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This paper emphasises the pivotal role of enhancing research and development (R&D) staff competence and boosting team innovation efficiency in configuring R&D project teams for artificial intelligence (AI) product development. Diverging from traditional studies primarily focused on time, quality, and cost objectives, this study proposes prioritising the skill increments of staff and team diversity, aligning with the overarching goals of the R&D cycle. Targeting multi-skilled R&D personnel for scheduling and configuration, a three-objective tradeoff optimisation model is established. The nonlinear mixed-integer constrained programming model incorporates a learning effect for calculating employee skill value and employs a heterogeneity efficiency formula for assessing team diversity, particularly emphasising the diversity of R&D personnel research backgrounds. An algorithm based on Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is designed to obtain the approximate Pareto optimal solution. Furthermore, it compares the algorithm with the multi-objective particle swarm optimisation (MOPSO), explores practical decision-making methods for Pareto solutions, and conducts sensitivity analyses on the learning rate and diversity level. Our research is relevant to enterprises seeking to enhance R&D capabilities with a certain degree of homogeneity among R&D employees. This paper exemplifies and validates the proposed model and solution approach using a new AI product from a healthcare company. [ABSTRACT FROM AUTHOR]

Rescheduling in a hybrid flowshop holds significant importance in modern industries that face uncertain events. Moreover, real-world manufacturing scenarios often utilise lot streaming to enhance market competitiveness. In light of escalating energy demands and their consequential environmental impacts, contemporary manufacturing companies are placing a heightened emphasis on energy efficiency. This study addressed a green hybrid flowshop rescheduling problem with consistent sublots (GHFRP_CS) in the context of urgent lot insertion. Initially, we establish an optimisation model aimed at minimising the makespan, total energy consumption, and system stability. To tackle this NP-hard multi-objective optimization problem, we develop a constructive heuristic generating promising solutions based on lot split, sequence, and local search rules. Further improvement is achieved through a multi-objective discrete artificial bee colony algorithm (MDABC). MDABC decomposes the problem into sub-problems, initiating solutions with the constructive heuristic and refining them through employed bee, onlooker bee, and scout bee phases. Computational experiments compare MDABC with other multi-objective evolutionary algorithms (MOEAs) on small- and large-scale problems. Results demonstrate MDABC's superiority, achieving fourfold accuracy and efficiency enhancement for small-scale instances and sixfold improvement for large-scale problems at low cost compared to other MOEAs. [ABSTRACT FROM AUTHOR]

In the automotive industry, some vehicles, failed vehicles, cannot be produced according to the planned schedule due to some reasons such as material shortage, paint failure, etc. These vehicles are pulled out of the sequence, potentially resulting in an increased work overload. On the other hand, the reinsertion of failed vehicles is executed dynamically as suitable positions occur. In case such positions do not occur enough, either the vehicles waiting for reinsertion accumulate or reinsertions are made to worse positions by sacrificing production efficiency. This study proposes a bi-objective two-stage stochastic program and formulation improvements for a mixed-model sequencing problem with stochastic product failures and integrated reinsertion process. Moreover, an evolutionary optimisation algorithm, a two-stage local search algorithm, and a hybrid approach are developed. Numerical experiments over a case study show that while the hybrid algorithm better explores the Pareto front representation, the local search algorithm provides more reliable solutions regarding work overload objective. Finally, the results of the dynamic reinsertion simulations show that we can decrease the work overload by 20% while significantly decreasing the waiting time of the failed vehicles by considering vehicle failures and integrating the reinsertion process into the mixed-model sequencing problem. [ABSTRACT FROM AUTHOR]

