Improving aerodynamic performance of intermodal trains through optimisation of loading plans

Indoor waste collection that utilizes mobile robots can solve the labor cost and manpower shortage but has the problem of limited energy resources, making it difficult to operate for long periods of time. Therefore, it is important to reduce the energy consumption for efficient waste collection. The waste collection robot can be modeled as a Capacitated Vehicle Routing Problem (CVRP), where heuristics algorithms can be deployed to search for the most energy-efficient path. This paper proposes the Ant Colony Optimization (ACO) algorithm for finding the optimal path of the waste collection robot. Energy consumption of the robot depends not only on the travel path but also on the weight of the waste it carries. Therefore, the proposed ACO algorithm utilizes the path distance and waste weight as the visibility. The travel distance and energy consumption are also used to determine the updated pheromone. Whereas the conventional and adapted ACO algorithms use only either the path distance or the waste weight as the visibility, respectively. The simulation experiments are conducted to compare the travel distance and the energy consumption that the waste collection robot takes by using the conventional, adapted, and proposed ACO algorithms. In the simulation experiments, the number of nodes, the waste weight, and the carrying capacity are used as parameters to verify the performance under the determined environment. The simulation results express that the proposed ACO algorithm provides a better energy optimal path in terms of travel distance and energy consumption than the conventional and adapted ACO algorithms.

In order to improve the safety of Unmanned Surface Vessel (USV) sailing in the ocean with movable obstacles, a new method based on greedy algorithm, ant colony algorithm and grid method is proposed. By adding periodic repeated calculation of movable obstacles with known paths, the route is periodically replanned. The approximation method is used to solve the problem that the traditional greedy ant colony algorithm is difficult to avoid the movable obstacles in the optimal course design of unmanned surface vessel. The algorithm uses greedy algorithm to plan the optimal route and ant colony algorithm to get rid of the situation that greedy algorithm is easy to enter the local convergence state. By rationalizing the movable obstacles on the known path, the navigation water limit of unmanned surface vessel caused by movable obstacles is avoided. The simulation results show that the algorithm is practical and reasonable for optimal route design when USV avoids movable obstacles. The simulation results show that the algorithm has the disadvantage of long search time.

A storage yard is a key resource in a container terminal. The effectiveness of its exploitation is an important factor to a port's productivity. Therefore, it is very important to adapt a good storage policy. In this paper, we propose a shared storage strategy, which is modeled mathematically with a mixed integer linear program. We consider a multi-modal container terminal, and take into account physical and operational constraints. This problem is Np-hard, and cannot be solved by optimization solvers when there are too many containers. So, we propose four algorithms to solve it: an ant colony algorithm, a bee algorithm, and two hybridizations. Numerical simulations prove the effectiveness and the efficiency of these algorithms.

The popularity of mobile devices with sensors is captivating the attention of researchers to modern techniques, such as the internet of things (IoT) and mobile crowdsensing (MCS). The core concept behind MCS is to use the power of mobile sensors to accomplish a difficult task collaboratively, with each mobile user completing much simpler micro-tasks. This paper discusses the task assignment problem in mobile crowdsensing, which is dependent on sensing time and path planning with the constraints of participant travel distance budgets and sensing time intervals. The goal is to minimize aggregate sensing time for mobile users, which reduces energy consumption to encourage more participants to engage in sensing activities and maximize total task quality. This paper introduces a two-phase task assignment framework called location time-based algorithm (LTBA). LTBA is a framework that enhances task assignment in MCS, whereas assigning tasks requires overlapping time intervals between tasks and mobile users’ tasks and the location of tasks and mobile users’ paths. The process of assigning the nearest task to the mobile user's current path depends on the ant colony optimization algorithm (ACO) and Euclidean distance. LTBA combines two algorithms: (1) greedy online allocation algorithm and (2) bio-inspired travel-distance-balance-based algorithm (B-DBA). The greedy algorithm was sensing time interval-based and worked on reducing the overall sensing time of the mobile user. B-DBA was location-based and worked on maximizing total task quality. The results demonstrate that the average task quality is 0.8158, 0.7093, and 0.7733 for LTBA, B-DBA, and greedy, respectively. The sensing time was reduced to 644, 1782, and 685 time units for LTBA, B-DBA, and greedy, respectively. Combining the algorithms improves task assignment in MCS for both total task quality and sensing time. The results demonstrate that combining the two algorithms in LTBA is the best performance for total task quality and total sensing time, and the greedy algorithm follows it then B-DBA.

