•The min-cost parallel drone scheduling vehicle routing problem (PDSVRP) is introduced.•A mixed integer linear program and a Ruin and Recreate (R&R) algorithm are proposed.•Extensive experiments are ...carried out to investigate the performance of the methods.•CART method is used to perform a sensitivity analysis on the impact of drone delivery.•R&R dominates other algorithms proposed for the PDSTSP in terms of solution quality.
Adopting unmanned aerial vehicles (UAV), also known as drones, into the last-mile-delivery sector and having them work alongside trucks with the aim of improving service quality and reducing the transportation cost gives rise to a new class of Vehicle Routing Problems (VRPs). In this paper, we introduce a new optimization problem called the min-cost Parallel Drone Scheduling Vehicle Routing Problem (PDSVRP). This problem is a variant of the well-known Parallel Drone Scheduling Traveling Salesman Problem (PDSTSP) recently introduced in the literature in which we allow multiple trucks and consider the objective of minimizing the total transportation costs. We formulate the problem as a Mixed Integer Linear Program and then develop a Ruin and Recreate (R&R) algorithm. Exploiting PDSVRP solution characteristics in an effective manner, our heuristic manages to introduce “sufficient” rooms to a solution via new removal operators during the ruin phase. It is expected to enhance the possibilities for improving solutions later in the recreate phase. Multiple experiments on a new set of randomly generated instances confirm the performance of our approach. To explore the benefits of drone delivery as well as the insight into the impact of related factors on the contribution of drones’ use to operational cost, a sensitivity analysis is conducted. We also adapt the proposed algorithm to solve the PDSTSP and validate it via benchmarks available in the literature. It is shown that our algorithm outperforms state-of-the-art algorithms in terms of solution quality. Out of 90 considered instances, it finds 26 new best known solutions.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
In order to predict the performance of designed electric machines, and also based on which the high efficiency of the drive systems can be obtained, the accurate calculation of the iron loss is ...necessary. Taking modeling accuracy versus modeling complexity into account, this article proposes a general and accurate iron loss calculation method considering harmonics based on the loss surface (LS) hysteresis model and finite-element method (FEM). Compared with the previous triangular wave excitation, this article uses the data under sinusoidal wave excitation to establish the LS model, which is more convenient for engineering application. A specimen is tested for different flux densities with harmonics, the extensive results verify the modeling method. Due to a powerful hysteresis model, the iron loss calculation can be realized by the direct integration of hysteresis loops. A separated iron loss calculation package is developed, and it is combined with the commercial finite-element software to compute the iron loss distribution. The method that combines the LS model and FEM is proved feasible by comparing the calculated iron loss with the measured one for the Epstein frame.
The uncertainties in design variables are unavoidable in the optimal design of electromagnetic devices, and there is an imperative demand to find a robust design, which is insensitive to the ...uncertainties and remains within the feasible region of constraints even perturbed by the uncertainties. In this paper, a gradient-based worst case optimization (G-WCO) algorithm is proposed in a limited uncertainty set to increase the numerical efficiency based on the worst case optimization (WCO) algorithm. Through applications to the robust optimal design of TEAM 22, the performances of the proposed G-WCO, conventional WCO, and multiobjective optimization approach using gradient index (GI) are compared.
The main difference between single and multi-objective optimizations using particle swarm optimization is how the guider to locate the global optimal and Pareto optimal solutions are defined in ...corresponding optimization problems. In general multi-objective particle swarm optimization, only one guider is selected, and, in order to reduce the non-dominated solution for more diversity in the external archive, only crowding distance in objective space is considered. This paper presents a new approach of selecting multiple guiders to lead a swarm toward a Pareto-front. Additionally, in order to overcome the local Pareto front, mutation operator is applied for not only particles but also members in an external archive. Furthermore, aside from considering the crowding distance of solutions in objective space to maintain the diversity of solutions, the crowding distance in variable space is also taken into account. The proposed algorithm is compared with recent approaches of multi-objective optimizer in solving a multi-objective version of the TEAM 22 benchmark optimization problem with three and eight design variables.