At present, the concept of green and sustainable development has been recognised by everyone. For industrial applications, the concept is both a matter of energy consumption and a matter of production methods. Based on the hybrid flow shop problem, a multi-energy supply system is constructed, and external renewable energy microgrid is introduced to further improve the low-carbon level. From the perspectives of energy supply and internal load, the output of wind power and photovoltaic, energy storage cost and energy consumption cost in the flow shop on a certain day are analysed under the demand–response electricity price. After considering the natural endowments and demand differences of different microgrids, construct a multi-energy complementarity model, determine the coordination transaction strategy among microgrids and obtain the solution of the subproblem and the equilibrium solution of the whole problem. Finally, an example is given to evaluate the effectiveness of the proposed strategy under time-of-use prices. The results show that the proposed strategy can effectively improve the utilisation rate of renewable energy in the flow shop while reducing the overall operating cost. [ABSTRACT FROM AUTHOR]

In the manufacturing industry, timely order fulfilment from diverse customers is important for operational profitability, given the constraints of limited production capacity. This paper proposes a novel queueing system for a production system, in which order processing times are categorised as regular or rush based on customer preference. The proposed queueing system employs variable service rates and service strategies in conjunction with staggered production to deal with heterogeneous arrivals. We compute the steady-state probability distribution and develop system performance indicators. The lower and upper bounds of the expected sojourn time for rush and regular orders are derived. Numerical results show that an appropriate switching policy can reduce rush orders by approximately 35% while regular orders increase by less than 10%, demonstrating significant improvement in order fulfilment management. We formulate a multi-objective optimisation problem and implement the NSGA-II algorithm to obtain Pareto optimal solutions. Finally, three regression models are proposed to determine the minimum cost and the associated optimal service rates, given the maximum acceptable value of the expected number of rush orders. The established models can explain at least 80% of the dataset of the optimal solutions and thus our numerical findings provide useful guidance for decision-makers and production managers. [ABSTRACT FROM AUTHOR]

As one of the primary sources of carbon emissions, transportation sector has proposed various measures to reduce its carbon emissions. Introducing energy-efficient and low-carbon autonomous trucks into freight transportation is highly promising, but faces various challenges, especially safety issues. This study addresses eco-friendly lane reservation-based autonomous truck transportation network design for transportation safety and low carbon emissions. It aims to optimally implement dedicated truck lanes in an existing network and design dedicated routes for autonomous truck transportation to simultaneously minimise the negative impact caused by dedicated truck lanes and carbon emissions of the entire transportation system. We first formulate this problem into a bi-objective integer linear program. Then, an ϵ-constraint-based two-stage algorithm (ETSA) is proposed to solve it based on explored problem properties. A case study based on the well-known Sioux Falls network is conducted to demonstrate the applicability of the proposed model and algorithm. Computational results for 310 instances from the literature demonstrate that the proposed algorithm significantly outperforms the ϵ-constraint combined with the proposed ILP in obtaining the Pareto front. Moreover, helpful managerial insights are derived based on sensitivity analysis. [ABSTRACT FROM AUTHOR]

Human-robot collaborative disassembly lines are widely used by remanufacturing companies to disassemble end-of-life (EOL) products. When disassembling large-sized EOL products, each workstation on a disassembly line is generally divided into multiple operating positions, so that different operators can disassemble the same product at their respective positions at the same time, thereby greatly improving efficiency. This paper focuses on a position-constrained human-robot collaborative disassembly planning (PC-HRCDP) problem for the above-mentioned lines, including three subproblems of disassembly sequence planning, disassembly line balancing and robot path planning. A multi-objective mixed integer programming model for PC-HRCDP is developed to solve smallscale instances. Furthermore, a multi-objective multi-fidelity optimisation (MO-MFO) algorithm is proposed to solve large-scale instances. Comprehensive experiments are conducted based on 10 problem instances generated in this study. Experimental results show that the proposed MO-MFO is better than a high-fidelity optimisation algorithm in terms of running time. In addition, benefiting from the strategy of MO-MFO to allocate the limited high-fidelity computational budget to solutions in the two stages of multi-objective optimisation and optimal sampling, MO-MFO is significantly better than the existing representative multi-fidelity optimisation algorithms in terms of the hyper-volume and the inverted generational distance. [ABSTRACT FROM AUTHOR]