This article addresses the problem of optimizing routes for unmanned aerial vehicles (UAVs) for the inspection of solar panels, which is a specific case of the Traveling Salesman Problem on a complete graph. The aim of the work is to develop a mathematical model for planning UAV routes with the minimization of route length and energy consumption, while ensuring complete coverage of all inspection points. A program has been developed that implements a UAV routing model, taking into account energy consumption constraints. Four algorithms were applied to solve the problem: greedy algorithm, 2-opt, Ant Colony Optimization, and Genetic Algorithm. Each method was evaluated based on three criteria: execution speed, route length, and energy efficiency. The experimental results showed that the greedy algorithm is the fastest, but it yields less optimal routes compared to other methods. The 2-opt algorithm did not provide satisfactory results due to a significant increase in energy consumption and route length. The Ant Colony Optimization and Genetic Algorithms showed the best results, providing optimal routes in terms of energy efficiency and minimization of path length. As a result of the analysis, it can be argued that the choice of algorithm depends on the specific requirements of the problem. For quickly obtaining an initial solution, it is advisable to use the greedy algorithm, for local optimization – the 2-opt algorithm, and for achieving the best results in minimizing energy consumption and route length – the Ant Colony Optimization or Genetic Algorithms.

Problem statement: The Container Loading Problem (CLP) considers packing a subset of given rectangular boxes into a rectangular container of fixed dimensions in the most optimum way. This was very important in the logistics industries and warehousing problems, since the cost can be reduced by increasing the space utilization ratio. Approach: This problem was solved in a two-phased Ant Colony Optimization (ACO) where a tower building approach was used as the inner heuristic. In the first phase, ACO with its probabilistic decision rule was used to construct a sequence of boxes. The boxes were then arranged into a container with the tower building heuristic in the second phase. The pheromone feedback of ACO using pheromone updating rule helped to improve the solutions. Results: Computational experiments were conducted on benchmark data set and the results obtained from the proposed algorithm are shown to be comparable with other methods from the literatures. Conclusion: ACO has the capability to solve the CLP.

Error correcting codes, also known as error controlling codes, are set of codes with redundancy that allows detecting errors. This is quite useful in transmitting data over a noisy channel or when retrieving data from a storage with possible physical defects. The idea is to use a set of code words that are maximally distant from each other, hence reducing the chance of changing a valid codeword to another valid codeword by flipping bits. The problem can be viewed as picking m codes out of 2 n available n- bit combinations, such that the aggregate hamming distance among those codewords is maximized. Due to the large solution spaces of such problems, greedy algorithms are sometimes used to generate quick and dirty solutions. However, modern evolutionary search algorithms like genetic algorithms, swarm particles, gravitational search and others, offer good alternatives, yielding near optimal solutions in exchange for some time. Chemical Reaction Optimization (CRO) has emerged as a new evolutionary algorithm to solve complex optimization problems. This algorithm mimics the molecular interactions towards finding a minimal energy state, which corresponds to an optimal solution for the problem in hand. In this research, we proposed a solution for the maximally distant codes allocation problem, through a binary knapsack mapping and compared the performance with the well established Ant Colony Optimization (ACO) algorithm, which is inspired by the ant's capability to find the shortest path between the nest and source of food. The binary knapsack mapping was used in the two algorithms. Test results showed that the CRO outperformed the ACO in every metric given any time budget.

This paper is concerned with the multiple container loading problem with rectangular boxes of different sizes and one or more containers, which means that the boxes should be loaded into the containers so that the waste space in the containers are minimized or the total value of the boxes loaded into the containers is maximized. Several approaches have been taken to solve this problem (Ivancic et al., 1989; Mohanty et al., 1994; George, 1996; Bortfeldt, 2000; Eley, 2002; Eley, 2003; Takahara, 2005; Takahara, 2006). The aim of this paper is to propose an efficient greedy approach for the multiple container loading problem and to contribute to develop a load planning software and applications. The problem considered in this paper is the three-dimensional packing problem, therefore it is known to NP-hard. This implies that no simple algorithm has been found so far. In this paper this problem is solved by a relatively simple method using a two-stage strategy. Namely, all boxes are numbered and for the sequence of the numbers a greedy algorithm of loading boxes into containers is considered. This greedy algorithm is based on first-fit concept (Johnson et al., 1974) and determines the arrangement of each box. This algorith try to load the boxes all kind of containers and select the best arrangement result. It’s requires a box loading sequence and a set of orientation orders of each box type for each container type. Each box is tried to load according to these dynamic parameters. Moreover, a static parameter overhang ratio is introduced here. This is a percentage of the bottom face area of the loading box that isn’t supported with other boxes that are below. As shown by Takahara (Takahara, 2006), if this parameter is different, the arrangement given this algorithm is different in spite of using a same pair of the loading sequence and the orientation orders. Some initial pairs of solution, that is a box loading sequence and a set of orientation orders of each box type, are selected among some rules, which are given beforehand. This solution is altered by using a multi-start local search approach. The local search approach is used to find a good pair of solutions that is brought an efficient arrangement by the greedy loading algorithm. The arrangement obtained in the iterations is estimated by volume utilization or total value of the loaded boxes. The effectiveness of the proposed approach is shown by comparing the results obtained with the approaches presented by using benchmark problems from the literature. Finally, the layout examples by the application using the proposed approach for container loading are illustrated. O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg

