The present era is characterised by many events that have influences on supply chains and supply networks. This concerns, e.g., war, epidemics, natural disasters, accidents, strikes, political instability, and political sanctions. These are generally grouped under the term “disruption”. In order to avoid the risk of supply chain disruption, major disruption of supply networks, or loss of customers associated with disruptions, it is necessary to take preventive and proactive measures in supply chain management in terms of planning. This paper is intended to briefly summarise the current state of knowledge with the most important facts and derive a new definition from it. In addition, an analogy to maintenance is established for the first time. In doing so, a comparison of the concepts and a listing of the important proactive measures derived from them for increasing resilience are made. In the course of this, the field of action considered is extended from the exchange of suppliers thro... [more]

In this paper, the no-wait flow shop problem with earliness and tardiness objectives is considered. The problem is proven to be NP-hard. Recent no-wait flow shop problem studies focused on familiar objectives, such as makespan, total flow time, and total completion time. However, the problem has limited studies with solution approaches covering the concomitant use of earliness and tardiness objectives. A novel methodology for the parallel simulated annealing algorithm is proposed to solve this problem in order to overcome the runtime drawback of classical simulated annealing and enhance its robustness. The well-known flow shop problem datasets in the literature are utilized for benchmarking the proposed algorithm, along with the classical simulated annealing, variants of tabu search, and particle swarm optimization algorithms. Statistical analyses were performed to compare the runtime and robustness of the algorithms. The results revealed the enhancement of the classical simulated anne... [more]

Mineral and metallurgical processing are crucial within the mineral value chain. These processes involve several stages wherein comminution is arguably the most important due to its high energy consumption, and its impact on subsequent extractive processes. Several geological properties of the orebody impact the efficiency of mineral processing and extractive metallurgy; scholars have therefore proposed to deal with the uncertain ore feed in terms of grades and rock types, incorporating operational modes that represent different plant configurations that provide coordinated system-wide responses. Even though these studies offer insights into how mine planning impacts the ore fed into the plant, the simultaneous optimization of mine plan and metallurgical plant design has been limited by the existing stochastic mine planning algorithms, which have only limited support for detailing operational modes. The present work offers to fill this gap for open-pit mines through a computationally e... [more]

Scheduling production is an important decision issue in the manufacturing domain. With the advent of the era of Industry 4.0, the basic generation of schedules becomes no longer sufficient to face the new constraints of flexibility and agility that characterize the new architecture of production systems. In this context, schedules must take into account an increasingly disrupted environment while maintaining a good performance level. This paper contributes to the identified field of smart manufacturing scheduling by proposing a complete process for assessing the robustness of schedule solutions: i.e., its ability to resist to uncertainties. This process focuses on helping the decision maker in choosing the best scheduling strategy to be implemented. It aims at considering the impact of uncertainties on the robustness performance of predictive schedules. Moreover, it is assumed that data upcoming from connected workshops are available, such that uncertainties can be identified and model... [more]

In the context of carbon trading, energy conservation and emissions reduction are the development directions of integrated energy systems. In order to meet the development requirements of energy conservation and emissions reduction in the power grid, considering the different responses of the system in different time periods, a wind-hydrogen integrated multi-time scale energy scheduling model was established to optimize the energy-consumption scheduling problem of the system. As the scheduling model is a multiobjective nonlinear problem, the artificial fish swarm algorithm−shuffled frog leaping algorithm (AFS-SFLA) was used to solve the scheduling model to achieve system optimization. In the experimental test process, the Griewank benchmark function and the Rosenbrock function were selected to test the performance of the proposed AFS-SFL algorithm. In the Griewank environment, compared to the SFLA algorithm, the AFS-SFL algorithm was able to find a feasible solution at an early stage,... [more]

Considering the complexity of the three-dimensional environment and the flexibility of the quadrotor aircraft, using the traditional A* algorithm for global path planning has the disadvantages of less search direction, more expanded nodes, and a longer planning path. Therefore, an improved A* algorithm is proposed, which is improved from two aspects. Firstly, a two-layer extended neighborhood strategy is proposed, which can increase the search direction and make better use of the flexibility of the aircraft. Secondly, the heuristic function is improved to make the heuristic function value closer to the actual planning path distance, which can reduce the expansion nodes and optimize the planning path. Finally, the path planning simulation of the improved A* algorithm is carried out and the results show that the path planned by the improved algorithm is shorter and the expanded nodes are fewer, which can guide the quadrotor to reach the destination better.

