Showing papers in "American Journal of Operations Research in 2013"
TL;DR: In this paper, the authors examined the effectiveness of the concept map as a assessment tool for students' systems thinking, a tool that provides researchers with a detailed picture of the systems thinking development taking place within individual students.
Abstract: In 2006 anew biology curriculum called “Human Biology: Emphasizing the Role of Homeostasis” was introduced into the Israeli high school system. Complex systems like those that make up the human body have become increasingly important as a focus of high school education. This study examines the effectiveness of the concept map as a assessment tool for students’ systems thinking, a tool that provides researchers with a detailed picture of the systems thinking development taking place within individual students. The content of the students’ concept maps was translated into information about students’ system thinking using the Systems Thinking Hierarchy (STH) model, a model in which system thinking is categorized according to eight hierarchical characteristics or abilities. The goal was to use the maps to characterize Israeli high school students’ understanding of the body’s systemic nature. To do this, we identified the extent to which the students understand three central elements of systems, namely hierarchy, homeostasis, and dynamism, and then analyzed this understanding according to its place within the hierarchical stages of the STH model. The extensive qualitative data analysis of 48 concept maps made by 11th grade biology majors suggest that the strength of the concept map is in its ability to describe the first two levels of system thinking (analysis and synthesis). However, it proved less successful in eliciting evidence of the third and highest level, particularly of students’ understanding of patterns, of homeostasis and their capacity for temporal thinking.
50 citations
TL;DR: This paper proposes an complex system based approach to the smart grid modeling, accentuating on the optimization by combining game theoretical and classical methods in different levels, so that the optimization can be achieved with flexibility and scalability, while keeping its generality.
Abstract: With urging problem of energy and pollution, smart grid is becoming ever important. By gradually changing the actual power grid system, smart grid may evovle into different systems by means of size, elements and strategies, but its fundamental requirements and objectives will not change such as optimizing production, transmission and consumption. Studying the smart grid through modeling and simulation provides us with valuable results which can not be obtained in real world due to time and cost related constraints. However, due to the complexity of the smart grid, achieving optimization is not an easy task, even using computer models. In this paper, we propose an complex system based approach to the smart grid modeling, accentuating on the optimization by combining game theoretical and classical methods in different levels. Thanks to this combination, the optimization can be achieved with flexibility and scalability, while keeping its generality.
31 citations
TL;DR: Some aspects of complexity behind health systems are reviewed and how they can be applied to improve the performance of healthcare organizations are reviewed.
Abstract: Health systems are paradigmatic examples of human organizations that blend a multitude of different professional and disciplinary features within a critically performance environment. Communication failure and defective processes in health systems have a tremendous impact in society, both in the financial and human aspects. Traditionally, health systems have been regarded as linear hierarchic structures. However, recent developments in the sciences of complexity point out to health systems as complex entities governed by non-linear interaction laws, self-organization and emergent phenomena. In this work we review some aspects of complexity behind health systems and how they can be applied to improve the performance of healthcare organizations.
30 citations
TL;DR: In this article, the authors developed a multi-period dynamic model in which managers decide in each period how much to invest in improving process reliability, which will minimize the firm's total costs, which are comprised of its preventive costs and failure costs.
Abstract: We develop a multi-period dynamic model in which managers decide in each period how much to invest in improving process reliability. The optimal investment decision will minimize the firm’s total costs, which are comprised of its preventive costs and failure costs. We explicitly characterize the optimal investment scheme under different output growth projections and where the firm considers project obsolescence and investment salvageability. Our findings include: for sufficiently small output growth, investment will be made upfront; for sufficiently large output growth, investment will be made periodically until project termination; and for intermediate growth, investment will be staged until some period after which there will be no more investment. The general nature of the cost function in this model allows for its application in various cost reduction settings.
27 citations
TL;DR: This paper explores the applicability and capability of an outranking method known as Evaluation of Mixed Data (EVAMIX) method combined with Analytical Hierarchy Process (AHP) for selection of right alternative.