The detent force of a permanent magnet linear synchronous motor (PMLSM) consists of cogging and drag forces, and should be minimized for high precision purpose application. This paper suggests a new ...9-pole 10-slot structure of a short primary PMLSM to remove the cogging force. Through theoretical and finite element analysis, the proposed structure is proven to remove most of the cogging force. The detent force is minimized by optimizing the length of armature core and shape of the exterior teeth simultaneously by using (1+lambda) evolution strategy coupled with response surface method using multi-quadric radial basis function. Additionally, simulation results for the proposed structures are verified by experimental measurements.
The differential evolution (DE) algorithm was initially developed for single-objective problems and was shown to be a fast, simple algorithm. In order to utilize these advantages in real-world ...problems it was adapted for multiobjective global optimization (MOGO) recently. In general multiobjective differential evolutionary algorithm, only use conventional DE strategies, and, in order to optimize performance constrains problems, the feasibility of the solutions was considered only at selection step. This paper presents a new multiobjective evolutionary algorithm based on differential evolution. In the mutation step, the proposed method which applied multiguiders instead of conventional base vector selection method is used. Therefore, feasibility of multiguiders, involving constraint optimization problems, is also considered. Furthermore, the approach also incorporates nondominated sorting method and secondary population for the nondominated solutions. The propose algorithm is compared with resent approaches of multiobjective optimizers in solving multiobjective version of Testing Electromagnetic Analysis Methods (TEAM) problem 22.
This paper presents an efficient optimization strategy which employs adaptive Taylor Kriging and Particle Swarm Optimization (PSO). In this method, the objective function of electromagnetic problem ...is interpolated by using adaptive Taylor Kriging, in which the covariance parameter is obtained by Maximum Likelihood Estimation (MLE). And then, PSO is used to search for optimal solutions of electromagnetic problem. The proposed algorithm is verified its validity by analytic functions and TEAM (Testing of Electromagnetic Analysis Method) problem 22.
In image compression and video coding, quantization error helps to reduce the amount of information of the high frequency components. However, in temporal prediction the quantization error ...contributes its value as noise in the total residual information. Therefore, the residual signal of the inter-picture prediction is greater than the expected one and always differs zero value even input video contains only homogeneous frames. In this paper, we reveal negative effects of quantization errors in inter prediction and propose a video encoding scheme which is able to avoid side effects of quantization errors in the stationary parts. We propose to implement a motion detection algorithm as the first stage of video encoding to separate the video into two parts: motion and static. The motion information allows us to force residual data of non-changed part to zero and keep the residual signal of motion regularly. Beside, we design block-based filters which improve motion results and filter those results fit into block encode size well. Fixed residual data of static information permits us to pre-calculate its quantized coefficient and create a bypass encoding path for it. Experimental results with the JPEG compression (MJPEG-DPCM) showed that the proposed method produces lower bitrate than the conventional MJPEG-DPCM at the same quantization parameter and a lower computational complexity.
An effective methodology for a robust global optimization of electromagnetic devices is developed based on the gradient index and multi-objective optimization method. The method transforms a given ...optimization problem into a multi-objective optimization one by adding another optimization target for minimizing the gradient index. The performance and robustness of the obtained optimal designs from the proposed algorithm are investigated through a numerical experiment with the TEAM Workshop Problem 22.
To improve the performance of a Thomson-coil actuator, a new global optimization algorithm is proposed with mixed integer-discrete-continuous variables. In the algorithm, an augmented objective ...function is constructed by introducing a penalty function and an extended function to treat the discrete quantities as continuous ones. For the discrete objective function, two kinds of continuous function approximation methods, step function and linear function approximations, are suggested. The coupling particles swarm optimization method is then applied to the augmented objective function to find all the global and local optimal points. Finally, the validity of the proposed method is proven by comparing obtained optimization results with those from the enumeration algorithm.