Minimizing the total completion time on a single machine with the learning effect and multiple availability constraints

This paper considers a single machine scheduling problem with the learning effect and multiple availability constraints that minimizes the total completion time. To solve this problem, a new binary integer programming model is presented, and a branch-and-bound algorithm is also developed for solving the given problem optimally. Since the problem is strongly NP-hard, to find the near-optimal solution for large-sized problems within a reasonable time, two meta-heuristics; namely, genetic algorithm and simulated annealing are developed. Finally, the computational results are provided to compare the result of the binary integer programming, branch-and-bound algorithm, genetic algorithm and simulated annealing. Then, the efficiency of the proposed algorithms is discussed.     

A robust optimization model for multi-product two-stage capacitated production planning under uncertainty

Production planning (PP) is one of the most important issues carried out in manufacturing environments which seeks efficient planning, scheduling and coordination of all production activities that optimizes the company’s objectives. In this paper, we studied a two-stage real world capacitated production system with lead time and setup decisions in which some parameters such as production costs and customer demand are uncertain. A robust optimization model is developed to formulate the problem in which minimization of the total costs including the setup costs, production costs, labor costs, inventory costs, and workforce changing costs is considered as performance measure. The robust approach is used to reduce the effects of fluctuations of the uncertain parameters with regards to all the possible future scenarios. A mixed-integer programming (MIP) model is developed to formulate the related robust production planning problem. In fact the robust proposed model is presented to generate an initial robust schedule. The performance of this schedule could be improved against of any possible occurrences of uncertain parameters. A case from an Iran refrigerator factory is studied and the characteristics of factory and its products are discussed. The computational results display the robustness and effectiveness of the model and highlight the importance of using robust optimization approach in generating more robust production plans in the uncertain environments. The tradeoff between solution robustness and model robustness is also analyzed.     

Solid-Phase Extraction of Metoprolol onto(Methacrylic acid-ethylene glycol dimethacrylate)-based Molecularly Imprinted Polymer and Its Spectrophotometric Determination

A new adsorbent for molecularly imprinted solid phase extraction (MISPE) of metoprolol was synthesized using methacrylic acid (MAA) as functional monomer and ethylene glycol dimethacrylate (EGDMA) as cross‐linking agent causing a non‐covalent, bulk, thermal radical‐polymerization. Control polymer (non‐imprinted polymer) was prepared under well defined conditions without the use of metoprolol. The synthesized polymers were characterized by IR spectroscopy, X‐ray diffraction and thermal analysis techniques. This polymer was used for the rapid extraction and preconcentration of metoprolol from real samples prior to spectrophotometric determination. Extraction efficiency and the influence of flow rates of sample and stripping solutions, pH, type of eluent for elution of metoprolol from polymer, break through volume and limit of detection were studied. The detection limit of the proposed method is 0.4 ng·mL^?1. The method was applied successfully to the recovery and determination of metoprolol in tablets, human urine and plasma samples.     

Symmetrization of the AMBER and CHARMM force fields

The AMBER and CHARMM force fields are analyzed from the viewpoint of the permutational symmetry of the potential for feasible exchanges of identical atoms and chemical groups in amino and nucleic acids. In each case, we propose schemes for symmetrizing the potentials, which greatly facilitate the bookkeeping associated with constructing kinetic transition networks via geometry optimization. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2010     

The measurement of relative efficiency using data envelopment analysis with assurance regions that link inputs and outputs

The most popular weight restrictions are assurance regions (ARs), which impose ratios between weights to be within certain ranges. ARs can be categorized into two types: ARs type I (ARI) and ARs type II (ARII). ARI specify bounds on ratios between input weights or between output weights, whilst ARII specify bounds on ratios that link input to output weights. DEA models with ARI successfully maximize relative efficiency, but in the presence of ARII the DEA models may under-estimate relative efficiency or may become infeasible. In this paper we discuss the problems that can occur in the presence of ARII and propose a new nonlinear model that overcomes the limitations discussed. Also, the dual model is described, which enables the assessment of relative efficiency when trade-offs between inputs and outputs are specified. The application of the model developed is illustrated in the efficiency assessment of Portuguese secondary schools.     

