A mathematical model and a heuristic approach for periodic material delivery in lean production environment

One of the important design elements for a good production system is material handling. In cases where it is not well-designed, it can be the bottleneck in the system. Moreover, it can cause a lot of wastes such as waiting time, idle time, and excessive transportation and cost. In this study, material handling in lean-based production environments is taken into account. Depending on the lean structure of the production systems such as being pull-based, smooth, and repetitive, delivering the materials to the stations periodically becomes important. At this point, milk-run trains are highly used in real applications since they enable the handling of required amount of materials on a planned basis. With this study, it is aimed to develop a specific model for milk-run trains which travel periodically in the production environment on a predefined route in equal cycle times with the aim of minimizing work-in-process and transportation costs. Since the milk-run trains having equal cycle times start their tours at the same time intervals, it becomes simple to manage them. For this reason, they are used in lean production systems where level scheduling is performed. The developed model is based on mixed-integer linear programming, and since it is difficult to find the optimum solution due to the combinatorial structure of the problem, a novel heuristic approach is developed. A numerical example is provided so as to show the applicability of the mathematical model and the heuristic approach.     

A Thienothiophene-Based Cation Treatment Allows Semitransparent Perovskite Solar Cells with Improved Efficiency and Stability

Perovskite surface treatment with additives has been reported to improve charge extraction, stability, and/or surface passivation. In this study, treatment of a 3D perovskite ((FAPbI₃)_1−x(MAPbBr₃)_x) layer with a thienothiophene-based organic cation (TTMAI), synthesized in this work, is investigated. Detailed analyses reveal that a 2D (n = 1) or quasi-2D layer does not form on the PbI₂-rich surface 3D perovskite. TTMAI-treated 3D perovskite solar cells (PSCs) fabricated in this study show improved fill factors, providing an increase in their power conversion efficiencies (PCEs) from 17% to over 20%. It is demonstrated that the enhancement is due to better hole extraction by drift-diffusion simulations. Furthermore, thanks to the hydrophobic nature of the TTMAI, PSC maintains 82% of its initial PCE under 15% humidity for over 380 h (the reference retains 38%). Additionally, semitransparent cells are demonstrated reaching 17.9% PCE with treated 3D perovskite, which is one of the highest reported efficiencies for double cationic 3D perovskites. Moreover, the semitransparent 3D PSC (TTMAI-treated) maintains 87% of its initial efficiency for six weeks (>1000 h) when kept in the dark at room temperature. These results clearly show that this study fills a critical void in perovskite research where highly efficient and stable semitransparent perovskite solar cells are scarce.     

Flow-line scheduling problem with controllable processing times

In this paper we address the simultaneous scheduling and optimal-processing-times selection problem in a multi-product deterministic flow line operated under a cyclic scheduling approach. The selection of processing times plays an important role in achieving the desired production rate with the least possible operating cost. We first formulate the important subproblem of optimal-processing-times selection for different objectives, when the sequence of jobs is fixed, and then develop an efficient solution procedure for it. The fast solution of the fixed sequence problem is necessary for the development of efficient approximate solution procedures for the simultaneous scheduling and optimal-processing-times problem. A computational study on the effectiveness of the proposed solution procedure is presented. For the solution of the simultaneous scheduling and optimal-processing-times problem we suggest an iterative solution procedure, and report our computational experience with this procedure. For the solution of large problems we present a genetic algorithm. The effectiveness of the algorithm is demonstrated through computational results and by evaluating the performance of the obtained solutions against lower bounds that we developed for the problem.     

Development of metal supported solid oxide fuel cells for operation at 500–600 °C

A novel metal-supported solid oxide fuel cell has been developed that is capable of operating at temperatures of 500–600 °C. The rationale behind the materials used to construct this fuel cell type is given, and results are presented from cell testing on hydrogen and reformed natural gas, including durability trials of some 2500 h duration. This new fuel cell variant is shown to be tolerant of carbon monoxide, durable, robust to thermal and redox cycling, and capable of delivering technologically relevant power densities. This paper was presented at the Fuel Cells: Materials, Processing, and Manufacturing Technologies Symposium sponsored by the Energy/Utilities Industrial Sector & Ground Transportation Industrial Sector and the Specialty Materials Critical Technologies Sector at the ASM International Materials Solutions Conference, October 13–15, 2003, in Pittsburgh, PA. The symposium was organized by P. Singh, Pacific Northwest National Laboratory, S.C. Deevi, Philip Morris USA, T. Armstrong, Oak Ridge National Laboratory, and T. Dubois, U.S. Army CECOM.     

