Development of a sustainable manufacturing strategy using analytic network process

This work explores the implementation of analytic network process (ANP) in developing a sustainable manufacturing strategy. Previous works fell short of addressing the need of developing a manufacturing strategy that simultaneously incorporates the demands of sustainability. Manufacturing strategy and sustainable manufacturing has been approached in the literature as two different concepts with having limited works done that holistically incorporate them. Thus, a proposed problem structure is developed that attempts to integrate these two concepts. The key aspect of the proposed framework is the explicit incorporation of stakeholders\u27 interests in the strategy development process. Due to the number of decision components and their inherent relationships that must be simultaneously addressed in developing a sustainable manufacturing strategy, ANP is used in this work. This work is aimed at: 1) identifying the content of a sustainable manufacturing strategy that integrates sustainability and classical manufacturing strategy; 2) demonstrating the applicability of ANP in the complex manufacturing strategy formulation process. A Monte Carlo simulation is also implemented to check the robustness of the ANP results. Managerial insights and future work were also discussed. © Copyright 2016 Inderscience Enterprises Ltd

Computing sustainable manufacturing index with fuzzy analytic hierarchy process

The growing industrial interest in adopting sustainability programmes has ushered in studies regarding sustainability indicators which have continually flourished in current literature. However, limited attention is given to the development of priority ranking, which is an important input for any adopting firm. This paper presents a hybrid multi-criteria approach in determining priority areas in sustainable manufacturing (SM). Using fuzzy analytic hierarchy process to address uncertainty in hierarchical decision-making, this paper determines SM priority strategies and eventually identifies even lower level strategies. The computed sustainable manufacturing index is presented at both the organizational and operational levels for a real case study of an industrial plastic manufacturing firm. This work provides a detailed and transparent hierarchical decision-making approach based on SM framework, the use of which could be valuable to practicing managers across industries in their pursuit of greater sustainability. © 2016 Informa UK Limited, trading as Taylor & Francis Group

A green chemistry-based decision modelling approach for optimal selection of nanomaterial\u27s synthesis method

Recent advances in nanotechnology have produced materials with superior performance in various industrial applications including medicine and advanced manufacturing. Synthesis of these so-called nanomaterials within the guiding philosophy of green chemistry has also continuously gaining attentions among researchers to address such emerging issue of sustainability. Green chemistry provides a set of principles which encourages the alternative design of product and processes that use renewable feedstock, and are energy efficient and safer with less hazardous pathway toward synthesis. However, optimal selection of such green synthesis method is a complex decision making problem that requires an integration of both tangible and intangible criteria. This work thus develops a metric from a green chemistry-inspired hierarchical decision model in prioritizing different synthesis methods of nanomaterial. A Monte Carlo simulation-aided Fuzzy Analytic Hierarchy Process (FAHP) coupled with Grey Relational Analysis (GRA) was used to rank the alternatives by integrating the knowledge from both peer-reviewed literature and experts in the field, while addressing the uncertainty involved in the decision making process. Such approach makes also the decision making transparent and open for new perspectives or criteria whenever relevant data becomes available. An illustrative example is presented for a case study of carbon nanotube synthesis. Copyright © 2017, AIDIC Servizi S.r.l

Optimal selection of desalination systems using fuzzy AHP and grey relational analysis

Water scarcity is an alarming global problem for a growing population with depleting sources of fresh water. Desalination is thus becoming an important solution for water management to address such looming shortage of the municipal water supply. At present, several technologies dominate the desalination industry which can be categorized either as a thermal process such as multi-stage flash distillation or a membrane process such as that of reverse osmosis. New desalination systems are also being developed to make the process more cost-effective and energy efficient. Hence, this work proposes a systematic approach for optimal selection of desalination systems using fuzzy analytic hierarchy process (FAHP) and grey relational analysis (GRA). Fuzzy AHP addresses the vagueness involve in the trade-off of the criteria or attributes used in evaluating the alternatives. On the other hand, the GRA solves the multiple criteria decision problem by aggregating the entire range of performance attribute values for every alternative into a single score in spite of incomplete information. An illustrative case study was presented wherein five desalination systems namely reverse osmosis (RO), combined reverse osmosis and forward osmosis (RO-FO), electrodialysis (ED), multi-stage flash distillation (MSF), and combined forward osmosis and membrane distillation (FO-MD) were evaluated. These desalination systems were compared to each other with respect to energy requirement, land footprint, system efficiency, economic viability, and maturity of technology. Sensitivity analysis was also done to determine the robustness of the modeling results from the variation of weights of the criteria. © 2016, AIDIC Servizi S.r.l

