Small organizations that maintain their own fleet and make their own deliveries are responsible for ensuring their drivers are utilizing the most efficient routes while delivering products to their customers. Furthermore, efficient delivery requires that drivers spend as little time as possible dropping off and picking up products, since these activities are referred to as “non-value added activities,” although they are necessary tasks in the order cycle process. To aid in reducing order cycle times, large organizations that can afford it have employed transportation management systems. Unfortunately, small organizations with limited resources are less likely to adopt transportation management systems, despite the need for such automation. One solution is to use available productivity software to track and manage driver route activity in an effort to improve and maintain driver productivity by reducing non-value time and identifying optimal routes. This paper will outline how office productivity software such as Microsoft® Access can meet the needs of small organizations with limited resources by describing the development and use of a route activity database that employs an easy-to-use multi-user interface. This paper also includes the details of the underlying infrastructure and the user interface.

Distribution is an indispensable component of logistics and supply chain management. Location-Routing Problem (LRP) is an NP-hard problem that simultaneously takes into consideration location, allocation, and vehicle routing decisions to design an optimal distribution network. Multi-layer and multi-product LRP is even more complex as it deals with the decisions at multiple layers of a distribution network where multiple products are transported within and between layers of the network. This paper focuses on modeling a complicated four-layer and multi-product LRP which has not been tackled yet. The distribution network consists of plants, central depots, regional depots, and customers. In this study, the structure, assumptions, and limitations of the distribution network are defined and the mathematical optimization programming model that can be used to obtain the optimal solution is developed. Presented by a mixed-integer programming model, the LRP considers the location problem at two layers, the allocation problem at three layers, the vehicle routing problem at three layers, and a transshipment problem. The mathematical model locates central and regional depots, allocates customers to plants, central depots, and regional depots, constructs tours from each plant or open depot to customers, and constructs transshipment paths from plants to depots and from depots to other depots. Considering realistic assumptions and limitations such as producing multiple products, limited production capacity, limited depot and vehicle capacity, and limited traveling distances enables the user to capture the real world situations.

In recent years, researchers have been interested in scheduling algorithms to avoid deadlock in Flexible Manufacturing Systems (FMS). FMS are discrete event systems characterized by the availability of resources to produce a set of products. Raw parts, which belong to various product types, enter the system at discrete times and are processed concurrently while sharing a limited number of resources. In such systems, a situation may occur in which parts become permanently block. This is called deadlock. This paper presents the sufficient conditions for deadlock to exist in a FMS; it models a FMS using digraphs to calculate slack, knot, order and space; it identifies three types of circuits that are fundamental in determining if a FMS is in deadlock.

Injuries due to inhalation of hot gas are commonly encountered when dealing with fire and combustible material, which is harmful and threatens human life. In the literature, various studies have been conducted to investigate heat and mass transfer characteristics in the human respiratory tract (HRT). This study focuses on assessing the injury taking place in the upper human respiratory tract and identifying acute tissue damage, based on level of exposure. A three-dimensional heat transfer simulation is performed using Computational Fluid Dynamics (CFD) software to study the temperature profile through the upper HRT consisting of the nasal cavity, oral cavity, trachea, and the first two generations of bronchi. The model developed is for the simultaneous oronasal breathing during the inspiration phase with a high volumetric flow rate of 90 liters/minute and the inspired air temperature of 100 degrees Celsius. The geometric model depicting the upper HRT is generated based on the data available and literature cited. The results of the simulation give the temperature distribution along the center and the surface tissue of the respiratory tract. This temperature distribution will help to assess the level of damage induced in the upper respiratory tract and appropriate treatment for the damage. A comparison of nasal breathing, oral breathing, and oronasal breathing is performed. Temperature distribution can be utilized in the design of the respirator systems where inlet temperature is regulated favoring the human body conditions.

Transmission Network Expansion Planning (TNEP) is a basic part of power network planning that determines where, when and how many new transmission lines should be added to the network. So, the TNEP is an optimization problem in which the expansion purposes are optimized. Artificial Intelligence (AI) tools such as Genetic Algorithm (GA), Simulated Annealing (SA), Tabu Search (TS) and Artificial Neural Networks (ANNs) are methods used for solving the TNEP problem. Today, by using the hybridization models of AI tools, we can solve the TNEP problem for large-scale systems, which shows the effectiveness of utilizing such models. In this paper, a new approach to the hybridization model of Probabilistic Neural Networks (PNNs) and Harmony Search Algorithm (HSA) was used to solve the TNEP problem. Finally, by considering the uncertain role of the load based on a scenario technique, this proposed model was tested on the Garver’s 6-bus network.

To meet the need for product variety, many companies are shifting from a mass-production mode to mass customization, which demands quick response to the needs of individual customers with high quality and low costs. The multifunctional nature of mechanical components necessitates that a designer redesign them each time when a component’s function changes. The functional Geometric Dimensioning & Tolerancing (GD & T) specification, also called functional tolerancing, must be updated for each component. Currently, this is done by humans, and thus can be very time-consuming and error-prone. Functional tolerancing is one of the main obstacles to practical mechanical product family modeling. In this paper, a graph-based functional tolerancing scheme in 3D CAD is proposed. In the scheme, a product is generated by applying production rules to the graph of the base product, following customers’ or manufacturing engineers’ requirements. Functional tolerancing of each component of a product in the family is formulated as a non-linear constrained optimization (or cost minimization) process. Certain critical aspects of the scheme have been implemented in SolidWorks®, by using its Application Programming Interface (API) and C++. LEDA® and MATLAB® have been used to solve the graph and optimization problems.