Posteriori Approach Research Articles

A posteriori multiobjective approach for techno-economic allocation of PV and BES units in a distribution system hosting PHEVs

This paper proposes a posteriori multiobjective approach to effectively integrate photovoltaic (PV) units into the distribution network. PV units are inherently non-dispatchable sources due to the intermittent nature of solar irradiance. To address this limitation, battery energy storage (BES) units are employed to support the PV units, thereby transforming them into dispatchable sources. The objective of this study is to optimally allocate dispatchable PV-BES units within a distribution network that accommodates plug-in hybrid electric vehicles (PHEVs). The PHEVs are modelled considering stochastic parameters at three different demand response levels. A multiobjective optimization problem is formulated to address the conflicting technical and economic objectives associated with the allocation of PV-BES units. The multiobjective manta ray foraging optimizer algorithm is utilized for solving the multiobjective optimization problem. Additionally, the technique for order of preference by similarity to ideal solution is used to identify the best trade-off solution, and multiple solutions are presented based on the decision-makers preferences regarding the conflicting objectives under consideration. The proposed approach is validated on 33-bus and 69-bus distribution networks, and extensive case studies are conducted to showcase the advantages of the proposed methodology.

A multi-objective optimisation strategy exploring the energy routing capability of a smart transformer while integrating hybrid energy hub into a distribution network

This study proposes an optimal planning approach for exploiting the energy routing capabilities of a smart transformer (ST) while integrating a hybrid energy hub (HEH) comprised of a wind turbine generator (WTG) and a battery storage system (BSS) into a distribution network (DN). An efficient energy management strategy (EMS) regulates energy exchanges within the HEH unit and with the utility grid. The work presents a mixed-integer nonlinear programming (MINLP) problem for optimising the HEH planning parameters with the purpose of minimising network performance costs such as line loading, real and reactive power losses, voltage profile, and asset management costs. Two approaches are used to achieve the goals, with the first strategy focused on network performance costs, while the second combines the network performance and the asset management expenses. The proposed methodology was tested using the 33-bus radial distribution network (RDN) alongwith a real-life 108-bus Indian RDN, and the results of various case studies are reported. The MATLAB R2020a programming platform has been used to solve the MINLP problem using the posteriori approach based paretosearch algorithm (PSA). The outcomes of the evaluation show an apparent energy loss reduction of 58.44%, a line loading improvement of 36.24%, and a voltage deviation improvement of 68.24%.

Comparing stochastic programming with posteriori approach for multi-objective optimization of distributed energy systems under uncertainty

Uncertainty complicates the optimization model of distributed energy systems, it is a challenge to address the fragility of optimal solutions, which calls for an effective but convenient approach to introduce uncertainties into multi-objective optimization. This study proposes and compares the priori and the posteriori modeling approaches for optimizing the design of distributed energy systems under uncertainty. The posteriori approach is developed as a Monte Carlo simulation combined with the deterministic programming model, while the priori approach is formulated as a two-stage stochastic programming model. Both approaches consider economic and environmental objectives and use the same set of uncertainty parameters based on a case study in Shanghai. The results show that, compared with the priori-approach model, the posteriori-approach model leads to an underestimation of the total annual cost, but their total annual carbon emission approximates. Besides, the Pareto frontier cliques from the posteriori approach demonstrate the distributions of system performance, whereas the priori approach can capture the uncertainties at the substantially higher computational cost. Finally, the tradeoff between model complexity and computational cost is discussed to generate more insights on the optimal design, i.e., configuration and dispatch, of distributed energy systems.

An approach to identify solutions of interest from multi and many-objective optimization problems

The result of a multiobjective or a many-objective optimization problem is a large set of non-dominated solutions. Once the Pareto Front (or a good approximation of it) has been found, then providing the decision maker with a smaller set of “interesting solutions” is a key step. Here, the focus is on how to select such a set of solutions of interest which, in contrast to previous approaches that relied on geometrical features, is carried out considering the decision maker’s preferences. The proposed a posteriori approach consists in assigning an interval of potential scores to every solution, where such scores depend on the decision maker’s preferences. The solutions are then compared and filtered according to their corresponding intervals, using a recently proposed possibility degree formula. Three examples, with two, three and many objectives are used to show the benefits of the proposal.

