Mathematical formulation and heuristic algorithms for optimisation of auto-part milk-run logistics network considering forward and reverse flow of pallets

The operational planning of distribution network for automotive industry is complex with many conditions to consider, including heterogeneous fleet, enforcing the feasibility of 3D-packing of pallets into vehicles to address the vehicle's capacity in terms of weight and volume, compatibility of orders in a vehicle, returning empty pallets from assembly-plants backwards to suppliers, and delivery time windows. A mathematical model (MILP) is proposed that takes account of these conditions to minimise total transportation costs. The network structure can be a combination of direct shipment and milk-run for both forward and reverse flow of pallets. The model is solved optimally for small-size problems. For solving larger problems, a heuristic algorithm (in two versions) is proposed that uses a similarity measure to generate a reasonable list of orders. Best/first-fit strategies are employed to generate a feasible solution with the aid of a relaxed version of the proposed MILP. Improvement heuristics are also designed. Unlike most of existing constructive heuristics, our aim for developing the heuristic approach is to force routing decision, with all of its considerations, being made optimal. We also use the proposed best-fit strategy in the body of grouping evolution strategy (GES) algorithm to attain an effective meta-heuristic approach. The effectiveness of heuristics is tested on generated instances which demonstrates they are optimal for small-size problems. They are also tested on the data of daily auto-parts shipments gathered from the largest Iranian automobile company. Results demonstrate there exists a significant potential for cost saving through milk-run strategy compared with the direct shipping strategy.     

CFD Simulation of Deep-Bed Paddy Drying Process and Performance

Computational Fluid Dynamics (CFD) was applied three-dimensionally to simulate the drying behavior of paddy in a deep-bed dryer. The commercial CFD software Fluent 6.3.26 was used. The deep-bed paddy drying process and performance were studied by incorporating user-defined function (UDF) in Fluent written in C language. The predicted drying parameters were compared with experimental data of deep-bed drying of paddy. The values of mean relative deviation (MRD), standard error of prediction (SEP), and maximum error of prediction (MEP) for prediction of grain moisture content, air temperature, and absolute humidity were less than 6, 10, and 9%; 0.33% (d.b), 1.24°C, and 0.06% (kg/kg of dry air); and 2.25% (d.b), 6.8°C, and 0.37% (kg/kg of dry air), respectively, which reflect reasonable accuracy. Moreover, the energetic and exergetic performance of deep-bed paddy drying were simulated and analyzed. The effects of inlet air temperature and mass flow rate on the performance parameters were investigated. It was shown that the application of higher levels of inlet air temperature and lower mass flow rates yielded higher exergy efficiencies of deep-bed paddy drying.     

High yield gold nanoparticle‐based DNA isolation method for human papillomaviruses genotypes from cervical cancer tissue samples

Gold nanoparticles (AuNPs) are commonly used in biosensors of various kinds. However, its application to extract DNA from cancer tissues has not been extensively studied. The purification of DNA from cancer tissues is an important step in diagnostic and therapeutic development. Almost, all cervical cancer cases are associated with high‐risk human papillomavirus (HR‐HPV) infection. Accurate viral diagnosis has so far relied on the extraction of adequate amounts of DNA from formalin‐fixed, paraffin‐embedded (FFPE) tissue samples. Till now, no specific and sensitive DNA purification method has been introduced for the extraction of HR‐HPV from FFPE tissue. Since the commercially available purification kits are not sensitive and specific enough for HR‐HPV DNA targets, in this study, a DNA purification method was designed based on AuNPs to purify sufficient amounts of HR‐HPV DNA from cervical cancer tissue samples. AuNPs were coated with a series of oligonucleotide probes to hybridize to specific DNA sequences of HR‐HPV genotypes. Results showed that 733 out of 800 copies of type‐specific HPV DNA were recovered with complete specificity, compared to 36 copies with a standard commercial kit (Qiagen FFPE). The high yield of DNA (91.6%) is the main advantage of the AuNPs‐probe purification method.Inspec keywords: cancer, biological tissues, microorganisms, gold, DNA, lab‐on‐a‐chip, gynaecology, purification, biochemistry, molecular biophysics, nanoparticles, nanomedicine, cellular biophysicsOther keywords: specific extraction method, cervical cancer tissue samples, high‐risk human papillomavirus infection, paraffin‐embedded tissue samples, specific DNA purification method, sensitive DNA purification method, FFPE tissue, HR‐HPV DNA targets, specific DNA sequences, HR‐HPV genotypes, type‐specific HPV DNA, AuNP‐probe purification method, gold nanoparticle‐based DNA isolation method, oligonucleotide, human papillomaviruse genotypes, clinical samples, Au     

A collision‐avoidance scheme for redundant manipulators: Theory and experiments

The development of a collision‐ and self‐collision‐avoidance scheme for redundant manipulators is discussed in this paper. The method is based on modeling the arm and its environment by simple geometric primitives (cylinders and spheres). A compact method of detecting collisions between two cylinders is introduced. By resorting to the notions of dual angles and dual vectors for representing the axes of cylinders in space, a characterization of different types of collisions is introduced. The performance of the proposed scheme is demonstrated for a seven degrees‐of‐freedom redundant manipulator via simulations and experiments. © 2005 Wiley Periodicals, Inc.     

