sensor node enabled by ultra-high-output swinging hybrid generator toward real-time and in-situ marine meteorological observations

With the intensification of global ocean exploration, there is an increasing need for extensive and dispersed wireless sensor networks (WSNs) to meet the requirements of real-time, in-situ, and high-spatial-temporal-density marine meteorological observations. Nevertheless, providing a reliable power source and ensuring the ongoing maintenance of these WSNs pose significant challenges. Here, an innovative self-powered wireless sensor node is developed to address these challenges, featuring an ultra-high-output triboelectric-electromagnetic hybrid generator (T-EHG) with a swinging mechanism. The T-EHG, which combines triboelectric (TENG) and electromagnetic (EMG) generation, assisted by a universal power management system (UPMS), efficiently converts wave energy into a consistent voltage output between 1.5 V to 3.3 V. Additionally, the self-powered WSNs integrated with T-EHG, UPMS circuits, meteorological sensors, and microcontroller units (MCUs) with LoRa wireless transmission functionality are capable of executing data collection and transmission operations every 10 s under simulated wave conditions. These systems can achieve a maximum wireless transmission range of 1.5 km. Notably, a marine meteorological monitoring system utilizes these autonomous sensor nodes, along with base stations, cloud servers, and terminal devices to effectively implement real-time, on-site measurements of temperature, humidity, and air pressure within the monitored zone. This research underscores the substantial practical utility and extensive application prospects of these advanced self-powered WSNs for marine meteorological surveillance.

Review of Underwater Wireless Sensor Networks Deployment Techniques and Challenges

Underwater Wireless Sensor Networks (UWSNs) have become increasingly important due to their critical roles in marine life monitoring, communication, ocean data collection, sampling, and military security operations. The success of UWSNs largely depends on efficient node deployment techniques that ensure optimal coverage, connectivity, cost-effectiveness, network lifetime, and energy utilization. This paper presents a comprehensive review of various node deployment types and techniques specifically designed for UWSNs. It covers depth-adjustment, movement-assisted, self-movement, and soft-computing techniques, highlighting their advantages, limitations, and application scenarios. Each technique is evaluated based on key performance metrics such as network coverage, connectivity, energy consumption, network lifetime, and deployment cost. Additionally, the paper discusses the challenges and identifies open research directions in the field, providing valuable insights for researchers and practitioners in selecting appropriate node deployment techniques for UWSNs.

Taking the MPI standard and the open MPI library to exascale

The Open MPI for Exascale (OMPI-X) project was one of two in the Exascale Computing Project (ECP) focused on advancing the MPI ecosystem. The OMPI-X team worked with other MPI Forum members to champion several important features for inclusion in the MPI 4.0, 4.1, and upcoming 5.0 MPI standard versions, in support of the needs of exascale applications and systems. The team also worked with the larger Open MPI community to bring implementations of these new features and other enhancements into Open MPI, one of the leading open-source implementations of the MPI interface. This paper describes the motivation for the work of the OMPI-X project in the context of exascale computing needs, the nature of the resulting new capabilities in the MPI standard, and how they were implemented in the Open MPI library. Features include improved support for “MPI + X” programming models through partitioned communications and support for user-level threading, sessions, fault tolerance through the user-level fault mitigation (ULFM) and Reinit models, and other features. We also discuss enhancements to Open MPI providing improved performance and scalability for existing features, such as collective operations, one-sided operations, support for the Slingshot-11 interconnect of the initial exascale systems, and how the OMPI-X team worked to improve quality assurance for the Open MPI library, particularly on platforms of interest to the Department of Energy community.

Energy Efficient strategy for Wireless Sensor Networks lifetime endurance using Genetic Algorithm

This study propose an optimized routing scheme for multiple hops Wireless Sensor Networks to enhance the lifetime of the network. Since the wireless sensor networks work as application-centric having the capabilities of data gathering and transmission to the base station. Therefore, a significant amount of sensor networks` energy spends during such operation of data collection and transmission if the route is not optimized. So, a meta-heuristic genetic algorithm plays a vital role in finding the optimized route. We will modify the genetic algorithm for better optimization results so that the best route can be selected among all possible routes and will save the wireless sensor networks` energy. All information is routed towards the sink via intermediate neighbor nodes, which only relay the data without performing any computation or operation. The genetic algorithm determines the route information to all nodes and sinks. Then, the data takes the most optimal route for data transmission. Thus, this study proved a considerably saved extra waste of energy.