With the development of Industry 4.0, artificial intelligence (AI) is gaining increasing attention for its performance in solving particularly complex problems in industrial chemistry and chemical engineering. Therefore, this review provides an overview of the application of AI techniques, in particular machine learning, in chemical design, synthesis, and process optimization over the past years. In this review, the focus is on the application of AI for structure-function relationship analysis, synthetic route planning, and automated synthesis. Finally, we discuss the challenges and future of AI in making chemical products.

To improve the efficiency of loading operation by researching the optimization of the pre-marshalling operation scheme in the export container block between the time when the ship stowage chart was published and the beginning time of loading, a two-stage mixed integer programming model was established. The first stage established an optimization model of the container reshuffling location, based on the objective function of the least time-consuming operation of a single-bay-yard crane, and designed an improved artificial bee colony algorithm to solve it. Based on the first stage, an optimization model of yard crane configuration and scheduling was built to minimize the maximum completion time of the yard crane in the export block, and an improved genetic algorithm was designed to solve the built model. Through comparative analysis, the performance of our algorithm was better than CPLEX and traditional heuristic algorithms. It could still solve the 30 bays quickly, and the solving quali... [more]

While carbon emissions reduction brings about environmental benefits, it can also create financial pressure on many manufacturing enterprises. Many manufacturing enterprises have begun to pledge their own carbon emissions right quotas for financing and the funds from this financing are being used to implement energy savings and emissions reduction strategies. To investigate the impact of carbon emissions right pledge financing on supply chains, this study constructed a two-echelon low-carbon supply chain, which consisted of a capital-constrained manufacturer and a retailer. The manufacturer invested in carbon reduction technologies using carbon emissions right pledge financing. On this basis, we analyzed the carbon emissions reduction levels and profits of the supply chain in three different power structures. The results showed that the manufacturer pledged the most carbon emissions rights to finance emissions reduction in the Nash model and, in this case, the carbon emissions reductio... [more]

The increasing use of high shares of renewable energy sources (RESs) in the current electricity network introduces challenges to the design and management of the electricity network due to the variation and uncertainty nature of the RESs. Some existing energy infrastructures, such as heat, gas, and transport, all have some level of inbuilt storage capacity and demand response (DR) potentials that can be exploited in an energy system integration to give the electricity network some level of flexibility and promote an efficient transition to a low-carbon, resilient, and robust energy system. The process of integrating different energy infrastructure is known as multi-vector energy systems (MESs). This paper reviews different studies on the planning of MESs using the energy hubs (EHs) approach. The EHs model used in this paper links different energy vectors such as gas, electricity, and heat energy vectors in its planning model, as opposed to planning each energy vector independently, in... [more]

The framework of energy transition poses significant challenges in subtransmission network development, where the increased renewable energy generation is collected, in order to efficiently convey power production, avoiding limitations in a range of operating conditions. In this paper, a method to evaluate possible margins for further renewable penetration due to electric network development is assessed, by means of scenario evaluation for the concretisation of renewable initiatives, combined producibility analysis, and load flow studies, accounting for operation islands in subtransmission network organisation, carried out in N and N-1 conditions. The method is applied to a provisional model of the southern part of the Italian power system.

After a large number of electric vehicles (EVs) are connected to the integrated energy system, disorderly charging and discharging of EVs will have a negative impact on the safe and stable operation of the system. In addition, EVs’ uncertain travel plans and the stochastic fluctuation of renewable energy output and load power will bring risks and challenges. In view of the above problems, this paper establishes a hierarchical stochastic optimal scheduling model of an electric thermal hydrogen integrated energy system (ETH-IES) considering the EVs vehicle-to-grid (V2G) mechanism. The EVs charging and discharging management layer aims to minimize the variance of the load curve and minimize the dissatisfaction of EV owners participating in V2G. The multi-objective sand cat swarm optimization (MSCSO) algorithm is used to solve the proposed model. On this basis, the daily stochastic economic scheduling of ETH-IES is carried out with the goal of minimizing the operation cost. The simulation... [more]

In this study, an operation strategy is introduced for distributed energy resources (DERs) in reconfigurable microgrids (MGs) to improve voltage profiles, minimize power losses, and boost the system performance. The proposed methodology aims to minimize power loss and energy not supplied (ENS) in MGs with an intelligent share of DERs and network reconfiguration in grid-connected and islanded modes. Due to the inherent uncertain nature of renewable DERs, these sources’ power output is considered as an uncertain parameter, and its effect on the system characteristics is analyzed. The state-of-the-art information gap decision theory (IGDT) approach is utilized to explore different decision-making strategies in the energy scheduling of reconfigurable MGs to deal with such uncertainty. To validate the effectiveness of the proposed method, the IEEE 33-bus radial system is utilized as the test MG. The simulation results show the importance of energy storage systems and reconfiguration in deal... [more]