Towards a sustainable reconfigurable manufacturing system (SRMS): multi-objective based approaches for process plan generation problem

Nowadays to meet the continuously growing worldwide demand for capital and consumer goods while considering the associated economic, environmental, and social aspects, new generation of manufacturing systems are developed and adopted. The goal is to respond to changing market needs while minimising adverse effects on the environment. Reconfigurable manufacturing system (RMS) – due to its flexibility and characteristics – can increase the system sustainability and responsiveness to satisfy the market needs. This paper considers an environmental oriented multi-objective process plan generation problem for a sustainable reconfigurable manufacturing system (SRMS). Three criteria are minimised respectively the sustainability-metric value, the total production time and the total production cost. As a new criterion, the sustainability-metric value considers both liquid hazardous waste and greenhouse gas emissions (GHG). First, a multi-objective integer linear programming (MOILP) model is proposed. Second, due to the problem complexity, three approaches are adapted respectively a posteriori approach called augmented ε-constraint (AUGECON) and two evolutionary approaches namely, non-dominated sorting genetic algorithm II (NSGA-II) and strength Pareto evolutionary algorithm II (SPEA-II). Finally, due to the lack of real world data and to illustrate the applicability of the three approaches, a simple numerical example is presented and the obtained numerical results are analysed.

Avoiding Invalid Transitions in Land Cover Trajectory Classification With a Compound Maximum a Posteriori Approach

Classifying remote sensing time-series in land cover trajectories can provide essential information about ecosystems functioning and about the impacts of natural phenomena or human activities over the environment. The existing approaches commonly used for this task can present serious drawbacks, such as the possibility to derive invalid trajectories, use of complex data inputs, and several classification steps. To provide a simple method for land cover trajectory classification, we present a novel algorithm named Compound Maximum a Posteriori (CMAP) classifier. CMAP incorporates the knowledge of land cover dynamics and the information of multi-temporal data sets to produce only valid land cover trajectories using a global generative classification approach and simple inputs. CMAP was tested in two case studies, in which we compared land cover trajectories obtained by CMAP to those obtained using the traditional Maximum Likelihood (ML) classifier in a post-classification comparison approach. In the first case study, we classified 6 images from the same sensor, using the same land cover legend. In the second case study, we classified 3 images from different types of sensors, using different land cover legend levels. Because of its formulation, CMAP does not return invalid transitions/trajectories as classification results. The use of ML and post-classification comparison, on the other hand, resulted in invalid land cover trajectories in more than 50% of the used images in both case studies. Furthermore, the use of CMAP leads to better accuracy indexes for land cover classification of each date and reduces the classification noise.

Awakening the Ubuntu episteme to embrace knowledge management in Africa

Purpose The knowledge management (KM) models in the African organisations are influenced by the interplay between human agents from diverse societies whose experiences, values, contextual information and insights that are perceived controversial in Africa. The purpose of this paper is to elucidate the indigenous assumptions related to knowledge and its management in Africa and the perceived contradictions in the existing models by adopting the Ubuntu philosophy. Design/methodology/approach The authors used a perspective lens to examine the existing management practices and propose an integrated framework that is appropriate for the utilisation of the Ubuntu epistemic knowledge management practices and at the same time provide highlights on the perceived paradoxes and how they can be managed to improve knowledge management and people management in African societies. Findings The inductive posteriori knowledge approach is perceived to be dynamic, applicable and more desirable in the African societies as it allows organisational managers and their work teams to embrace knowledge construction, dependent on experiences in form of stories and metaphors that demonstrate successful work samples. The Ubuntu dramaturgical knowledge management approach adds value to the posteriori knowledge by refining the rhetoric stories and metaphors into empirical performance scripts that are tailored to the audiences’ expectations. Research limitations/implications The paper adapted a perspective view to explain knowledge management; therefore, it was not possible to provide empirical data on the metaphysical and dramaturgical elements that are assumed to influence knowledge management in Africa. However, based on theoretical analysis, the authors have proposed a coherent knowledge management framework based on the interaction between posteriori KM assumptions and Ubuntu dramaturgy. Practical implications Ubuntu ideology has been appreciated since it treasures interdependency and interconnectedness among people. Therefore, collaborating partners working in Africa would be expected to act as interdependent agents, whereby this interdependency is perceived as an integral part of the knowledge management process. The proposed Ubuntu knowledge management model is grounded on the posteriori knowledge approach which assumes that experience is the source of knowledge. Through social interactions and experiences sharing, organisational members can create new processes, innovative technologies and dynamic context based performance scripts that can drive productivity. Social implications The authors concluded that a coherent framework that is tailored to social interactions and contextual needs of the people and their communities can promote productive knowledge and knowledge management systems in the African contexts. Moreover knowledge management requires one to acknowledge the complexity of Ubuntu ideology in a sense that it recognises the past experiences and contributions of the diverse individuals in the same community/organisation. Originality/value This paper focused on examining how the Ubuntu philosophy can promote knowledge development and management strategies that are tailored to social and contextual needs of the organisations in Africa to curtail the perceived paradoxes in the existing knowledge management models.

