Integrated Decision Support Model Research Articles

An Integrated Multi-Criteria Decision Support Model for Sustainable Ship Queuing Policy Application via Vessel Traffic Service (VTS)

An Integrated Multi-Criteria Decision Support Model for Sustainable Ship Queuing Policy Application via Vessel Traffic Service (VTS)

An integrated decision support model for effective institutional coordination framework in planning for public transportation

An integrated decision support model for effective institutional coordination framework in planning for public transportation

Choosing The Right Photovoltaic Panel for Electric Vehicles: An Integrated Decision Support Model

In the current era, global carbon emissions are on the rise and to achieve environmental sustainability, greenhouse gas emissions must be reduced to net zero levels with greater reliance on renewable energy sources. Due to the increasing demand for sustainable transportation options, the integration of photovoltaic (PV) panels in electric vehicles (EVs) is considered a promising solution to boost energy efficiency and reduce greenhouse gas emissions. However, selecting the most suitable photovoltaic panel for EVs is a complex process that involves multiple criteria and considerations. This research article presents an integrated decision support model using the Best-Worst Method (BWM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to assist in selecting the optimal module. The BWM is employed to compute the weights of eight identified criteria, reflecting the preferences and priorities of decision experts. Subsequently, the TOPSIS method is utilized to evaluate and rank a set of PV panel options based on their performance against the identified criteria. The results reveal that a mono-crystalline bulk silicon module is the best alternative followed by multi-silicon modules. This study proposes a structured decision approach for EV manufacturers to select the right PV panel, promoting energy-efficient transportation solutions.

Green Mining Strategy Selection via an Integrated SWOT-PEST Analysis and Fuzzy AHP-MARCOS Approach

Deciding on an appropriate development strategy is one of the most crucial aspects of the mining industry’s green transition. This research introduces a novel integrated decision support model that can be applied to analyze various environmental factors and determine development strategies. In this study, a strengths, weaknesses, opportunities, and threats (SWOT) analysis is employed from multiple perspectives, including political, economic, social, and technological (PEST), to assess the internal and external factors that influence green mining. The fuzzy analytic hierarchy process (AHP) is used to analyze the factor weights quantitatively, and the fuzzy Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method is used to rank and select development strategies. According to the results, “grasp the trend of green development and improve the protection and exploitation level of mineral resources” is found to be the final optimal strategy. Comparative analysis and sensitivity analysis confirmed the accuracy of the model and the case study results.

A Remaining Useful Life Model for Optimizing Maintenance cost and Spare-parts replacement of Production Systems in the Context of Sustainability

In reliability and maintenance engineering, predictive methods play a major role for estimating the remaining useful life (RUL) of equipment. However, in the most cases the RUL estimation is based on two types of approaches: model-based solution and data-driven solution. Data-driven solution is a very realistic solution, but requires the availability of large quantity of collected data using sensors networks, big storage capacity, supercomputers for processing, and high-level algorithms such as machines learning, convolutional Neural Networks, Hidden Markov Models. While model-based solution methods are less difficult to deploy, they use techniques based on the mean residual life (MRL) value. This paper proposes an integrated decision support model for minimizing the maintenance expected cost, and for reducing the excess of spare-parts usage of multi-component production systems. This decision support model includes three performance indicators that are: the renewal function (RF), the mean residual lifetime (MRL) and the renewal mean residual lifetime (RMLR). The contribution of this research consists of (1) introducing the MRL function for a class of useful probability distributions, (2) proposing the Renewal MRL (RMRL) as a predictive maintenance strategy, (3) applying the approach-MRL to maintenance scheduling problems, (4) dressing a comparative numerical study between the different proposed models using an industrial case study. Besides, the comparative study performing the RF, MRL, and RMRL is carried out using basic and modified replacement strategies consisting of an age replacement policy (ARP) and an opportunistic maintenance solution. In addition, a case study from an Electrical power system is proposed by providing numerical results that discuss and illustrate the outcome gains in terms of maintenance costs saving and spare parts replacements’ minimization. However, the proposed solution is demonstrated to have a positive effect to enhance sustainable development systems, and it is provided soon to cover other theoretical and application aspects of the MRL.