Abstract: Selection of best alternative among multiple alternatives is a tough task for decision makers in many industrial situations. This paper explores the applicability and capability of an outranking method known as Evaluation of Mixed Data (EVAMIX) method combined with Analytical Hierarchy Process (AHP) for selection of right alternative. The novelty of the proposed methodology is its capability of dealing with both ordinal and cardinal information. The integrated approach is a significant tool of the decision making process in industrial environments. Five examples are illustrated to show the effectiveness of method.
22 citations
TL;DR: The general findings demonstrated that the ANN model is superior compared to the ANFIS and ARFIMA models in predicting the CPO prices.
Abstract: An accurate prediction of crude palm
oil (CPO) prices is important especially when investors deal with
ever-increasing risks and uncertainties in the future. Therefore, the
applicability of the forecasting approaches in predicting the CPO prices is
becoming the matter into concerns. In this study, two artificial intelligence
approaches, has been used namely artificial neural network (ANN) and adaptive
neuro fuzzy inference system (ANFIS). We employed in-sample forecasting on
daily free-on-board CPO prices in Malaysia and the series data stretching from
a period of January first, 2004 to the end of December 2011. The predictability
power of the artificial intelligence approaches was also made in regard with
the statistical forecasting approach such as the autoregressive fractionally
integrated moving average (ARFIMA) model. The general findings demonstrated
that the ANN model is superior compared to the ANFIS and ARFIMA models in
predicting the CPO prices.
20 citations
TL;DR: In this paper, the effect of altruism on retailer's and manufacturer's pricing strategy in two classic dual-channel supply chains by presenting Stackelberg game models was analyzed.
Abstract: With the development of behavioral operational management, human behavior such as altruism, fairness and trust has received considerable attention. This paper studies the effect of altruism on retailer’s and manufacturer’s pricing strategy in two classic dual-channel supply chains by presenting Stackelberg game models. The analysis shows that the player’s altruism preference strongly affects their pricing strategies. The more altruistic one player is, the more profits the other player obtains. Moreover, the effect of manufacturer’s altruistic preference is larger than that of retailer’s. In addition, online price is always lower than offline price in dual-channel supply chain, which still holds true considering altruism. The results also reveal that the product web-fit has significant effect on the player’s optimal pricing strategies. The more compatible with online market the product is, the lower the retail price is set, and the more profit the manufacturer obtains whereas the less the retailer gets.
16 citations
TL;DR: The examples demonstrate possibilities to use modeling results from different application spheres and to go in making decision “from a pragmatical filtration of information to generation of the proved ideas and effective decisions”.
Abstract: The paper is concerned with the development and application of the original probability models and supporting them software tools to predict and optimize quality and risks for complex systems. The examples demonstrate possibilities to use modeling results from different application spheres and to go in making decision “from a pragmatical filtration of information to generation of the proved ideas and effective decisions”.
16 citations
TL;DR: The proposed model is formulated as a 0 - 1 integer programming problem which can be applied by existing useful combinatorial optimization and soft computing algorithms and equivalently transformed into several existing tour planning problems using some natural assumptions.
Abstract: This paper proposes a new personal tour planning problem with time-dependent satisfactions, traveling and activity duration times for sightseeing. It is difficult to represent the time-dependent model using general static network models, and hence, Time-Expanded Network (TEN) is introduced. The TEN contains a copy to the set of nodes in the underlying static network for each discrete time step, and it turns the problem of determining an optimal flow over time into a classical static network flow problem. Using the proposed TEN-based model, it is possible not only to construct various variations with time of costs and satisfactions flexibly in a single network, but also to select optimal departure places and accommodations according to the tour route with tourist’s favorite places and to obtain the time scheduling of tour route, simultaneously. The proposed model is formulated as a 0 - 1 integer programming problem which can be applied by existing useful combinatorial optimization and soft computing algorithms. It’s also equivalently transformed into several existing tour planning problems using some natural assumptions. Furthermore, comparing the proposed model with some previous models using a numerical example with time-dependent parameters, both the similarity of these models in the static network and the advantage of the proposed TEN-based model are obtained.