Habitual Dietary Patterns of Patients on Hemodialysis Indicate Nutritional Risk

This study aimed to (i) determine habitual dietary patterns of Malaysian patients on hemodialysis (HD) and (ii) examine their association with nutritional status. An à posteriori approach examined 3-day dietary recalls of 382 multiethnic Malaysian patients on HD, leading to short-listing of 31 food groups. Dietary patterns were derived through principal component analysis. Sociodemographic and lifestyle characteristics together with nutritional parameters were examined for associations with specific dietary patterns. Four dietary patterns emerged, namely, "Home Food," "Eating Out (EO)-Rice," "EO-Sugar sweetened beverages," and "EO-Noodle." Younger patients, male gender, Malay, and patients with working status were more likely to follow "EO-Rice" and "EO-Sugar sweetened beverages" patterns, while Chinese patients were more likely to consume "EO-Noodle" pattern (all P values<.05). The EO frequency was directly associated with "EO-Rice" (P=.030), "EO-Sugar sweetened beverages" (P=.040), and "EO-Noodle" (P=.001) patterns. The highest tertile of the "Home Food" pattern related to higher handgrip strength (T3=21.3±0.74 vs. 18.0±0.73kg, P=.006), higher serum albumin (T3=3.99±0.04 vs. T1=3.84±0.04g/dL, P=.027), and lower Malnutrition-Inflammation Score (T3=4.9±0.36 vs. T1=6.4±0.34, P=.010), along with lower Diet Monotony Index (T3=29.0±1.1 vs. T1=33.0±1.0, P=.030). while "EO-Rice" and "EO-Sugar sweetened beverage" patterns were associated only with higher energy intake (all P values<.001). These results indicated that a home-based diet inclusive of healthy food choices was associated with better nutritional status in this HD population.

A speech measure for early stratification of fast and slow progressors of bulbar amyotrophic lateral sclerosis: lip movement jitter

Objective: To assess the utility of novel measures derived from a rapid syllable repetition task (i.e. oral dysdiadochokinesis [DDK]) in early stratification of fast and slow progressive bulbar amyotrophic lateral sclerosis (ALS) and prediction of bulbar disease progression rate. Methods: Fifty-four individuals with ALS were tracked longitudinally on their oral DDK and global bulbar/speech performance (i.e. bulbar subscore on the ALS Functional Rating Scale-Revised [ALSFRS-R]; articulation rate during passage reading) for a four-month average duration. Based on the bulbar deterioration rate over the tracked period, the participants were stratified as 14 fast bulbar progressors and 40 slow bulbar progressors using a posteriori classification approach. To determine if oral DDK performance predicts the differential bulbar disease progression trajectories in these individuals during the early stages of the tracked period (prior to significant bulbar/speech signs), twenty-two measures of lip motor performance in an oral DDK task were derived to (1) differentiate fast and slow bulbar progressors using the Receiver Operating Characteristic analysis and (2) predict bulbar disease progression rates across all individuals using linear regressions. Results: Movement jitter, a measure of temporal variability of alternating lip movement during DDK, showed 80% sensitivity and 95% specificity in differentiating fast and slow bulbar progressors early in the disease, and outperformed the ALSFRS-R bulbar subscore and articulation rate. Movement jitter also predicted bulbar disease progression rates across participants. Conclusion: Findings provided preliminary validation of the clinical value of movement jitter during oral DDK in patient stratification and bulbar disease prognosis.

