Based on current trends and policies aimed at decarbonizing energy systems, the conversion of biomass to bioenergy has the potential to grow rapidly, but such growth depends on the development of efficient, sustainable, and competitive biomass supply chains. As a result, the biomass supply chain has stimulated the interest of a diverse group of researchers across academia, government, and industry, and there is a need to synthesize and categorize the rapidly expanding literature in this field. We conducted a literature review using advanced bibliometric analysis and visualization of 1711 peer-reviewed articles published from January 1992 to August 2022 with the aim of promoting impactful research in both growing and neglected areas of investigation. The results show that there are potential research gaps and opportunities in six critical areas: globalization of supply chain research; incorporation of uncertainty, stochasticity, and risk into supply chain models; investigation of multi-... [more]

This paper proposes a three-stage model for managing energy communities for local energy sharing and providing grid flexibility services to tackle local distribution grid constraints. The first stage addresses the minimization of each prosumer’s individual energy bill by optimizing the schedules of their flexible resources. The second stage optimizes the energy bill of the whole energy community by sharing the prosumers’ energy surplus internally and re-dispatching their batteries, while guaranteeing that each prosumer’s new energy bill is always be equal to or less than the bill that results for this prosumer from stage one. This collective optimization is designed to ensure an additional collective benefit, without loss for any community member. The third stage, which can be performed by the distribution system operator (DSO), aims to solve the local grid constraints by re-dispatching the flexible resources and, if still necessary, by curtailing local generation or consumption. Stage... [more]

There is an ongoing, revolutionary transformation occurring across the globe. This transformation is altering established processes, disrupting traditional business models and changing how people live their lives. The power sector is no exception and is going through a radical transformation of its own. Renewable energy, distributed energy sources, electric vehicles, advanced metering and communication infrastructure, management algorithms, energy efficiency programs and new digital solutions drive change in the power sector. These changes are fundamentally altering energy supply chains, shifting geopolitical powers and revising energy landscapes. Underlying infrastructural components are expected to generate enormous amounts of data to support these applications. Facilitating a flow of information coming from the system′s components is a prerequisite for applying Artificial Intelligence (AI) solutions in the power sector. New components, data flows and AI techniques will play a key ro... [more]

Photovoltaic (PV) systems are the leading solutions for reducing carbon dioxide (CO2) emissions in Iran’s energy system. However, there are some challenges to investing in PV systems in Iran, such as the low energy market price and the high investment cost of PV systems. Although the flat feed-in tariff (FiT) is defined to help purchase energy from the PV systems, it is not attractive to investors. In this paper, a mathematical formulation is developed for the planning problem of the PV systems with battery energy storages (BESs) considering two incentive policies: (1) Designing time-of-use FiT to encourage the PV systems to sell energy to the grid at peak hours (2) Participating in the carbon trading energy market. The insolation in Iran is calculated regarding mathematical formulations which divide Iran into eight zones. The results of the base case show high payback periods for all zones. In the presence of the incentive policies, the payback period decreases considerably from 5.46... [more]

Distributed energy resources (DERs) based on renewable power, such as photovoltaic (PV), have been increasing worldwide. To support this growth, some technologies have been developed to increase the hosting capacity (HC) of distribution networks (DNs), such as the Soft Open Point (SOP), which can replace normally open switches in DNs with the advantage of allowing power and voltage control. The benefits of SOPs in terms of increasing distributed generation (DG) hosting capacity can be enhanced by network reconfiguration (NR). In this work, an optimization-based approach is proposed for placing SOP in DN with simultaneous NR; that is, the proposed algorithm consists of a promising alternative to previous works in the literature that deal with SOP placement and NR in an iteratively way or in a two-step procedure, considering that better results can be obtained by simultaneously handling both options, as shown in the introduced case studies. The optimization problem is modeled as nonlinea... [more]

