A Network-Based Solution for the Fair Student-to-School Allocation

The study aims to optimize the vehicle routing problem, considering infeasible routing, to minimize losses for the company. Firstly, a vehicle routing model with hard time windows and infeasible route constraints is established, considering both the minimization of total vehicle travel distance and the maximization of customer satisfaction. Subsequently, a Floyd-based improved genetic algorithm that incorporates local search is designed. Finally, the computational experiment demonstrates that compared with the classic genetic algorithm, the improved genetic algorithm reduced the average travel distance by 20.6% when focusing on travel distance and 18.4% when prioritizing customer satisfaction. In both scenarios, there was also a reduction of one in the average number of vehicles used. The proposed method effectively addresses the model introduced in this study, resulting in a reduction in total distance and an enhancement of customer satisfaction.

The Capacitated Vehicle Routing Problem (CVRP) presents significant challenges in shipping route optimization and logistics management. These challenges include balancing vehicle capacity, minimizing travel distance, and efficiently grouping delivery points, all of which are crucial for enhancing operational efficiency and reducing costs. This research aims to apply a combination of the Sweep and Nearest Neighbor algorithms to address the CVRP, seeking to improve route efficiency and manage vehicle capacity effectively. The Sweep algorithm is employed to cluster pickup points based on their polar angle from the depot, facilitating efficient grouping and optimal vehicle capacity management. Within each cluster, the Nearest Neighbor algorithm is implemented to optimize the sequence of visits, minimizing total travel distance by sequentially selecting the next closest point. The Haversine Distance is used to calculate the distances between points, ensuring geographical accuracy compared to the Euclidean method. Experimental results demonstrate that this hybrid approach yields shorter routes. Quantitative analysis shows a significant reduction 13% in total travel distance when using this combination of algorithms, highlighting its effectiveness in solving the CVRP. This research demonstrates that combining the Sweep and Nearest Neighbor algorithms provides an efficient solution to the CVRP, improving route optimization and vehicle capacity management. The findings contribute valuable insights to logistics management, with practical implications for enhancing shipping route efficiency.

Industri konstruksi khususnya di Indonesia termasuk dalam kategori sebagai negara yang responsive terhadap penyelenggaraan konstruksi di dunia. Dengan dimulainya era green construction, fokus penyelenggaraan infrastruktur dewasa ini tidak saja hanya memfokuskan perhatiannya pada aspek sosial dan ekonomi, namun juga aspek lingkungan hidup.

In this paper, two new formulations are presented for trajectory optimization in the patrolling problem. It is assumed that the starting depot is not prespecified; an assumption that distinguishes the present work from the existing literature. A number of viewpoints are assigned to be visited in a certain sequence to minimize the total travel distance. The problem turns out to be a variant of the well-known Traveling Salesmen Problem (TSP), namely the Single depot multiple Traveling Salesmen Problem (mTSP). Comparisons between the commonly-used prespecified starting depot approach and the proposed formulations are performed and the efficacy of the results is presented through simulations. It is noted that by using the new approach, the total travel distance can be improved by an average of about 20 % compared to the case where the starting depot is prespecified, and by about 40 % in the worst-case scenario (in terms of the starting depot).

Handling the distribution of broilers has special attention. The distribution of poultry should be done in the shortest possible time to decrease broiler mortality rate. Real-time monitoring of poultry distribution is expected to control the stress level of poultry due to the length of travel time by limiting the delivery time window. In order to control the distribution time and distance, it is necessary to integrate vehicle routing and mobile technology. By adopting the digital twin-enabled framework model and combining it with Google APIs, web and mobile applications are generated to accommodate the real-time traveling time, vehicle capacity, and pickup-delivery activity. There is a significant change in travel distance in one delivery cycle after implementing the developed technology. The total travel distance was 493 KM decreased to 436 KM, which means a decrease of 11.5% from the initial total travel distance. In addition, there was a change in the total penalty time (delay) from 102 minutes to 85 minutes, decreasing delay time by 16.6%. This mobile technology's development can indirectly increase vehicle utilization by not overloading the vehicle capacity and reducing vehicle travel distance.