MON-100 DIETARY PHOSPHATE INTAKE PATTERNS IN MALAYSIAN HEMODIALYSIS PATIENTS: UNDERSTANDING DETERMINANTS OF PATIENT BEHAVIORS

A neglected area of dialysis patient care in Malaysia is the dietary management of hyperphosphatemia. Information on patient’s food habits is critical in determining strategies in nutrition intervention and monitoring services. This study accordingly investigated dietary patterns (DPs) of hemodialysis (HD) patients to determine food consumption behaviors detrimental to serum phosphate levels. We used screening data (n=382) from the ongoing multicentered (n=12) Palm Tocotrienols in Chronic Hemodialysis(PaTCH) study. An à posteriori approach examined 3-day dietary records leading to short-listing 31 food groups. DPs were derived through principal component analysis with eigenvalue >1.0. Dietary phosphorous as per total, organic (animal and plant) and inorganic phosphates, phosphate to protein (Pho:Pro) ratio, along with serum phosphate were parameters examined as per tertiles of DP consumption. In final analyses (n=366) four DPs emerged namely, Home foods (HF), Eating out (EO), Sugar-sweetened beverages (SSB) and Monotonous diet (MD). Within DP Tertiles (T3 vs T1), younger patients chose EO (T3 = 49.96 ± 15.15 vs T1 = 57.42 ± 13.18 years) and SSB (T3 = 50.65 ± 14.54 vs T1 = 58.20 ± 12.86 years) patterns. T3 of HF pattern reflective of greater food variety, carried the highest organic phosphate load from animal foods (287 ± 160 mg vs 228 ± 130 mg, p = 0.007) and least from inorganic phosphates (85 ± 82 mg vs 117 ± 102 mg, p= 0.032). The EO pattern, mainly cereal-based, carried the highest load of plant organic phosphates (323 ± 130 mg/T2 and 289± 107 mg/T3). T3 of MD pattern reflective of limited food variety, carried the lowest Pho:Pro ratio (11.42 ± 2.24 mg/g). But T3 of SSB pattern which significantly correlated to the highest serum phosphate levels (1.90 ± 0.54 vs 1.74 ± 0.46 mmol/L, p = 0.048), also carried the highest load of inorganic phosphate content (144.2 ± 109.4 vs 80.5 ± 76.8 mg, p = 0.001) and Pho:Pro ratio (12.93 ± 2.34 vs 11.89 ± 3.00 mg/g, p = 0.011). The SSB pattern was dominated by beverages (1206 ± 304 vs 1000 ± 326 ml, p = 0.001) carrying inorganic phosphates mainly from non-dairy creamers and condensed milk. The SSB pattern appears detrimental to serum phosphate control in Malaysian HD patients, as it increases inorganic phosphate load. Interventions should target patient behaviors towards a healthier DP such as the HF pattern which had greater food variety.

Automatic Segmentation and Detection of Small Bowel Angioectasias in WCE Images.

Angioectasias are lesions that occur in the blood vessels of the bowel and are the cause of more than 8% of all gastrointestinal bleeding episodes. They are usually classified as bleeding related lesions, however current state-of-the-art bleeding detection algorithms present low sensitivity in the detection of these lesions. This paper proposes a methodology for the automatic detection of angioectasias in wireless capsule endoscopy (WCE) videos. This method relies on the automatic selection of a region of interest, selected by using an image segmentation module based on the Maximum a Posteriori (MAP) approach where a new accelerated version of the Expectation-Maximization (EM) algorithm is also proposed. Spatial context information is modeled in the prior probability density function by using Markov Random Fields with the inclusion of a weighted boundary function. Higher order statistics computed in the CIELab color space with the luminance component removed and intensity normalization of high reflectance regions, showed to be effective features regarding angioectasia detection. The proposed method outperforms some current state of the art algorithms, achieving sensitivity and specificity values of more than 96% in a database containing 800 WCE frames labeled by two gastroenterologists.