A branch-and-bound algorithm for the concave cost supply problem

Effective supplier selection and allocation of order quantity among multiple suppliers are indispensable to the success of a manufacturing company. While companies have begun to turn into a comprehensive multi-criteria approach, most buyers still consider purchasing cost to be their primary concern in selecting their suppliers. In this paper, we consider the concave cost supply problem where a manufacturer seeks to select the suppliers and simultaneously procure the quantity of material/component required for production at the minimum total cost during a standard production period. We provide and validate an effective and efficient branch-and-bound algorithm that is finite and that finds the global optimal solution of the problem without any restrictions on the cost functions or on the set of input parameters used in the problem. Numerical experiments are conducted to evaluate the performance of the proposed algorithm.     

Synthesis and characterization of 2‐hydroxyethylmethacrylate/2‐(3‐indol‐yl)ethylmethacrylamide‐based novel hydrogels as drug carrier with in vitro antibacterial properties

In this study, a new cationic monomer 2‐(3‐indol‐yl)ethylmethacrylamide (IEMA) derived from tryptamine was synthesized in a single step and characterized by Fourier transform infrared (FTIR), ¹H‐NMR, and ¹³C‐NMR. Then, one‐step preparation of novel poly[2‐hydroxyethylmethacrylate‐c‐2‐(3‐indol‐yl)ethylmethacrylamide], or p(HEMA‐c‐IEMA), copolymeric hydrogels has been performed successfully with IEMA and 2‐hydroxyethylmethacrylate (HEMA) as monomers using free radical aqueous polymerization. The hydrogels were characterized with scanning electron microscopy, FTIR, elemental analysis, thermogravimetric analysis, and texture profile analysis instruments. p(HEMA‐c‐IEMA) hydrogels were used for swelling, diffusion, drug release, and antibacterial activity studies. The drug‐release behavior of the hydrogels was determined as a function of time at 37 °C in pH 1.2 and 7.2. The swelling and drug‐release studies showed that an increased IEMA amount caused a higher increase in swelling and drug‐release values. Additionally, zero‐order, first‐order, and Higuchi equation kinetic models were applied to the drug‐release data, and the data fit well in the Higuchi model, and the Peppas power‐law model was applied to the release mechanism. Finally, the antibacterial activities of the hydrogels were screened against Gram‐positive bacteria (Bacillus cereus and Staphylococcus aureus) and Gram‐negative bacteria (Escherichia coli and Salmonella typhimurium). © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134, 45550.     

Photocatalytic and Photoelectrocatalytic Performance of 1% Pt Doped TiO2 for the Detoxification of Water

The degradation of formic acid (HCOOH), FA (a surrogate contaminant) using titanium dioxide (TiO₂) and 1% Pt doped TiO₂ electrodes, prepared by sol—gel methods, was investigated in a photoelectrocatalytic (PEC) system in order to determine the effect of Pt doping on the oxidation potential of TiO₂. Pt doping shifts the position of band edge and therefore the direct and indirect oxidation potentials of TiO₂ in PEC systems. As a result, the degradation of formic acid via the generation of hydrogen peroxide production on 1%Pt—TiO₂ electrodes was much better than that on non-doped electrodes. The degradation of HCOOH was also examined with respect to the faradaic efficiency of this process. It was found that the 1%Pt—TiO₂ photoanode had a 30% higher efficiency than that of non-doped TiO₂ photoanodes.     

Fault detection on robot manipulators using artificial neural networks

Nowadays, gas welding applications on vehicle’s parts with robot manipulators have increased in automobile industry. Therefore, the speed of end-effectors of robot manipulator is affected on each joint during the welding process with complex trajectory. For that reason, it is necessary to analyze the noise and vibration of robot’s joints for predicting faults. This paper presents an experimental investigation on a robot manipulator, using neural network for analyzing the vibration condition on joints. Firstly, robot manipulator’s joints are tested with prescribed of trajectory end-effectors for the different joints speeds. Furthermore, noise and vibration of each joint are measured. And then, the related parameters are tested with neural network predictor to predict servicing period. In order to find robust and adaptive neural network structure, two types of neural predictors are employed in this investigation. The results of two approaches improved that an RBNN type can be employed to predict the vibrations on industrial robots.     

Modeling the order picking function in supply chain systems: formulation, experimentation, and insights

Order picking is found at the interface of production and distribution networks within supply chain systems. In this study the order picking function is modeled to explore its role with respect to inventory deployment and centralized distribution planning, in the presence of production economies of scale and deterministic demand. The results of 5400 numerical experiments suggest that, for certain system cost structures, there may exist an inherent tension between commercially independent stages, owed to the consequences of decentralized planning. Increasing total system cost due to pull style planning over centralized planning appeared to be most heavily influenced by the proportion of costs shared by various system stages around the order picker, and this gap averaged over 30% in worst-case scenarios.