In nowadays production, fluctuations in demand, shortening product life-cycles, and highly configurable products require an adaptive and robust control approach to maintain competitiveness. This approach must not only optimise desired production objectives but also cope with unforeseen machine failures, rush orders, and changes in short-term demand. Previous control approaches were often implemented using a single operations layer and a standalone deep learning approach, which may not adequately address the complex organisational demands of modern manufacturing systems. To address this challenge, we propose a hyper-heuristics control model within a semi-heterarchical production system, in which multiple manufacturing and distribution agents are spread across pre-defined modules. The agents employ a deep reinforcement learning algorithm to learn a policy for selecting low-level heuristics in a situation-specific manner, thereby leveraging system performance and adaptability. We tested our approach in simulation and transferred it to a hybrid production environment. By that, we were able to demonstrate its multi-objective optimisation capabilities compared to conventional approaches in terms of mean throughput time, tardiness, and processing of prioritised orders in a multi-layered production system. The modular design is promising in reducing the overall system complexity and facilitates a quick and seamless integration into other scenarios. [ABSTRACT FROM AUTHOR]

This paper proposes a novel intelligent scheduling method based on deep reinforcement learning (DRL) to solve the multi-objective steelmaking-continuous casting (SCC) scheduling problem, under time-of-use (TOU) tariffs for the first time. The intelligent scheduling system architecture is designed, and a mathematical model is established to minimise the total sojourn time and electricity cost. To effectively reduce production costs by avoiding peak periods of electricity consumption, the 'start time' of the system is generated based on the Markov Decision Process (MDP), and heuristic scheduling rules related to power cost are used as the action space, with corresponding reward functions designed according to the characteristics of these two objectives. To satisfy the continuous casting which is a particular SCC constraint, a backward strategy is developed. Additionally, a branching duelling double deep Q-network (BD3QN) is adapted to guide action selection and avoid blind search in the iteration process, and then applied to real-time scheduling. Numerical experiments demonstrate that the proposed method outperforms comparison algorithms in terms of solution quality and CPU times by a large margin. [ABSTRACT FROM AUTHOR]

Manufacturing systems are socio-technical systems, with explicit interactions between humans and technologies in shared workspaces. These shared workspaces could also be called hybrid collaborative manufacturing systems, which involve workers as well as technological equipment and combine the benefits of human workers and new Industry 4.0 technologies, such systems are particularly useful in a context requiring flexibility and adaptability. Furthermore, the new Industry 5.0 approach has the objective to shift toward more human-centric and resilient manufacturing systems. The key problems to solve in the design of collaborative manufacturing systems are the combinatorial assembly line balancing problem and the equipment selection problem. An efficient and sustainable line requires a cost-effective choice of equipment while improving the ergonomics and the safety of workers. Both decisions of balancing workload and the assignment of equipment impact the ergonomics of a collaborative system and present conflicting criteria. To this end, we propose a multi-objective approach, the objectives are the optimisation of the investment costs and the ergonomics with a fatigue and recovery criterion. We propose to linearise the fatigue and recovery to formulate a new Mixed Integer Linear Programming formulation. We developed an exact multi-objective solving algorithm based on the ϵ-constraint to obtain the trade-off between these objectives. We conducted numerical experiments with different instances from the literature with promising results for instances with up to 45 operations. Finally, we discuss insightful managerial conclusions and future research perspectives. [ABSTRACT FROM AUTHOR]

Lot splitting is one of the effective technologies of time-based strategy and has been widely studied in a variety of production environments. Nevertheless, the literature on its application in seru production has been highly scarce until now. Seru system is composed of simple equipment and multi-skilled workers, which can be quickly converted from the traditional assembly line to seru units. As an innovative production mode, seru production inevitably allows applying lot splitting in the real world. Therefore, a multi-objective model is studied for line–cell conversion with lot splitting, aiming at determining the trade-off among makespan, inter-seru system balancing, and intra-seru system balancing. Due to the proposed model's NP-hard nature, an improved NSGA-II was developed to solve it. Finally, extensive numerical simulations are conducted. Compared to no lot splitting, lot splitting is improved by 4.2% and 3.7% in terms of inter-seru system balancing and makespan respectively. The better efficiency and effectiveness of the improved NSGA-II are proved by comparisons with other state-of-the-art algorithms. Additionally, a sensitivity analysis is conducted to ascertain the degree of contribution of the model parameters towards the value of objectives, which provides management implications to support the decision-making of seru production for the enterprise. [ABSTRACT FROM AUTHOR]