An a posteriori decision support methodology for solving the multi-criteria supplier selection problem

This research presents a novel, state-of-the-art methodology for solving a multi-criteria supplier selection problem considering risk and sustainability. It combines multi-objective optimization with the analytic network process to take into account sustainability requirements of a supplier portfolio configuration. To integrate ‘risk’ into the supplier selection problem, we develop a multi-objective optimization model based on the investment portfolio theory introduced by Markowitz. The proposed model is a non-standard portfolio selection problem with four objectives: (1) minimizing the purchasing costs, (2) selecting the supplier portfolio with the highest logistics service, (3) minimizing the supply risk, and (4) ordering as much as possible from those suppliers with outstanding sustainability performance. The optimization model, which has three linear and one quadratic objective function, is solved by an algorithm that analytically computes a set of efficient solutions and provides graphical decision support through a visualization of the complete and exactly-computed Pareto front (a posteriori approach). The possibility of computing all Pareto-optimal supplier portfolios is beneficial for decision makers as they can compare all optimal solutions at once, identify the trade-offs between the criteria, and study how the different objectives of supplier portfolio configuration may be balanced to finally choose the composition that satisfies the purchasing company's strategy best. The approach has been applied to a real-world supplier portfolio configuration case to demonstrate its applicability and to analyze how the consideration of sustainability requirements may affect the traditional supplier selection and purchasing goals in a real-life setting.

Improving the quality characteristics of abrasive water jet machining of marble material using multi-objective artificial bee colony algorithm

Abstract Although abrasive water jet machining has proved its capabilities for cutting marble material in a most economic and environment friendly manner, is facing serious issues related to dimensional inaccuracy and striation marks. This has put limit on its applications. Also, due to complex nature of abrasive water jet machining process, it is very difficult to control all three quality factors i.e. kerf taper, kerf width, striation marks simultaneously to achieve desired quality. This work therefore deals with multi-objective optimization considering three objectives as: minimization of kerf width, minimization of kerf taper, and maximization of depth of striation free surface in abrasive water jet machining process. The response surface modeling is used to establish the relation between various input parameters such as stand of distance, traverse speed, water pressure, and abrasive flow rate, with objectives mentioned above. Application of well-known meta-heuristics named artificial bee colony algorithm is extended to multi-objective optimization with posteriori approach by incorporating the concept of non-dominated sorting. Set of Pareto optimal solutions obtained by this proposed approach provides a ready reference for selecting most appropriate parameter setting on the machine with respect to objectives considered in this work. Highlights Provides methodology to concurrently minimize the dimensional inaccuracy along with striation marks for cutting marble material with abrasive water jet machining process. Application of artificial bee colony algorithm is extended to multi-objective optimization. The set of Pareto-optimal solution obtained using proposed approach can be used as a ready reference by the process engineers for cutting marble material by AWJM process.

Transcriptome sequence-based phylogeny of chalcidoid wasps (Hymenoptera: Chalcidoidea) reveals a history of rapid radiations, convergence, and evolutionary success

Chalcidoidea are a megadiverse group of mostly parasitoid wasps of major ecological and economical importance that are omnipresent in almost all extant terrestrial habitats. The timing and pattern of chalcidoid diversification is so far poorly understood and has left many important questions on the evolutionary history of Chalcidoidea unanswered. In this study, we infer the early divergence events within Chalcidoidea and address the question of whether or not ancestral chalcidoids were small egg parasitoids. We also trace the evolution of some key traits: jumping ability, development of enlarged hind femora, and associations with figs. Our phylogenetic inference is based on the analysis of 3,239 single-copy genes across 48 chalcidoid wasps and outgroups representatives. We applied an innovative a posteriori evaluation approach to molecular clock-dating based on nine carefully validated fossils, resulting in the first molecular clock-based estimation of deep Chalcidoidea divergence times. Our results suggest a late Jurassic origin of Chalcidoidea, with a first divergence of morphologically and biologically distinct groups in the early to mid Cretaceous, between 129 and 81 million years ago (mya). Diversification of most extant lineages happened rapidly after the Cretaceous in the early Paleogene, between 75 and 53 mya. The inferred Chalcidoidea tree suggests a transition from ancestral minute egg parasitoids to larger-bodied parasitoids of other host stages during the early history of chalcidoid evolution. The ability to jump evolved independently at least three times, namely in Eupelmidae, Encyrtidae, and Tanaostigmatidae. Furthermore, the large-bodied strongly sclerotized species with enlarged hind femora in Chalcididae and Leucospidae are not closely related. Finally, the close association of some chalcidoid wasps with figs, either as pollinators, or as inquilines/gallers or as parasitoids, likely evolved at least twice independently: in the Eocene, giving rise to fig pollinators, and in the Oligocene or Miocene, resulting in non-pollinating fig-wasps, including gallers and parasitoids. The origins of very speciose lineages (e.g., Mymaridae, Eulophidae, Pteromalinae) are evenly spread across the period of chalcidoid evolution from early Cretaceous to the late Eocene. Several shifts in biology and morphology (e.g., in host exploitation, body shape and size, life history), each followed by rapid radiations, have likely enabled the evolutionary success of Chalcidoidea.

