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Supply chain challenges have been significantly affected by both demand and supply on a global level. The selection of manufacturing countries has become critical to firms and their boards, even more so coming out of the COVID-19 global pandemic. The present study focuses on how firms select countries and regions to de-risk future global apparel sourcing, as countries that have been dependable in the past may not be in the future based on frequent environmental jolts, legacy supply chain failures, shifting government policy, and extreme volatility. The result of this study is a decision model for manufacturing country selection. This research was focused on the apparel industry; however, further research may indicate that it is applicable to other industries. A group of criteria was selected, the relative significance of these criterion was determined using the Analytical Hierarchy Process (AHP). The AHP methodology was applied in a case study as a decision-making tool to enable decision-makers to assess the most suitable countries for manufacturing country selection. The result of this study is a decision model for manufacturing country selection based on multiple criteria weighted by industry experts using Analytical Hierarchy Process (AHP). In developing the model we utilize data from 61 countries representing over 95% of all the global apparel exports, with criteria utilized originating from 10 indices., 2023, Includes bibliography., Degree granted: Dissertation (PhD)--Florida Atlantic University, 2023., Collection: FAU Electronic Theses and Dissertations Collection

The world is ever-changing with technological advancement. National economies and private organizations are shifting their infrastructure to adapt to innovation and technology. We are seeing a major shift in our transportation ecosystem as well. Automotive manufacturers are launching fully electric semi-truck (EST) on the road for freight transportation. Electric trucks will have a long-term effect on many industries and the national economy in the United States. Compared to conventional automobiles, the limited range of electric vehicles is a major obstacle. To adapt electric vehicles (EVs) to our transportation system, the U.S. needs a proper charging infrastructure in our grid. Though we have been adapting the passenger EVs, the EST needs larger charging infrastructure capabilities to charge the large batteries of these trucks to complete the journey. The most important aspect is the geographical locations of these mega charging stations along U.S. highways. To analyze the optimal locations of these charging infrastructures, we use the framework from Csiszár et al. (2020), an origin-destination (O-D) data model. OD is classified as the original location of the freight to the end destination. We also use the flow-refueling location model (FRLM) from He et al. (2019). This framework showcases the optimal locations in each route in order to complete the OD pairs. We use data from the U.S. department of energy for the locations of charging stations. Furthermore, we use U.S. department of transportation highway & transportation data to procure the major O-Ds of freight transportation., 2023, Includes bibliography., Degree granted: Dissertation (PhD)--Florida Atlantic University, 2023., Collection: FAU Electronic Theses and Dissertations Collection

While the complexities and challenges facing healthcare continue to grow, the focus on improving surgical practices remains constant. Possessing a strong influence over patient referral patterns, public reputation/prominence, and financial performance, surgical practices command heightened attention on operational performance and clinical outcomes. Executive leadership cannot support (nor improve) a surgical practice without comprehending the importance of team dynamics in the operating room (OR) environment. Previous literature offers mixed and incomplete results on themes of team familiarity and OR efficiency, frequently citing handoffs, late starts, and task disruptions as catalysts for negative performance. Studies routinely use historical interaction counts to measure team familiarity, which often neglect the degree of participation (engagement) across prior experiences. Similarly, counts of handoffs or individuals entering an OR do not offer an accurate assessment of team performance. Guided by historical studies, four hypotheses are presented and argue that enhancing surgical team dynamics yield favorable improvements for operational performance and clinical outcomes. Utilizing data from 9,049 neurologic surgery cases performed at two separate campuses (belonging to the same organization) over a three-year timeframe (March 2019 to November 2021), this study measures surgical team dynamics in a highly complex setting through the lens of case continuity and surgeon familiarity to assess key outputs: case scheduling errors (proxy for operational performance) and post-operative complications within 30-days of surgery (proxy for clinical outcomes)., 2023, Includes bibliography., Degree granted: Dissertation (PhD)--Florida Atlantic University, 2023., Collection: FAU Electronic Theses and Dissertations Collection

Increasing fossil fuel consumption and consequently the effects of greenhouse gases (GHGs) on the environment and economy are a major concern for all nations and governments. Electric vehicles (EVs) with plug-in capabilities have the potential to ease such problems. However, the extracted power from the grid for charging the EVs' batteries will significantly impact daily power demand. To satisfy the increasing demand and ensure generation capacity adequacy, the generation expansion planning (GEP) problem is solved to determine the investment decisions for electricity generation sources. Even though there are no centralized utilities for generation planning in most markets, there is still a need to realistically solve the GEP problems and find the optimal investment decisions to tailor the incentives used by most governments to guide the market. There is also a need for a tool to analyze the effect of different charging power levels, charging policies, and penetration levels. The main goal of this paper is to provide a tool to determine realistic optimal investment plans and evaluate different cases. It is also very important to consider the stochastic nature of the electricity demand in GEP problems. We propose a scenario-based stochastic programming model to incorporate the variability in the electricity demand due to EV charging through a set of scenarios generated by Monte Carlo Simulation. The methodology starts with applying a simulation method to generate the electricity demand of EVs by considering all the possible factors affecting EVs' demand. Each iteration of this simulation represents a possible demand profile as a result of penetrating the EVs into the market. Using all these demand profiles in GEP is preferable, but it is not computationally efficient. Computational tractability is achieved by using the clustering technique to reduce the size of such scenarios. We propose clustering methods to select a representative set from the data sets generated by, NA

Rural areas in developing countries have the highest concentrations of unelectrified communities. There is a clear link between electricity consumption and the Human Development Index, as highlighted by the 7th Development Goal of the United Nations. Estimating the energy needs of the previously nonelectrified population is imprecise when designing rural electrification projects. Indeed, daily energy demand and peak power assessments are complex, since these values must be valid over the project’s lifetime, while tight budgets do not allow for the systems to be oversized. In order to assist project promoters, this study proposes a fuzzy mixed integer linear programming model (FMILP) for the design of wind–PV rural electrification systems including uncertainty in the demand requirements. Two different FMILP approaches were developed that maximized the minimum or the average satisfaction of the users. Next, the FMILP approaches were applied to six Latin American communities from three countries. Compared with the deterministic MILP (where the energy and peak power needs are considered as specific values), the FMILP results achieved a better balance between the project cost and the users’ satisfaction regarding the energy and peak power supplied. Regarding the two approaches, maximizing the users’ minimum satisfaction obtained globally better solutions., Postprint (published version)