The efficiency of material supply for workstations using Automatic Guided Vehicles (AGVs) is largely determined by the performance of the AGV dispatching scheme. This paper proposes a new solution approach for the AGV dispatching problem (AGVDP) for material replenishment in a general manufacturing workshop where workstations are in a matrix layout, and where uncertainty in replenishment time of workstations and stochastic unloading efficiencies of AGVs are dynamic contextual factors. We first extend the literature proposing a mixed integer optimisation model with a delivery satisfaction soft constraint of material orders and two objectives: transportation costs and delivery time deviation. We then develop a new knowledge-guided estimation of distribution algorithm with delivery satisfaction evaluation for solving the model. Our algorithm fuses three knowledge-guided strategies to enhance optimisation capabilities at its respective execution stages. Comprehensive numerical experiments with instances built from a real-world scenario validate the proposed model and algorithm. Results demonstrate that the new algorithm outperforms three popular multi-objective evolutionary algorithms, a discrete version of a recent multi-objective particle swarm optimisation, and a multi-objective estimation of distribution algorithm. Findings of this work provide major implications for workshop management and algorithm design. [ABSTRACT FROM AUTHOR]

This paper considers the optimisation of Mixed-model assembly lines with continuous paced line control. The two minimisation goals have a mixed-integer linear multi-objective dispute. The paper proposes a criterion-space method to define the Pareto front for this class of problems. The method combines Pareto fronts obtained from integer solutions and gradually refines them until the instance's global front is determined. Comparing paced to unpaced line controls can be challenging, since they can produce the same cycle time given sufficiently long line lengths or buffers. Hence, determining Pareto fronts between cycle time and line length for paced lines allows meaningful comparisons between line controls. An industrial case study shows that line length acts as continuously distributable buffers for paced lines, leading to weaker diminishing returns. This result suggests that paced lines are more efficient than unpaced ones for lower cycle time ranges. [ABSTRACT FROM AUTHOR]

Farms usually plan the planting and harvest of crops independently and without considering the plans of the rest of supply chain (SC) members. This generates large imbalances between crops' supply and demand in markets, as well as significant price fluctuations. Shifting how crop production is planned to joint SC decision-making would offer significant advantages in preserving agricultural SC sustainability. This paper explores this idea by proposing a multi-objective optimisation model for planning the planting, cultivation, harvesting, storage, packaging, transport, labouring, and sale of fresh crops. The tool considers five objectives aligned with sustainability: maximising SC profits (economic), minimising waste (environmental), minimising unsatisfied demand (social), maximising sold crops' freshness (environmental and social), and minimising economic injustice perceived by farmers (social). The tool is validated through its application to the La Plata fresh tomato SC case study in Argentina and it is shown how the tool could be used by real decision-maker(s). [ABSTRACT FROM AUTHOR]

Specification of design margins serves the purpose of mitigating the ubiquitous uncertainty of the engineering design process. This paper addresses the twofold challenge of establishing a suitable concept of margins early in the design process, before the design is mature enough for detailed modelling, and evaluating margin inter-dependencies. As margins are often specified in an unstructured manner, there is a risk that one margin might lead to a need for more margins, e.g. tight tolerances, downstream. To avoid adverse margin dependencies, five Multi-Objective Robustness Indicators based on multi-objective design exploration are presented: The Relative Utopia Index, the Relative Pareto Shape Index, the Relative Compromise Index, the Relative Average Sensitivity Index, and the Average Feasible Variable Range. They aggregate optimality, trade-offs and sensitivity of design configurations. The indicators are applied to a case example, a flat head tank design, to illustrate the effect and inter-dependency of margin specification. Three margin types were identified. The first type affects only the Objective Space size, the second type affects only the Pareto set size and the third type affects Pareto set shape. The third margin type is the most critical as it leads to harder compromises between design objectives later in the design process. [ABSTRACT FROM AUTHOR]