15 citations
TL;DR: In this article, the authors address bilevel multi-objective programming problems (BMPP) in which the decision maker at each level has multiple objective functions conflicting with each other.
Abstract: In this paper, we address bilevel multi-objective programming problems (BMPP) in which the decision maker at each level has multiple objective functions conflicting with each other. Given a BMPP, we show how to construct two artificial multiobjective programming problems such that any point that is efficient for both the two problems is an efficient solution of the BMPP. Some necessary and sufficient conditions for which the obtained result is applicable are provided. A complete procedure of the implementation of an algorithm for generating efficient solutions for the linear case of BMPP is presented. A numerical example is provided to illustrate how the algorithm operates.
15 citations
TL;DR: Two-Dimensional Irregular Strip Packing Problem is a classical cutting/packing problem to assign, a set of 2-D irregular-shaped items to a rectangular sheet and a sequence-based approach is developed and tested.
Abstract: Two-Dimensional
Irregular Strip Packing Problem is a classical cutting/packing problem The
problem is to assign, a set of 2-D irregular-shaped items to a rectangular
sheet The width of the sheet is fixed, while its length is extendable and has
to be minimized A sequence-based approach is developed and tested The
approach involves two phases; optimization phase and placement phase The
optimization phase searches for the packing sequence that would lead to an
optimal (or best) solution when translated to an actual pattern through the
placement phase A Particle Swarm Optimization algorithm is applied in this
optimization phase Regarding the placement phase, a combined algorithm based
on traditional placement methods is developed Competitive results are
obtained, where the best solutions are found to be better than, or at least
equal to, the best known solutions for 10 out of 31 benchmark data sets A
Statistical Design of Experiments and a random generator of test problems are
also used to characterize the performance of the entire algorithm
TL;DR: A two stage queuing model where the server provides two stages of service one by one in succession is studied, and steady state solutions are derived by using supplementary variable technique and the mean queue length and mean waiting time are calculated.
Abstract: We study a two stage queuing model
where the server provides two stages of service one by one in succession. We
consider reneging to occur when the server is unavailable during the system
breakdown or vacation periods. We concentrate on deriving the steady state
solutions by using supplementary variable technique and calculate the mean
queue length and mean waiting time. Further some special cases are also
discussed and numerical examples are presented.
TL;DR: Investigation of traffic dynamics and performance of complex networks reveals that the parameters chosen to reflect network structure, including node betweenness centrality, network polarization, and average path length, play important roles in different states of the underlying networks.
Abstract: This paper explores traffic dynamics and performance of complex networks. Complex networks of various structures are studied. We use node betweenness centrality, network polarization, and average path length to capture the structural characteristics of a network. Network throughput, delay, and packet loss are used as network performance measures. We investigate how internal traffic, through put, delay, and packet loss change as a function of packet generation rate, network structure, queue type, and queuing discipline through simulation. Three network states are classified. Further, our work reveals that the parameters chosen to reflect network structure, including node betweenness centrality, network polarization, and average path length, play important roles in different states of the underlying networks.
TL;DR: In this article, the authors validate a Balanced Scorecard (BSC) model and a Strategic Map for the University by studying the relationships of efficiency between its dimensions, employing Data Envelopment Analysis (DEA).