Many countries are still facing a common hurdle: last-mile rural electrification. Great skill is required to face this challenge, which generally involves the most isolated, inaccessible and complex regions. Many Latin American countries have already undertaken universal access to energy schemes and have built an experience base that, if shared, can add a wealth of knowledge to facilitate the implementation of future initiatives. In this regard, this work evaluates the sustainability of the main electrification initiatives developed in Brazil and Venezuela, by performing an ex-post comparative multicriteria evaluation on 18 quantitative and qualitative social, institutional, economic, technical and managerial indicators. Eight discussion threads are drawn from the programs’ design and implementation strategies and outcomes. The main insights are: the suitability of renewable-based distributed energy resources for covering last-mile rural electrification; the effectiveness of a private context for the development of the technological market and the creation of policy instruments; and the effectiveness of a public context to implement a greater diversity of technological solutions focused on improving social well-being. The lessons learned aim to guide rural electrification promoters and decision makers in developing more sustainable and successful last-mile electrification initiatives., Peer Reviewed, Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant, Postprint (published version)

New forms of consumption, including online streaming, have interfered with film exhibition, promoting important changes. These services need to generate significant traffic and a look for a wide range of home subscribers to provide them with an extensive catalogue of movies. The domestic confinement in almost all countries caused by COVID 19 has led the internet to break historical traffic records. Cinemas are no longer the only place where a film can be shown. The novelty of films is the main factor that convinces viewers to pay for a cinema ticket. The exhibitor must pursue excellence and offer a truly superior experience at a reasonable price, because of the limited budget for leisure activities. This research uses Structural Equation Modelling (SEM) to carry out an empirical analysis on the results of a questionnaire with a focus on the factors influencing the preference for watching films at a cinema, Peer Reviewed, Preprint

We consider a public health emergency, during which a high number of patients and their varying health conditions necessitate prioritizing patients receiving home health care. Moreover, the dynamic emergence of patients needing urgent care during the day should be handled by rescheduling these patients. In this article, we present a reoptimization framework for this dynamic problem to periodically determine which patients will be visited in which order on each day to maximize the total priority of visited patients and to minimize the overtime for the health-care provider. This optimization framework also aims to minimize total routing time. A mixed-integer programming (MIP) model is formulated and solved at predetermined reoptimization times, to assure that urgent patients are visited within the current day, while visits of others may be postponed, if overtime is not desired or limited. The effectiveness of a schedule is evaluated with respect to several performance metrics, such as the number of patients whose visits are postponed to the next day, waiting time of urgent patients, and required overtime. The MIP-based approach is compared to two practical heuristics that achieve satisfactory performance under a nervous service system by excelling in different criteria. The MIP-based reoptimization approach is demonstrated for a case during the COVID-19 pandemic. We contribute to the home health-care literature by managing dynamic/urgent patient arrivals under a multiperiod setting with prioritized patients, where we optimize different rescheduling objectives via three alternative reoptimization approaches., NA

Understanding the underlying biological mechanisms of primary tumors is crucial for predicting how tumors respond to therapies and exploring accurate treatment strategies. miRNA-mRNA interactions have a major effect on many biological processes that are important in the formation and progression of cancer. In this study, we introduced a computational pipeline to extract tissue- and cohort-specific miRNA-mRNA regulatory modules of multiple cancer types from the same origin using miRNA and mRNA expression profiles of primary tumors. Our model identified regulatory modules of underlying cancer types (i.e., cohort-specific) and shared regulatory modules between cohorts (i.e., tissue-specific). MicroRNA (miRNA) alterations significantly impact the formation and progression of human cancers. miRNAs interact with messenger RNAs (mRNAs) to facilitate degradation or translational repression. Thus, identifying miRNA-mRNA regulatory modules in cohorts of primary tumor tissues are fundamental for understanding the biology of tumor heterogeneity and precise diagnosis and treatment. We established a multitask learning sparse regularized factor regression (MSRFR) method to determine key tissue- and cohort-specific miRNA-mRNA regulatory modules from expression profiles of tumors. MSRFR simultaneously models the sparse relationship between miRNAs and mRNAs and extracts tissue- and cohort-specific miRNA-mRNA regulatory modules separately. We tested the model's ability to determine cohort-specific regulatory modules of multiple cancer cohorts from the same tissue and their underlying tissue-specific regulatory modules by extracting similarities between cancer cohorts (i.e., blood, kidney, and lung). We also detected tissue-specific and cohort-specific signatures in the corresponding regulatory modules by comparing our findings from various other tissues. We show that MSRFR effectively determines cancer-related miRNAs in cohort-specific regulatory modules, distinguishes tissue- and coh, M.G. was supported by the Turkish Academy of Sciences (TUBA-GEB.IP; The Young Scientist Award Program) and the Science Academy of Turkey (BAGEP; The Young Scientist Award Program). P.G.M. holds a Canada Research Chair Tier 2 in Non-coding Disease Mechanisms.

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Rural electrification should plan the multi-stage implementation of electrification systems considering different distribution options (national grid extension and decentralized off-grid systems) with different energy sources (photovoltaic, wind, diesel, etc.). Most of the existing planning assessment tools search for a static final design and only consider economical and technical criteria, although social and environmental issues have proven essential in ensuring long-term sustainability. In this context, the objective of this paper is to investigate and define how different actions of electricity extension in a region can be gradually organized to maximize the region’s overall satisfaction, and which technology options for electricity generation and distribution should be promoted on a regional scale according to economic, technical, socio-institutional and environmental criteria. The obtained solution defines, at each planning stage, to which communities should electricity access be extended and which configuration and sources are used in each community in order to maximize the total satisfaction of the region. This total satisfaction is calculated by means of multicriteria indices that evaluate the energy provided and the suitability of the chosen system designs according to stakeholders’ opinions (experts, promoters, users, among others). A mixed-integer linear programming (MILP) model is developed to solve the regional multi-stage planning. The model is validated with a computational experiment of 540 randomly generated instances; the results highlight its efficiency. Finally, it is applied to solve a case study of 544 real non-electrified communities in Plateau State (Nigeria) calculating two electrification plans (with a more and less budget-constrained scenario) which are compared with the currently available one. The results show that the proposed new plans significantly increase the total satisfaction obtained in the electrification of the region. The co, Peer Reviewed, Postprint (published version)