With the expansion of global trade and the deterioration of the marine environment, research on the sustainability of marine transport has drawn increasing scientific attention. This study takes the marine supply chain composed of Maersk and ports in 17 coastal cities in China as decision-making units (DMUs). It then chooses indicators from the three dimensions of economy, environment and society to evaluate the sustainable efficiency of the marine supply chain, Maersk and ports. In order to deal with the uncertain variables of the sustainability evaluation index, this study develops an uncertain network DEA model based on the uncertainty theory, and the computable equivalent form and proof are also provided. In addition, this study divides the decentralized marine supply chain into two modes, i.e., Maersk as leader and the port as leader, and it calculates their sustainable efficiency, respectively. These results suggest that the sustainable performance of ports is superior to that of... [more]

Flexographic printing is a highly sought-after technique within the realm of packaging and labeling due to its versatility, cost-effectiveness, high speed, high-quality images, and environmentally friendly nature. A major challenge in flexographic printing is the need to optimize energy usage, which requires diligent attention to resolve. This research combines lean principles and machine learning to improve energy efficiency in selected flexographic printing machines; i.e., Miraflex and F&K. By implementing the 5Why root cause analysis and Kaizen, the study found that the idle time was reduced by 30% for the Miraflex machine and the F&K machine, resulting in energy savings of 34.198% and 38.635% per meter, respectively. Additionally, a multi-linear regression model was developed using machine learning and a range of input parameters, such as machine speed, production meter, substrate density, machine idle time, machine working time, and total machine run time, to predict energy consum... [more]

The use of regenerative braking systems is an important approach for improving the travel mileage of electric vehicles, and the use of an auxiliary hydraulic braking energy recovery system can improve the efficiency of the braking energy recovery process. In this paper, we present an algorithm for optimizing the energy recovery efficiency of a hydraulic regenerative braking system (HRBS) based on fuzzy Q-Learning (FQL). First, we built a test bench, which was used to verify the accuracy of the hydraulic regenerative braking simulation model. Second, we combined the HRBS with the electric vehicle in ADVISOR. Third, we modified the regenerative braking control strategy by introducing the FQL algorithm and comparing it with a fuzzy-control-based energy recovery strategy. The simulation results showed that the power savings of the vehicle optimized by the FQL algorithm were improved by about 9.62% and 8.91% after 1015 cycles and under urban dynamometer driving schedule (UDDS) cycle conditi... [more]

This study describes the implementation of an energy sector community to examine the practice of cybersecurity for operational technology environments and their supply chains. Evaluating cybersecurity from the perspectives of different actors participating in the energy sector, the progress and challenges of operators and suppliers in delivering cybersecurity for the sector are explored. While regulatory frameworks incentivize individual organizations to improve their cybersecurity, operational services contain contributions from many organizations, and this supply chain of activity needs to be influenced and managed to achieve desired security and resilience outcomes. Through collaborations and systems engineering approaches, a reference model is created to facilitate improvements in managing the cybersecurity of supply chains for different actors, including service operators, maintainers, manufacturers, and systems integrators. This study provides an illustration of implementing a co... [more]

This paper investigates the design problem of an electric bus (E-bus) route with charging stations to smooth the operations between E-bus service and charging. The design variables include the locations of E-bus stops, number of charging piles at charging stations, fare, and headway. A mathematical programming model is proposed to maximize social welfare in consideration of the uncertain charging demand at charging stations. The model solution algorithm is also designed. The model and algorithm are demonstrated on the E-bus route 931 in the city of Suzhou, China. The results of the case studies show that (i) the right number of stops on a bus route can contribute to the highest social welfare; (ii) the pile−bus ratio decreases with the increase of E-bus fleet size, thereby improving the E-bus charging efficiency at charging stations; and (iii) deploying charging stations at one end of a bus route can achieve a shorter waiting time for E-bus compared with deployment at two ends.