Abstract: The principal objective of the research reported in this article is to validate a Balanced Scorecard (BSC) model and a Strategic Map for the University by studying the relationships of efficiency between its dimensions. Subsequently, the validation is completed by establishing hypotheses of efficiency relationships between the perspectives proposed, employing Data Envelopment Analysis (DEA). Empirical evidence has been obtained on the validity of the proposed BSC for a unit of academic management in the university. The first contribution of this work is the establishment of a framework of analysis of the hypothetical cause-effect relationships in the BSC in university institutions. The second contribution is to obtain the determining factors of the performance in this type of institution and, therefore, the Strategic Map. Specifically, these factors are: the participation of teaching staff in innovation activities; the number of doctorate-level staff; the academic subjects and credits in the Virtual Campus; and the scores in the surveys of student satisfaction. With respect to research, the determining factors of the performance are: the research sexennials; the funding obtained from contracts with companies; the number of research projects obtained; their financing; and the participation of teachers in these projects.
TL;DR: In this paper, the authors considered a special class of transportation problems where in the total transportation, flow is restricted to a known specified level and an algorithm is presented to solve a capacitated linear plus linear fractional transportation problem.
Abstract: In this paper, a transportation problem with
an objective function as the sum of a linear and fractional function is considered. The linear function
represents the total transportation cost incurred when the goods are shipped
from various sources to the destinations and the fractional function gives the
ratio of sales tax to the total public expenditure. Our objective is to
determine the transportation schedule which minimizes the sum of total
transportation cost and ratio of total sales tax paid to the total public
expenditure. Sometimes, situations
arise where either reserve stocks have to be kept at the supply points, for
emergencies or there may be extra demand in the markets. In such situations,
the total flow needs to be controlled or enhanced. In this paper, a special
class of transportation problems is studied where in the total transportation
flow is restricted to a known specified level. A related transportation
problem is formulated and it is shown that to each basic feasible solution
which is called corner feasible solution to related transportation problem,
there is a corresponding feasible solution to this restricted flow problem. The
optimal solution to restricted flow problem may be obtained from the optimal
solution to related transportation problem. An algorithm is presented to solve
a capacitated linear plus linear fractional transportation problem with
restricted flow. The algorithm is supported by a real life example of a
manufacturing company.
TL;DR: In this article, the authors address a set of key methodological choices, crucial in shaping the multi-criteria decision support and illuminate how they followed from transdisciplinary collaboration and negotiation: diversity tolerance, dealing with uncertainty and difference of opinion, weight of information and the epistemological divide between traditional closed and alternative open paradigms.
Abstract: The complexity we take into account when dealing with complex issues and the way we deal with that complexity is not given or self-evident, it is framed and negotiated. Based on two environmental health decision support case studies we address a set of key methodological choices, crucial in shaping the multi-criteria decision support and illuminate how they followed from transdisciplinary collaboration and negotiation: diversity tolerance, dealing with uncertainty and difference of opinion, weight of information and the epistemological divide between traditional closed and alternative open paradigms. The case studies exemplify the growing conviction amongst methodologists that, especially regarding complex issues, best methods do not exist as such: methods are chosen and tailored in practice and the quality to a large extent is dependent on the process in which methodological development is embedded. We hope to contribute to making explicit the importance of methodological decision making regarding environmental health complexity.
TL;DR: Four mixed-integer linear programming models are presented to help supply chain managers make decisions for multiple products subject to multiple constraints when suppliers offer quantity discounts and shippers offer freight discounts.
Abstract: One of the most important responsibilities of a supply chain manager is to decide “how much” (or “many”) of inventory items to order and how to transport them. This paper presents four mixed-integer linear programming models to help supply chain managers make these decisions for multiple products subject to multiple constraints when suppliers offer quantity discounts and shippers offer freight discounts. Each model deals with one of the possible combinations of all-units, incremental quantity discounts, all-weight and incremental freight discounts. The models are based on a piecewise linear approximation of the number of orders function. They allow any number of linear constraints and determine if independent or common (fixed) cycle ordering has a lower total cost. Results of computational experiments on an example problem are also presented.
TL;DR: A framework of hierarchical modeling of a complex network system, based on a recursive unsupervised spectral clustering method, is proposed, with the aim of providing risk managers with information on the risk/safety significance of system structures and components.