Access to electricity for the rural and indigenous population of Ecuador’s Amazon Region (RAE) is considered a critical issue by the national authorities. The RAE is an isolated zone with communities scattered throughout the rainforest, where the expansion of the national grid is not a viable option. Therefore, autonomous electrification systems based on solar energy constitute an important solution, allowing the development of indigenous populations. This work proposes a tool for the design of stand-alone rural electrification systems based on photovoltaic technologies, including both microgrid or individual supply configurations. This tool is formulated as a Mixed Integer Linear Programming model including economic, technical and social aspects. This approach is used to design electrification systems (equipment location and sizing, microgrid configurations) in three real communities of the RAE. The results highlight the benefits of the developed tool and provide guidelines regarding RAE’s electrification., Peer Reviewed, Postprint (published version)

© 2022. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0, This article focuses on the Mixed-Model Assembly Line Balancing single-target problem of type 2 with single-sided linear assembly line configurations, which is common in the industrial environment of small and medium-sized enterprises (SMEs). The main objective is to achieve Algorithmic Transparency (AT) when using Genetic Algorithms for the resolution of balancing operation times. This is done by means of a new matrix methodology that requires working with product functionalities instead of product references. The achieved AT makes it easier for process engineers to interpret the obtained solutions using Genetic Algorithms and the factors that influence decisions made by algorithms, thereby helping in the later decision-making process. Additionally, through the proposed new matrix methodology, the computational cost is reduced with respect to the stand-alone use of Genetic Algorithms. The AT produced using the new matrix methodology is validated through its application in an industry-based paradigmatic example., Peer Reviewed, Postprint (published version)

Developing efficient performance evaluation methods is important to design and control complex production systems effectively. We present an approximation method (SLQNA) to predict the performance measures of queueing networks composed of multi-server stations operating under different service disciplines with correlated interarrival and service times with merge, split, and batching blocks separated with infinite capacity buffers. SLQNA yields the mean, coefficient of variation, and first-lag autocorrelation of the inter-departure times and the distribution of the time spent in the block, referred as the cycle time at each block. The method generates the training data by simulating different blocks for different parameters and uses Gaussian Process Regression to predict the inter-departure time and the cycle time distribution characteristics of each block in isolation. The predictions obtained for one block are fed into the next block in the network. The cycle time distributions of the blocks are used to approximate the distribution of the total time spent in the network (total cycle time). This approach eliminates the need to generate new data and train new models for each given network. We present SLQNA as a versatile, accurate, and efficient method to evaluate the cycle time distribution and other performance measures in queueing networks.

Efficient performance evaluation methods are needed to design and control production systems. We propose a method to analyse single-server open queueing network models of manufacturing systems composed of delay, batching, merge and split blocks with correlated interarrival and service times. Our method (SLQNA) is based on using a supervised learning approach to determine the mean, the coefficient of variation, and the first-lag autocorrelation of the inter-departure time process as functions of the mean, coefficient of variation and first-lag autocorrelations of the interarrival and service times for each block, and then using the predicted inter-departure time process as the input to the next block in the network. The training data for the supervised learning algorithm is obtained by simulating the systems for a wide range of parameters. Gaussian Process Regression is used as a supervised learning algorithm. The algorithm is trained once for each block. SLQNA does not require generating additional training data for each unique network. The results are compared with simulation and also with the approximations that are based on Markov Arrival Process modelling, robust queueing, and G/G/1 approximations. Our results show that SLQNA is flexible, computationally efficient, and significantly more accurate and faster compared to the other methods.

Given the extensive data being collected in manufacturing systems, there is a need for developing a systematic method to implement data-driven production control policies. For an effective implementation, first, the relevant information sources must be selected. Then, a control policy that uses the real-time signals collected from these sources must be implemented. We analyse the production control policy implementation problem in three levels: choosing the information sources, forming clusters of information signals to be used by the policy and determining the optimal policy parameters. Due to the search-space size, a machine-learning-based framework is proposed. Using machine learning speeds up optimisation and allows utilising the collected data with simulation. Through two experiments, we show the effectiveness of this approach. In the first experiment, the problem of selecting the right machines and buffers for controlling the release of materials in a production/inventory system is considered. In the second experiment, the best dispatching policy based on the selected information sources is identified. We show that selecting the right information sources and controlling a production system based on the real-time signals from the selected sources with the right policy improve the system performance significantly. Furthermore, the proposed machine learning framework facilitates this task effectively.

Making a decision usually means selecting one from different alternatives to solve a problem according to a set of criteria. Multicriteria analysis usually offers a quantitative approach to ease decision-making by ranking the alternatives. However, uncertainty can arise when rating the importance of criteria and the adequacy of each alternative for each criterion, due to two factors: first, answers are usually expressed in linguistic terms that do not have a unique quantification; and second, there might be a lack of confidence in the response. Most multicriteria procedures combine fuzzy numbers and linguistic scales to deal with the first factor, but underestimate confidence issues. In this context, this work develops a Methodology for Integrated Multicriteria Decision-making with Uncertainty (MIMDU), which considers both factors of uncertainty. MIMDU is structured in three phases: (1) a novel procedure based on fuzzy rating scales to model uncertain opinions; (2) a fuzzy formulation of the compromised ranking method to rank the alternatives; and (3) a systematic procedure for results’ interpretation comparing a crisp ranking (without uncertainty) and a fuzzy-based ranking (with uncertainty). The methodology is illustrated with a generic example case, aiming to prove its potential application in any sector. Results show that MIMDU helps decision-makers to choose the most reliable alternative, since significant differences in ranking with and without uncertainty can be addressed. A sensitivity analysis is carried out to bare the effect of confidence in the alternatives evaluation, concluding that worse rankings are obtained for alternatives that are less confidently evaluated. A final comparison with the standard fuzzy VIKOR method shows MIMDU’s major preciseness in modelling non-confident opinions and providing more useful and complimentary information to better assist decision-making., Peer Reviewed, Postprint (author's final draft)