A tree search algorithm for the container loading problem

Container retrieval and wagon assignment planning at container rail terminals

Vehicle routing problem (VRP) concerns the transport of items from a depot to its number of customers using group of vehicles. VRP needs method to solve problem in terms of best route to service the customers. The solution must ensure that all the customer s are served under the operational constraints and minimizing the overall cost. The solution can be obtained using one of the metaheuristic techniques Ant Colony Optimization (ACO). In Ant colony optimization a colony of artificial ants altogether find good solutions to difficult discrete optimization problems. It is not possible for each ant to find a solution to the problem under consideration which is probably a poor one, good-quality solutions can only emerge only if there is collective interaction among the ants. They act concurrently and independently and there, by their decision making process, finds the most efficient route. ACO helps in finding the best optimal path in vehicle routing as well. This paper presents a review on the application of ACO for solving VRP. The paper is mainly concerned with Capacitated VRP (CVRP) to explain the ACO optimization of VPR problem. Keywords: VRP, ACO, Pheromone, metaheuristic, CVRP

Aerodynamic drag is a major cause of energy losses during alpine ski racing. Here we developed two models for monitoring the aerodynamic drag on elite alpine skiers in the technical disciplines. While 10 skiers assumed standard positions (high, middle, tuck) with exposure to different wind speeds (40, 60, and 80 km/h) in a wind tunnel, aerodynamic drag was assessed with a force plate, shoulder height with video-based kinematics, and cross-sectional area with interactive image segmentation. The two regression models developed had 3.9–7.7% coefficients of variation and 4.5–16.5% relative limits of agreement. The first was based on the product of the coefficient of aerodynamic drag and cross-sectional area (Cd∙S) and the second on the coefficient of aerodynamic drag Cd and normalized cross-sectional area of the skier Sn, both expressed as a function of normalized shoulder height (hn). In addition, normative values for Cd (0.75 ± 0.09–1.17 ± 0.09), Sn (0.51 ± 0.03–0.99 ± 0.05), hn (0.48 ± 0.03–0.79 ± 0.02), and Cd∙S (0.23 ± 0.03–0.66 ± 0.09 m2) were determined for the three different positions and wind speeds. Since the uncertainty in the determination of energy losses due to aerodynamic drag relative to total energy loss with these models is expected to be <2.5%, they provide a valuable tool for analysis of skiing performance.

This paper investigates optimum path planning for CNC drilling machines for a special class of products that involve a large number of holes arranged in a rectangular matrix. Examples of such products include boiler plates, drum and trammel screens, connection flanges in steel structures, food-processing separators, as well as certain portions of printed circuit boards. While most commercial CAD software packages include modules that allow for automated generation of the CNC code, the tool path planning generated from the commercial CAD software is often not fully optimised in terms of the tool travel distance, and ultimately, the total machining time. This is mainly due to the fact that minimisation of the tool travel distance is a travelling salesman problem (TSP). The TSP is a hard problem in the discrete programming context with no known general solution that can be obtained in polynomial time. Several heuristic optimisation algorithms have been applied in the literature to the TSP, with varying levels of success. Among the most successful algorithms for TSP is the ant colony optimisation (ACO) algorithm, which mimics the behaviour of ants in nature. The research in this paper applies the ACO algorithm to the path planning of a CNC drilling tool between holes in a rectangular matrix. In order to take advantage of the rectangular layout of the holes, two modifications to the basic ACO algorithm are proposed. Simulation case studies show that the average discovered path via the modified ACO algorithms exhibit significant reduction in the total tool travel distance compared to the basic ACO algorithm or a typical genetic algorithm.

A hybrid ant colony optimization based on Genetic Algorithm (GA) is applied to solving complex packing problem in the paper. Firstly, it searches for a set of rough solutions with the random search ability and the rapid global convergence of GA. Then, this set of solutions are used as the initial input of Ant Colony Optimization(ACO), using the positive feedback mechanism, the parallelism and the high efficiency of ACO to find the optimal solution of container loading problem. Finally, a design example is given in which 700 pieces of goods are loaded into a 40-foot container. The experimental results show that the hybrid algorithm can enhance the utilization of the container and it improves the performance of ACO and GA.

In this paper an ant colony optimization (ACO) algorithm for the minimum connected dominating set problem (MCDSP) is presented. The MCDSP become increasingly important in recent years due to its applicability to the mobile ad hoc networks (MANETs) and sensor grids. We have implemented a one-step ACO algorithm based on a known simple greedy algorithm that has a significant drawback of being easily trapped in local optima. We have shown that by adding a pheromone correction strategy and dedicating special attention to the initial condition of the ACO algorithm this negative effect can be avoided. Using this approach it is possible to achieve good results without using the complex two-step ACO algorithm previously developed. We have tested our method on standard benchmark data and shown that it is competitive to the existing algorithms.