A two-phase hybrid metaheuristic for the vehicle routing problem with time windows

(1) Background: This work focuses on improving the efficiency of warehouse operations with the goal of promoting efficiency in the logistics industry and mitigating logistics-related labor shortages. Many factors are involved in warehouse operations, such as the optimal allocation of manpower, the optimal layout design, and the use of automatic guided vehicles, which together affect operational efficiency. (2) Methods: In this work, we developed an optimal method for operating a limited number of workers or picking robots in a specific area, coping with cases of sudden disruptions such as a change in picking order or the blockage of aisles. For this purpose, the number of pickers, the storage capacity, and other constraints such as sudden changes in picking orders during the picking process, as well as blockages in the aisles of a warehouse site, are considered. The total travel distance is minimized using Gurobi, an optimization solver. (3) Results: The picking routes were optimized in three different scenarios using the shortest route between the starting point and the picking points, resulting in up to a 31% efficiency improvement in terms of the total distance traveled. (4) Conclusions: The main contribution of this work is that it focuses on the day-to-day work situations of sudden changes in the picking order and the presence of route blocks in real-world logistics warehouse sites. It demonstrates the feasibility of responding to sudden disruptions and simultaneously optimizing picking routes in real time. This work contributes to the overall efficiency of logistics by providing a simple, yet practical, data-driven solution for the optimization of warehouse operations.

Youth dress, influenced by commercialism, impacts students' attitudes and behaviors in public schools. This article reports quantitative data and qualitative data on Black educators' views on student dress and behavior, including the use of uniforms. Findings indicate that educators support the use of school uniforms because they believe that uniforms reduced the risk of both psychological harm and school related problems. Younger students (sixth graders) agreed with the educators while the older students disagreed. These findings generally reflect the views of many educators who are grappling with effective strategies to reduce dress-related problems among youth in public schools. Children and adolescents are socialized around dress across several settings such as the family, school, marketplace, workplace, and others. Research has been conducted on dress socialization and its influence on children's development and has identified many dress-related challenges and problems within these various contexts (LaPoint, Alleyne, Lee, & Mitchell, 2003). Many public schools, especially at the elementary and middle school levels, have begun to implement student uniforms in an effort to promote student health and safety (Lopez, 2003). Very little empirical research exists on the effectiveness of school uniforms on student achievement and social competence. Moreover, little research exists in educators' views on the nature of dress-related problems at school, especially the views of educators of color (LaPoint et al., 2003). This article reports qualitative and quantitative data on Black educators' views on dress and behavior in a public middle school in a large Northeastern city. It is part of a larger study on youth dress and behavior and includes data collected from students (LaPoint et al., 2003). The article includes a literature review, a description of the study including the sample and methods, analysis and findings, and implications for further research, policies, and programs relating to the use of uniforms in public schools. LITERATURE REVIEW While literature on student dress and behavior in public schools often cites educators' views on youth dress and behavior in public schools, it is often anecdotal (LaPoint et al., 2003). The literature generally focuses on educators' (e.g., teachers, administrators, staff) views of challenges of youth dress to classroom and school behaviors and the solutions to theses challenges, including the use of uniforms (King, Walker, & Minor, 2002; King, 1998; LaPoint et al., 2003; Lopez, 2003; Mitchell & Knechtle, 2003). Research and reports are inconclusive about the positive impact of uniforms on various academic and academic-related behaviors (Brunsma & Rockquemore, 1998; LaPoint et al., 2003). Moreover, some research indicates mixed support for the use of uniforms in public schools (National Association of Elementary School Principals, 1998) even though the federal government issued guidelines encouraging the use of uniforms in public schools (U.S. Department of Education, 1996). Finally, the literature, generally does not relate commercialism influences on youth dress in schools to broader commercialism influences in other settings (LaPoint & Alleyne, 2001; LaPoint et al., 2003). The present study provides empirical data, both quantitative and qualitative, on educators' views on the use student uniforms in one public middle school. It also provides data on educators' views about commercial influences on students' attitudes and dress in the school and other socialization settings. THE RESEARCH STUDY The study used two approaches to obtain educators' views on student dress and behavior in a public middle school in a northeastern U.S. city. The first was a survey that examined Black educators' attitudes toward dress and behavior among students. These data were compared with the data obtained from all students in the school (grades 6, 7, and 8). …

Optimizing Helicopter Air Ambulance Dispatch to Improve Sustainability in Interfacility Transfers.

A coordinated traffic control on urban expressways with modified particle swarm optimization

Special education students are usually routed on different buses than non-disabled general education students. To make the routes more efficient, this study proposes serving general students and special education students on the same bus at the same time (mixed ride) while allowing heterogeneous fleets and mixed loads. ESRI Location-Allocation tools and Google OR-Tools are used for bus stop selection, route generation, and bus stop optimization. Parallel cheapest insertion heuristic and metaheuristic, simulated annealing, are adopted to generate school bus routes. The effectiveness of the mixed ride approach is tested for three schools with 178 synthetic students’ locations data (including 12 with wheelchair) in New York City using a fleet of 14 buses spread over four types. The results show the mixed ride approach achieved 14.32% reduction in total travel distance and 10.46% reduction in total travel time. The mixed ride approach tends to return solutions with fewer vehicles and fewer bus stops, less average travel distance, and shorter average travel time.