En els darrers anys les vendes de Vehicles Elèctrics (VE) han experimentat un increment considerable. Si ens fixem només en Europa, les vendes de cotxes elèctric i híbrids endollables, fins a finals d’octubre de 2019, es van incrementar per sobre del 39% respecte les vendes del període Q1-Q3 de l’any anterior, tot i que a Espanya aquest increment va ser només del 6%, Postprint (author's final draft)

Isolated small-scale renewable energy projects are a suitable option to provide access to electricity in rural areas. In such projects, establishing a suitable management model is a key issue for ensuring sustainability. The management models can be classified according to the ruling institution: government, municipality, private institution, microenterprise or cooperative. Also, they may differ on sensitive features, such as the systems' ownership, level of users' participation, responsibility for systems operation and maintenance, users’ involvement in systems installation processes and administration of tariff payments. Existing literature lacks of a comparative study highlighting the strengths and weaknesses of each kind of management model and their contribution to achieve technical, economic, social, organisational and environmental sustainability. To fill this gap, this study analyses and compares the impact of different management models through a number of case studies in Peru, Ecuador and Bolivia after 4 years of on-site work. Final general recommendations are given as results to strengthen all sustainability dimensions: among them, realise comprehensive training programs to enhance local maintenance capabilities; reach agreements with final electricity users to set a fair and affordable tariff rate; and incentivize population involvement and self-organisation to rise a sense of ownership. Such aspects are more completely fulfilled by management models based on microenterprises and cooperatives, due to its decisively higher community engagement, and should be incentivized by policy makers in the region., Peer Reviewed, Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant, Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.1 - Per a 2030, garantir l’accés universal a serveis d’energia assequibles, confiables i moderns, Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.2 - Per a 2030, augmentar substancialment el percentatge d’energia renovable en el con­junt de fonts d’energia, Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.a - Per a 2030, augmentar la cooperació internacional per tal de facilitar l’accés a la investigació i a les tecnolo­gies energètiques no contaminants, incloses les fonts d’energia renovables, l’eficiència energètica i les tecnologies de combustibles fòssils avançades i menys contaminants, i promoure la inversió en infraestructures energètiques i tecnologies d’energia no contaminant, Postprint (published version)

The problem of balancing parallel assembly lines with multi-line stations have recently aroused great interest. In this type of lines, it is usual to work with different cycle times, which can cause multi-line stations to have to produce in batches and, thus, buffers may be necessary to locate some units. In this paper, mathematical models are presented to solve the problem of buffer sizing in multi-line stations., Postprint (published version)

In recent years, reductions in the price of photovoltaic panels and batteries have made them profitable. However, the achievement of grid parity, i.e. whether these systems are cheaper than the national grid for residential users, is still being debated. This paper quantifies the proportion of demand that could be covered assuming that solar-battery adoption is decided based on the maximum profit, the maximum autarky with no extra cost or the maximum autarky with limited extra-cost. A simulation model is developed which performs a half-hourly analysis for one year, considering the solar radiation, the consumption pattern and characteristics of equipment. London is examined using a database gathering consumption from 5567 households. In particular, the techno-economic performance of the systems is studied according to different reward schemes (from a non-subsidized to a high compensation one). Results are discussed according to the optimisation strategy: maximising profit, for users seeking economic performance; and maximum autarky, for users willing independence from the grid. Complementarily, the correlation between characteristics of consumption profiles and autarky is analysed. Results show that installations are profitable for a reward of 0.03 £/kWh, under profit maximisation, and can attain 90% autarky. The injection reward is still essential to make batteries profitable., Peer Reviewed, Postprint (published version)

Parallel Assembly Lines with multi-line stations (PALBs) have been widely studied in recent years because PALBs can help improve the efficiency of a production system. However, the need of buffers has not been considered in the PALBP literature. In this study, for the first time in the literature, it is shown and demonstrated the need to consider the use of buffers in this type of line when their cycle times are different. Moreover, the buffer sizing problem in a multi-line workstation is presented and formalized. It is shown that the sequencing and scheduling of the task influences the buffer size. Finally, the designed resolution method is shown: solve a mixed integer linear programming model with a commercial software, with the objective of minimizing the size of the buffers., Peer Reviewed, Postprint (published version)

Purpose: This paper, devoted to the selection of strategic sourcing options as a part of the supply chain design, aims to provide supply chain designers with guidelines for selecting the best strategic sourcing option for each item (i.e., products, modules, parts and services). Design/methodology/approach: The study is based on a review of the literature on sourcing options, buyer-supplier relationships, factors and criteria to select a sourcing strategy as well as the authors experience in this field. Findings: We propose a multi-step process by considering the three dimensions of the strategic sourcing options (who and how many will make the item; in cases of outsourcing, the kind of relationship between buyer and supplier(s); and where the item is to be made) together with the characteristics of the context, demand and product, and the relevant criteria to evaluate them. Originality/value: The main contributions are the review of the factors and criteria for selecting the best strategic sourcing option for each item (i.e., products, modules, parts and services) as well as the guidelines provided for each step of the process., Peer Reviewed, Postprint (published version)

In recent years, solar price drops and regulations have helped residential users to invest in grid-connected photovoltaic (PV) facilities. In Spain, a novel law promotes self-consumption by discounting electricity fed into the grid from the utility bill. However, the performance of PV-based facilities depends on diverse factors. The contribution of this paper is to evaluate the techno-economic performance of such installations for different considerations linked to the Spanish law. A simulation model is used to examine different representative cities, load profiles and alternative objectives: maximising profitability and self-sufficiency. For profit maximisation, results show that load profile variations entail PV size changes up to 5 kWp for the same location, together with huge economic and self-sufficiency differences. In contrast, the solar radiation and compensation rate have a more limited influence. For self-sufficiency maximisation, the economic performance drops close to EUR 0, as benefits are used to double the PV size, buy batteries and reach close to 70% self-sufficiency. Finally, a sensitivity analysis shows a limited impact of the utility tariff and the technology cost on the PV size, but a relevant influence on the benefits. These results can help investors and families to quantify the risks and benefits of domestic self-consumption facilities, Peer Reviewed, Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.2 - Per a 2030, augmentar substancialment el percentatge d’energia renovable en el con­junt de fonts d’energia, Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant, Postprint (published version)