Abstract: The complexity of large-scale network systems made of a large number of nonlinearly interconnected components is a restrictive facet for their modeling and analysis. In this paper, we propose a framework of hierarchical modeling of a complex network system, based on a recursive unsupervised spectral clustering method. The hierarchical model serves the purpose of facilitating the management of complexity in the analysis of real-world critical infrastructures. We exemplify this by referring to the reliability analysis of the 380 kV Italian Power Transmission Network (IPTN). In this work of analysis, the classical component Importance Measures (IMs) of reliability theory have been extended to render them compatible and applicable to a complex distributed network system. By utilizing these extended IMs, the reliability properties of the IPTN system can be evaluated in the framework of the hierarchical system model, with the aim of providing risk managers with information on the risk/safety significance of system structures and components.
TL;DR: A computational comparison in which the basis inverse is computed with five different updating schemes, and a parallel implementation of two updating schemes on a CPU-GPU System using MATLAB and CUDA environment is proposed.
Abstract: The computation of the basis inverse is the most time-consuming step
in simplex type algorithms. This inverse does not have to be computed from
scratch at any iteration, but updating schemes can be applied to accelerate
this calculation. In this paper, we perform a computational comparison in which
the basis inverse is computed with five different updating schemes. Then, we
propose a parallel implementation of two updating schemes on a CPU-GPU System
using MATLAB and CUDA environment. Finally, a computational study on randomly
generated full dense linear programs is preented to
establish the practical value of GPU-based implementation.
TL;DR: A method to obtain the nearest trapezoidal approximation of fuzzy numbers so that preserving conditions expect interval and include the core of a fuzzy number is introduced.
Abstract: In this paper, we introduce a method to obtain the nearest trapezoidal approximation of fuzzy numbers so that preserving conditions expect interval and include the core of a fuzzy number.
TL;DR: The concept of quality requirements definition method based on the SQuaRE quality models define in ISO/IEC25030 is proposed and the result of verification about effectiveness of quantitative quality requirement definition approach from the view point of six quality characteristics described in ISO /IEC9126 is proposed.
Abstract: For a purpose of suitable system product development and acquisition successfully, it is extremely important to specify the quality requirement for target system during system design phase, and developing or selecting a most suitable product finally from among the alternative candidate products. However, the quality requirements analysis is a very difficult matter because it is non-formulaic and demands much of product customer’s technical perceptiveness. Traditionally, analysis of quality requirements of a product has been conducted through questionnaires or interviews with customers based on survey investigator’s personal experience. This kind of lack of structure and incompleteness in the traditional approach introduced missing requirements and errors in prioritizing requirements to implement in the requirement analysis of a product. On the other hand, we have been working on developing the techniques (SQuaRE) for quality requirements and evaluation for system and software product for a long time in ISO/IECJTC1/SC7WG 6. For the purpose of specify the quality requirement of system product, the method of quantitative quality requirement definitions based on ISO/IEC9126 [1] quality model that includes six characteristics is widely recognized. However, independency among each quality characteristic is not sure and the suitability of method by using these six quality characteristics for quality requirement is not certified statistically. In this paper, we propose the concept of quality requirements definition method based on the SQuaRE quality models define in ISO/IEC25030 [2,3]. Also, this paper proposes the result of verification about effectiveness of quantitative quality requirement definition approach from the view point of six quality characteristics described in ISO/IEC9126.
TL;DR: An integrated design system for solving complexity during multi-disciplinary design that uses the multi-viewpoint concept to allow experts to share their information and knowledge in common views and can structure and solve the complexity of design problems.
Abstract: The engineering problems today become more and more complex particularly in the area of new product development. It requires the multi-disciplinary design method to solve complex problems. This paper presents an integrated design system for solving complexity during multi-disciplinary design. Complexity could be solved if the design problems, given by any individuals who are concerned, are structured. The design system uses the multi-viewpoint concept to allow experts to share their information and knowledge in common views. Knowledge modules are used to store semantics from the experts of different disciplines. Then the system agent acts as an internal designer to help support the individuals to translate any semantics provided from one discipline and then propagate to other related disciplines. With these tools, the integrated design system can structure and solve the complexity of design problems.