An a posteriori species clustering for quantifying the effects of species interactions on ecosystem functioning

Abstract Quantifying the effects of species interactions is key to understanding the relationships between biodiversity and ecosystem functioning but remains elusive due to combinatorics issues. Functional groups have been commonly used to capture the diversity of forms and functions and thus simplify the reality. However, the explicit incorporation of species interactions is still lacking in functional group‐based approaches. Here, we propose a new approach based on an a posteriori clustering of species to quantify the effects of species interactions on ecosystem functioning. We first decompose the observed ecosystem function using null models, in which species diversity does not affect ecosystem function, to separate the effects of species interactions and species composition. This allows the identification of a posteriori functional groups that have contrasting diversity effects on ecosystem functioning. We then develop a formal combinatorial model of species interactions in which an ecosystem is described as a combination of co‐occurring functional groups, which we call an assembly motif. Each assembly motif corresponds to a particular biotic environment. We demonstrate the relevance of our approach using datasets from a microbial experiment and the long‐term Cedar Creek Biodiversity II experiment. We show that our a posteriori approach is more accurate, more efficient and more parsimonious than a priori approaches. The discrepancy between a priori and a posteriori approaches results from the way each clustering is set up: a priori approaches are based on ecosystem or species properties, such as ecosystem size (number of species or functional groups) or species’ functional traits, whereas our a posteriori approach is based only on the observed interaction and composition effects on ecosystem functioning. Our findings demonstrate that an a posteriori approach is highly explanatory: it identifies who interacts with whom, and quantifies the effects of species interactions on ecosystem functioning. They also highlight that a combinatorial modelling of ecosystem functioning can predict the functioning of an ecosystem without any hypothesis about the biotic or environmental determinants or any information on species functional traits. It only requires the species composition of the ecosystem and the observed functioning of others that share the same assembly motif.

Using cystoscopy to segment bladder tumors with a multivariate approach in different color spaces.

Nowadays the diagnosis of bladder lesions relies upon cystoscopy examination and depends on the interpreter's experience. State of the art of bladder tumor identification are based on 3D reconstruction, using CT images (Virtual Cystoscopy) or images where the structures are exalted with the use of pigmentation, but none uses white light cystoscopy images. An initial attempt to automatically identify tumoral tissue was already developed by the authors and this paper will develop this idea. Traditional cystoscopy images processing has a huge potential to improve early tumor detection and allows a more effective treatment. In this paper is described a multivariate approach to do segmentation of bladder cystoscopy images, that will be used to automatically detect and improve physician diagnose. Each region can be assumed as a normal distribution with specific parameters, leading to the assumption that the distribution of intensities is a Gaussian Mixture Model (GMM). Region of high grade and low grade tumors, usually appears with higher intensity than normal regions. This paper proposes a Maximum a Posteriori (MAP) approach based on pixel intensities read simultaneously in different color channels from RGB, HSV and CIELab color spaces. The Expectation-Maximization (EM) algorithm is used to estimate the best multivariate GMM parameters. Experimental results show that the proposed method does bladder tumor segmentation into two classes in a more efficient way in RGB even in cases where the tumor shape is not well defined. Results also show that the elimination of component L from CIELab color space does not allow definition of the tumor shape.