A comparative assessment of air quality across European countries using an integrated decision support model

Reducing air pollution including greenhouse gas emissions originating from extensive use of fossil fuels is critical for European countries aiming at improving their environment and at carbon neutrality by the middle of this century. To optimally reduce the air pollutants and mitigate the climate change, not only national or European Union level regulation need to be considered, but also international agreements such as the Sustainable Development Goals, Kyoto Protocol, and Paris Climate Agreement need to be included in these strategies. Managing such a complex framework would benefit from reliable multi-criteria decision-making approaches. Current models to enhance air quality often concentrate on one criterion at a time and focus on momentary improvements only, unable to offer longstanding enhancement. Therefore, comparative analysis of emissions of several air pollutants simultaneously is highly relevant empowering decision-makers with better tools for policy development. The focus of this study is on a decision support model based on the Best-Worst Method and the Measurement of Alternatives and Ranking According to Compromise Solution method to comparatively analyze air pollutants of 22 European countries. This study is the first in its kind to develop an integrated decision model for air quality assessment considering six air pollutants. Extensive sensitivity analyses were performed to highlight the impacts from different scenarios on the decision-making. The results indicate that Sweden, Latvia, France, Lithuania, Hungary, and Italy ranked as the top six countries with the lowest emission. However, Finland, Poland, the Czech Republic, Luxembourg, and Estonia had the lowest overall ranking and the highest per capita emissions. The proposed methodology and evaluation framework can provide a helpful tool for developing regional and national strategies to minimize air pollutants and to improve environmental sustainability.

An Integrated Decision Support Model Based on BWM and Fuzzy-VIKOR Techniques for Contractor Selection in Construction Projects

Sustainable development of any country to some extent depends on successful accomplishment of construction projects, particularly infrastructures. Contractors have a key role in the success of these projects. Hence, the selection of a competent contractor as a complicated and hard decision process has a vital importance in the destiny of any construction project. Contractor selection is in essence a multicriteria decision-making that ought to encompass so many aspects of the project and the client’s requirements on one hand and the capabilities and past records of the contractors on the other hand. Failure in selecting a competent contractor may cause time and cost overruns; quality shortcomings; increasing in claims, disputes and change orders; and even failure of the project. In spite of deficiencies of selecting a contractor by the rule of “the lowest bid price”, it still prevails in many countries including Iran. In this paper, a new contractor selection model based on the best-worst method (BWM) and well-known Fuzzy-VIKOR techniques is proposed as a solution to overcome the deficiencies of the traditional “lowest bid price” rule. An illustrative example of a water channel construction project verified the applicability of the proposed model in practice.

An Integrated Decision Support Model for Evaluating Public Transport Quality

Big cities suffer from serious complex problems such as air pollution, congestion, and traffic accidents. Developing public transport quality in such cities is considered an efficient remedy to obviate these critical issues. This paper aims to determine the significant supply quality criteria of public transportation. As a methodology, a hybrid Analytic Hierarchy Process (AHP) combined with the Best Worst Method (BWM) is applied. The proposed model is basically a hierarchy structure with at least a 5 × 5 pairwise comparison matrix or larger. A real-world complex problem was examined to validate the created model (public transport quality improvement). An urban bus transport system in the Jordanian capital city, Amman, was used as a case study; three stakeholder groups (passengers, nonpassengers, and representatives of the local government) participated in the evaluation process. The conventional Analytic Hierarchy Process (AHP) leads to weak consistency in the case of existing 5 × 5 pairwise comparison matrices or larger, particularly in estimating complex problems. To avoid this critical issue in AHP, we used Best Worst Method (BWM) comparisons, which make the evaluation process easier for decision makers; moreover, it saves survey time and provides more consistency when compared to AHP pairwise comparisons. The model adopted highlighted the most significant service quality criteria that influence urban bus transport systems. Furthermore, the sensitivity analysis conducted detected the stability of the criteria ranking in the three levels of the hierarchical structure. Since the proposed AHP–BWM model (which is the sole example of this sort of combination) is independent from the decision attributes, it can be applied to arbitrary hierarchically structured decision problems with a relatively large number of pairwise comparisons.

An Integrated Decision Support Model For Enhancing Continuous Improvement Of Academic Programs

An Integrated Decision Support Model For Enhancing Continuous Improvement Of Academic Programs

An Integrated Decision Support Model For Enhancing Continuous Improvement Of Academic Programs

Continuous Quality Improvement (CQI) in higher educational institutions is considered an aspect that plays a vital role in maintaining excellence in teaching and learning. It focuses on several critical phases such as designing Program Learning Outcomes (PLOs) assessment plans, collecting data, evaluating the of PLO achievement results, and designing and implementing improvement actions to achieve educational goals. CQI is very critical to assess and improve the educational process. As it appears, academic program decision-makers find difficult to formulate and implement an authentic CQI for their programs without the help of decision support systems (DSSs). Therefore, this paper proposes an integrated decision support model that provides CQI decision support to enhance academic programs. Moreover, it provides authentication of the CQI process by auto-checking the accuracy of the CQI phases (assessment, evaluation, designing, and implementing improvement). After the development and implementation of the proposed model in a bachelor-level academic institution, the results show that the proposed model provides decision support for enhancing academic program’s CQI, and authentication of the CQI accuracy, which will lead to the improvement of the educational process and the achievement of educational goals.

