Reliability and quality assurance have become major considerations in the design and manufacture of today’s parts and products. Survivability of green compact using powder metallurgy technology is considered as one of the major quality attributes in manufacturing systems today. During powder metallurgy (PM) production, the compaction conditions and behavior of the metal powder dictate the stress and density distribution in the green compact prior to sintering. These parameters greatly influence the mechanical properties and overall strength of the final component. In order to improve these properties, higher compaction pressures are usually employed, which make unloading and ejection of green compacts more challenging, especially for the powder-compacted parts with relatively complicated shapes. This study looked at a mathematical survivability model concerning green compact characteristics in PM technology and the stress-strength failure model in reliability engineering. This model depicts the relationship between mechanical loads (stress) during ejection, experimentally determined green strength and survivability of green compact. The resulting survivability is the probability that a green compact survives during and after ejection. This survivability model can be used as an efficient tool for selecting the appropriate parameters for the process planning stage in PM technology. A case study is presented here in order to demonstrate the application of the proposed survivability model.

In competitive electricity markets, power needed for the network’s reserve is purchased from the ancillary service market. In this market, producing units and buyers alike announce their offers. As will be seen, energy market and reserve market implementation is possible with simultaneous method and serial method by choosing each of the methods based on the type of market and other conditions. In this paper, the energy market and the active power reserve market are simulated in two formations as serial and simultaneous for a uniform pricing system. In each method, limitations of transferring power over the lines, based on available transfer capacity (ATC), is considered alongside the other constraints in the energy market and the active power reserve market. Then, during network overload, economic dispatch is accomplished between winner units in the reserve market by using a linear optimization problem, and needed power is provided from these units at a minimal cost. Finally, our proposed methods are implemented on an IEEE 39-bus test system and results are analyzed.

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.

The classical production-inventory model assumes that both demand and set-up costs are constant. However, in real manufacturing environment, managers usually embark on continuous improvement programmes that often lead to more effective use of tools and machineries and consequently reduction in set-up costs. In fact, constant emphasis on reduction of set-up costs is usually cited as one of the factors responsible for the efficiency of Japanese manufacturing methods. On the other hand, the demand for seasonal product is often characterized by a mixture of time-dependent patterns over the entire season. This paper investigates the effect of learning-based reduction in set-up costs on the optimal schedules and costs of a production-inventory system for deteriorating seasonal products. The demand pattern is a general three-phase ramp-type demand function that represents the various phases of demand commonly observed in many seasonal products in the market. A two-parameter Weibull-distribution function is used for the deterioration of items in order to make the model more generalized and realistic. The study further presents two different multi-period production strategies that can ensure a fast-response to customers’ demand and compare them with the usual single period strategy. The Numerical example and sensitivity analysis shows that learning-based reduction in set-up costs leads to higher production frequency and shorter production runs which are vital aspects of the just-in-time (JIT) philosophy.

The aim of this paper is to present a new genetic algorithm approach for large scale multiple resource-constrained project-scheduling problems (RCPSP). It also presents a heuristic approach to achieve proper solutions for large scale problems. This research area is very common in industry especially when a set of activities needs to be finished as soon as possible subject to two sets of constraints, precedence constraints and resource constraints. The emphasis in this research is on investigating the complexity of scheduling problems and developing a new GA approach to solve this problem in such a way that the advantages of GA are appropriately utilized by applying a novel method to reduce the complexity of the problem. Computational results are also reported for the most famous classical problems taken from the operational research literature.