Southern Africa has suffered from multiple power disruptions in the past decade due to inadequate electrical generation capacity, as well as load developments in locations that were not suitably planned for. Southern African countries are able to have reliable, sustainable, and efficient electrical power grids. The use of power interconnections for exchange power, especially for long-distance transmission networks, is important. Installing a suitable high-voltage alternating current (HVAC) with a high-voltage direct current (HVdc) will improve the active−reactive power compensation when transmitting electrical power over long distances (when transmitting bulk power is possible). Flexible alternating current transmission system (FACTS) devices are typically combinations of shunt and series converters. These approaches are capable of improving the power stability and voltage while allowing power to be transferred with minimal losses to an alternating current transmission system for the p... [more]

The aim of the article is to identify problems related to the siting of wind farms, both those that have arisen as a result of recent legislative revisions and those arising from social developments in Poland. In 2022 a map defining ‘exclusion zones’ around wind turbines, i.e., areas where residential development was prohibited, was released in Poland. It was only then that many territorial governments realised the scale of the problems generated by the entry into force of the 2016 Wind Farm Act. It turned out that this group of municipalities included towns that might suffer some consequences despite the fact that there are no or few wind farms in their area. The aim of this paper is to identify towns and cities where more than one quarter of the area is land within the H10 zones, where the construction of wind farms is banned, if their distance from the nearest building or from the boundary of a national park is less than ten times the height of the turbine mast. The example of the t... [more]

With the changing world energy structure, the development of renewable energy sources is gradually accelerating. Among them, close attention has been given to geothermal energy because of its abundant resources and supply stability. In this article, a deep borehole heat exchanger (DBHE) is coupled with a heat pump system to calculate the heat supply and daily electricity consumption of the system. To make better use of the peaks and valleys in electricity prices, the following three daily operating modes were studied: 24-h operation (Mode 1), 8-h operation plus 16-h non-operation (Mode 2), and two cycles of 4-h operation and 8-h non-operation (Mode 3). Simulation results show that scheduled non-continuous operation can effectively improve the outlet temperature of the heat extraction fluid circulating in the DBHE. The heat extraction rates of Mode 1 is 190.9 kW for mass flowrate of 9 kg/s; in Mode 2 and Mode 3 cases, the rates change to 304.7 kW and 293.0 kW, respectively. The daily op... [more]

NSGA-II is an evolutionary multi-objective optimization algorithm that has been applied to a wide variety of search and optimization problems since its publication in 2000. This study presents a review and bibliometric analysis of numerous NSGA-II adaptations in addressing scheduling problems. This paper is divided into two parts. The first part discusses the main ideas of scheduling and different evolutionary computation methods for scheduling and provides a review of different scheduling problems, such as production and personnel scheduling. Moreover, a brief comparison of different evolutionary multi-objective optimization algorithms is provided, followed by a summary of state-of-the-art works on the application of NSGA-II in scheduling. The next part presents a detailed bibliometric analysis focusing on NSGA-II for scheduling applications obtained from the Scopus and Web of Science (WoS) databases based on keyword and network analyses that were conducted to identify the most intere... [more]

Bird damage to fruit crops causes significant monetary losses to farmers annually. The application of traditional bird repelling methods such as bird cannons and tree netting become inefficient in the long run, requiring high maintenance and reducing mobility. Due to their versatility, Unmanned Aerial Vehicles (UAVs) can be beneficial to solve this problem. However, due to their low battery capacity that equals low flight duration, it is necessary to evolve path planning optimization. A novel path planning optimization algorithm of UAVs based on Particle Swarm Optimization (PSO) is presented in this paper. This path planning optimization algorithm aims to manage the drone’s distance and flight time, applying optimization and randomness techniques to overcome the disadvantages of the traditional systems. The proposed algorithm’s performance was tested in three study cases: two of them in simulation to test the variation of each parameter and one in the field to test the influence on bat... [more]

