Decision Support with Fuzzy Production Systems

PurposeThe purpose of this study is to ensure productive, robust and sustainable production systems by enabling future investments in maintenance. This study aims to provide a deeper understanding of the investment process and thereby facilitate future maintenance-related investments. The objectives are to describe the investment process, map the decision support and roles involved and identify factors influencing the process.Design/methodology/approachThe study was designed as a multiple-case study, with three industrial cases of maintenance-related investments. A structured coding procedure was used to analyse the empirical data from the cases.FindingsThis paper provides a deeper understanding of the process of maintenance-related investments. Eleven factors influencing the investment process could be identified, three of which were seen in all three cases. These three factors are: fact-based decision-support, internal integration and foresight.Practical implicationsInvestments in modern maintenance are needed to ensure productive, robust and sustainable production in the future. However, it is a challenge in manufacturing industry to justify maintenance-related investments. This challenge may be solved by developing a decision-support system, or a structured work procedure, that considers the findings of this study.Originality/valueFrom this study, an extended view of the relation between quantifying effects of maintenance and maintenance-related investment is proposed, including surrounding factors influencing the investment process. The factors were identified using a structured and transparent coding procedure which is rarely used in maintenance research.

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A simulation-based decision support for eco-efficiency improvements in production systems

Journal of Cleaner Production ◽

10.1016/j.jclepro.2014.12.082 ◽

2015 ◽

Vol 105 ◽

pp. 389-405 ◽

Cited By ~ 59

Author(s):

A. Sproedt ◽

J. Plehn ◽

P. Schönsleben ◽

C. Herrmann

Keyword(s):

Decision Support ◽

Production Systems ◽

Simulation Based

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A decision support system for order acceptance/rejection in hybrid MTS/MTO production systems

Applied Mathematical Modelling ◽

10.1016/j.apm.2010.09.015 ◽

2011 ◽

Vol 35 (3) ◽

pp. 1363-1377 ◽

Cited By ~ 47

Author(s):

Mahdokht Kalantari ◽

Masoud Rabbani ◽

Mahmood Ebadian

Keyword(s):

Decision Support ◽

Decision Support System ◽

Support System ◽

Production Systems ◽

Order Acceptance

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Farm-Scale Greenhouse Gas Emissions’ Decision Support Systems

Engineering Proceedings ◽

10.3390/engproc2021009022 ◽

2021 ◽

Vol 9 (1) ◽

pp. 22

Author(s):

Evangelos Alexandropoulos ◽

Vasileios Anestis ◽

Thomas Bartzanas

Keyword(s):

Decision Support ◽

Beef Cattle ◽

Decision Support Systems ◽

Greenhouse Gas ◽

Production Systems ◽

Estimation Procedure ◽

Cattle Production ◽

Green House Gas ◽

Farm Scale ◽

Beef Cattle Production

In this paper, 15 farm-scale Green House Gas-based (GHG-based) decision support (DS) tools were evaluated based on a number of criteria (descriptive evaluation), as well as the parameters requested as inputs and the outputs, all of which are considered important for the estimation procedure and the decision support approach. The tools were grouped as emission calculators and tools providing indicators in terms of more than one pillar of sustainability. The results suggest an absence of automatic consultation in decision support in most of the tools. Furthermore, dairy and beef cattle production systems are the most represented in the tools examined. This research confirms a number of important functionalities of modern GHG-based DS tools.

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Development of a Multi-criteria Decision Model for Nigerian Refinery Bottlenecks

European Journal of Engineering and Technology Research ◽

10.24018/ejers.2021.6.5.2492 ◽

2021 ◽

Vol 6 (5) ◽

pp. 80-83

Author(s):

T. B. Adeleke ◽

R. O. Edokpia ◽

M. K. Onifade ◽

N. B. Chime

Keyword(s):

Decision Support ◽

Decision Model ◽

Production Systems ◽

Petroleum Industry ◽

Ideal Solution ◽

Policy Makers ◽

Support Strategy ◽

Government Interference ◽

Individual Criterion ◽

Hierarchy Process

Production systems are continually surrounded by bottleneck problems that limit their overall performances. The petroleum industry today faces a lot of challenges which border on production bottlenecks that tend to limit production throughput and hence output. The purpose of this study is to provide a decision support strategy for refinery operators and mangers as well as other stakeholders. The multi-criteria models used were the Analytical Hierarchy Process (AHP) and the Test of Preference by Similarity to Ideal Solution (TOPSIS). A 9-point saaty scale and 10-point linguistic scale questionnaires were used to elicit responses from experts in the refinery. The statistical computations with the Multi-criteria Decision Model were carried out with the aid of (AHP -OS) BPMSG software on nine criteria which are bottleneck variables which impact on refinery operations and the comparison was made by nine decision makers who are refinery experts while TOPSIS was used for alternatives selection. The result of the AHP showed the contributing weights of individual criterion with “Government Interference” ranking first, exerting a weight of 19.84%. The result also generated a normalized total matrix which is approximately one (1), consistency index of 0.09694 and a consistency ratio of 0.06685 which is within acceptable limit and finally from TOPSIS modeling, “Denationalization” with the highest value of 0.7598 was found to be closest to the ideal solution for the optimal refinery performance. This study has developed a multi-criteria decision model for selecting the best alternative for optimal performance based on inputs from experts and this provides a veritable framework that serves as a decision support strategy for policy makers and stakeholders in the operations of the refinery.

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