A Heuristic Procedure for Improving the Routing of Urban Waste Collection Vehicles Using ArcGIS

This paper proposes a heuristic procedure created to improve the collection routes obtained with the support of the ArcGIS software and its complement, Network Analyst. After a series of experiments, it was found that the software presents several inconsistencies with logistical and operational management concepts, such as the unnecessary realization of U-turns in a dead end and unnecessary access to areas with difficult access to a single customer. These are issues that a collection route planner must consider to make a good decision that considers the cost of visiting areas with difficult access and the benefits of reaching that area. In this sense, our heuristic procedure considers a set of best practices in operational and logistical strategies that could be programmed within the Network Analyst. As it is well known in the science of vehicle routing, U-turns and sub-tours in the routes travelled by vehicles increase distances and operating costs, so in our work, we propose a systematic heuristic procedure to reduce the number of U-turns performed by a municipal waste collection truck, while using the ArcGIS-Network Analyst add-on to reduce the number of sub-tours in the route under the Directed-Capacitated Arc Routing Problem approach. It is then shown how the routes improved using our conceptual heuristic procedure, which provides better quality than the original routes obtained with ArcGIS and Network Analyst. Specifically, reducing the total distances travelled by the vehicle fleet, increasing the coverage of sidewalks visited by the truck in the urban perimeter of a city, and minimizing the time used in municipal solid waste collection operations. The importance of our work lies in the fact that effective MSW management is an essential municipal service whose regulation can drive innovation, sustainability, and efficiency in the MSW sector.

Analysis of spin-squeezing generation in cavity-coupled atomic ensembles with continuous measurements

We analyze the generation of spin-squeezed states via coupling of three-level atoms to an optical cavity and continuous quantum measurement of the transmitted cavity field in order to monitor the evolution of the atomic ensemble. Using analytical treatment and microscopic simulations of the dynamics, we show that one can achieve significant spin squeezing, favorably scaling with the number of atoms N. However, contrary to some previous literature, we clarify that it is not possible to obtain Heisenberg scaling without the continuous feedback that is proposed in optimal approaches. In fact, in the adiabatic cavity removal approximation and large N limit, we find the scaling behavior N−2/3 for spin squeezing and N−1/3 for the corresponding protocol duration. These results can be obtained only by considering the curvature of the Bloch sphere, since linearizing the collective spin operators tangentially to its equator yields inaccurate predictions. With full simulations, we characterize how spin-squeezing generation depends on the system parameters and departs from the bad cavity regime, by gradually mixing with cavity-filling dynamics until metrological advantage is lost. Finally, we discuss the relevance of this spin-squeezing protocol to state-of-the-art optical clocks.

Intelligent Waste Management System using IOT

The escalating population density in urban areas has fueled a surge in waste generation, underscoring the urgent need for effective waste management solutions. In response to this challenge this study proposes an innovative approach to waste management through the integration of IOT components. The system focuses on real-time monitoring and segregation of dry and wet waste using a network of sensors. The system also leverages platform to automate data logging and notification processes ,ensuring seamless oversight of waste collection operations. Our Proposed Intelligent Waste Management System employs IOT components such as sensors ,detectors, actuators to optimize waste collection and route planning. This integrated IOT-based approach represents a promising avenue for sustainable waste management in both large and small urban environments , paving the way for enhanced city livability and environmental stewardship.

Quantum thermodynamics of boundary time-crystals

Time-translation symmetry breaking is a mechanism for the emergence of non-stationary many-body phases, so-called time-crystals, in Markovian open quantum systems. Dynamical aspects of time-crystals have been extensively explored over the recent years. However, much less is known about their thermodynamic properties, also due to the intrinsic nonequilibrium nature of these phases. Here, we consider the paradigmatic boundary time-crystal system, in a finite-temperature environment, and demonstrate the persistence of the time-crystalline phase at any temperature. Furthermore, we analyze thermodynamic aspects of the model investigating, in particular, heat currents, power exchange and irreversible entropy production. Our work sheds light on the thermodynamic cost of sustaining nonequilibrium time-crystalline phases and provides a framework for characterizing time-crystals as possible resources for, e.g. quantum sensing. Our results may be verified in experiments, for example with trapped ions or superconducting circuits, since we connect thermodynamic quantities with mean value and covariance of collective (magnetization) operators.