TL;DR: The method of Laplace transforms is used to find the distribution function, mean, and variance of the number of renewals of a renewal process whose inter-arrival time distribution has a rational Laplace transform as mentioned in this paper.
Abstract: The method of Laplace transforms is used to find the distribution function, mean, and variance of the number of renewals of a renewal process whose inter-arrival time distribution has a rational Laplace transform. Where the Laplace transform is not rational, we use the Pade approximation method. We apply our method to certain examples and the results are compared to those reported by other researchers.
TL;DR: This review is an update of two earlier review papers concerning the application of the methodology of mathematical systems theory to population ecology, and three topics illustrate the recent developments in monitoring methodology of ecological systems: stock estimation of a fish population with reserve area; and observer construction for two vertically structured population systems.
Abstract: The paper is an update of two earlier review papers concerning the application of the methodology of mathematical systems theory to population ecology, a research line initiated two decades ago. At the beginning the research was concentrated on basic qualitative properties of ecological models, such as observability and controllability. Observability is closely related to the monitoring problem of ecosystems, while controllability concerns both sustainable harvesting of population systems and equilibrium control of such systems, which is a major concern of conservation biology. For population system, observability means that, e.g. from partial observation of the system (observing only certain indicator species), in principle the whole state process can be recovered. Recently, for different ecosystems, the so-called observer systems (or state estimators) have been constructed that enable us to effectively estimate the whole state process from the observation. This technique offers an efficient methodology for monitoring of complex ecosystems (including spatially and stage-structured population systems). In this way, from the observation of a few indicator species the state of the whole complex system can be monitored, in particular certain abiotic effects such as environmental contamination can be identified. In this review, with simple and transparent examples, three topics illustrate the recent developments in monitoring methodology of ecological systems: stock estimation of a fish population with reserve area; and observer construction for two vertically structured population systems (verticum-type systems): a four-level ecological chain and a stage-structured fishery model with reserve area.
TL;DR: A consistent and asymptotically normal estimator of intensities ρ1 , ρ2 for a queueing network with feedback (in which a job may return to previously visited nodes) with distribution-free inter-arrival and service times is proposed.
Abstract: In this paper we propose a consistent and asymptotically
normal estimator (CAN) of intensities ρ1 , ρ2 for a queueing network
with feedback (in which a job may return to previously visited nodes) with
distribution-free inter-arrival and service times. Using this estimator and its
estimated variance, some 100(1-α)% asymptotic confidence
intervals of intensities are constructed. Also bootstrap approaches such as
Standard bootstrap, Bayesian bootstrap, Percentile bootstrap and Bias-corrected
and accelerated bootstrap are also applied to develop the confidence intervals
of intensities. A comparative analysis is conducted to demonstrate performances
of the confidence intervals of intensities for a queueing network with short
run data.
TL;DR: In this article, the authors analyzed the convergence of provincial per capita GDP stability in Vietnam over the period of 1991-2007 by two approaches including bias data-based regression method for testing convergence and Markov chain model for describing features of long-term tendency of per capita income in Vietnam growth in provinces.
Abstract: Convergence problem of an economic variable represents an underlying forecast of neoclassical economic growth model. This paper aims to analyze the convergence of provincial per capita GDP stability in Vietnam over the period of 1991-2007. This can be done by two approaches including bias data-based regression method for testing convergence and Markov chain model for describing features of long-term tendency of per capita income in Vietnam growth in provinces. The regression method results in the signs of convergence. To apply Markov process, we divide total pattern into 5 per capita income classes. Result estimated from the Markov chain model shows the poor convergence.
TL;DR: In this paper, the authors derived the distribution of the deviation distance to visit an alternative fuel station, where the distribution explicitly considers the vehicle range and whether the round trip between origin and destination can be made.