Optimal selection of aerobic biological treatment for a petroleum refinery plant

© 2016, AIDIC Servizi S.r.l. Aerobic biological treatment has been known to be an integral part of a typical wastewater treatment plant that reduces the pollution load of wastewater from either municipality or industry having soluble organic contaminants. For example, the conventional activated sludge process has been widely used in a long time but a variety of biological treatment systems were also being considered in recent years to meet a more stringent discharge standards. In designing such wastewater treatment plant, several criteria have to be taken into account that includes the technical, socio-economic and environmental aspects of the decision problem. This work thus applies a multiple criteria analysis based on Group Fuzzy AHP for optimal selection of the different aerobic biological treatment technologies. This technique decomposes the decision problem into a hierarchic structure and derives priority weights for the ranking of the alternatives. The decision model also incorporates the ambiguity-type uncertainty when eliciting pairwise comparison judgment from a domain expert. A case study applied to a petroleum refinery plant is presented considering the following alternatives: 1) conventional activated sludge system (CAS); 2) sequencing batch reactor (SBR); 3) integrated fixed film activated sludge system (IFAS); and 4) membrane bioreactor (MBR). These wastewater treatment systems were then evaluated and compared with respect to the following criteria: 1) economic sub-criteria such as the capital and operating cost; 2) environmental sub-criteria such as the treated effluent quality, ability to adjust to hydraulic and pollutant loading, ability to cope with oil ingress, and land footprint; 3) technical sub-criteria such as pre-treatment and secondary clarifier requirement, reliability and validity of technology, and complexity to operate and control. The Group Fuzzy AHP model showed that SBR is the most preferred option followed by CAS as regard to aerobic biological system for the treatment of petroleum refinery wastewater. Indications suggest from sensitivity analysis that the ranking of the alternatives is influenced largely by the weighting of economic and environmental aspects

A fuzzy programming approach to multi-objective optimization for geopolymer product design

Geopolymer is an inorganic polymer binder formed from the alkaline activation of reactive alumino-silicate materials resulting in two- or three-dimensional polymeric network. It is a promising alternative to Portland cement-based materials because of its lower embodied energy and carbon footprint with potential for waste valorization. Studies have been done to develop such material with desired engineering specification by using statistical design of experiment and optimizing the process conditions or mix formulation of waste materials. However, it is not only the engineering properties such as its mechanical and thermal properties, but also other properties pertaining to green materials (e.g., embodied energy and carbon footprint) have to be considered. Conflicting objectives may also have to be satisfied simultaneously to find a compromised solution in the product design such as that of maximizing the strength and minimizing the volumetric weight. This work thus proposes a weighted max-min aggregation approach to multi-objective optimization of the geopolymer product using fuzzy programming approach. The optimization formulation was introduced such that fuzzy sets represent both the aspired product desirability and soft constraints; the optimal mix is then found by maximizing the simultaneous satisfaction of target properties of the desired product. This work also proposes an extension of such fuzzy optimization formulation wherein the nature of trade-off between improving the product desirability and satisfying the fuzzy constraints are made explicit. The relative importance of the properties as represented by priority weights were derived systematically using Analytic Hierarchy Process (AHP). A case study on a ternary blended geopolymer from coal fly ash, coal bottom ash, and rice hull ash is presented to illustrate the proposed method.© 2017 Elsevier B.V

An input-output approach to analysing the relative influence of journals: The case of process integration journals