Today’s global business trends are causing a significant and complex data revolution in the healthcare industry, culminating in the use of artificial intelligence and predictive modeling to improve health outcomes and performance. The dataset, which was referred to is based on consumption data from 2015 to 2019, included approximately 500 goods. Based on a series of data pre-processing activities, the top ten (10) essential medicines most used were chosen, namely cotrimoxazole 480 mg, amoxicillin 250 mg, paracetamol 500 mg, oral rehydration salts (O.R.S) sachet 20.5 g, chlorpheniramine 4 mg, nevirapine 200 mg, aminophylline 100 mg, artemether 20 mg + lumefantrine (AL) 120 mg, Cromoglycate ophthalmic. Our study concentrated on the application of machine learning (ML) to forecast future trends in the demand for essential drugs in Rwanda. The following models were created and applied: linear regression, artificial neural network, and random forest. The random forest was able to predict 10... [more]

There have been many literature reviews carried out on Lean implementation in larger organisations with specific focus on the automobile industry. Lean implementation in the medical device industry has not been extensively investigated. Thus, this research endeavored to analyse the benefits of Lean, tools utilised and challenges and results of Lean implementation in Medtech companies. This article aims to bridge a gap in the literature by reviewing the literature that discusses Lean implementation in MedTech companies in Ireland with a perspective of identifying the benefits and challenges faced. The quantitative methodology allows us to review the comprehensive numbers and data which were collected from 20 Enterprise Ireland MedTech case studies. There are very few published case studies in the literature on Lean due to the highly regulated nature of MedTech sector and the vast array of medical devices, which are often under privacy and confidentiality constraints. The results showed... [more]

This paper presents an investigation into the enhancement of availability of a curing machine deployed in the rubber industry, located in Tamilnadu in India. Machine maintenance is a major task in the rubber industry, due to the demand for product. Critical component identification in curing machines is necessary to prevent rapid failure followed by subsequent repairs that extend curing machine downtime. A reward in the Reinforcement Learning Algorithm (RLA) prevents frequent downtime by improving the availability of the curing machine at time when unscheduled long-term maintenance would interfere with operation, due to the occurrence of unspecified failure to a critical component. Over time, depreciation and degradation of components in a machine are unavoidable, as is shown in the present investigation through intelligent assessment of the lifespan of components. So far, no effective methodology has been implemented in a real-time maintenance environment. RLAs seem to be a more effec... [more]

The cloud energy storage system (CES) is a shared distributed energy storage resource. The random disordered charging and discharging of large-scale distributed energy storage equipment has a great impact on the power grid. This paper solves two problems. On one hand, to present detailed plans for designing an orderly controlled CES system in a realistic power system. On the other hand, Monte Carlo simulation (MCS) is used for analyzing the load curves of five types of distributed energy storage systems to manage and operate the CES system. A method of its planning and the principles of CES for applied in a power grid, are presented by analyzing the impact based on five load curves including the electric vehicle (EV), the ice storage system, the demand response, the heat storage system, and the decentralized electrochemical energy storage system. The MCS simulates the random charging and discharging of the system over a five-year planned scaling of distributed energy storage from 2021... [more]

The data based on location/activity sensing technology is exploding and integrating multi-source data provides us with a new perspective to observe tourist behavior. On the one hand, tourist preferences can be extracted from the attractions generated by clustering. On the other hand, potentially extracted tourist information can provide decision-making support for tourism management departments in tourism planning and resource development. Therefore, developing smart tourism services for tourists and promoting the realization of “smart scenic spots.” A field survey was conducted in Zhongshan Botanical Garden, China, from 3 February to 3 April 2019. This empirical study combines a handheld GPS tracking device and questionnaire survey using SEE to optimize k-means clustering algorithm and explores the spatial−temporal behavior patterns of tourists. The results showed that tourists in the botanical garden could be divided into three behavioral patterns. They are recreation and leisure, bi... [more]