This paper deals with the problem of scheduling jobs in a parallel flow shop environment without buffers between machines and with sequence-dependent setup times in order to minimize the maximum completion time of jobs. The blocking constraint normally leads to an increase in the maximum completion time of jobs due to the blockage of machines, which can increase even more so when setup times are considerable. Hence, the heuristic to solve this problem must take into account these specificities in order to minimize the timeout of machines. Because the procedures designed to solve the parallel flow shop scheduling problem must deal not only with the sequencing of jobs but also with their allocation to the flow shops, 36 heuristics have been tested in this paper, of which 35 combine sequencing rules with allocation methods while the last one takes a different approach that is more related to the nature of this problem. The computational evaluation of the implemented heuristics showed good performance of the heuristic designed especially for the problem (RCP0) when the setup times are considerable. Furthermore, the evaluation has also allowed us to propose a combined heuristic that leads to good solutions in a short CPU time., Peer Reviewed, Postprint (author's final draft)

Controlling production and release of material into a manufacturing system effectively can lower work-in-progress inventory and cycle time while ensuring the desired throughput. With the extensive data collected from manufacturing systems, developing an effective real-time control policy helps achieving this goal. Validating new control methods using the real manufacturing systems may not be possible before implementation. Similarly, using simulation models can result in overlooking critical aspects of the performance of a new control method. In order to overcome these shortcomings, using a lab-scale physical model of a given manufacturing system can be beneficial. We discuss the construction and the usage of a lab-scale physical model to investigate the implementation of a data-driven production control policy in a production/inventory system. As a data-driven production control policy, the marking-dependent threshold policy is used. This policy leverages the partial information gathered from the demand and production processes by using joint simulation and optimization to determine the optimal thresholds. We illustrate the construction of the lab-scale model by using LEGO Technic parts and controlling the model with the marking-dependent policy with the data collected from the system. By collecting data directly from the lab-scale production/inventory system, we show how and why the analytical modeling of the system can be erroneous in predicting the dynamics of the system and how it can be improved. These errors affect optimization of the system using these models adversely. In comparison, the data-driven method presented in this study is considerably less prone to be affected by the differences between the physical system and its analytical representation. These experiments show that using a lab-scale manufacturing system environment is very useful to investigate different data-driven control policies before their implementation and the marking-dependent thresho, NA

In interdependent networks, nodes are connected to each other with respect to their failure dependency relations. As a result of this dependency, a failure in one of the nodes of one of the networks within a system of several interdependent networks can cause the failure of the entire system. Diagnosing the initial source of the failure in a collapsed system of interdependent networks is an important problem to be addressed. We study an online failure diagnosis problem defined on a collapsed system of interdependent networks where the source of the failure is at an unknown node (v). In this problem, each node of the system has a positive inspection cost and the source of the failure is diagnosed when v is inspected. The objective is to provide an online algorithm which considers dependency relations between nodes and diagnoses v with minimum total inspection cost. We address this problem from worst-case competitive analysis perspective for the first time. In this approach, solutions which are provided under incomplete information are compared with the best solution that is provided in presence of complete information using the competitive ratio (CR) notion. We give a lower bound of the CR for deterministic online algorithms and prove its tightness by providing an optimal deterministic online algorithm. Furthermore, we provide a lower bound on the expected CR of randomized online algorithms and prove its tightness by presenting an optimal randomized online algorithm. We prove that randomized algorithms are able to obtain better CR compared to deterministic algorithms in the expected sense for this online problem., NA

Given the extensive data being collected in manufacturing systems, there is a need for developing a systematic method to implement data-driven production control policies. For an effective implementation, first, the relevant information sources must be selected. Then, a control policy that uses the real-time signals collected from these sources must be implemented. We analyse the production control policy implementation problem in three levels: choosing the information sources, forming clusters of information signals to be used by the policy and determining the optimal policy parameters. Due to the search-space size, a machine-learning-based framework is proposed. Using machine learning speeds up optimisation and allows utilising the collected data with simulation. Through two experiments, we show the effectiveness of this approach. In the first experiment, the problem of selecting the right machines and buffers for controlling the release of materials in a production/inventory system is considered. In the second experiment, the best dispatching policy based on the selected information sources is identified. We show that selecting the right information sources and controlling a production system based on the real-time signals from the selected sources with the right policy improve the system performance significantly. Furthermore, the proposed machine learning framework facilitates this task effectively., European Union (EU); Horizon 2020; EU ECSEL Joint Undertaking; Project Productive4.0; Scientific and Technological Research Council of Turkey (TÜBİTAK)

Background: identification of molecular mechanisms that determine tumour progression in cancer patients is a prerequisite for developing new disease treatment guidelines. Even though the predictive performance of current machine learning models is promising, extracting significant and meaningful knowledge from the data simultaneously during the learning process is a difficult task considering the high-dimensional and highly correlated nature of genomic datasets. Thus, there is a need for models that not only predict tumour volume from gene expression data of patients but also use prior information coming from pathway/gene sets during the learning process, to distinguish molecular mechanisms which play crucial role in tumour progression and therefore, disease prognosis. Results: in this study, instead of initially choosing several pathways/gene sets from an available set and training a model on this previously chosen subset of genomic features, we built a novel machine learning algorithm, PrognosiT, that accomplishes both tasks together. We tested our algorithm on thyroid carcinoma patients using gene expression profiles and cancer-specific pathways/gene sets. Predictive performance of our novel multiple kernel learning algorithm (PrognosiT) was comparable or even better than random forest (RF) and support vector regression (SVR). It is also notable that, to predict tumour volume, PrognosiT used gene expression features less than one-tenth of what RF and SVR algorithms used. Conclusions: PrognosiT was able to obtain comparable or even better predictive performance than SVR and RF. Moreover, we demonstrated that during the learning process, our algorithm managed to extract relevant and meaningful pathway/gene sets information related to the studied cancer type, which provides insights about its progression and aggressiveness. We also compared gene expressions of the selected genes by our algorithm in tumour and normal tissues, and we then discussed up- and down-regula, Scientific and Technological Research Council of Turkey (TÜBİTAK); Turkish Academy of Science (TÜBA-GEBİP) The Young Scientist Award Program; Science Academy of Turkey (BAGEP) The Young Scientist Award Program

