The circular economy is an economic concept opposite to the current linear system. One of its main principles is to seek to minimise waste by reusing seemingly useless raw materials. Biogas plants are places where energy can be recovered from waste. In order to boost the environmental benefits of this concept, it is important to rely on local systems (including supply chains). Therefore, the aim of this study was to examine whether biogas plants in Poland operate in a circular manner from the perspective of locality. The analysis was based on questionnaire surveys concerning the nature of the facilities’ operations, divided into biogas plants located at sewage treatment plants, biogas plants based on municipal waste and agricultural biogas plants. On the basis of the data obtained, statistical and spatial analyses were carried out to verify the installed capacity of the facilities, the distance from which they obtain their substrate and the use of the biogas produced. The results of th... [more]

The coordinated use of electricity and a heat energy system can effectively improve the energy structure during winter heating in the northern part of China and improve the environmental pollution problem. In this paper, an economic scheduling model of an electric−thermal integrated energy system, including a wind turbine, regenerative electric boiler, solar heat collection system, biomass boiler, ground source heat pump and battery is proposed, and a biomass boiler was selected as the auxiliary heat source of the solar heat collection system. A mixed integer linear programming model was established to take the operating cost of the whole system as the target. A day-ahead optimization scheduling strategy considering the demand side response and improving new energy consumption is proposed. In order to verify the influence of the coordinated utilization of the flexible load and energy storage equipment on the optimal scheduling in the model built, three scenarios were set up. Scenario 3... [more]

This study proposes an optimal scheduling method for complex integrated energy systems. The proposed method employs a heuristic algorithm to maximize its energy, economy, and environment indices and optimize the system operation plan. It uses the k-means combined with box plots (Imk-means) to improve the convergence speed of the heuristic algorithm by forming its initial conditions. Thus, the optimization scheduling speed is enhanced. First of all, considering the system source and load factors, the Imk-means is presented to find the typical and extreme days in a historical optimization dataset. The output results for these typical and extreme days can represent common and abnormal optimization results, respectively. Thus, based on the representative historical data, a traditional heuristic algorithm with an initial solution set, such as the genetic algorithm, can be accelerated greatly. Secondly, the initial populations of the genetic algorithm are dispersed at the historical outputs... [more]

Sustainable energy projects in Africa are particularly vulnerable in terms of sourcing vital alternative fuels due to the complexity of sourcing processes, contract agreements and relationships between society managers or directors and supplier chain entities. These challenges can affect any phase of a sustainable project, and the losses can be as high as 3.2 EURO/GJ. In addition, there is reduced competition and fair trade, low profits and poor quality of the fuel purchased. Technology (mobile application) is one powerful tool that can solve the above challenges by controlling or managing the supply and demand of biomass-based fuels, agriculture residue, industrial waste and many more. Thus, the main objective of this study is to evaluate the feasibility of a developed digital platform to remove barriers in the trade of alternative fuels. Data collection began with the identification of the key production areas (sources) and quantities of three selected AFs. Secondly, data on the seas... [more]

The transition from internal combustion engines to electric vehicles (EVs) has received significant attention and investment due to its potential in reducing greenhouse gas emissions. The integration of EVs into electric and transport systems presents both benefits and challenges in energy management. The scheduling of EV charging can alleviate congestion in the electric system and reduce waiting times for EV owners. The use of renewable energy sources (RESs) for EV charging and supporting the grid can help mitigate the uncertainty of these energy resources. Vehicle-to-grid (V2G) technology can be used as an alternative approach in the event of sudden high consumption of the grid. Additionally, cost minimization through large-scale coordinated planning is crucial for the future of e-mobility systems. This review paper focuses on the latest trends considering the various approaches and features in coordinated EV scheduling, as well as the influence of different stakeholders, categorized... [more]

Rapid shifts in consumer preferences have prompted enterprises to offer products in small quantities and various options. To meet market demands, enterprises must be able to research the development of modern conceptions of manufacturing systems which has revolved around new practical and scientific results that are able to meet the assumptions of focused flexible manufacturing systems (FMSs) and the challenges of the Industry 4.0 philosophy. These FMSs, which incorporate automated facilities and computer control systems, play a crucial role in boosting the productivity of enterprises. In this study, the development trajectory and applications of FMS research were investigated. Scopus was used to collect and organize voluminous data, and main path analysis was used to identify the most relevant studies on FMS research. The results revealed that early FMS research concentrated on fundamental property analysis. After the flexibility and productivity of these systems were enhanced, the el... [more]

