Energy-Efficient Location-Routing Problem with Time Windows with Dynamic Demand

Sustainability and energy savings have attracted considerable attention in recent years. However, in the traditional location-routing problem (LRP), the objective function has yet to minimize the distance traveled regardless of the amount of energy consumed. Although, distance is one of the major factors determining the energy consumption of a distribution network, it is not the only factor. Therefore, this paper explains the development of a novel formulation of the LRP that considers energy minimization, which is called the energy-efficient location-routing problem (EELRP). The energy consumed by a vehicle to travel between two nodes in a system depends on many forces. Among those, rolling resistance (RR) and aerodynamic drag are considered in this paper to be the major contributing forces. The presented mixed-integer non-linear program (MINLP) finds the best location-allocation routing plan with the objective function of minimizing total costs, including energy, emissions, and depot establishment. The proposed model can also handle the vehicle-selection problem with respect to a vehicles’ capacity, source of energy, and aerodynamic characteristics. The formulation proposed can also solve the problems with hard and soft time window constraints. Also, the model is enhanced to handle the EELRP with dynamic customers’ demands. Some examples are presented to illustrate the formulations presented in this paper

An integrated model for cash transfer system design problem

This paper presents an integrated model that incorporates strategic, tactical, and operational decisions for a cash transfer management system of a bank. The aim of the model is to decide on the location of cash management centers, number and routes of vehicles, and the cash inventory management policies to minimize the cost of owning and operating a cash transfer system while maintaining a pre-defined service level. Owing to the difficulty of finding optimal decisions in such integrated models, an iterative solution approach is proposed in which strategic, tactical, and operational problems are solved separately via a feedback mechanism. Numerical results show that such an approach is quite effective in reaching greatly improved solutions with just a few iterations, making it a promising approach for similar integrated models

Routing Unmanned Vehicles in GPS-Denied Environments

Most of the routing algorithms for unmanned vehicles, that arise in data gathering and monitoring applications in the literature, rely on the Global Positioning System (GPS) information for localization. However, disruption of GPS signals either intentionally or unintentionally could potentially render these algorithms not applicable. In this article, we present a novel method to address this difficulty by combining methods from cooperative localization and routing. In particular, the article formulates a fundamental combinatorial optimization problem to plan routes for an unmanned vehicle in a GPS-restricted environment while enabling localization for the vehicle. We also develop algorithms to compute optimal paths for the vehicle using the proposed formulation. Extensive simulation results are also presented to corroborate the effectiveness and performance of the proposed formulation and algorithms.Comment: Publised in International Conference on Umanned Aerial System