Android Application for Waste Collection Management

Abstract: The value and rate of logical waste discarding will remain to rise as long as mortal conditioning remains. The result of the scurrility of logical waste, which is a great peril to the world and to mortal health, calls for the want to amend the situation before it can get out of phase. This leads us to exercise the stylish ways to take logical waste collection following the rearmost trends in movable technology. This study developed a waste collection model with the end of intending a movable waste operation operation model that will support ameliorate quality operation of logical waste collection and better environmental jolt reduction. The model places waste creators in the middle ground of the waste collection system as they play a crucial part in producing logical waste, aiding waste companies during waste collection and providing an operative device for external councils to epitomise this appreciation. all conditioning related to logical waste collection and directing their opinions. In this study we have handed all the necessary and over- to- assignation movable technology and the model can work to achieve an operative logical waste collection system at home.

IOT-BASED WASTE COLLECTION MANAGEMENT SYSTEM FOR CITIES: CASE STUDY OF PARAKOU CITY IN BENIN

Waste management is a critical issue for cities around the world, and Parakou City in Benin is no exception. In this article, we propose an innovative solution to address the challenges of waste collection and management in urban areas. Our approach utilizes the Internet of Things (IoT) technology to create an efficient Waste Collection Management System tailored specifically for Parakou. The core of our system lies in equipping waste bins with sensors capable of detecting their filling levels. These sensors continuously monitor the status of each bin, and when a bin reaches its capacity, the system triggers an alert. This real-time data collection ensures that waste collection resources are deployed only when needed, optimizing operational efficiency and reducing unnecessary costs. The case study conducted in Parakou City highlights the significance of our IoT-based approach. Parakou faces unique challenges in waste management due to its urban landscape and growing population. With limited resources, it becomes essential to maximize the efficiency of waste collection operations. By implementing our system in Parakou, we aim to demonstrate how technology can be harnessed to tackle these challenges effectively.

Machine learning for water demand forecasting: Case study in a Brazilian coastal city

ABSTRACT Water resources management is crucial for human well-being and contemporary socio-economic development. However, the increasing use of water has led to various problems that affect its quality and availability. To address these issues, accurate forecasting of water consumption is essential for the optimal operation of water collection, treatment, and distribution systems. This study aims to compare four machine learning methods for predicting daily urban water demand in a Brazilian coastal tourist city (Guaratuba – Paraná). Historical data from the city’s water distribution system, spanning from 2016 to 2019 (1,461 measurements in total), were considered along with meteorological and calendar data to conduct the investigation. Three time series cross-validation approaches were considered for each method, thus totaling 12 evaluation settings. All models were subjected to hyperparameter optimization and evaluated using appropriate performance metrics from the literature. Results demonstrate the importance of using nonlinear models to predict short-term water demand, highlighting the problem’s complexity. From the compared models, multilayer perceptron provided the best results. Finally, regardless of the model, the best results were obtained by applying an expanding window time series cross-validation, indicating that the more historical data available, the better, in this particular case.

On the operation and implications of grid-interactive renewable energy communities

The increasing installations of photovoltaic systems, electric vehicle chargers, heat pumps, and battery storage systems challenge distribution system operators to prevent critical grid operations. Nonetheless, flexibility potential from customer-owned assets remains untapped due to lacking federated coordination and incentives. In Europe, emerging renewable energy communities can address this challenge by facilitating collective optimal operation of flexible assets and providing grid services. This paper presents an approach for renewable energy communities to offer participants’ flexibility through grid-interactive operations. Distribution system operators then based on optimal power flow calculations request flexibility use to prevent voltage violations, transformer overloads, and line overloads. A case study with simulations for a future rural low-voltage grid is conducted. The results show that, compared to business-as-usual operations or renewable energy communities with fixed import and export limits, the grid-interactive approach ensures non-critical grid operations cost-efficiently and reduces curtailment by 60% to 90%, down to 1.1% of total generation. By providing both upward and downward flexibility and aligning with distribution system operators’ requirements, grid-interactive renewable energy communities can be key in enhancing the efficient use and stability of distribution grids.