Effect of reconfiguration costs on planning for capacity scalability in reconfigurable manufacturing systems

In a globally competitive market for products, manufacturers are faced with an increasing need to improve their flexibility, reliability, and responsiveness to meet the demands of their customers. Reconfigurable manufacturing systems (RMS) have become an important manufacturing paradigm, because they broadly encompass the ability to react efficiently to this environment by providing the exact capacity and functionality needed when needed. This paper studies how such new systems can manage their capacity scalability planning in a cost effective manner. An approach for modeling capacity scalability planning is proposed. The development of the model is based on set theory and the regeneration point theorem which is mapped to the reconfigurable manufacturing paradigm as the capacity scalability points of that system. The cost function of the model incorporates both the physical capacity cost based on capacity size and costs associated with the reconfiguration process which referred to as the scalability penalty cost and scalability effort cost. A dynamic programming (DP) approach is manipulated for the development of optimal capacity scalability plans. The effect of the reconfiguration costs on the capacity scalability planning horizon and overall cost is investigated. The results showed the relation between deciding on the optimal capacity scalability planning horizon and the different reconfiguration costs. Results also highlighted the fact that decreasing costs of reconfiguration will lead to cost effective implementation of reconfigurable manufacturing systems.     

A voltage tunable CMOS differential active resistor and its application

We propose a novel CMOS differential voltage-controlled equivalent active resistor based on a modified common-source cross-coupled pair. The equivalent resistance exhibited by the circuit can be tuned to be either positive or negative through the tail current, which is controlled by a bias voltage. The complete cell comprises four transistors, two resistors in addition to the bias voltage, and tail current source. Application to the design of oscillators is demonstrated by theoretical analysis, circuit simulations, and verified experimentally through measurements on prototypes implemented using discrete transistors.     

Assessing capacity scalability policies in RMS using system dynamics

This paper presents a model for assessing different capacity scalability policies in Reconfigurable Manufacturing System (RMS) for different changing demand scenarios. The novelty of this approach is two fold: (1) it is the first attempt to explore different capacity scalability policies in RMS based on multiple performance measures, mainly scaling rate, Work In Process level, inventory level and backlog level; and (2) the dynamic scalability process in RMS is modeled for the first time using System Dynamics. Different policies for capacity scalability for various demand scenarios were assessed. Numerical simulation results obtained using the developed capacity scalability model showed that the best capacity scalability policy to be adopted for RMS is dependent on the anticipated demand pattern as well as the various manufacturing objectives. The presented assessment results will help the capacity scalability planners better decide the different tradeoffs between the competing strategic and operational objectives of the manufacturing enterprise, before setting the suitable capacity scalability plan parameters.

Applying several soft computing techniques for prediction of bearing capacity of driven piles

Engineering with Computers - Pile as a type of foundation is a structure which can transfer heavy structural loads into the ground. Determination and proper prediction of pile bearing capacity are...     

An Innovative Genetic Algorithm for a Multi-Objective Optimization of Two-Dimensional Cutting-Stock Problem

This paper addressed an important variant of two-dimensional cutting stock problem. The objective was not only to minimize trim loss, as in traditional cutting stock problems, but rather to minimize the number of machine setups. This additional objective is crucial for the life of the machines and affects both the time and the cost of cutting operations. Since cutting stock problems are well known to be NP-hard, we proposed an approximate method to solve this problem in a reasonable time. This approach differs from the previous works by generating a front with many interesting solutions. By this way, the decision maker or production manager can choose the best one from the set based on other additional constraints. This approach combined a genetic algorithm with a linear programming model to estimate the optimal Pareto front of these two objectives. The effectiveness of this approach was evaluated through a set of instances collected from the literature. The experimental results for different-size problems show that this algorithm provides Pareto fronts very near to the optimal ones.     