Formulating a reasonable and feasible unit maintenance scheme is a promising way to eliminate potential risks and improve the reliability of power systems. However, the uncertainty and volatility of new energy outputs, such as wind power, increase the difficulty of scheme formulation. To overcome the complexity of uncertainty, a robust unit maintenance scheme considering the uncertainty of new energy output and electrolytic aluminum load is established in this paper. Considering the significant time-series characteristics of new energy, this paper first introduces the definition and mathematical model of information granulation (IG), through which the initial new energy output data can be transformed into fuzzy particles used for prediction and analysis. Moreover, a support-vector machine (SVM) regression prediction model is adopted, and a corresponding progressive search algorithm is designed to determine SVM parameters efficiently. Then, a robust unit maintenance model is established... [more]

With the extensive development of remanufacturing, remanufacturing trade, as an essential part of it, has also attracted much attention from researchers. Quite a large number of studies related to remanufacturing trade, such as pricing, sales, competition, channel expansion and service strategy, have been published in various journals. This paper aims to focus on the research status on remanufacturing trade through bibliometric analysis that can provide the primary research trends and the future research hotspots by analyzing the progress, parties and themes of the research. In this paper, the review and analysis are conducted on over 121 articles from 2000 until July 2021 with the help of VOS viewer (Leiden University, Leiden, The Netherlands) and Citespace (Drexel University, Philadelphia, PA, USA). The results of the analysis of research progress and research parties suggest that: (a) more and more researchers have started to pay attention to consumers during modeling; (b) sustainab... [more]

The urgent need for CO2 reduction is calling upon the industry to contribute. However, changes within local energy supply systems including efficiency enhancement are bound to several economical and technical constraints, which results in interfering trade-offs that make it difficult to find the optimal investment option for CO2 mitigation. In this article, a new optimization model is presented that allows to optimize the design and operation of a supply and heat recovery system and production scheduling simultaneously. The model was used for retrofitting of a small brewery’s local energy system to identify decarbonization measures for eight potential future scenarios with different technical, economical and ecological boundary conditions. The results show that the proposed cost-optimized changes to the current energy system only slightly reduce carbon emissions if decarbonization is not enforced since the optimal solutions prioritize integration of photo voltaic (PV) modules that main... [more]

To solve the problem of inserting new job into flexible job-shops, this paper proposes a dynamic self-learning artificial bee colony (DSLABC) optimization algorithm to solve dynamic flexible job-shop scheduling problem (DFJSP). Through the reasonable arrangement of the processing sequence of the jobs and the corresponding relationship between the operations and the machines, the makespan can be shortened, the economic benefit of the job-shop and the utilization rate of the processing machine can be improved. Firstly, the Q-learning algorithm and the traditional artificial bee colony (ABC) algorithm are combined to form the self-learning artificial bee colony (SLABC) algorithm. Using the learning characteristics of the Q-learning algorithm, the update dimension of each iteration of the ABC algorithm can be dynamically adjusted, which improves the convergence accuracy of the ABC algorithm. Secondly, the specific method of dynamic scheduling is determined, and the DSLABC algorithm is prop... [more]

An efficient and flexible production system can contribute to production solutions. These advantages of flexibility and efficiency are a benefit for small series productions or for individual articles. The aim of this research was to produce a genetic production system schedule similar to the sustainable production scheduling problem of a discrete product assembly plant, with more heterogeneous production lines, and controlled by one-time orders. First, we present a detailed mathematical model of the system under investigation. Then, we present the IT for a solution based on a soft calculation method. In connection with this model, a computer application was created that analyzed various versions of the model with several practical problems. The applicability of the method was analyzed with software specifically developed for this algorithm and was demonstrated on a practical example. The model handles the different products within an order, as well as their different versions. These w... [more]