A journal\u27s influence is commonly measured through well-known metrics such as the h-index, Impact Factor (IF), and Source Normalized Impact per Paper (SNIP); many new metrics have emerged in the recent years to quantify the hierarchy among journals in each discipline. However, a measure including the interdependence among journals through the article reference and citations has not been designed. Interdependence among journals is important to gauge the influence of journals as a source of knowledge or as an avenue for communicating new developments. In this paper, an input-output network model is proposed to quantify the interdependencies that exist in a given cohort of journals. This method yields measures derived from indices used with input-output models in other domains (e.g., economic modelling) that quantifies a journal\u27s influence relative to other journals in the same cohort. This approach is illustrated using publication and citation statistics from four journals that have been regularly associated with PRES. Results show that journals may have high citation ratings but they may not necessarily have a strong influence in driving scientific discussion in other journals. Copyright © 2017, AIDIC Servizi S.r.l

Induction approach via P-graph to rank clean technologies

Identification of appropriate clean technologies for industrial implementation requires systematic evaluation based on a set of criteria that normally reflect economic, technical, environmental and other aspects. Such multiple attribute decision-making (MADM) problems involve rating a finite set of alternatives with respect to multiple potentially conflicting criteria. Conventional MADM approaches often involve explicit trade-offs in between criteria based on the expert\u27s or decision maker\u27s priorities. In practice, many experts arrive at decisions based on their tacit knowledge. This paper presents a new induction approach, wherein the implicit preference rules that estimate the expert\u27s thinking pathways can be induced. P-graph framework is applied to the induction approach as it adds the advantage of being able to determine both optimal and near-optimal solutions that best approximate the decision structure of an expert. The method elicits the knowledge of experts from their ranking of a small set of sample alternatives. Then, the information is processed to induce implicit rules which are subsequently used to rank new alternatives. Hence, the expert\u27s preferences are approximated by the new rankings. The proposed induction approach is demonstrated in the case study on the ranking of Negative Emission Technologies (NETs) viability for industry implementation. © 2019 The Author

Problematique approach to analyse barriers in implementing industrial ecology in Philippine industrial parks

Industrial ecology is recognized as an important framework toward a circular economy wherein industrial systems minimize their environmental burden by mimicking the material cycles and energy cascades found in biological ecosystems. Advocates of such framework in planning of eco-industrial parks suggest that both economic and environmental gains can be attained by transforming the industrial production from a linear to a closed loop system. However, it is imperative first to understand and analyze systematically the barriers in implementing the concept of industrial ecology in industrial parks even at the early planning stage. This work thus proposes a problematique approach to understand and analyse such barriers toward a successful development of eco-industrial parks. A problematique is a term coined by Warfield referring to concepts and tools for a structural model of relationships among members of a set of problems. The problematique is shown to be effective in analyzing the structure that underlies problematic situations, thus increasing the potential for crafting solution through human intervention. An illustrative case study was presented using a methodological framework built from Decision Making Trial and Evaluation Laboratory (DEMATEL), Interpretive Structural Modeling (ISM) and Analytic Network Process (ANP). Among the identified barriers in an industrial park situated in Philippines, The method reveals the strength and direction of interaction, hierarchical network structure, prioritization of components, and the causal loop mapping to aid stakeholders in systems thinking and problem solving for such complex issues. © 2016, AIDIC Servizi S.r.l

Optimal multi-criteria selection of energy storage systems for grid applications

Currently, a wide variety of energy storage alternatives are available, each with a unique set of characteristics advantageous on selective applications. Current studies focus only on levelized costs on predicting the best-fit technology for specific applications. The study addresses this limitation by considering multiple factors on the selection process among technologies for specific applications. A systematic approach on the selection of energy storage technologies based on multiple and possible conflicting factors was proposed in this study for two specific applications: frequency regulation and load levelling. Fuzzy Analytic Hierarchy Process was utilized to generate the relative importance of each criterion. Monte Carlo simulations were performed to reflect the effect of battery characteristics and operating parameters uncertainties on the resulting scores of technologies. Grey Relational Analysis was used to aggregate the performance attributes of alternatives into a single score reflecting the desirability of alternatives. The levelized costs dominated all other criteria for both applications. Lithium ion battery dominated all technologies for both applications resulting from its well-rounded performance across all considered attributes. Results emphasized the importance of considering socio-economic indicators alongside techno-economic parameters on selecting the technology for future deployment. Thorough analysis on the results is important not only for decision-makers but for developers and innovators as well to direct future research. Copyright © 2019, AIDIC Servizi S.r.l