Suitability of artificial bulk viscosity for large-eddy simulation of turbulent flows with shocks

The artificial bulk viscosity method to numerically capture shocks is investigated for large-eddy simulation (LES). Different variations of this method are tested on a turbulent flow over a cylinder at Reynolds number of 10,000 and free-stream Mach number of 0.85. The artificial bulk viscosity model by Cook and Cabot, which is parameterized by the strain rate magnitude, is found to provide unnecessary bulk viscosity in turbulent regions away from shocks. While developed turbulent structures are found unaffected, this extra bulk viscosity is shown to significantly damp the sound field. An alternative formulation of the model which is parameterized by the rate of dilatation is proposed. This formulation is shown to avoid the unnecessary bulk viscosity and enhance the sound-prediction capability of the model. It was found that standard LES combined with artificial bulk viscosity is a promising approach for simulation of turbulent flows with shocks. The formulation of the model on curvilinear coordinates is presented in the appendix.     

Small signal gain and saturation intensity of a Yb:Silica fiber MOPA system

In this work, the small signal gain (γ₀) and signal saturation power (P_sat) of a continuous wave (CW) single-frequency ytterbium-doped (YD) double-clad (DC) fiber master oscillator power amplifier (MOPA) array has been determined in the optimum length. At first, we have obtained output versus input signal powers in the amplifier, using the coupled steady-state rate equations for the various end diode pumping such as co/counter propagation and bidirectional modes. On the other hand, the steady-state amplification relation was employed at a presumed γ₀ and P_sat as initial guess, to generate output signals. Then, using the least square method (LSM), those input/output signal powers were fitted by the best regression curve obtained from the amplification relation to determine the converged γ₀ and P_sat values. The analytical formulae for γ₀ and P_sat were derived using the rate equations as well.     

Flexible job shop scheduling with overlapping in operations

In this paper, flexible job shop scheduling problem with a new approach, overlapping in operations, is discussed. In many flexible job shops, a customer demand can be released more than one for each job, where demand determines the quantity of each finished job ordered by a customer. In these models each job has a demand more than one. This assumption is an important and practical issue for many flexible job shops such as petrochemical industries. To consider this assumption, we use a new approach, named overlapping in operations. In this approach, embedded operations of each job can be performed due to overlap considerations in which each operation may be overlapped with the others because of its nature. The overlapping is limited by structural constraints, such as the dimensions of the box to be packed or the capacity of the container used to move the pieces from one machine to the next. Since this problem is well known as NP-Hard class, a hierarchical approach used simulated annealing algorithm is developed to solve large problem instances. Moreover, a mixed integer linear programming (MILP) method is presented. To evaluate the validity of the proposed SA algorithm, the results are compared with the optimal solution obtained with the traditional optimization technique (The Branch and Bound method). The computational results validate the efficiency and effectiveness of the proposed algorithm. Also the computational results show that the overlapping considering can improve the makespan and machines utilization measures. So the proposed algorithm can be applied easily in real factory conditions and for the large size problems and it should thus be useful to both practitioners and researchers.     

Room Temperature Ionic Liquids (RTILs) and Multiwalled Carbon Nanotubes (MWCNTs) as Modifiers for Improvement of Carbon Paste Ion Selective Electrode Response; A Comparison Study with PVC Membrane

Room temperature ionic liquids (RTILs), 1‐n‐butyl‐3‐methylimidazolium tetrafluoroborate, [bmim]BF₄, and multiwalled carbon nanotubes (MWCNTs) were used for improvement of a praseodymium carbon paste ion selective sensor response. [bmim]BF₄ can be a better binder than mineral oils. MWCNTs have a good conductivity which helps the transduction of the signal in carbon paste electrode. The characteristics of these electrodes as potentiometric sensors were evaluated and compared with PVC membrane sensor. The results indicate that potentiometric sensor constructed with ionic liquid shows an increase in performance in terms of Nernstian slope, selectivity, response time, and response stability compared to Pr(III) PVC membrane sensor.     

A novel high selective and sensitive para-nitrophenol voltammetric sensor, based on a molecularly imprinted polymer-carbon paste electrode

By using a molecularly imprinted polymer (MIP) as a recognition element, the design and construction of a high selective voltammetric sensor for para-nitrophenol was formed. Para-nitrophenol selective MIP and a non-imprinted polymer (NIP) were synthesized, and then used for carbon paste (CP) electrode preparation. The MIP-CP electrode showed greater recognition ability in comparison to the NIP-CP. It was shown that electrode washing after para-nitrophenol extraction led to enhanced selectivity, without noticeably decreasing the sensitivity. Some parameters affecting sensor response were optimized and a calibration curve was plotted. A dynamic linear range of 8 × 10⁻⁹ to 5 × 10⁻⁶ mol L⁻¹ was obtained. The detection limit of the sensor was calculated as 3 × 10⁻⁹ mol L⁻¹. Thus, this sensor was used successfully for the para-nitrophenol determination in different water samples.