Integrated decision support model with fuzzy AHP and fuzzy TOPSIS: Teacher selection in private school

Çok sayıda alternatif ve kriterin bulunduğu karar problemlerinin çözümü için geliştirilen yöntemler, çok kriterli karar verme yöntemleri olarak tanımlanmaktadır. Birbirleriyle çelişen çok sayıda kriter ve alternatifin bulunduğu karar problemlerinde bulanıklık ortaya çıkabilmektedir. Bu çalışmada Bulanık Analitik Hiyerarşi (AHP) ve Bulanık TOPSIS yöntemleri ile bütünleşik bir karar destek modeli önerilmiştir. Çalışmada; karar vermede etkili olan kriterlerin önem ağırlıkları Bulanık AHP ile belirlenmiş, alternatiflerin sıralama ve seçimi ise Bulanık TOPSIS yöntemi ile yapılmıştır. Uygulama kısmında özel okullarda öğretmen seçimi, önerilen karar destek yöntemi ile gerçekleştirilmiştir. Önerilen yöntemin karar destek modeli olarak başarılı bir şekilde kullanılabileceği görülmüştür.

A Linguistic Intuitionistic Cloud Decision Support Model with Sentiment Analysis for Product Selection in E-commerce

Online product reviews significantly impact the online purchase decisions of consumers. However, extant decision support models have neglected the randomness and fuzziness of online reviews and the interrelationships among product features. This study presents an integrated decision support model that can help customers discover desirable products online. This proposed model encompasses three modules: information acquisition, information transformation, and integration model. We use the information acquisition module to gather linguistic intuitionistic fuzzy information in each review through sentiment analysis. We also apply the information transformation module to convert the linguistic intuitionistic fuzzy information into linguistic intuitionistic normal clouds (LINCs). The integration module is employed to obtain the overall LINCs for each product. A ranked list of alternative products is determined. A case study on Taobao.com is then provided to illustrate the effectiveness and feasibility of the proposal, along with sensitivity and comparison analyses, to verify its stability and superiority. Finally, conclusions and future research directions are suggested.

Investment risk evaluation for new energy resources: An integrated decision support model based on regret theory and ELECTRE III

As conventional energy resources are limited and polluting, new energy resources, being renewable and environmentally friendly, have been receiving increasing attention in recent years. However, no study on new energy investment, which acts a significant role in promoting the development and use of new energy resources, has been conducted. To cover this gap, an applicable decision support model is established by integrating Z-numbers, regret theory and elimination and choice translating reality III (ELECTRE III) to address new energy investment risk evaluation problems. In this way, Z-numbers are used to describe the decision-making information involved in the problems, a suggested method is combined with regret theory to determine the utility, rejoice and regret values of Z-information, and ELECTRE III is introduced to handle multiple criteria evaluation comprehensively. To elucidate and validate the application of the established model, a case study for new energy investment in Qingshuitang Industrial Zone is conducted and in-depth results analysis and discussion are implemented. The study shows that solar energy is the best investment project and environment is the most important investment factor. Moreover, the results demonstrate that the established model can effectively support new energy investment decision-making and it performs better than other existing methods.

An integrated decision support model for selecting the most feasible crane at heavy construction sites

Abstract Heavy industrial projects, especially oil refinery facilities, consist of numerous components such as modules and vessels, which are large, heavy, and long objects. The successful completion of these projects requires precise planning and selection of safe and efficient single- and/or tandem crane lifts, which are strongly influenced by the crane types and models used. Thus, selecting not only the best crane type and model, but also the most efficient crane operation among alternatives, can result in productivity and safety improvements. In this respect, this paper proposes an integrated decision support model which designs, simulates, and evaluates potential crane operations in accordance with feasible crane types and models based on equipment, cost, site, and environmental aspects of the project. The proposed methodology consists of two components: (1) 3D-based crane selection simulation, which enhances the process of crane selection by not only accurately verifying and identifying engineering factors, but also analyzing feasibility (procedures) of tandem and single-crane operations, from crane installation to lift completion and disassembly of the crane configuration, in a 3D environment; and (2) a crane selection matrix which calculates the crane evaluation scores by measuring various factors of each feasible crane against the project criteria and constraints. The methodology is tested in a case study in order to illustrate its effectiveness.