Joint QoE-based user association and efficient cell–carrier distribution for enabling fully hybrid spectrum sharing approach in 5G mmWave cellular networks

Densifying the network by adding more minicell towers or relays throughout a hot spot area while extensively reusing the available spectrum is an essential choice to improve QoS. Unfortunately, this approach can be prohibitively costly. One possible solution to reduce the capital and operating expenditure in such overdensified networks is the adoption of the spectrum-sharing approach. However, both approaches would complicate the interference phenomenon either among inter- or intraoperators, which may cause serious performance degradation. In this paper, a fully hybrid spectrum-sharing (FHSS) approach aided by an efficient cell–carrier distribution was proposed with consideration to the interference dilemma. Moreover, an adaptive hybrid QoE-based mmWave user association (mUA) scheme was presented to assign a typical user to the serving mmWave base station (mBS), which offers the highest achievable data rate. The proposed FHSS approach (with the presented QoE-based mUA) was compared with recent works and with both FHSS approach using the conventional max-SINR-based mUA, which assigns a typical user to the tagged mBS carrying the highest signal-to-interference-plus noise ratio and the baseline scenario (licensed spectrum access). In particular, three spectrum access methods (licensed, semipooled, and fully pooled) were integrated in a hybrid manner to engage improved data rates to users. Numerical results show that the joint cell–carrier distribution and FHSS approach with QoE-based mUA outperform both baselines FHSS with the max-SINR mUA scheme and the licensed spectrum access. Furthermore, results demonstrate the effectiveness of the proposed approach in terms of both operators’ independence and fairness.

     

Analysis of malware propagation behavior in Social Internet of Things

Social Internet of Things (SIoT) is an evolution of the Internet of Things, where objects interact socially with each other in the sense that they can independently establish new relationships, offer, or discover services, in order to accomplish their tasks with minimum involvement of the user. This additional convenience comes at the expense of higher risk of speeding up malware propagation through the dynamically created relationships. Because of the undesirable effects of malware (eg, disruption of device operation), it is essential to understand their spreading behavior in order to minimize their negative impacts. In this paper, we analyze malware propagation behavior in SIoT and investigate different parameters that influence spreading of malware. Toward that end, a simulator has been developed to simulate the spreading process of malware in SIoT. Many propagation scenarios were analyzed using the proposed simulator. Simulation results show that adding more relationships in the SIoT or increasing the number of owned objects per user has increased malware spreading rate. For example, the time to infect all objects is faster by 45% when objects communicate through four relationships compared with the case when objects communicate through only two relationships in SIoT. We also investigated ways to restrict the malware spreading. Results show that preventing objects from establishing dynamic social relationship slows down the infection by 40% compared with the next best scenario (ie, blocking co‐location relationships), which means more time for vendors to patch up their products.     

An effective back‐off selection technique for reliable beacon reception in VANETs

In safety‐critical scenarios, reliable reception of beacons transmitted by a subject vehicle is critical to avoid vehicle collision. According to the employed contention window sizes in IEEE 802.11p, beacons are transmitted with a small contention window size. As a result, multiple vehicles contend for the shared channel access by selecting the same back‐off slot. This is a perfect recipe for synchronous collisions wherein reliable beacon delivery cannot be guaranteed for any vehicle. We consider the problem of selecting the back‐off slots from the current contention window to provide reliable delivery of beacons transmitted by a subject vehicle to its neighbors. Given a safety scenario, we propose a Pseudo‐Random Number Generator (PRNG)‐inspired back‐off selection (PBS) technique. The proposed technique works on the hypothesis that synchronous collisions of beacons transmitted by a subject vehicle can be reduced if all its neighbors select different back‐off slots (ie, not the back‐off slot selected by the subject vehicle). The discrete‐event simulations demonstrate that PBS can increase the overall message reception from a subject vehicle, in comparison with the uniform random probability back‐off selection in IEEE 802.11p.     

Cons and pros: prison education through the eyes of the prison educated*

ABSTRACT

While much has been written within academic journals about prisoners, rarely is there anything written by prisoners. In this essay, we, a group of prisoners who are earning or have earned college degrees while incarcerated in Texas, address the purpose, merits, and pitfalls of prison education and reform. Written as a response to the essays appearing in this special issue, we discuss our experiences of being othered as inmates, the impact of societal bias against us, our perspectives on prison education, and our own ideas for reforming prisons and making them more humane.