Devising an efficient approach to determine the optimal sequence of from-to matrix

Sequencing is the most impact factor in many production areas, such as assembly lines, batch production, Travelling Salesman Problem (TSP), product sequences, process sequences, etc. The flow and analysis from one item to another can be presented by the square matrix in which the number of rows or columns is equal to the number of manipulated items, this special matrix form is called “From-To matrix”. The matrix suffers from many drawbacks when it is applied to determine the optimal sequences, such as the number of variables must be as small as possible, there is no flexibility to determine the start or the end sequence to find the best sequencing with some conditions. Also, there is no possibility to add relations to point a variable as wanted or prevented from the sequence. In this paper, we solve the From-To matrix by binary linear programming (BLP). The proposed BLP approach has been applied in Ur company to solve the From-To matrix. This company has a production line that can manufacture four products: A, B, C, and D, the setup time matrix is considered as From-To matrix and the goal of this company is to get an optimum sequence of products with minimum time. The solution of state transition of the From-To matrix using BLP can be formulated in the following five model cases according to transition requirement condition and desired: the first case gives all possible sequence items, the second case lists the sequence items when the first sequence is known, the third case lists the sequence items when the last sequence is known, the fourth case gives all possible sequence items with a condition that prevents occurring of an undesired sequence, and the fifth case gives all possible sequence items with the condition of a wanted occurring of the desired sequence. Furthermore, we found the optimum sequences for states by determining the start or end sequences, and also add the wanted relations or prevented. The mathematical formulas for the number of all sequences under some conditions are derived and proved

Optimal Techno-economic Sequence-based Set of Diagnostic Tests for Distribution Transformers Using Genetic Algorithm

The diagnostic measurement and tests of transformers are essential. Also, the costs of diagnostic tests are considerable. Hence, proposing a method to determine an economic-technical sequence-bases set of diagnostic tests for transformers is useful and interesting. In this paper, a new method is proposed to determine the optimal sequence-based set of diagnostic tests for distribution transformers. A new objective function based on the branch and bound concept is developed in this paper. The proposed optimization problem is solved by using the Genetic Algorithm (GA). The statistical data regarding the experimental diagnostic tests for more than 20 distribution transformers of South-West Power System Company (Pivdenno-Zakhidna Power System) located in Ukraine have been used. The usage of the actual statistical data of distribution transformers is one of the most important contributions of this paper. The comparison of the obtained optimum test results and those of a typical conventional non-optimum sequence of diagnostic tests illustrate the advantages of the proposed method. By applying the proposed method, it is achievable to perform the comprehensive diagnostic tests with the minimum required costs.

A Flowchart Approach to Industrial Hubs

The chapter introduces the flowchart approach, a theory for the construction of an industrial hub or an agglomeration that consists of segments such as physical infrastructure and regulation. The workflow is the dynamic process of building the segments of an agglomeration. Economies of sequence are defined as the selection of any two out of all the segments of an agglomeration and efficiently building them. The integration of all the economies of sequence into a flowchart approach represents the overall process of building an agglomeration. In the flowchart approach to a manufacturing industry agglomeration policy, the optimum sequence involves the construction of traffic infrastructure such as ports and highways, institutional reforms such as liberalization and the formation of one-stop services, the construction of industrial zones to reduce fixed costs, and the invitation to anchor firms. By way of example, the chapter presents the flowchart approach to an information and communications technology agglomeration.

Modeling of Effective Antimicrobials to Reduce Staphylococcus aureus Virulence Gene Expression Using a Two-Compartment Hollow Fiber Infection Model