This article aims to target the key factors that could positively affect crowdfunding success for green products in order to promote crowdfunding efficiency and green supply chain management. Methods: Data were collected through expert interviews and questionnaires and then processed through analytic hierarchy process (AHP) analysis. Statistical tool: This study used Expert Choice as the software for AHP analysis. Sampling: There were 20 participants (20 effective) in pretesting and 30 (23 effective) in formal testing. Participants were followers of green products in Taiwan. Results: (1) Twenty-four factors were abstracted to form the final construct; (2) the 24 key factors could be divided into 2 hierarchies, with 5 primary factors and 19 secondary factors; (3) among the 5 primary factors, “green diversified context” was the most influential; (4) among the 19 secondary factors, “product material is safe and non-toxic” was the most important. Conclusions: Funders would be likely to fin... [more]

Al assisted alloying of carbon steel in Submerged Arc Welding (SAW) by Al-Cr-Ti-Cu unconstrained metal powders is applied. A base case without metal powder additions is compared to two metal powder addition schedules, Al-Cu-Ti and Al-Cu-Ti-Cr. Al powder is used as a deoxidiser element to control the oxygen partial pressure at the weld pool−molten flux interface to ensure that most of the Ti and Cr metal powder is transferred into the weld pool and that the weld metal ppm O is controlled within acceptable limits of 200 to 500 ppm O. The likely sequence of alloy melt formation is deduced from the relevant alloy phase diagrams. The effect of Fe addition into the initial Al-Cu-Ti and Al-Cu-Ti-Cr alloy melt is illustrated in thermochemical calculations. Increased metal deposition productivity with metal powder addition in SAW is confirmed. The metal deposition rates increased by 19% and 40% when Al-Cu-Ti and Al-Cu-Ti-Cr powders were applied at the same weld heat input used in the absence of... [more]

The primary goal of business organization is optimally maximizing their productivity and profit whilst reducing the cost resulting from lost sales and services given to their customers, which can be achieved by exceeding the balance between the demand and supply. Analyzing real-world situations, including integrated queuing-inventory systems, such as M/M/1-systems and M/M/1/∞-systems, can help business organizations reach this goal. This research analyzes integrated queuing-inventory systems with lost sales validated under a deterministic and uniformly distributed order size scheme under continuous review. The limited integrated inventory-queuing M/M/1/N-1-system was chosen as subject of our interest due to its closeness to reality. Thus, this system with exponentially distributed deteriorating products and random planning time with lost sales was simulated. This research aimed to analyze customers’ sanctification by studying the addition of the deterioration parameter γ to the model u... [more]

This paper focuses on the problem of event-triggered H∞ asynchronous filtering for Markov jump nonlinear systems with varying delay and unknown probabilities. An event-triggered scheduling scheme is adopted to decrease the transmission rate of measured outputs. The devised filter is mode dependent and asynchronous with the original system, which is represented by a hidden Markov model (HMM). Both the probability information involved in the original system and the filter are assumed to be only partly available. Under this framework, via employing the Lyapunov−Krasovskii functional and matrix inequality transformation techniques, a sufficient condition is given and the filter is further devised to ensure that the resulting filtering error dynamic system is stochastically stable with a desired H∞ disturbance attenuation performance. Lastly, the validity of the presented filter design scheme is verified through a numerical example.

The production process of a smart factory is complex and dynamic. As the core of manufacturing management, the research into the flexible job shop scheduling problem (FJSP) focuses on optimizing scheduling decisions in real time, according to the changes in the production environment. In this paper, deep reinforcement learning (DRL) is proposed to solve the dynamic FJSP (DFJSP) with random job arrival, with the goal of minimizing penalties for earliness and tardiness. A double deep Q-networks (DDQN) architecture is proposed and state features, actions and rewards are designed. A soft ε-greedy behavior policy is designed according to the scale of the problem. The experimental results show that the proposed DRL is better than other reinforcement learning (RL) algorithms, heuristics and metaheuristics in terms of solution quality and generalization. In addition, the soft ε-greedy strategy reasonably balances exploration and exploitation, thereby improving the learning efficiency of the sc... [more]