Abstract: This paper derives the distribution of the deviation distance to visit an alternative fuel station. Distance is measured as the Euclidean distance on a continuous plane. The distribution explicitly considers the vehicle range and whether the round trip between origin and destination can be made. Three cases are examined: fuel is available at both origin and destination, fuel is available at either origin or destination, and fuel is available at neither origin nor destination. The analytical expressions for the distribution demonstrate how the vehicle range, the shortest distance, and the refueling availability at origin and destination affect the deviation distance. The distribution will thus be useful to estimate the number of vehicles refueled at a station.
TL;DR: In this article, the authors introduce and study the system of generalized======vector quasi-variational-like inequalities in Hausdorff topological vector spaces, which include vector variational and quasi-varianational inequalities, and several other systems as special cases.
Abstract: In this paper, we introduce and study the system of generalized
vector quasi-variational-like inequalities in Hausdorff topological vector spaces,
which include the system of vector quasi-variational-like inequalities, the
system of vector variational-like inequalities, the system of vector
quasi-variational inequalities, and several other systems as special cases.
Moreover, a number of C-diagonal quasiconvexity properties are proposed for
set-valued maps, which are natural generalizations of the g-diagonal
quasiconvexity for real functions. Together with an application of continuous
selection and fixed-point theorems, these conditions enable us to prove unified
existence results of solutions for the system of generalized vector
quasi-variational-like inequalities. The results of this paper can be seen as
extensions and generalizations of several known results in the
literature.
TL;DR: The relationship between inherent attributes of the product and quantitative result of a measured value of total customer satisfaction from the view point of six quality characteristics statistically is confirmed.
Abstract: The profitability of the system product is decided on the sales of the product. Furthermore, a customer satisfaction for products quality and a price have a big influence on the sales of the product. It spends limited financial resources effectively to raise the profitability of the system product, and it is necessary to realize the high quality product correspond to the customer needs as much as possible. There may be close relationship between cost of a product and an expense to implement the individual inherent attribute of system product. For the purpose of improvement of the customer satisfaction for quality of system product, the method of quantitative quality requirement and evaluation based on the ISO/IEC9126 quality model that includes six quality characteristics is widely recognized. However, independency among each quality characteristic has not been sure and the suitability of method for quality requirement of system product by using these six quality characteristics could not certified statistically. In the precedent study, introduced the requirements definition method for the quality of system product based on the system quality model defined in ISO/IEC9126 and proposed the effectiveness of it statistically. This study have measured the customer satisfaction for the system quality from the viewpoint of six quality characteristics quantitatively and confirmed the effectiveness of the technique to evaluate. In this study, we have confirmed the relationship between inherent attributes of the product and quantitative result of a measured value of total customer satisfaction from the view point of six quality characteristics statistically. This study performed the trial to clarify the relations with the inherent attributes that quantitative result of a measurement of the customer satisfaction based on six quality characteristics by the quality model of ISO/IEC9126. In addition, this study performed the development of the prediction model to estimate the total customer satisfaction for the system product from the view point of inherent attribute of the product. In this paper, we propose the effectiveness of application of the estimated prediction model and possibility of improvement of the total customer satisfaction of a system product.
TL;DR: In this article, a new active-set, cutting-plane Constraint Optimal Selection Technique (COST) was proposed for solving general linear programming problems, which considers both the depth of constraints as well as their angles from the objective function.
Abstract: We describe a new active-set, cutting-plane Constraint Optimal Selection Technique (COST) for solving general linear programming problems. We describe strategies to bound the initial problem and simultaneously add multiple constraints. We give an interpretation of the new COST’s selection rule, which considers both the depth of constraints as well as their angles from the objective function. We provide computational comparisons of the COST with existing linear programming algorithms, including other COSTs in the literature, for some large-scale problems. Finally, we discuss conclusions and future research.