Toxins produced by community-associated methicillin-resistant Staphylococcus aureus (CA-MRSA) contribute to virulence. We developed a statistical approach to determine an optimum sequence of antimicrobials to treat CA-MRSA infections based on an antimicrobial’s ability to reduce virulence. In an in vitro pharmacodynamic hollow fiber model, expression of six virulence genes (lukSF-PV, sek, seq, ssl8, ear, and lpl10) in CA-MRSA USA300 was measured by RT-PCR at six time points with or without human-simulated, pharmacokinetic dosing of five antimicrobials (clindamycin, minocycline, vancomycin, linezolid, and trimethoprim/sulfamethoxazole (SXT)). Statistical modeling identified the antimicrobial causing the greatest decrease in virulence gene expression at each time-point. The optimum sequence was SXT at T0 and T4, linezolid at T8, and clindamycin at T24–T72 when lukSF-PV was weighted as the most important gene or when all six genes were weighted equally. This changed to SXT at T0–T24, linezolid at T48, and clindamycin at T72 when lukSF-PV was weighted as unimportant. The empirical p-value for each optimum sequence according to the different weights was 0.001, 0.0009, and 0.0018 with 10,000 permutations, respectively, indicating statistical significance. A statistical method integrating data on change in gene expression upon multiple antimicrobial exposures is a promising tool for identifying a sequence of antimicrobials that is effective in sustaining reduced CA-MRSA virulence.

Optimizing outcomes and treatment sequences inEGFRmutation-positive non-small-cell lung cancer: recent updates

The availability of several EGFR tyrosine kinase inhibitors (TKIs) for the treatment of EGFR mutation-positive NSCLC poses important questions regarding the optimum sequence of therapy. A key consideration is how best to use the third-generation TKI, osimertinib. While osimertinib has demonstrated impressive efficacy and tolerability in a first-line setting, there are currently no standard targeted treatment options following progression. There is an argument, therefore, for reserving osimertinib for second-line use in patients who acquire the T790M resistance mutation after first- or second-generation TKIs. This article reviews recent clinical studies that have assessed the activity of sequential EGFR TKI regimens. These studies support the hypothesis that sequential use of EGFR TKIs represents a viable treatment option in ‘real-world’ clinical practice.

Improving Project Management Teaching Using Scaffolding Based on Cladistics Parsimony Analysis

There are numerous educational paradigms each with their advocates and critics. The cognitive science approach is based on modelling memory as short term and long term each with their different characteristics. All learning consists of an iterative cycle of assimilate and retrieve between these two types of memory. The objective is the construction of an ordered mental structure called a schema in long term memory. With this approach it is possible to define schemas according to an optimal learning sequence. An optimum sequence has minimal cognitive load and hence the ideal teaching sequence. Previous work has clearly demonstrated that this method may be applied to network technology education. This paper applies the same method of teaching financial instruments in project management. Results to date demonstrate that scaffolding, based on cladistics parsimony analysis is a generic method and can be applied to different disciplines. Using this method an optimal learning sequence for project management financial instruments may be produced.

The Optimal Sequence of Production Orders, Taking into Account the Cost of Delays

In flexible manufacturing systems the most important element in determining the proper course of technological processes, transport and storage is the control and planning subsystem. The key planning task is to determine the optimal sequence of production orders. This paper proposes a new method of determining the optimal sequence of production orders in view of the sum of the costs related to the delayed execution of orders. It takes into account the different unit costs of delays of individual orders and the amount of allowable delays of orders involving no delay costs. The optimum sequence of orders, in the single-machine problem, in view of the sum of the costs of delays may be significantly different from the optimal order, taking into account the sum of delay times.

Maximizing Demineralization during Chemical Leaching of Coal through Optimal Reagent Addition Policy

The main objective of the optimal reagent addition was to maximize the quantity of product with minimal quantity of feed. In the present study, the optimal addition of reagents during the chemical leaching of coal was computed. Chemical leaching of coal was carried out using aqueous solution of caustic to dissolve and remove the mineral matter. Simulation studies were carried out using the optimal reagent addition for chemical leaching of coal in batch reactors. This was experimentally validated, using the bench-scale reactor setup with hierarchical optimization architecture. Chemical leaching experiments were conducted using West Bokaro coal. Samples collected at various time intervals during the experiment were analyzed. Variations in silica (SiO2) and alumina (Al2O3) concentrations, which were main constituents present in coal ash, were evaluated with respect to time for different concentrations of caustic. The simulation studies for optimal addition were carried out at 6, 8 and 10 intervals. An objective function, required for maximum ash removal, was solved, using sequential quadratic programming (SQP) algorithm to find out the optimum sequence for reagent dosing. An improvement of about 1% (wt) ash reduction on an average was observed with implementation of optimal reagent addition.