Industrial Multiple Criteria Decision Making Problems Handled by Means of Fuzzy Inference-Based Decision Support Systems

Cateni, Silvia; Vannucci, Marco; Colla, Valentina

doi:10.1109/isms.2013.11

“…Therefore, it is imperative that a system be created that will allow release management to automatically and implicitly incorporate human expertise into the formulized solution of release planning problems. In many decision making applications [87,88], linguistic rules have proved highly effective in capturing human expertise. In linguistic rules, the antecedents and premises are described using linguistic terms.…”

Section: Introductionmentioning

confidence: 99%

Release planning for multi-tenant software as a service (SaaS) applications

Alrashoud¹

2021

Preprint

0

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In multi-tenant Software as a Service (SaaS) applications, the providers are required to regularly deliver new releases of the software in order to satisfy the evolving requirements of tenants. The first step in a release development lifecycle is the release planning process. This thesis formulates the problem of the "next release" planning for multi-tenant Software as a Service (SaaS) applications. Two variables that influence release planning in SaaS applications are introduced: the degree of commonality of features and the contractual constraints. The commonality of a feature denotes the number of tenants that have requested that feature. The contractual constraints denote the effects of service levels to which tenants have subscribed on the release planning process. Furthermore, this thesis proposes three novel approaches in order to tackle the problem of the "next release" planning for multi-tenant SaaS applications. The first one is a prioritization approach that employs a Fuzzy Inference System (FIS) engine in order to speed up the release planning process and overcome the uncertainty associated with the human judgment. In this approach, the human expertise, which is represented by fuzzy rules, is considered automatically in the release planning process. The second and third approaches consider release planning as an optimization problem. The second approach uses an exact optimization method (Binary Linear Programming (BLP)) in order to generate an optimal release plan, while the third approach uses heuristic optimization method (Genetic Algorithm (GA)). All of the three approaches aim to generate a plan for the next release that maximizes the degree of overall tenants’ satisfaction, maximizes the degree of commonality, and minimizes the potential risk while taking into account contractual, effort, and dependencies constraints. Moreover, the thesis presents an experimental study of the proposed approaches in order to determine which approach is best suited to different sets of scenarios. In this experiment, the performance of the proposed approaches is evaluated using four criteria: the overall tenants’ satisfaction, the commonality, the adherence to the risk, and the running time. Additionally, the thesis presents an experiment that compares the proposed approaches with a compared model that is selected from the literature.

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“…Therefore, it is imperative that a system be created that will allow release management to automatically and implicitly incorporate human expertise into the formulized solution of release planning problems. In many decision making applications [87,88], linguistic rules have proved highly effective in capturing human expertise. In linguistic rules, the antecedents and premises are described using linguistic terms.…”

Section: Introductionmentioning

confidence: 99%

Release planning for multi-tenant software as a service (SaaS) applications

Alrashoud¹

2021

Preprint

0

View full text Add to dashboard Cite

In multi-tenant Software as a Service (SaaS) applications, the providers are required to regularly deliver new releases of the software in order to satisfy the evolving requirements of tenants. The first step in a release development lifecycle is the release planning process. This thesis formulates the problem of the "next release" planning for multi-tenant Software as a Service (SaaS) applications. Two variables that influence release planning in SaaS applications are introduced: the degree of commonality of features and the contractual constraints. The commonality of a feature denotes the number of tenants that have requested that feature. The contractual constraints denote the effects of service levels to which tenants have subscribed on the release planning process. Furthermore, this thesis proposes three novel approaches in order to tackle the problem of the "next release" planning for multi-tenant SaaS applications. The first one is a prioritization approach that employs a Fuzzy Inference System (FIS) engine in order to speed up the release planning process and overcome the uncertainty associated with the human judgment. In this approach, the human expertise, which is represented by fuzzy rules, is considered automatically in the release planning process. The second and third approaches consider release planning as an optimization problem. The second approach uses an exact optimization method (Binary Linear Programming (BLP)) in order to generate an optimal release plan, while the third approach uses heuristic optimization method (Genetic Algorithm (GA)). All of the three approaches aim to generate a plan for the next release that maximizes the degree of overall tenants’ satisfaction, maximizes the degree of commonality, and minimizes the potential risk while taking into account contractual, effort, and dependencies constraints. Moreover, the thesis presents an experimental study of the proposed approaches in order to determine which approach is best suited to different sets of scenarios. In this experiment, the performance of the proposed approaches is evaluated using four criteria: the overall tenants’ satisfaction, the commonality, the adherence to the risk, and the running time. Additionally, the thesis presents an experiment that compares the proposed approaches with a compared model that is selected from the literature.

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“…Sometimes the information was useful if some conditions were true; Allahviranloo and Ezadi [20] investigated Z-advance numbers, aiming to solve the uncertain information problem which was reliable depends on some conditions. e second category is the practical application of Z-numbers; many scholars applied Z-number to linguistic calculation [21,22], and some used it as a tool for fuzzy inference [23] and pattern recognition [24]. Z-number is more likely to be used for MADM.…”

Section: Introductionmentioning

confidence: 99%

A Method of Uncertainty Measurements for Multidimensional Z-number and Their Applications

Li

¹

,

Wu

²

,

Mao

³

et al. 2020

Mathematical Problems in Engineering

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Z-number provides the reliability of evaluation information, and it is widely used in many fields. However, people usually describe things from various aspects, so multidimensional Z-number has more advantages over traditional Z-number in describing evaluation information. In view of the uncertainty of the multidimensional Z-number, the entropy of multidimensional Z-number is defined and an entropy formula of multidimensional Z-number is established. Furthermore, the entropy is used to construct an average operator of multidimensional Z-numbers. In addition, a novel distance measure is introduced to measure the distance between two multidimensional Z-numbers. Moreover, the group decision model in the multidimensional Z-number environment is constructed by combining the average operator with the TOPSIS decision-making method. Finally, an illustrative example is given to verify the feasibility and effectiveness of the proposed method.

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Industrial Multiple Criteria Decision Making Problems Handled by Means of Fuzzy Inference-Based Decision Support Systems

Cited by 7 publications

References 25 publications

Release planning for multi-tenant software as a service (SaaS) applications

Release planning for multi-tenant software as a service (SaaS) applications

Release planning for multi-tenant software as a service (SaaS) applications

A Method of Uncertainty Measurements for Multidimensional Z-number and Their Applications

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