A bi-level network interdiction model for solving the hazmat routing problem

In the current work, we considered the problem of hazardous material distribution where the distributer chooses the routes on the network, and a regulatory agency controls the behaviour of the distributer to traverse the specified routes. In these circumstances, the distributer sets to select some routes to minimise the total distributing costs. Mostly, this occurs due to selecting risky arcs in which more individuals are exposed to risk. To prevent this and increase the capability to deal with the risk of hazardous material transportation through roads, the regulatory agency obliges carriers to traverse through the most secure arcs, though imposing more distribution costs. The problem is modelled as a bi-level routing problem. The bi-level model is difficult to solve and may be ill-posed. Two meta-heuristic algorithms are proposed to solve the bi-level model, and some randomly generated problems are applied to show the applicability and efficiency of the proposed algorithms.     

Private Investor-based Transmission Expansion Planning in Deregulated Environments

Abstract—Deregulation in power systems creates new uncertainties and escalates the previous ones. The presence of these uncertainties causes the transmission network to remain a monopoly and the private investors not to be interested in investing in new transmission lines. This article presents a new merchant-based transmission network expansion planning algorithm from the viewpoint of private investors. The point estimation method is used to handle the uncertainties, and the genetic algorithm is used as the optimization tool. The proposed method provides insights for private investors to deal with the uncertainties and to find appropriate transmission projects in which to invest.     

Generating Texture for 3D Human Avatar from a Single Image using Sampling and Refinement Networks

There has been significant progress in generating an animatable 3D human avatar from a single image. However, recovering texture for the 3D human avatar from a single image has been relatively less addressed. Because the generated 3D human avatar reveals the occluded texture of the given image as it moves, it is critical to synthesize the occluded texture pattern that is unseen from the source image. To generate a plausible texture map for 3D human avatars, the occluded texture pattern needs to be synthesized with respect to the visible texture from the given image. Moreover, the generated texture should align with the surface of the target 3D mesh. In this paper, we propose a texture synthesis method for a 3D human avatar that incorporates geometry information. The proposed method consists of two convolutional networks for the sampling and refining process. The sampler network fills in the occluded regions of the source image and aligns the texture with the surface of the target 3D mesh using the geometry information. The sampled texture is further refined and adjusted by the refiner network. To maintain the clear details in the given image, both sampled and refined texture is blended to produce the final texture map. To effectively guide the sampler network to achieve its goal, we designed a curriculum learning scheme that starts from a simple sampling task and gradually progresses to the task where the alignment needs to be considered. We conducted experiments to show that our method outperforms previous methods qualitatively and quantitatively.     

Using cross-docking operations in a reverse logistics network design: a new approach

Increasing development of competitive market has forced organizations to make great efforts in supplying, production and distribution of goods in their company so that they are capable of responding the customers different needs at the minimum delivery time and lowest cost. Cross-docking is a practical strategy in distribution cycle which has significantly attracted the attention of experts and industrialists in different areas. In this paper, the problem of designing a multi-echelon reverse logistics network with applying cross-docking centers is presented—as the first attempt to propose the new approach of using cross-docking centers in reverse logistics network. In this regard, a mixed-integer linear programming is utilized to model the problem for the goals of increasing shipment rate, decreasing fixed, variable costs and better management of returned products. Finally, the validation and sensitivity analysis are done by using the GAMS software. Considered the above requirements, the model facilitates objective-oriented reverse logistic performance.