Developing efficient performance evaluation methods is important to design and control complex production systems effectively. We present an approximation method (SLQNA) to predict the performance measures of queueing networks composed of multi-server stations operating under different service disciplines with correlated interarrival and service times with merge, split, and batching blocks separated with infinite capacity buffers. SLQNA yields the mean, coefficient of variation, and first-lag autocorrelation of the inter-departure times and the distribution of the time spent in the block, referred as the cycle time at each block. The method generates the training data by simulating different blocks for different parameters and uses Gaussian Process Regression to predict the inter-departure time and the cycle time distribution characteristics of each block in isolation. The predictions obtained for one block are fed into the next block in the network. The cycle time distributions of the blocks are used to approximate the distribution of the total time spent in the network (total cycle time). This approach eliminates the need to generate new data and train new models for each given network. We present SLQNA as a versatile, accurate, and efficient method to evaluate the cycle time distribution and other performance measures in queueing networks., European Union (EU); Horizon 2020; EU ECSEL Joint Undertaking; Project Productive4.0; Scientific and Technological Research Council of Turkey (TÜBİTAK)

Efficient performance evaluation methods are needed to design and control production systems. We propose a method to analyse single-server open queueing network models of manufacturing systems composed of delay, batching, merge and split blocks with correlated interarrival and service times. Our method (SLQNA) is based on using a supervised learning approach to determine the mean, the coefficient of variation, and the first-lag autocorrelation of the inter-departure time process as functions of the mean, coefficient of variation and first-lag autocorrelations of the interarrival and service times for each block, and then using the predicted inter-departure time process as the input to the next block in the network. The training data for the supervised learning algorithm is obtained by simulating the systems for a wide range of parameters. Gaussian Process Regression is used as a supervised learning algorithm. The algorithm is trained once for each block. SLQNA does not require generating additional training data for each unique network. The results are compared with simulation and also with the approximations that are based on Markov Arrival Process modelling, robust queueing, and G/G/1 approximations. Our results show that SLQNA is flexible, computationally efficient, and significantly more accurate and faster compared to the other methods., European Union (EU); Horizon 2020; EU ECSEL Joint Undertaking; Scientific and Technological Research Council of Turkey (TÜBİTAK)

Las medidas de confinamiento y reducción de la movilidad impuestas para limitar la expansión del coronavirus tuvieron un impacto positivo en la mejora de la calidad del aire y la reducción del ruido en la ciudad. Del 16 al 22 de marzo, primera semana de confinamiento, en Barcelona se redujo el nivel de dióxido de nitrógeno (NO2) un 40 % y en Madrid un 56 %. Muchas ciudades ya habían implementado medidas para disuadir el uso de vehículos privados. Por ejemplo, reduciendo los carriles de circulación o implementando zonas de bajas emisiones que limitan la circulación a los vehículos más contaminantes. Algunas, además, aprovecharon el periodo de confinamiento para acelerar cambios que ya habían sido diseñados previamente. Un ejemplo lo encontramos en Barcelona, con el proyecto de supermanzanas, diseñado para devolver el espacio público a los peatones, eliminando los coches aparcados en superficie y restringiendo la circulación a vehículos motorizados., Postprint (published version)

Stand-alone electrification systems based on renewable energies are suitable for electrifying isolated rural communities. After some years of operation, their impact on the development of beneficiaries must be evaluated; in order to understand the strengths and weaknesses of each design option and to learn useful lessons for future projects. The literature lacks a unified study that compares the long-term impact on population of several projects combining different options for electricity generation (solar PV, wind and hydro) and distribution (microgrids and individual systems). In this context, this work performs a two-phase assessment of 9 electrification projects combining different systems and located in 6 rural Peruvian communities. First, a qualitative analysis is performed to monitor the progress experienced by the communities, considering previous evaluations carried out shortly after the projects’ implementation. Second, an in-depth analysis of the systems and the development of the communities is quantitatively performed through 28 indicators, evaluating 9 sustainability objectives organised into 4 dimensions: technical, social, economic and environmental. The results show that microgrid projects require and encourage community involvement to promote effective maintenance of the shared equipment. Conversely, individual systems can work for longer periods without as much regular maintenance, but still need an efficient management model to raise funds for repairs. Also, hydro is confirmed as providing continuous and high-quality supply, while solar and wind options allow more flexible designs. In all cases, a well-planned management model is essential for reducing the default rate, organizing effective maintenance and allowing the sustainable development of the community., Peer Reviewed, Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.1 - Per a 2030, garantir l’accés universal a serveis d’energia assequibles, confiables i moderns, Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.2 - Per a 2030, augmentar substancialment el percentatge d’energia renovable en el con­junt de fonts d’energia, Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.a - Per a 2030, augmentar la cooperació internacional per tal de facilitar l’accés a la investigació i a les tecnolo­gies energètiques no contaminants, incloses les fonts d’energia renovables, l’eficiència energètica i les tecnologies de combustibles fòssils avançades i menys contaminants, i promoure la inversió en infraestructures energètiques i tecnologies d’energia no contaminant, Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant, Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.b - Per a 2030, ampliar la infraestructura i millorar la tecnologia per tal d’oferir serveis d’energia moderns i sos­tenibles per a tots els països en desenvolupament, en particular els països menys avançats, els petits estats insulars en desenvolupament i els països en desenvolupament sense litoral, d’acord amb els programes de suport respectius, Objectius de Desenvolupament Sostenible::10 - Reducció de les Desigualtats, Objectius de Desenvolupament Sostenible::10 - Reducció de les Desigualtats::10.2 - Per a 2030, potenciar i promoure la inclusió social, econòmica i política de totes les persones, independentment de l’edat, sexe, discapacitat, raça, ètnia, origen, religió, situació econòmica o altra condició, Postprint (author's final draft)