During the last few decades, various strategic planning models have been suggested in the literature. It is difficult for a company to decide which of these models is most useful to adopt, as each of them shows different strengths and weaknesses. We consider this problem a multicriteria decision problem and investigate the evaluation of six strategic planning models in the context of smaller and medium-sized manufacturing companies in Iran. We consider a methodology that supports the analysis of the input from several decision-makers based on multiple criteria and assume vagueness in the input data elicited from them. For the purpose considered, the fuzzy best worst method (FBWM) appears appropriate. Based on a literature review, six evaluation criteria for strategic management models are considered: formality, clarity, measurability, objectivity, coverage, and consistency. These criteria are evaluated based on the input provided by thirteen managers using linguistic variables. FBWM is... [more]

This paper investigates common (slack) due-date assignment single-machine scheduling with controllable processing times within a group technology environment. Under linear and convex resource allocation functions, the cost function minimizes scheduling (including the weighted sum of earliness, tardiness, and due-date assignment, where the weights are position-dependent) and resource-allocation costs. Given some optimal properties of the problem, if the size of jobs in each group is identical, the optimal group sequence can be obtained via an assignment problem. We then illustrate that the problem is polynomially solvable in O(℘3) time, where ℘ is the number of jobs.

This feasibility study utilized regression models to predict makespan robustness in dynamic production processes with uncertain processing times. Previous methods for robustness determination were computationally intensive (Monte Carlo experiments) or inaccurate (surrogate measures). However, calculating robustness efficiently is crucial for field-synchronous scheduling techniques. Regression models with multiple input features considering uncertain processing times on the critical path outperform traditional surrogate measures. Well-trained regression models internalize the behavior of a dynamic simulation and can quickly predict accurate robustness (correlation: r>0.98). The proposed method was successfully applied to a permutation flow shop scheduling problem, balancing makespan and robustness. Integrating regression models into a metaheuristic model, schedules could be generated that have a similar quality to using Monte Carlo experiments. These results suggest that employing machi... [more]

The cement industry is an important industrial entity responsible for implementing carbon emission reduction targets. Considering the carbon trading and green certificate trading mechanisms, this paper presents a multi-stage planning approach for the constructed Cement-Industrial Integrated Energy System (Cement-IIES). Carbon reduction technologies represented by low-temperature waste heat recovery, as well as phased changes in economic and technical parameters, are considered in the model. The case study shows that the proposed method not only optimizes the design economy of the Cement-IIES but also achieves a substantial carbon emission reduction in the cement production process and energy supply system. Compared with the traditional single-stage planning, the proposed method improves the system’s economic efficiency by 13.88% and flexibly adapts to changes in policies such as “coal reform”, green certificate trading and carbon quotas. The low-temperature waste heat recovery technolo... [more]

This study explores the responses of manufacturing and service provision companies in Santiago de Cali to stimuli during the COVID-19 pandemic. The responses included changes in demand, absenteeism, and the development of new products, which affected the supply chain (SC). The qualitative methodology of the multiple-case study was used. The evidence for the multiple-case studies was collected through semi-structured interviews, where the interviewees were SC experts from four manufacturing companies and one service company. The data analysis was performed in two phases. In phase one, the case study protocol was completed, and in phase two, thematic analysis was used to identify supply chain response (SCR) patterns. The results revealed two aspects of the SCR. First, to respond to a stimulus, SC adapted activities other than those of suppliers and manufacturers, such as product design and development, human resources, budgeting, and logistics. Second, the SCs used several alternatives t... [more]

The inventory level has a significant influence on the cost of process scheduling. The stochastic cutting stock problem (SCSP) is a complicated inventory-level scheduling problem due to the existence of random variables. In this study, we applied a model-free on-policy reinforcement learning (RL) approach based on a well-known RL method, called the Advantage Actor-Critic, to solve a SCSP example. To achieve the two goals of our RL model, namely, avoiding violating the constraints and minimizing cost, we proposed a two-stage discount factor algorithm to balance these goals during different training stages and adopted the game concept of an episode ending when an action violates any constraint. Experimental results demonstrate that our proposed method obtains solutions with low costs and is good at continuously generating actions that satisfy the constraints. Additionally, the two-stage discount factor algorithm trained the model faster while maintaining a good balance between the two af... [more]

Today, fog and cloud computing environments can be used to further develop the Internet of Things (IoT). In such environments, task scheduling is very efficient for executing user requests, and the optimal scheduling of IoT task requests increases the productivity of the IoT-fog-cloud system. In this paper, a hybrid meta-heuristic (MH) algorithm is developed to schedule the IoT requests in IoT-fog-cloud networks using the Aquila Optimizer (AO) and African Vultures Optimization Algorithm (AVOA) called AO_AVOA. In AO_AVOA, the exploration phase of AVOA is improved by using AO operators to obtain the best solution during the process of finding the optimal scheduling solution. A comparison between AO_AVOA and methods of AVOA, AO, Firefly Algorithm (FA), particle swarm optimization (PSO), and Harris Hawks Optimization (HHO) according to performance metrics such as makespan and throughput shows the high ability of AO_AVOA to solve the scheduling problem in IoT-fog-cloud networks.

