The Capacitated Vehicle Routing Problem (CVRP) is a significant variant of the vehicle routing problem that incorporates constraints related to customer demand and vehicle capacity. Owing to its extensive applications in logistics and transportation, CVRP has attracted substantial research attention, with numerous algorithms proposed from the perspective of intelligent search. A common solution strategy involves two phases: first, assigning customers to different vehicles to form feasible routes, and second, optimizing these routes. This paper presents a two-phase CVRP solution framework through the clustering concept with intelligent search to improve route planning. In the first phase, a set of clustering methods - fuzzy c-means, k-means, and k-medoids - combined with a nearest neighbor heuristic search, are applied to generate feasible routes for each vehicle. In the second phase, these routes are iteratively optimized using the Simulated Annealing (SA) algorithm. The process yields three distinct solution pathways: fuzzy c-means with SA, k-means with SA, and k-medoids with SA. For performance evaluation, 46 benchmark CVRP datasets from a publicly available library are used. Simulation results demonstrate that k-means with SA performs the best, surpassing the other two approaches and outperforming other clustering-based two-phase state-of-the-art algorithms in terms of solution quality.

Background and Objectives: Thermal problem is one of the main challenges in 3D on-chip networks. Inappropriate traffic distribution, poor heat dissipation, cooling restriction for layers away from the chip heatsink are the main reasons for this problem. Methods: This paper proposes a new intelligent routing algorithm called (IntTAR) to solve these problems. Int-TAR applies a routing for managing the heat in 3D on-chip networks dynamically. The main idea behind Int-TAR is to save the past states of the system and, according to these states, predict the future behavior of the network and perform routing dynamically. It is done by the threshold of routers dynamically based on the current status of the routers. Results: The simulation results show that Int-TAR decreases the temperature of the network by 13% and improves performance efficiently. Conclusion: The proposed idea shows the better benchmark for the thermal problem in 3D on-chip network. Also, the higher memory for storing the past state of the network can make accurate and the further performance for the network.

Routing in ad-hoc networks, specifically intelligent-based ones, is a highly interested research topic in recent years. Most of them are simulation-based study. Large percentages have not even mentioned some of the fundamental parameters. This strictly reduces their validity and reliability. On the other hand, there is not a comprehensive framework to classify routing algorithms in wireless sensor networks yet. In this paper, we present a novel comprehensive taxonomy for routing algorithms along with a complete experimental evaluation framework. It makes the ability to put each routing algorithm in its place. It also provides a complete view of the algorithm behavior. At the end, a proper framework is introduced to express essential simulation parameters too. This can lead to improve the quality of scientific practices in the simulation studies.

This paper aims at proposing a novel quadratic assignment-based mathematical model for designing an optimal facility layout in each period of the stochastic dynamic facility layout problem (SDFLP). Considering routing flexibility is the main assumption of this problem so that parts can pass through multiple routes. It is also assumed that product demands are independent, normally distributed random variables with known expected value and variance changing from period to period at random. In addition, to solve the proposed model, a new hybrid meta-heuristic algorithm is developed by combining simulated annealing (SA) and the CRAFT approaches. Finally, the proposed model and the hybrid algorithm are verified and validated using design of experiment, real case study and sensitivity analysis methods as well as solving some numerical examples. The results show that the hybrid algorithm has an outstanding performance from both solution quality and computational time perspectives. Moreover, the proposed model can be used to design the layout of facilities in both of the stochastic and deterministic environments of traditional and modern manufacturing systems.

Traffic congestion and route guidance are integral parts of urban development in large cities. How cars are routed and the traffic flowing have a direct impact on each other. Therefore, the first step is to determine a criterion for assessing the traffic situation. The type of vehicles should also be considered in routing. Emergency vehicles must arrive at their mission site as soon as possible. Public transportations must also travel according to their plans. Ordinary vehicle drivers can either choose a road as an intermediate destination (out of interest, pick up someone, etc.). In this paper, two new algorithms are proposed to 1) route with intermediate destination selection for ordinary vehicles, and 2) schedule traffic lights to decrease traffic density and routing delay. The first algorithm proposed an agent-based route guidance model that in addition to finding the least expected travel time (LET) routes, drivers could select a part of the route as intermediate destinations according to their interests, to raise their satisfaction level. The second algorithm considers the density of traffic flow and the presence of emergency vehicles. This algorithm evaluates the status of the traffic flow by fuzzy logic. The evaluation is conducted by considering traffic flow speed and density. The output of fuzzy logic is used by the Gradational Search Algorithm (GSA). GSA regards the status of the flow, the priority of the traffic flow, and the distance of the emergency vehicles to the traffic light. The simulation results prove that the proposed algorithms have better performances.