A novel image fusion algorithm based on 2D scale-mixing complex wavelet transform and Bayesian MAP estimation for multimodal medical images

In this paper, we propose a new image fusion algorithm based on two-dimensional Scale-Mixing Complex Wavelet Transform (2D-SMCWT). The fusion of the detail 2D-SMCWT coefficients is performed via a Bayesian Maximum a Posteriori (MAP) approach by considering a trivariate statistical model for the local neighboring of 2D-SMCWT coefficients. For the approximation coefficients, a new fusion rule based on the Principal Component Analysis (PCA) is applied. We conduct several experiments using three different groups of multimodal medical images to evaluate the performance of the proposed method. The obtained results prove the superiority of the proposed method over the state of the art fusion methods in terms of visual quality and several commonly used metrics. Robustness of the proposed method is further tested against different types of noise. The plots of fusion metrics establish the accuracy of the proposed fusion method.

An a Posteriori Multi-Objective Supplier Portfolio Selection Approach Under Risk and Sustainability Considerations

This research presents a novel, state-of-the-art methodology for solving the multi-criteria supplier selection problem under risk and sustainability considerations. The approach combines multi-objective optimization with the analytic network process to meet the requirements for supplier portfolio configuration with sustainability considerations. To integrate the aspect ‘risk’ into the supplier selection problem, this research develops a multi-objective optimization model based on the investment portfolio theory introduced by Markowitz. The proposed model is a non-standard portfolio selection problem with four objectives: to minimize the purchasing costs, to select the supplier portfolio with the highest logistics service, to minimize the supply risk, and to order as much as possible from those suppliers with outstanding sustainability performance. The optimization model, which has three linear and one quadratic objective function, is solved by an algorithm which analytically computes a set of efficient solutions and provides graphical decision support by a visualization of the complete and exactly-computed Pareto front (a posteriori approach). The possibility of computing all Pareto optimal supplier portfolios is beneficial for decision makers as they can compare all optimal solutions at once, identify the trade-offs between the criteria, and study how the different aspects of supplier portfolio configuration may be balanced to finally choose the composition that satisfies the purchasing company’s strategy at best. The approach has been applied to a real-world supplier portfolio configuration case to demonstrate its applicability and to analyze how the consideration of sustainability requirements may affect the traditional supplier selection and purchasing goals in a real-life setting.

Parametric optimisation of abrasive water jet machining of glass fibre reinforced plastic composite using non-dominated sorting genetic algorithm-II

Machining of glass fibre reinforced plastic (GFRP) possesses several challenges; it is completely different from metal machining. Abrasive water jet machining has proved to be an interesting manufacturing process to machine GFRP in environment friendly manner. In this paper, experimentation is conducted and mathematical model has been developed to establish the correlation between process variables: water jet pressure, traverse rate, abrasive mass flow rate and standoff distance with performance measures: surface roughness, kerf width and kerf taper angle using response surface methodology. A well-known multi-objective optimisation method, non-dominated sorting genetic algorithm-II, is applied to obtain the set of Pareto-optimal solutions, which can be used as a ready reference by the process engineers. Rarely if ever, multiple responses are considered but that too are attempted with priori approach considering finite solutions. Whereas in practice, the problem has to be dealt with infinite solutions with posteriori approach, which is attempted in the proposed method.

Parametric optimisation of abrasive water jet machining of glass fibre reinforced plastic composite using non-dominated sorting genetic algorithm-II

Machining of glass fibre reinforced plastic (GFRP) possesses several challenges; it is completely different from metal machining. Abrasive water jet machining has proved to be an interesting manufacturing process to machine GFRP in environment friendly manner. In this paper, experimentation is conducted and mathematical model has been developed to establish the correlation between process variables: water jet pressure, traverse rate, abrasive mass flow rate and standoff distance with performance measures: surface roughness, kerf width and kerf taper angle using response surface methodology. A well-known multi-objective optimisation method, non-dominated sorting genetic algorithm-II, is applied to obtain the set of Pareto-optimal solutions, which can be used as a ready reference by the process engineers. Rarely if ever, multiple responses are considered but that too are attempted with priori approach considering finite solutions. Whereas in practice, the problem has to be dealt with infinite solutions with posteriori approach, which is attempted in the proposed method.