Time, cost, and quality are critical factors that impact the production of intelligent manufacturing enterprises. Achieving optimal values of production parameters is a complex problem known as an NP-hard problem, involving balancing various constraints. To address this issue, a workflow multi-objective optimization algorithm, based on the dynamic virtual staged pruning (DVSP) strategy, was proposed to optimize multi-stage nonlinear production processes. The algorithm establishes a virtual workflow model based on the actual production process and proposes a pruning strategy to eliminate the indirect constraint relationship between tasks. A virtual hierarchical strategy is employed to divide the task node set, and the Pareto optimal service set is calculated through backward iteration in stages. The optimal path is generated through forward scheduling, and the global optimal solution is obtained. The algorithm was compared with the minimum critical path algorithm (MCP) and the partial c... [more]

Work-flow scheduling is for finding the allocation method to achieve optimal resource utilization. In the scheduling process, constraints, such as time, cost and quality, need to be considered. How to balance these parameters is a NP-hard problem, and the nonlinear manufacturing process increases the difficulty of scheduling, so it is necessary to provide an effective heuristic algorithm. Aiming at these problems, a multi-objective nonlinear virtual work-flow model was set up, and a multi-objective staged scheduling optimization algorithm with the objectives of minimizing cost and time and maximizing quality was proposed. The algorithm includes three phases: the virtualization phase abstracts tasks and services into virtual nodes to generate a virtual work-flow model; the virtual scheduling phase divides optimized segments and obtains the solution set through reverse iteration; the generation phase obtains the scheduling path according to the Pareto dominance. The proposed algorithm pe... [more]

Water conservancy project scheduling is an extension to the classic resource-constrained project scheduling problem (RCPSP). It is limited by special time constraints called “forbidden time windows” during which certain activities cannot be executed. To address this issue, a specific RCPSP model is proposed, and an approach is designated for it which incorporates both a priority rule-based heuristic algorithm to obtain an acceptable solution, and a hybrid genetic algorithm to further improve the quality of the solution. In the genetic algorithm, we introduce a new crossover operator for the forbidden time window and adopt double justification and elitism strategies. Finally, we conduct simulated experiments on a project scheduling problem library to compare the proposed algorithm with other priority-rule based heuristics, and the results demonstrate the superiority of our algorithm.

Task offloading and real-time scheduling are hot topics in fog computing. This paper aims to address the challenges of complex modeling and solving multi-objective task scheduling in fog computing environments caused by widely distributed resources and strong load uncertainties. Firstly, a task unloading model based on dynamic priority adjustment is proposed. Secondly, a multi-objective optimization model is constructed for task scheduling based on the task unloading model, which optimizes time delay and energy consumption. The experimental results show that M-E-AWA (MOEA/D with adaptive weight adjustment based on external archives) can effectively handle multi-objective optimization problems with complex Pareto fronts and reduce the response time and energy consumption costs of task scheduling.

The management of the supply chain for enterprise-wide operations generally consists of strategic, tactical, and operational decision stages dependent on one another and affecting various time scales. Their integration usually leads to multiscale models that are computationally intractable. The design and operation of energy hubs faces similar challenges. Renewable energies are challenging to model due to the high level of intermittency and uncertainty. The multiscale (i.e., planning and scheduling) energy hub systems that incorporate renewable energy resources become more challenging to model due to an integration of the multiscale and high level of intermittency associated with renewable energy. In this work, a mixed-integer programming (MILP) superstructure is proposed for clustering shape-based time series data featuring multiple attributes using a multi-objective optimization approach. Additionally, a data-driven statistical method is used to represent the intermittent behavior of... [more]

In recent years, most of the research in the field of smart grids integrating renewable energy sources assumed energy efficiency as a scheduling objective. However, the aspects of energy consumption or energy demand have not been described clearly, even though they have been proven to be an effective way of reducing energy consumption. In this context, this study aimed to cover a key research challenge in the field, such as the development of an intelligent strategy for solving energy consumption scheduling problems. The added value of our proposal consists of classifying individual consumption profiles assigned to each operation cycle phase, instead of considering an average of non-varying consumption of household appliances. Within this hybrid approach, the proposed explainable system, based on self-organizing maps of neural networks, fuzzy clustering algorithm, and scheduling technics, correlates the complex interrelation between power generated from renewable energy sources in a sm... [more]