Efficiency and accuracy have been challenging in the design optimisation process driven by building simulation. The literature review identified the limitations of previous studies, prompting this study to explore the performance of single-objective versus multi-objective efficiency and accuracy on equivalent problems based on control variables and to consider more algorithmic options for a broader range of designs. This study constructed a comparative energy-related experiment whose results are in the same unit, either as a single-objective optimisation or split into two objectives. The project aims to reduce annual energy consumption and increase solar utilisation potential. Our approach focuses on the use of a surrogate modelling algorithm, Radial Basis Function Optimisation Algorithm (RBFOpt), with its multi-objective version RBFMOpt, to optimise the energy performance while quickly identifying new energy requirements for an iterative office building design logic, contrast to traditional genetic-algorithm-driven. In addition, the research also conducted a comparative study between RBFOpt and Covariance Matrix Adaptation Evolutionary Strategies (CMAES) in a single-objective comparison and between RBFMOpt and Nondominated Sorting Genetic Algorithm II (NSGA-II) in a multi-objective optimisation process. The comparison of these sets of Opt algorithms with evolutionary algorithms helps to provide data-driven evidence to support early design decisions. [ABSTRACT FROM AUTHOR]

Methanol (CH3OH) is a versatile compound used in various industries. Catalytic reactors used in CH3OH production are expensive due to high energy and raw material costs. Multi-objective optimization (MOO) is used to optimize CH3OH production, but it is still lacking. Researchers use alternative strategies or modify existing ones to achieve better results. This study applied model-based optimization using an ASPEN Plus simulator and Multi-objective Ant Lion Optimization (MOALO) to address the issue. The results revealed the highest conversion and product rate, with the lowest energy cost, side product, and bare module cost, CBM. The decision variable plots indicate that the reactor's pressure significantly affects the optimal solution. This study provides valuable insight into optimizing CH3OH production. [ABSTRACT FROM AUTHOR]

One of the most widely used fuzzy linear programming models is the multi‐objective interval type‐2 fuzzy linear programming (IT2FLP) model, which is of particular importance due to the simultaneous integration of multiple criteria and objectives in a single problem, the fuzzy nature of this type of problems, and thus, its closer similarity to real‐world problems. So far, many studies have been done for the IT2FLP problem with uncertainties of the vagueness type. However, not enough studies have been done regarding the multi‐objective interval type‐2 fuzzy linear programming (MOIT2FLP) problem with uncertainties of the vagueness type. As an innovation, this study investigates the MOIT2FLP problem with vagueness‐type uncertainties, which are represented by membership functions (MFs) in the problem. Depending on the localisation of vagueness in the problem, that is, vagueness in the objective function vector, vagueness in the technological coefficients, vagueness in the resources vector, and any possible combination of them, various problems may arise. Furthermore, to solve problems with MOIT2FLP, first, using the weighted sum method as an efficient and effective method, each of the MOIT2FLP problems is converted into a single‐objective problem. In this research, these types of problems are introduced, their MFs are stated, and different solution methods are suggested. For each of the proposed methods, the authors have provided an example and presented the results in the corresponding tables. [ABSTRACT FROM AUTHOR]

