An efficient optimal algorithm for the quantity discount problem in material requirement planning

An optimal algorithm based on branch-and-bound approach is presented in this paper to determine lot sizes for a single item in material requirement planning environments with deterministic time-phased demand and constant ordering cost with zero lead time, where all-units discounts are available from vendors and backlog is not permitted. On the basis of the proven properties of optimal order policy, a tree-search procedure is presented to construct the sequence of optimal orders. Some useful fathom rules have been proven, which make the algorithm very efficient. To compare the performance of this algorithm with the other existing optimal algorithms, an experimental design with various environments has been developed. Experimental results show that the performance of our optimal algorithm is much better than the performance of other existing optimal algorithms. Considering computational time as the performance measure, this algorithm is considered the best among the existing optimal algorithms for real problems with large dimensions (i.e. large number of periods and discount levels).     

Synthesis of modified catalyst and stabilization of CuO/NH4‐ZSM‐5 for conversion of methanol to gasoline

In this article, the catalytic conversion of methanol to gasoline range hydrocarbons has been studied over CuO/ NH₄‐ZSM‐5(3,5,7,9%) catalysts prepared via sono‐chemistry methods. In order to improve, copper oxide can be used as a booster on NH₄‐ZSM‐5 this catalyst property. Accordingly, the conversion process of Methanol to Gasoline (MTG) was conducted under a pressure of 1 atm and temperature of 400°C by a fixed‐bed reactor on copper oxide catalysts which were prepared based on synthetic NH₄‐ZSM‐5. The synthetic catalyst was investigated by such analyses as BET, XRD, FT‐IR, and SEM. Formation of copper oxide phase and proper distribution of copper oxide were proven on the basic level of using XRD analysis. BET analysis showed the reduction in catalyst level and SEM images depicted the proper distribution of particles. The present investigation is to study the effect of CuO loading on NH4‐ZSM‐5 support for conversion of methanol to gasoline range hydrocarbons. A series of CuO/ NH4‐ZSM‐5 catalysts were prepared, characterized, and experimented for their performance on methanol conversion and hydrocarbon yield.     

The Effects of SiO2/Al2O3 and H2O/Al2O3 Molar Ratios on SAPO-34 Catalyst in the Methanol to Olefin Process

Silicon - Informed through synthesis and characterization of NH 3 TPD, BET techniques, the current study investigated the right contribution of silicon and water content on a 9 sample SAPO-34...     

Development and physico-mechanical characterization of carrageenan and poloxamer-based lyophilized matrix as a potential buccal drug delivery system

Context and objectives: The buccal mucosa presents a unique surface for non-invasive drug delivery and also avoids first-pass metabolism. The objective of this study was the formulation development of polymeric mucoadhesive lyophilized wafers as a matrix for potential buccal drug delivery.

Materials and methods: Differential scanning calorimetry (DSC) was used to develop an optimum freeze-cycle, incorporating an annealing step. The wafers were prepared by lyophilization of gels containing three polymers, κ-carrageenan (CAR 911), poloxamer (P407) and polyethylene glycol 600 (PEG 600). The formulations were characterized using texture analysis (for mechanical and mucoadhesion properties), hydration studies, thermogravimetric analysis (TGA), DSC, X-ray powder diffraction (XRPD) and scanning electron microscopy (SEM).

Results and discussion: DSC showed the eutectic temperature (12.8?°C) of the system where the liquid solution and pure solids both existed at a fixed pressure which helped determine the freeze-annealing cycle at 55?°C for 7?h. Mechanical resistance to compression, hydration and mucoadhesion studies showed that optimized wafers were obtained from aqueous gels containing 2% w/w CAR 911, 4% w/w P407 and 4.4% w/w PEG 600. TGA showed residual water of approximately 1% and SEM showed a porous polymeric network that made ease of hydration possible.

Conclusions: Lyophilized wafers by freeze-drying gels containing 2% w/w CAR 911, 4% w/w P407 and 4.4% w/w PEG 600 with optimum physico-mechanical properties has been achieved.     

New dispatching rules to minimize rejection and tardiness costs in a dynamic flexible flow shop

This paper studies a flexible flow shop system considering dynamic arrival of jobs and the ability of acceptance and rejection of new jobs. The problem objective is to determine a schedule that minimizes sum of the tardiness and rejection costs of jobs. A 0–1 mixed integer model of the problem is formulated. Since this problem class is NP-hard, four dispatching rules have been developed to solve the problem approximately. Moreover, a discrete event simulation model of the flexible flow shop system is developed for the purpose of experimentation. Four dispatching rules from the literature and four new dispatching rules proposed in this paper are incorporated in the simulation model. Simulation experiments have been conducted under various experimental conditions characterized by factors such as shop utilization level, due date tightness and number of stages in flexible flow shop. The results indicate that proposed dispatching rules provide better performance under problem assumptions.     