In the last ten years, there has been a progressive improvement in rural electrification indexes in developing countries, and renewable energies are progressively being integrated into electrification programs. In Cuba, the government has set a target of 700 MW in solar photovoltaic energy by 2030, including rural electrification and off-grid systems. Within this framework, 10,000 modular systems of 300 Wp are being installed in isolated communities. Nowadays, previously diesel-electrified settlements are migrating into renewable energy technologies projects in rural Cuba. The objective of this research is to evaluate the sustainability of these changes in order to identify the implications for other developing countries, taking four different dimensions into account: environmental, technical, socioeconomic, and institutional. For this purpose, the rural communities of Yaguá (diesel-based) and Río Abajo (solar-based) in the province of Sancti Spiritus are visited and studied. Results show that the institutional dimension of sustainability is positive thanks to improvements in energy security and promotion of the Cuban national plan goals. Moreover, results confirm that the energy transition from diesel-based to solar PV is environmentally sustainable in Cuba, but improvements are still necessary in the power capacity of solar modules to strengthen the socioeconomic and technical dimensions., Peer Reviewed, Postprint (published version)

Currently, around 1.1 billion people lack access to electricity, mainly in developing countries. Solar photovoltaic systems can provide electricity, but the design is complex, having to size and site the equipment. Besides, since forecasting consumption habits for newly electrified populations is complex, the estimation of the electricity needs is uncertain. This work addresses, for the first time, demand uncertainty to assist promoters in designing electricity access projects, simultaneously solving the sizing and siting problems, using fuzzy logic. In particular, a mathematical model is proposed, introducing uncertainty through five modelling approaches with different fuzzy logic assumptions: three based on the literature and two novel ones according to the conflicting problem nature (project cost minimisation vs electricity supply maximisation). The five approaches are compared and the new ones obtain solutions achieving a higher satisfaction regarding the cost and electricity supply. Then, the most efficient approach is applied in two Peruvian communities, comparing the solutions with those obtained without uncertainty. The results of the proposal show a better balance between the project cost and the demand supplied. Hence, the proposal can help promoters in developing countries to better design electricity access projects where the demand estimation is complex., Peer Reviewed, Postprint (author's final draft)

This paper deals with the problem of scheduling jobs in a parallel flow shop configuration under the blocking constraint, in which the setup time of machines depends not only on the job to be processed but also on the previously processed one, i.e., there are sequence-dependent setup times. The performance analysis of several iterated greedy algorithms with different initial solution procedures and local searches lets us define an efficient algorithm to minimize the maximum job completion time. Moreover, the computational evaluation showed the efficiency of searching in different neighborhood structures and noted the significant influence of the initial solution. However, contrary to other scheduling problems, starting with a high quality solution does not guarantee better performance of the algorithm., Peer Reviewed, Postprint (published version)

TPM (Total Productive Maintenance), which is part of the Lean Management philosophy, aims to increase the company's productivity by reducing efficiency losses during manufacturing. TPM allows organizations to know the real efficiency with which they are working and the losses of the production process, and with the application of this methodology to improve the efficiency of the equipment and the competitiveness of the company., Peer Reviewed, Postprint (published version)

Brasil es responsable por varios programas de becas en la educación superior, estos patrocinados por el gobierno e instituciones privadas que otorgan becas nacionales e internacionales. Este articulo tiene como objetivo analizar el impacto de los programas de becas de Brasil en la movilidad de estudiantes al extranjero, en particular de los que eligen España para la realización de los estudios superiores. Para este estudio se ha utilizado los datos de los programas de becas del gobierno de Brasil, desarrollados a través del Consejo Nacional de Desarrollo Científico y Tecnológico (CNPq), Ciencia sin fronteras (CsF) y Coordinación de Perfeccionamiento de Personal de Nivel Superior (Capes) y también los datos de la UNESCO - Instituto de Estadística (UIS) series historiales de entradas (inbound) y salidas (outbound). Los resultados han demostrado que los programas de becas de Brasil tienen una importancia muy destacada entre los estudiantes brasileños, este factor tiene una presencia menor en los estudios de la literatura referidos a otros flujos, a pesar de ello, los estudios con estudiantes extranjeros para acudir a España son todavía escasos. En concreto, el flujo de estudiantes desde Brasil hasta España no ha sido estudiado con anterioridad, así que este estudio pretende cubrir esta carencia., Postprint (published version)

Managing exploitative innovation and branding orientation resources in small service firms is important to build a sustainable competitive advantage. In British Columbia, Canada, such firms’ knowledge about allocating resources to innovation and branding is not well documented. Research measuring two or more marketing strategies is encouraged by the academic community. This study uses the resource-based view of the firm to understand owners’ and managers’ level of agreement with two marketing strategies—exploitative innovation and branding orientation—revealing the strategies’ effects on performance at the firm level. This correlation and multivariate study collected quantitative data from 299 owners/managers of service firms with less than 50 employees/contractors prior to the COVID-19 pandemic. Small service firm sectors included retail, health care services, arts, entertainment, recreation services, accommodation, food services, construction, personal care services, and professional, scientific, and technical services in British Columbia. An online survey collected data using an anonymized SurveyMonkey Audience Panel, in order to answer the research question: “What are the interactions between branding orientation and exploitative innovation and how do they relate to firm performance?” Four controls were considered that impact business strategies: age of business, size of business, and business environment hostility and uncertainty. The study found that exploitative innovation and branding orientation had a significant positive interaction effect on firm performance. A covariate relationship between branding orientation and exploitative innovation was found, as well as a relationship between branding orientation and exploitative innovation, with firm performance. Business environment uncertainty was significant for firm performance, branding orientation, and exploitative innovation. Age of business and business environment hostility had significant effects on exp, 2020-07

The paper assumes that, at the end of the operational period of a Spanish nuclear power plant, an Independent Spent Fuel Storage Installation will be used for long-term storage. Spent fuel assemblies are selected and transferred to casks for dry storage, with a series of imposed restrictions (e.g., limiting the thermal load). In this context, we present a variant of the problem of spent nuclear fuel cask loading in one stage (i.e., the fuel is completely transferred from the spent fuel pool to the casks at once), offering a multi-start metaheuristic of three phases. (1) A mixed integer linear programming (MILP-1) model is used to minimize the cost of the casks required. (2) A deterministic algorithm (A1) assigns the spent fuel assemblies to a specific region of a specific cask based on an MILP-1 solution. (3) Starting from the A1 solutions, a local search algorithm (A2) minimizes the standard deviation of the thermal load among casks. Instances with 1200 fuel assemblies (and six intervals for the decay heat) are optimally solved by MILP-1 plus A1 in less than one second. Additionally, A2 gets a Pearson’s coefficient of variation lower than 0.75% in less than 260s CPU (1000 iterations), Peer Reviewed, Postprint (published version)