This work addresses the internalization of externalities in energy decision making and in generation expansion planning (GEP). Although the linkage between externalities and energy is well recognized, the issue of the internalization in GEP models and from a sustainability perspective is still far from being fully explored. A critical literature review is presented, including scientific articles published in the period from 2011 to 2021 and selected from scientific databases according to a set of pre-defined keywords. The literature is vast and quite heterogeneous in the models and methods used to deal with these externalities, and therefore a categorization of these studies was attempted. This categorization was based on the methods used, the geographical scope, the externalities included in the planning model and the strategies for their inclusion. As a result, it was possible to perceive that most studies tend to focus on the internalization of externalities related to CO2 and equiv... [more]

Dealing with multi-objective problems has several interesting benefits, one of which is that it supplies the decision-maker with complete information regarding the Pareto front, as well as a clear overview of the various trade-offs that are involved in the problem. The selection of such a representative set is, in and of itself, a multi-objective problem that must take into consideration the number of choices to show the uniformity of the representation and/or the coverage of the representation in order to ensure the quality of the solution. In this study, day-ahead scheduling has been transformed into a multi-objective optimization problem due to the inclusion of objectives, such as the operating cost of multi-energy multi-microgrids (MMGs) and the profit of the Distribution Company (DISCO). The purpose of the proposed system is to determine the best day-ahead operation of a combined heat and power (CHP) unit, gas boiler, energy storage, and demand response program, as well as the tra... [more]

Electricity load prediction is an essential tool for power system planning, operation and management. The critical information it provides can be used by energy providers to maximise power system operation efficiency and minimise system operation costs. Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) are two suitable methods that have been successfully used for analysing time series problems. In this paper, the two algorithms are explored further for load prediction; two load prediction algorithms are developed and verified by using the half-hourly load data from the University of Warwick campus energy centre with four different prediction time horizons. The novelty lies in comparing and analysing the prediction accuracy of two intelligent algorithms with multiple time scales and in exploring better scenarios for their prediction applications. High-resolution load forecasting over a long range of time is also conducted in this paper. The MAPE values for the LSTM are 2.50... [more]

The global energy and environmental crisis promotes the development of electric vehicles (EVs), and the rational planning of EV fast charging stations is an important influencing factor for their development. In this paper, for the EV fast charging station capacity planning problem, a joint-optimization model for optimal planning of EV fast charging stations and the economic operation of a distribution network is constructed, considering the impact of user preference selection and EV access on the regional distribution network. To address the problems of low efficiency and local convergence found in traditional heuristic optimization algorithms, an improved krill swarm optimization algorithm (CKHA) that introduces chaotic optimization parameters to make the initial population as uniformly distributed as possible is proposed to find the optimal planning scheme for EV fast charging stations. The case results show that the optimal planning model and its solution method are effective.

Solar PhotoVoltaics (PV) integration into the electricity grids significantly increases the complexity of Transmission Expansion Planning (TEP) because solar PV power generation is uncertain and difficult to predict. Therefore, this paper proposes the optimal planning method for transmission expansion combined with uncertain solar PV generation. The problem of uncertain solar PV generation is solved by using Long Short-Term Memory (LSTM) for forecasting solar radiation with high accuracy. The objective function is to minimize total system cost, including the investment cost of new transmission lines and the operating cost of power generation. The optimal TEP problem is solved by the Binary Differential Evolution (BDE) algorithm. To investigate and demonstrate the performance of the proposed method, the IEEE 24-bus system and solar radiation data in Thailand are selected as a study case for TEP. The MATPOWER program written in MATLAB software is used for solving optimal power flow probl... [more]

The deployment of CO2 capture and storage (CCS) and negative emissions technologies (NETs) are crucial to meeting the net-zero emissions target by the year 2050, as emphasised by the Glasgow Climate Pact. Over the years, several energy planning models have been developed to address the temporal aspects of carbon management. However, limited works have incorporated CCS and NETs for bottom-up energy planning at the individual plant scale, which is considered in this work. The novel formulation is implemented in an open-source energy system software that has been developed in this work for optimal decarbonisation planning. The DECarbonation Options Optimisation (DECO2) software considers multiperiod energy planning with a superstructural model and was developed in Python with an integrated user interface in Microsoft Excel. The software application is demonstrated with two scenarios that differ in terms of the availabilities of mitigation technologies. For the more conservative Scenario 1... [more]

The energy transition and the resulting expansion of renewable energy resources increasingly pose a challenge to the energy system due to their volatile and intermittent nature. In this context, energy management systems are central as they coordinate energy flows and optimize them toward economic, technical, ecological, and social objectives. While numerous scientific publications study the infrastructure, optimization, and implementation of residential energy management systems, only little research exists on industrial energy management systems. However, results are not easily transferable due to differences in complexity, dependency, and load curves. Therefore, we present a systematic literature review on state-of-the-art research for residential and industrial energy management systems to identify trends, challenges, and future research directions. More specifically, we analyze the energy system infrastructure, discuss data-driven monitoring and analysis, and review the decision-m... [more]