Integrated Decision Support Model for Hedge Trading and Production Planning in the Petrochemical Industry

Using an optimization technique, this study proposes a new decision support system to improve the economics of petrochemical industries. In achieving the goal, we first develop two optimization models to support the decisions on (1) the hedge trade by minimizing purchase costs of raw materials, and (2) the production planning by maximizing profits from product sales. We then integrated these two models into a single decision support system using a recursive two-stage programming framework. On the basis of the integrated framework, we systematically identify the optimal solution, that is, the maximum profit, by simultaneously determining timing, amount, and price of raw materials to purchase, and the strategies for facility operation and product sales. To illustrate the capability of the proposed system, it was then applied to the business model of the petrochemical company in Korea. As a result, compared to the base case, it is revealed that the profitability of the petrochemical company could be improved...

An integrated decision support model for design and operation of a wind-based hydrogen supply system

We aim to develop a new optimization-based approach for the strategic planning of a renewable hydrogen supply system using onshore and offshore wind energy. To achieve this goal, we develop an optimization model to design and analyze a wind-powered hydrogen supply (WPHS) system using a mixed-integer linear programming technique. In this model, we include decision variables to account for a wide range of issues regarding the proposed system, from the technical selection of the wind turbine and wind farm layout design to strategies for onshore/offshore wind farm and hydrogen supply network development. To illustrate the capability of the proposed approach, we present a case study pertaining to the design of the WPHS system for the road transportation sector of Jeju Island, Korea. Using the proposed approach, we are able to i) identify the configuration and operational practices of an optimal WPHS system, ii) analyze the cost distribution and major cost drivers through economic evaluation, and iii) provide decision-making guidance to stakeholders and policy makers for planning an economically viable and a sustainable hydrogen supply system using various policies to encourage offshore wind farm development.

Implementing of a three-phase integrated decision support model for parts machining outsourcing

Parts machining outsourcing (PMO) can be regarded as a strategic weapon to increase product quality and productivity as well as cost cutting in many manufacturing enterprises. However, making an outsourcing decision based on accumulated experience is neither effective nor scientific due to subjective judgement and lack of systematic analysis. Therefore, to systematically help the decision-maker survey and select suppliers to outsource and also to timely achieve the most beneficial portfolio among suppliers in a non-cooperative game structure, this paper proposes an integrated decision support model for PMO using ontology, constraint-based reasoning, fuzzy VIKOR and game theory in three phases, namely supplier prequalification, ultimate selection and order coordination. In the decision-making process, decision factors such as quality, cost and production capability are categorised and put into different phases, respectively, by their characteristics for being measured at a more reasonable method. Finally, a case from a leading printing machinery enterprise is analysed to validate the proposed model. This research is expected to improve the quality and effectiveness of decision-making in PMO.

An integrated decision support model for a knowledge city's strategy formulation

PurposeThe concept of knowledge cities (KCs) offers advantages to any urban region. Many cities globally claim themselves as being already KCs, while other cities have elaborated strategic plans in order to integrate this concept into their operational structures. The examination of their approaches reveals however that these initiatives are fragmented. The purpose of this paper is to present a multi‐dimensional and integrated decision support model for a KC's strategy formulation.Design/methodology/approachReference is made to a methodological approach (KnowCis) for the integrated development of a KC, consisting of five main phases and taking into account nine different dimensions. The strategy formulation phase is a particularly complex procedure for any authority (e.g. local government or city's development agency). The reasons for this complexity are related to the amplitude of the KC concept, to the variety of the factors to be considered as well as to the challenge for balancing the needs and interests of different target groups.FindingsThe proposed model consists of the following building blocks: identification of the appropriate actions (based on the KnowCis methodology), modeling of the city's current status as a KC (via the development of related indicators), assessment of actions' necessity (based on the indicators' outcomes and through the benchmarking of other successful KCs cases), selection of the most appropriate form for each proposed action (based on their efficiency during the last reference period) and, finally, prioritisation of the proposed actions (based on a multi‐criteria approach).Research limitations/implicationsThe main suggestion for future research is the development of an intelligent information system which will incorporate the building blocks of the proposed model.Originality/valueThe originality and value of the paper is that the proposed model can be a really helpful decision support tool for any city which is developing a knowledge‐based strategy.