The Vehicle Routing Problem with Time Windows involves finding the lowest-cost set of routes to deliver goods to customers, which have service time windows, using a homogeneous fleet of vehicles with limited capacity. In this paper, we propose and analyze the performance of an improved multi-objective evolutionary algorithm, that simultaneously minimizes the number of routes, the total travel distance, and the delivery time. Empirical results indicate that the simultaneous minimization of all three objectives leads the algorithm to find similar or better results than any combination of only two objectives. These results, although not the best in all respects, are better in some aspects than all previously published approaches, and fully multi-objective comparisons show clear improvement over the popular NSGA-II algorithm.KeywordsTime WindowMutation OperatorTravel DistanceDelivery TimeVehicle Route ProblemThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This paper proposes a model predictive control method based on dynamic multi-objective optimization algorithms (MPC_CPDMO-NSGA-II) for reducing freeway congestion and relieving environment impact simultaneously. A new dynamic multi-objective optimization algorithm based on clustering and prediction with NSGA-II (CPDMO-NSGA-II) is proposed. The proposed CPDMO-NSGA-II algorithm is used to realize on-line optimization at each control step in model predictive control. The performance indicators considered in model predictive control consists of total time spent, total travel distance, total emissions and total fuel consumption. Then TOPSIS method is adopted to select an optimal solution from Pareto front obtained from MPC_CPDMO-NSGA-II algorithm and is applied to the VISSIM environment. The control strategies are variable speed limit (VSL) and ramp metering (RM). In order to verify the performance of the proposed algorithm, the proposed algorithm is tested under the simulation environment originated from a real freeway network in Shanghai with one on-ramp. The result is compared with fixed speed limit strategy and single optimization method respectively. Simulation results show that it can effectively alleviate traffic congestion, reduce emissions and fuel consumption, as compared with fixed speed limit strategy and classical model predictive control method based on single optimization method.

The nexus of two relatively recent technologies, additive manufacturing and unmanned aircraft systems (UAS), has enabled new and unique capabilities that have only started to be realized in integrated systems. This article explores and quantifies the impact of 3D printing parts for UAS, or entire UAS systems, on an agent platform, while this agent travels to multiple locations as part of a mission objective. The fully printed or enhanced UAS can then be released at launch points farther away from the goal locations. This, in turn, can accelerate mission completion times and reduce travel costs depending upon the ratio between vehicle speed and 3D printing rate. Thousands of scenarios are optimized across the design space to minimize the travel path length for the agent platform as a result of 3D printing en route to the locations of interest. Results indicate that based on the print capability and agent travel speed, an exponential decay in the amount of travel distance of the agent platform occurs. For unity ratios of print speed and agent speed in the considered design space, a decrease of 55% in the total required distance of our agent is observed. This reduction in total travel distance can reduce time, fuel, cost, and other aspects including other environmental and social impacts. A generalized optimization formulation is also presented at the end to enable similar analyses with other en route range-extending technology such as battery charging.

Different urban transportation flows (e.g., passenger journeys, freight distribution, and waste management) are conventionally separately handled by corresponding single‐purpose vehicles (SVs). The multi‐purpose vehicle (MV) is a novel vehicle concept that can enable the sequential sharing of different transportation flows by changing the so‐called modules, thus theoretically improving the efficiency of urban transportation through the utilization of higher vehicles. In this study, a variant of the pick‐up and delivery problem with time windows is established to describe the sequential sharing problem considering both MVs and SVs with features of multiple depots, partial recharging strategies, and fleet sizing. MVs can change their load modules to carry all item types that can also be carried by SVs. To solve the routing problem, an adaptive large neighborhood search (ALNS) algorithm is developed with new problem‐specific heuristics. The proposed ALNS is tested on 15 small‐size cases and evaluated using a commercial MIP solver. Results show that the proposed algorithm is time‐efficient and able to generate robust and high‐quality solutions. We investigate the performance of the ALNS algorithm by analyzing convergence and selection probabilities of the heuristic solution that destroy and repair operators. On 15 large‐size instances, we compare results for pure SV, pure MV, and mixed fleets, showing that the introduction of MVs can allow smaller fleet sizes while approximately keeping the same total travel distance as for pure SVs.