Optimal production control and marketing plan in two-machine unreliable flexible manufacturing systems

In this paper, we have developed a production planning and marketing model in unreliable flexible manufacturing systems with inconstant demand rate that its rate depends on the level of advertisement on that product. The proposed model is more realistic and useful from a practical point of view. The flexible manufacturing system is composed of two machines that produce a single product. Markovian models frequently have been used in modeling a wide variety of real-world systems under uncertainties. Therefore, in this paper, the inventory balance equation is represented by a continuous-time model with Markov jump process to take into account machines breakdown. The objective is to minimize the expected total cost of the firm over an infinite time horizon. While the total cost consists of the cost of the product surplus, the cost of the production, and the cost of the advertisement. In the process of finding a solution to the problem, we first characterize an optimal control by a class of linear stochastic system where some parameter values are subject to random jump. By defining quadratic cost functions and characterizing the associated limiting optimal control problem, a discrete-time approximation model and an asymptotic optimal control model are developed. It is clear that such a solution exists and can be obtained as a limit of a monotonic sequence with solving the steady-state Riccati equation.     

Photocatalytic degradation and mineralization of dye pollutants from wastewater under visible light using synthetic CuO-VO2/TiO2 nanotubes/nanosheets

CuO-VO₂/TiO₂ as a new nanocomposite was synthesized through hydrothermal method and identified by various spectroscopic techniques including X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), high-resolution transmission electron microscopy (HR-TEM), energy dispersive X-ray analysis (EDX), UV–visible, differential reflectance spectroscopy (DRS), and Mott–Schottky. The presence of nanotubes/nanosheets in the synthesized nanocomposite was confirmed by HR-TEM. The anatase and rutile crystalline forms of TiO₂ were detected by Raman spectroscopy and X-ray diffraction (XRD). XPS analysis confirmed the presence of CuO and VO₂ in the nanocomposite. The surface area and the band-gap energy of the nanocomposite were determined via N₂ adsorption–desorption analysis and DRS. The presence of a p–n junction between TiO₂ (n-type) and CuO/VO₂ (p-type) was confirmed by the Mott–Schottky analysis. The photocatalytic activity of the nanocomposite against methylene blue (MB), methyl orange (MO), and cango red (CR) was studied under visible-light irradiation. The times of degradation for the decomposition of the dyes were 10–25 min. The rate constants of degradation for MB, MO, and CR were calculated as 0.34, 0.090, and 0.155 min^?1, respectively. The catalyst was recovered four times. In addition, the mineralization of the dyes was investigated by chemical oxygen demand (COD). The reaction was performed in the presence of different radical scavengers, and the ·OH was found to be the predominantly active species in the photodegradation of the dyes.

     

Study of stochastic sequence-dependent flexible flow shop via developing a dispatching rule and a hybrid GA

A flexible flow shop is a generalized flow shop with multiple machines in some stages. This system is fairly common in flexible manufacturing and in process industry. In most practical environments, scheduling is an ongoing reactive process where the presence of real time information continually forces reconsideration of pre-established schedules. This paper studies a flexible flow shop system considering non-deterministic and dynamic arrival of jobs and also sequence dependent setup times. The problem objective is to determine a schedule that minimizes average tardiness of jobs. Since the problem class is NP-hard, a novel dispatching rule and hybrid genetic algorithm have been developed to solve the problem approximately. Moreover, a discrete event simulation model of the problem is developed for the purpose of experimentation. The most commonly used dispatching rules from the literature and two new methods presented in this paper are incorporated in the simulation model. Simulation experiments have been conducted under various experimental conditions characterized by factors such as shop utilization, setup time level and number of stages. The results indicate that methods proposed in this study are much better than the traditional dispatching rules.     

An efficient hybrid genetic algorithm to solve assembly line balancing problem with sequence-dependent setup times

In this paper the setup assembly line balancing and scheduling problem (SUALBSP) is considered. Since this problem is NP-hard, a hybrid genetic algorithm (GA) is proposed to solve the problem. This problem involves assigning the tasks to the stations and scheduling them inside each station. A simple permutation is used to determine the sequence of tasks. To determine the assignment of tasks to stations, the algorithm is hybridized using a dynamic programming procedure. Using dynamic programming, at any time a chromosome can be converted to an optimal solution (subject to the chromosome sequence).