Tuning PI and fractional order PI controllers with an additional fractional order Pole

The PI controller with an additional pole (PI?+?P) already has been proposed to decrease the noise effect on the control signal. In this paper, a fractional order pole is employed to increase the PI?+?P controller performance. The fractional order is obtained by adjusting the Nyquist plot slope in gain crossover frequency. This condition as well as gain crossover frequency and phase margin specifications are utilized to design the PI controller augmented with an additional fractional order pole (PI?+?FO[P]). To design these two controllers, a first-order plus delay time (FOPDT) model is utilized. For plants that could not be described by this model, its fractional order version (FFOPDT) could be utilized. In this case, a FOPI?+?FO[P] controller is obtained that could improve the transient response of the closed-loop system. The numerical simulations accomplished on various plant models (including chemical plants) demonstrate the effectiveness of the proposed controller comparing with the PI?+?P, PID, FOPI, and fractional order proportional-integral-derivative controllers.     

Engineering modeling frameworks for microbial food safety at various scales

The landscape of mathematical model-based understanding of microbial food safety is wide and deep, covering interdisciplinary fields of food science, microbiology, physics, and engineering. With rapidly growing interest in such model-based approaches that increasingly include more fundamental mechanisms of microbial processes, there is a need to build a general framework that steers this evolutionary process by synthesizing literature spread over many disciplines. The framework proposed here shows four interconnected, complementary levels of microbial food processes covering sub-cellular scale, microbial population scale, food scale, and human population scale (risk). A continuum of completely mechanistic to completely empirical models, widely-used and emerging, are integrated into the framework; well-known predictive microbiology modeling being a part of this spectrum. The framework emphasizes fundamentals-based approaches that should get enriched over time, such as the basic building blocks of microbial population scale processes (attachment, migration, growth, death/inactivation and communication) and of food processes (e.g., heat and moisture transfer). A spectrum of models are included, for example, microbial population modeling covers traditional predictive microbiology models to individual-based models and cellular automata. The models are shown in sufficient quantitative detail to make obvious their coupling, or their integration over various levels. Guidelines to combine sub-processes over various spatial and time scales into a complete interdisciplinary and multiphysics model (i.e., a system) are provided, covering microbial growth/inactivation/transport and physical processes such as fluid flow and heat transfer. As food safety becomes increasingly predictive at various scales, this synthesis should provide its roadmap. This big picture and framework should be futuristic in driving novel research and educational approaches.     

Generalized projective synchronization of uncertain chaotic systems with external disturbance

This paper proposes the generalized projective synchronization (GPS) of uncertain chaotic systems with external disturbance via Gaussian radial basis adaptive sliding mode control (GRBASMC). A sliding surface is adopted to ensure the stability of the error dynamics in sliding mode control. In the neural sliding mode controller, a Gaussian radial basis function is utilized to online estimate the system dynamic function. The adaptation law of the control system is derived in the sense of Lyapunov function, thus the system can be guaranteed to be asymptotically stable.The proposed method allows us to arbitrarily adjust the desired scaling by controlling the slave system. It is not necessary to calculate the Lyapunov exponents and the eigen values of the Jacobian matrix, which makes it simple and convenient. Also, it is a systematic procedure for GPS of chaotic systems and it can be applied to a variety of chaotic systems no matter whether it contains external excitation or not. Note that it needs only one controller to realize GPS no matter how much dimensions the chaotic system contains and the controller is easy to be implemented.The proposed method is applied to three chaotic systems: Genesio system, Lur’e like system and Duffing system.     

Simulation and Validation of Microwave-Assisted Fluidized Bed Drying of Soybeans

A general mathematical model of heat and mass transfer was developed to simulate the microwave-assisted fluidized bed drying of bulk grain. The model was solved using the well-known Runge-Kutta-Gill method. The model is capable of predicting the moisture content of soybean as well as the drying air parameters (i.e., drying air temperature and moisture content) during drying. The values of mean relative deviation (MRD) were less than 8 and 10% for prediction of grain moisture content and outlet air parameters, respectively, which reflects an acceptable accuracy. In comparison with conventional fluidized bed drying of soybean, microwave-assisted fluidized bed drying led to 83.39–98.07% savings in drying time and 82.07–95.22% savings in specific energy consumption when reducing soybean moisture content from 18.32 to 12% (db).     

A case study of two-robot-arm workcell material handling

More problems are involved in collaborating multi-robot-arm systems than in single robot arms. These problems stem from mutual dynamic and kinematic effects between the arms. This work is confined to only the kinematics of two robot arms; other problems like control, force distribution, and so on are not addressed here. A particular case of a material handling problem with two collaborating robot arms loading/unloading long objects from a conveyor is studied. The feasibility of the task from a kinematics point of view, and the necessary conditions and constraints for the relative set-up of the two manipulators are discussed. These conditions originate from the working envelope of the two arms which depends on three factors: the working envelope of each individual arm, the spacing between them, and the dimensions of the workpiece. To assist this study, an efficient algorithm for determining the two-dimensional contours of the workspace of a single arm is included.