The offloading of computationally intensive tasks to edge servers is indispensable in the mobile edge computing (MEC) environment. Once the tasks are offloaded, the subsequent challenges lie in buffering them and assigning them to edge virtual machine (VM) resources to meet the multicriteria requirement. Furthermore, the edge resources’ availability is dynamic in nature and needs a joint prediction and optimal allocation for the efficient usage of resources and fulfillment of the tasks’ requirements. To this end, this work has three contributions. First, a delay sensitivity-based priority scheduling (DSPS) policy is presented to schedule the tasks as per their deadline. Secondly, based on exploratory data analysis and inferred seasonal patterns in the usage of edge CPU resources from the GWA-T-12 Bitbrains VM utilization dataset, the availability of VM resources is predicted by using a Holt−Winters-based univariate algorithm (HWVMR) and a vector autoregression-based multivariate algori... [more]

Studies have reported the incorporation of microorganisms into cement to promote the formation of calcium carbonate in cracks of concrete, a process known as biomineralization. The paper aims to improve the process of the cascade system for biomineralization in cement by identifying the best hydrodynamic conditions in a reaction cell in order to increase the useful life of concrete structures and, therefore, bring energy and environmental benefits. Two central composite rotatable designs were used to establish the positioning of the air inlet and outlet in the lateral or upper region of the geometry of the reaction cell. The geometries of the reaction cell were constructed in SOLIDWORKS®, and computational fluid dynamics was performed using the Flow Simulation tool of the same software. The results were submitted to statistical analysis. The best combination of meshes for the simulation was global mesh 4 and local mesh 5. The statistical analysis applied to gas velocity and pressure re... [more]

Petroleum has become a strategic resource to the national economy, and forecasting its demand is a critical step to supporting energy planning and policy-making for carbon reduction. We first conducted a characteristic analysis of end consumption for petroleum products, and the key affecting factors are identified through an extended logarithmic mean Divisia index (LMDI) method. Afterwards, the long-range energy alternatives planning system (LEAP) was adopted to predict the petroleum products demand by considering the potential impacts of different policies on the identified key factors. Through comparative analysis of three scenarios including five sub-scenarios, the findings show that the dual carbon constraints are crucial to petroleum demand control. Under the enforcement of existing carbon peaking policies, the petroleum products demand will peak around 2043 at 731.5 million tons, and the impact of energy intensity-related policies is more significant than that of activity level.... [more]

Energy systems around the world are going through tremendous transformations, mainly driven by carbon footprint reductions and related policy imperatives and low-carbon technological development. These transformations pose unprecedented technical challenges to the energy sector, but they also bring opportunities for energy systems to develop, adapt, and evolve. With rising complexity and increased digitalization, there has been significant growth in the amount of data in the power/energy sector (data ranging from power grid to household levels). Utilization of this large data (or “big data”), along with the use of proper data analytics, will allow for useful insights to be drawn that will help energy systems to deliver an increased amount of technical, operational, economic, and environmental benefits. This paper reviews various categories of data available in the current and future energy systems and the potential benefits of utilizing those data categories in energy system planning a... [more]

The existing public utility building belonging to the Forest Experimental Station of the Poznań University of Life Sciences, due to high energy consumption and related costs, has qualified for deep energy modernisation or consideration for the construction of a new building. One of the goals is to achieve carbon neutrality and have a positive energy balance. The article uses the hybrid DEMATEL-AHP/ANP-VIKOR method. The methodology used is distinguished by the creation of a set of decision-making criteria and the identification of the relationship between them, which is determined by conducting a survey of a group of experts using the Delphi method, as well as determining the preferences of the decision-maker using a survey of the target group using social research. Two different models of the decision-maker’s preferences have been developed, taking into account the selected decision criteria, and four acceptable technical solutions have been identified. As a result of the calculations... [more]

The growing demand for electricity and the reconstruction of poor areas in Africa require an effective and reliable energy supply system. The construction of reliable, clean, and inexpensive microgrids, whether isolated or connected to the main grid, has great importance in solving energy supply problems in remote desert areas. It is a complex interaction between the level of reliability, economical operation, and reduced emissions. This paper investigates the establishment of an efficient and cost-effective microgrid in a remote area located in the Djado Plateau, which lies in the Sahara Ténéré desert in northeastern Niger. Three cases are presented and compared to find the best one in terms of low costs. In case 1, the residential area is supplied by PVs and a battery energy storage system (BESS), while in the second case, PVs, a BESS, and a diesel generator (DG) are utilized to supply the load. In the third case, the grid will take on load-feeding responsibilities alongside PVs, a B... [more]