Assembly lines are mass production systems which are significant in the industrial production of both standard and customised products. Currently, industrial companies offer several products and it is common for an assembly system to have multiple assembly lines. Parallel assembly lines are multiple lines located in such a way as to allow improvements in the system’s efficiency through the use of common resources. In recent years several research studies have been made on parallel assembly lines. In this paper, we survey the parallel assembly lines balancing problem (PALBP) studies. Moreover, a classification scheme is provided to ease understanding. Finally, the main gaps in the literature are described and future research directions are presented., Peer Reviewed, Postprint (author's final draft)

El negocio cinematográfico se ha visto inmerso en el desarrollo tecnológico y las innovaciones de los últimos años, básicamente la introducción de la tecnología digital, aunque también la globalización o la televisión de pago. Todo ello ha comportado efectos como reducir el ciclo de vida del producto película en la sala de cine o los costes al implantar outsorcing en la producción. Este trabajo analiza el nuevo modelo de negocio cinematográfico según el exhibidor. Las conclusiones se derivan de la valoración de algunos exhibidores cinematográficos de España analizando decisiones como la renovación de proyectores en este cambio de gestión., Peer Reviewed, Postprint (published version)

Electrification with micro-grids is receiving increasing attention to electrify rural areas in developing countries. However, determining the best local supply solution is a complex problem that requires considering different generation technologies (i.e. solar PV, wind or diesel) and different system configurations (off-grid or on-grid). Most existing decision aid tools to assess this design only consider economical and technical issues in a single optimization process. However, social and environmental considerations have been proven key issues to ensure long-term sustainability of the projects. In this context, the objective of this work is to develop a multicriteria procedure to allow comparing electrification designs with on-grid or isolated micro-grids and different tech-nologies considering multiple aspects. This multicriteria procedure is integrated in a two-phased methodology to assist the design of the system to electrification promoters in a structured process. First, different electrification alternatives are generated with an open-source techno-economic optimization model; next, these alternatives are evaluated and ranked with the multicriteria procedure, which considers 12 criteria representing economic, technical, socio-institutional and environmental aspects. The whole design methodology is validated with a real case study of 26 population settlements in Plateau State, Nigeria. Experts in rural electrification within the Nigerian context have been consulted to weight the criteria and particularize their evaluation for the specific case study. Results show that solar PV technology based systems are the most suitable electrification designs for communities in Nigeria, while grid connection feasibility depends on the size of the community and the distance to the closest national grid consumption point., Peer Reviewed, Postprint (author's final draft)

Se presenta un modelo de PLEM para minimizar el coste de almacenamiento de elementos combustibles nucleares gastados en contenedores tipo MPC-32 procedentes de la piscina de una central nuclear. La carga de contenedores se realiza en una sola etapa y en una fecha concreta., En la gestión de residuos de combustible de las centrales nucleares españolas, está previsto activar los almacenes temporales individualizados (ATI) cuando se alcanza la saturación de la piscina destinada a albergar elementos de combustible nuclear gastado durante el periodo de explotación de la instalación; al finalizar dicho periodo, un ATI puede utilizarse como almacén transitorio a corto o largo plazo hasta que el almacén temporal central (ATC) esté disponible. Los elementos combustibles (EC), gastados y ubicados en la piscina de la central nuclear (CN), se seleccionan para su almacenado en seco en contenedores (cápsulas) atendiendo a una serie de restricciones impuestas a cada ATI por el órgano regulador competente en función de los requerimientos que afectan específicamente a los contenedores utilizados (v.gr. la limitación de la carga térmica o el número de posiciones para elementos combustibles). Las características de estas cápsulas condicionan el tiempo mínimo de enfriamiento del combustible en la piscina de la central nuclear y, por consiguiente, el tiempo necesario para completar el vaciado completo de dicha piscina. En este contexto, presentamos un modelo de programación lineal entera mixta (Mixed Integer Linear Programming) con el objetivo de minimizar, en una sola carga de elementos con fecha preestablecida, el coste de utilización de las cápsulas MPC (Multi Purpose Canister) necesarias para reubicar en un almacén ATI el número máximo de elementos de combustible gastado que están disponibles en la piscina de la central en tal fecha. El modelo propuesto se ha explotado mediante el solucionador CPLEX utilizando un caso de estudio inspirado en la realidad de la CN Ascó ubicada a unos 160 km de Barcelona. El método de resolución que proponemos es capaz de resolver instancias con 1500 elementos de combustible, 7 tipos de regiones y 4 tipos de contenedores en un tiempo de CPU inferior a 2 segundos., Preprint

With the rising focus on integrating high shares of renewable energy into energy supply systems, the need to meet the viability of these renewable sources becomes pressing. Apart from storing electricity in electrochemical storage units, the concept of sector coupling could promise to provide the needed flexibility and storage capacities. A strong metric is needed to determine the viability and economic feasibility of different sector-coupled energy systems. This conference study presents an empirical method to develop a list of key performance indicators (KPIs), as a direct adaption of the KPI of the energy system with a single energy vector is not always possible. The list was developed based on a stakeholder workshop within the H2020 research project E-Land. The authors proposed the introduction of three new indicators for the evaluation of sector-coupled energy systems, namely degree of autonomy, levelised cost of energy and degree of sector coupling. A sector-coupled case study is evaluated to validate the performance of such new indicators while proving their utility to better assist decision-making., Peer Reviewed, Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.2 - Per a 2030, augmentar substancialment el percentatge d’energia renovable en el con­junt de fonts d’energia, Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant, Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.a - Per a 2030, augmentar la cooperació internacional per tal de facilitar l’accés a la investigació i a les tecnolo­gies energètiques no contaminants, incloses les fonts d’energia renovables, l’eficiència energètica i les tecnologies de combustibles fòssils avançades i menys contaminants, i promoure la inversió en infraestructures energètiques i tecnologies d’energia no contaminant, Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.1 - Per a 2030, garantir l’accés universal a serveis d’energia assequibles, confiables i moderns, Postprint (published version)