The Unit Re-Balancing Problem

We describe the problem of re-balancing a number of units distributed over a geographic area. Each unit consists of a number of components. A value between 0 and 1 describes the current rating of each component. By a piecewise linear function, this value is converted into a nominal status assessment. The lowest of the statuses determines the efficiency of a unit, and the highest status its cost. An unbalanced unit has a gap between these two. To re-balance the units, components can be transferred. The goal is to maximize the efficiency of all units. On a secondary level, the cost for the re-balancing should be minimal. We present a mixed-integer nonlinear programming formulation for this problem, which describes the potential movement of components as a multi-commodity flow. The piecewise linear functions needed to obtain the status values are reformulated using inequalities and binary variables. This results in a mixed-integer linear program, and numerical standard solvers are able to compute proven optimal solutions for instances with up to 100 units. We present numerical solutions for a set of open test instances and a bi-criteria objective function, and discuss the trade-off between cost and efficiency.

A network model of the egg supply chain in Germany implemented as a FSKX compliant object

In our days, food supply chains are becoming more and more complex, generating global networks involving production, processing, distribution and sale of food products. To follow the "farm to fork" paradigm when assessing risks from various hazards linked to food products, supply chain network models are useful and versatile tools. The objective of the present "egg supply chain network model" is to allow users to predict and visualise the spatial commodity flow within the German egg supply chain. The network model provides for the user the option to select values for the input parameter "actor" in order to allow simulation of estimates for different supply chain scenarios. It generates a data frame as output regarding the estimates of food flows for the product "chicken eggs" in Germany on NUTS-3 level according to the selected parameter and a chloropleth map for illustrating the distribution of product quantities. The network model and all required resources are provided as a fully annotated file compliant to the community standard Food Safety Knowledge Exchange (FSKX) and can be executed online or with the desktop FSK-Lab software.

Optimal Sizing of Electric Vehicle Charging Stations Considering Urban Traffic Flow for Smart Cities

Achieving high penetration of electric vehicles (EVs) is one of the objectives proposed by the scientific community to mitigate the negative environmental impact caused by conventional mobility. The limited autonomy and the excessive time to recharge the battery discourage the final consumer from opting for new environmentally friendly mobility alternatives. Consequently, it is essential to provide the urban road network with charging infrastructure (CI) to ensure that the demand generated by EV users is met. The types of terminals to be considered in charging stations (CS) are fast and ultra-fast. The high energy requirements in these types of terminals could stress the electrical systems, reducing the quality of service. To size and forecast the resources needed in CI, it is of great interest to model and predict the maximum number of EVs, in each hour, that each CS will have to serve according to the geographic area in which they are located. Our proposal is not based on an assumed number of vehicles to be served by each CS, but rather it is based on the analysis of vehicular traffic in geo-referenced areas, so that the load managers can design the topology of the CS. The maximum vehicular concentration is determined by some considerations such as the road system, direction of the route, length of the road segment, the intersections, and the economic zone to which it belongs. The topology of the road map is freely extracted from OpenStreetMap to know the latitude and longitude coordinates. Vehicular traffic will be modeled through the topology obtained from OpenStreetMap and other microscopic variables to understand the traffic engineering constraints. In addition, the Hungarian algorithm is used as a minimum cost decision tool to allocate demand to the CS by observing vehicular traffic as a cost variable. The multi commodity flow problem (MCFP) algorithm aims to make commodities flow through the road network with the minimum cost without exceeding the capacities of the road sections. Therefore, it is proposed to solve the transportation problem by directing the vehicular flow to the CS while minimizing the travel time. This situation will contribute significantly to the design of the topology of each CS, which will be studied in future research.

Development of a Novel Framework for Hazardous Materials Placard Recognition System to Conduct Commodity Flow Studies Using Artificial Intelligence AlexNet Convolutional Neural Network

Conducting hazardous materials (HAZMAT) commodity flow studies (CFS) is crucial for emergency management agencies. Identifying the types and amounts of hazardous materials being transported through a specified geographic area will ensure timely response if a HAZMAT incident takes place. CFS are usually conducted using manual data collection methods, which may expose the personnel to some risks by them being subjected to road traffic and different weather conditions for several hours. On other hand, the quality and accuracy of the collected HAZMAT data are affected by the skill and alertness of the data collectors. This study introduces a framework to collect HAZMAT transportation data exploiting advanced image processing and machine learning techniques on video feed. A promising convolutional neural network (CNN), named AlexNet was used to develop and test the automatic HAZMAT placard recognition framework. A solar-powered mobile video recording system was developed using high-resolution infra-red (IR) cameras, connected to a network video recorder (NVR) mounted on a mobile trailer. The developed system was used as the continuous data collection system. Manual data collection was also conducted at the same locations to calibrate and validate the newly developed system. The results showed that the proposed framework could achieve an accuracy of 95% in identifying HAZMAT placard information. The developed system showed significant benefits in reducing the cost of conducting HAZMAT CFS, as well as eliminating the associated risks that data collection personnel could face.

Adaptation of Random Binomial Graphs for Testing Network Flow Problems Algorithms

Algorithms for network flow problems, such as maximum flow, minimum cost flow, and multi-commodity flow problems, are continuously developed and improved, and so, random network generators become indispensable to simulate the functionality and to test the correctness and the execution speed of these algorithms. For this purpose, in this paper, the well-known Erdős–Rényi model is adapted to generate random flow (transportation) networks. The developed algorithm is fast and based on the natural property of the flow that can be decomposed into directed elementary s-t paths and cycles. So, the proposed algorithm can be used to quickly build a vast number of networks as well as large-scale networks especially designed for s-t flows.

The wargame commodity course of action automated analysis method

This research presents the wargaming commodity course of action automated analysis method (WCCAAM) – a novel approach to assist wargame commanders in developing and analyzing courses of action (COAs) through semi-automation of the military decision making process (MDMP). MDMP is a seven-step iterative method that commanders and mission partners follow to build an operational course of action to achieve strategic objectives. MDMP requires time, resources, and coordination – all competing items the commander weighs to make the optimal decision. WCCAAM receives the MDMP’s Mission Analysis phase as input, converts the wargame into a directed graph, processes a multi-commodity flow algorithm on the nodes and edges, where the commodities represent units, and the nodes represent blue bases and red threats, and then programmatically processes the MDMP steps to output the recommended COA. To demonstrate its use, a military scenario developed in the Advanced Framework for Simulation, Integration, and Modeling (AFSIM) processes the various factors through WCCAAM and produces an optimal, minimal risk COA.

ANALYSIS OF METHODS FOR MODELING THE DEMAND FOR FREIGHT TRANSPORTATION TAKING INTO ACCOUNT THEIR CHARACTERISTICS

Existing models of demand for freight transportation are considered within the framework of various approaches. A review of approaches to urban modeling shows that the most commonly used metrics are: traffic flow, trip generation, vehicle load and commodity flow. Their advantages and disad-vantages are highlighted. To solve problems in the field of freight transport, scalable tools are need-ed that reflect as many attributes as possible at an acceptable cost in a given time period.