The routing problem for rapid public transit is described as the determination of the optimal place or location for stations and their alignment in a city. This can be modeled as an optimization problem, with maximization of coverage of stations as the objective function. It can then be subjected to station intervals and length of the alignment. The benefit of this is optimization of the transport network, allowing more people to use public transport, thereby reducing costs and emissions. On the other hand, heuristics offer some advantages, such as reduced complexity and their high run speed, which often attracts the attention of researchers. This paper introduces a heuristic method based on the maximization of the population coverage of stations and access to the main destinations in the city. It is therefore expected that a high level of trip coverage is the main object. This solution is a combination and development of two previous methods, known as population coverage and travel coverage. Comparing the results from this method with those from the two previous methods shows that the travel coverage achieved with the new method is increased. The study in this paper has been carried out on the transportation network in the city of Tabriz.

Objective: This study tackles the reviewer assignment problem by proposing a model that optimizes reviewer-proposal matching based on thematic coverage, fairness, and expertise, while considering workload balance and team size constraints. The model incorporates practical constraints such as limits on the number of proposals each reviewer can handle and team composition requirements. This approach is especially relevant to institutions like academic conferences, journals, and funding organizations, aiming to enhance the integrity and efficiency of the review process.Methods: This study is classified as descriptive research with a practical orientation and relies on data collection through applied methods. The approach is grounded in mathematical modeling. Initially, the selected articles are grouped into clusters. Reviewers are then assigned to these clusters using a multi-objective binary integer programming model that incorporates all relevant criteria and constraints. To implement this model, 150 articles were selected through purposive sampling. The model was optimized using Python, employing both the branch-and-bound algorithm and a genetic metaheuristic algorithm to maximize the degree of reviewer-proposal matching within the proposed framework. Results: The proposed model demonstrates strong practical relevance by closely reflecting real-world reviewer assignment challenges. By simultaneously optimizing thematic coverage, evaluation fairness, and reviewer expertise, the model captures the complexity of actual allocation scenarios. To validate its effectiveness, the model was solved using both the branch-and-bound algorithm and a genetic algorithm. The branch-and-bound method yielded an objective value of 177.349 in approximately one hour, while the genetic algorithm reached 120.35 in just seven minutes. Although branch-and-bound guarantees optimality, its longer runtime makes it less practical for larger datasets. Given the similarity of results, the genetic approach is a reliable and scalable alternative.Conclusion: This study introduces a new allocation strategy and mathematical model for reviewer assignment, addressing often-overlooked factors such as reviewer expertise, grouping, and conflicts of interest. By integrating these elements, the proposed model better reflects real-world conditions. Future work is encouraged to expand on these findings with new frameworks and methods.

Since the binomial distribution is discrete‎, ‎finding accurate confidence intervals for its parameters is not easily achievable‎. ‎Many approximate confidence intervals have been suggested for the binomial distribution's ratio parameter thus far‎. ‎As we know‎, ‎these confidence intervals have not been compared based on the maximum coverage probabilities‎. ‎This article aims to evaluate six widely used confidence intervals for the binomial distribution's ratio parameter‎, ‎focusing on their accurate maximum coverage probabilities criterion.

Roadside units are crucial elements of intelligent transportation systems that provide vehicle–vehicle and vehicle–equipment information communication. Due to the high cost of installation, the deployment of roadside units is the most critical. Aim of this study is developing a model to optimize of roadside units placement to achieve maximum coverage. A multi–objective mathematical model presented, based on the three main parameters. These parameters are traffic volume, incident rate and adjacency to important centers, which determine for alternative points. The maximum coverage problem is NP–hard. Consequently, conventional mathematical methods are not accurate for large scale problem. A meta–heuristic method based on the greedy algorithm was developed which conciders marking points as definitive–select or non–selectable. Result of the model were evaluated through testing of three scenarios, 200, 500 and 1000 meters coverage in District 5 of Tehran by using MATLAB and the best one, 1000 meters was chosen with 71% coverage. Observations showed the effect of various parameters such as equipment coverage radius, number of equipment and budget on the results of distribution. This algorithm makes it possible to solve the problem on a large scale by using the geolocation of the candidate points.

Background and Aims: Metal-free and fiber-reinforced composite (FRC) restorations have drawn considerable attention and interest in recent years for restoring in the posterior area due to their improved esthetics. Fracture resistance is one of the most important mechanical properties of materials because of 500-600 N load of occlusion. The restorations should tolerate this load. The purpose of this in vitro study was to compare the fracture resistance of FRC with that of zirconia all-ceramic for posterior partial dentures.Materials and Methods: Forty extracted human intact teeth (20 first premolars and 20 first molars) selected for fabricating 10 pairs of fiber-reinforced composite and 10 pairs of zirconia all-ceramic bridges. After receiving standard tooth preparation, the teeth were mounted with 7.5 mm distance between each other. The bridges were made and cemented on the teeth. Then the restorations were stored in 37oC water for 30 days. The fracture resistance was measured using mechanical testing machine with cross-head speed of 1mm/min. Data were evaluated by Independent sample t test.Results: The mean fracture resistance in the ceramic group was 1329.41 N and for the F.R.C group was 1118.528 N with significant differences between them (P=0.034). The failure modes were mainly cohesive at pontic area for ceramic samples, but adhesive for FRC samples at pontic area.Conclusion: Both systems showed sufficient fracture resistance for using in posterior area.

Today, intense competition in global markets has forced companies to design and manage of supply chains in a better way. Since the role of three factors: location, routing and inventory is important to continue the life of a supply chain so, integration of these three elements will create an efficient and effective supply chain. In this study, we investigate the problem of supply chain network design that including routing and inventory problem consist of flow allocation, vehicle routing between facilities, locating distribution centers and also consider the maximum coverage for respond to customer demand. Proposed mathematical model is a nonlinear mixed integer programming model for location-routing-inventory problem in the four-echelon supply chain by considering the multiple conflicting goals of total cost, travel time and maximum coverage. In order to solve the proposed model, three meta-heuristic algorithms (MOPSO, MSGA_II and NRGA) has been used. The accuracy of mathematical model and proposed algorithms are evaluated through numerical examples

In this paper, we present a bi-objective virtual-force local search particle swarm optimization (BVFPSO) algorithm to improve the placement of sensors in wireless sensor networks while it simultaneously increases the coverage rate and preserves the battery energy of the sensors. Mostly, sensor nodes in a wireless sensor network are first randomly deployed in the target area, and their deployment should be then modified such that some objective functions are obtained. In the proposed BVFPSO algorithm, PSO is used as the basic meta-heuristic algorithm and the virtual-force operator is used as the local search. As far as we know, this is the first time that a bi-objective PSO algorithm has been combined with a virtual force operator to improve the coverage rate of sensors while preserving their battery energy. The results of the simulations on some initial random deployments with the different numbers of sensors show that the BVFPSO algorithm by combining two objectives and using virtual-force local search is enabled to achieve a more efficient deployment in comparison to the competitive algorithms PSO, GA, FRED and VFA with providing simultaneously maximum coverage rate and the minimum energy consumption.