Hardware‐based sensing frameworks such as cooperative fuel research engines are conventionally used to monitor research octane number (RON) in the petroleum refining industry. Machine learning techniques are employed to predict the RON of integrated naphtha reforming and isomerisation processes. A dynamic Aspen HYSYS model was used to generate data by introducing artificial uncertainties in the range of ±5% in process conditions, such as temperature, flow rates, etc. The generated data was used to train support vector machines (SVM), Gaussian process regression (GPR), artificial neural networks (ANN), regression trees (RT), and ensemble trees (ET). Hyperparameter tuning was performed to enhance the prediction capabilities of GPR, ANN, SVM, ET and RT models. Performance analysis of the models indicates that GPR, ANN, and SVM with R2 values of 0.99, 0.978, and 0.979 and RMSE values of 0.108, 0.262, and 0.258, respectively performed better than the remaining models and had the prediction capability to capture the RON dependence on predictor variables. ET and RT had an R2 value of 0.94 and 0.89, respectively. The GPR model was used as a surrogate model for fitness function evaluations in two optimisation frameworks based on genetic algorithm and particle swarm method. Optimal parameter values found by the optimisation methodology increased the RON value by 3.52%. The proposed methodology of surrogate‐based optimisation will provide a platform for plant‐level implementation to realise the concept of industry 4.0 in the refinery. [ABSTRACT FROM AUTHOR]

To study the optimal design of the section of the double-system composite guideway under the economic, steel consumption, and carbon emission characteristics, this paper introduced the multi-objective constrained optimisation model, which was established by the non-dominated sorting genetic algorithm II. In addition, the finite element model was established to further analyse the optimised section's deformation and summarise the rail girder's deformation law under different loads. The results showed that compared with the original design scheme, the optimised scheme can effectively reduce carbon emission during the construction of the double-system composite guideway, by 23.67% for Scheme I and 42.03% for Scheme II. On the other hand, steel had the largest share in the economic targets of the three design options, accounting for about 75% to 88.5% of the total cost. Concrete had the highest share of carbon emissions, ranging from 90% to 95% of the total carbon emissions. The distribution patterns of horizontal and vertical deformations in the three design options were independent of the load type as well as the load magnitude, but the vertical deformations were related to the load type, especially the self-weight load. The conclusions of this paper aim to fill the gap in the theoretical study of section optimisation of the double-system composite guideway and lay the theoretical foundation for developing the multi-system monorail transportation system. [ABSTRACT FROM AUTHOR]

To solve the problem of high peak force and large load fluctuation of combined energy-absorbing structures, a coordinated design strategy based on staggered compensation of load curves was proposed. First, an energy-absorbing unit of metal thin-walled tapered square tube was designed, and its crashworthiness was verified by experiment and simulation, and the peak force was reduced by introducing a groove inducing structure. Then, a combined structure with multi-level energy-absorbing units was proposed through layout arrangement and dimensional design. The parameter analysis was performed using a validated surrogate model. It is found that the thickness of the outer tube of the energy-absorbing units has a significant effect on peak crushing force (PCF), crushing force uniformity (CFU) and specific energy absorption (SEA). To improve the crashworthiness of the combined structure, a multi-objective optimisation was applied to achieve the maximum CFU and SEA values, and weight factors were introduced to select design schemes. The results show that the optimised combined structure reduces PCF by 44.48% and increases CFU by 50.78%, while improving the SEA. [ABSTRACT FROM AUTHOR]

Recently, the demands for structural impact protection in automobile, train, aerospace and other fields have become increasingly intense, and the bionic energy absorption structures have attracted widespread attention for their excellent structural properties and crushing characteristics. In this study, inspired by the great impact resistance of the frog legs, a novel bionic frog leg tube was proposed and investigated by numerical simulation and parametric study. Firstly, according to the structural forms of the legs for frogs, humans and pigs, three types of bionic leg tubes were designed, and the energy absorption characteristics of these tubes subjected to axial loading were investigated, it was found that the peaking crushing force ( PCF ) and undulation of load-carrying capacity ( ULC ) seem to have a strong relationship with the jumping ability for animals, lower PCF and ULC could decrease the injury transmitted to the body. Then, the parametric studies were conducted to investigate the effects of structural parameters on the crushing behaviour of the bionic tubes, the results indicated that a bionic frog leg tube with smaller L 1 tends to present the concertina deformation mode, and a larger L 1 will lead to the diamond mode or mixed mode. Lastly, the multi-objective optimisation of the bionic frog leg tube was carried out to simultaneously improve specific energy absorption ( SEA ), crushing force efficiency ( CFE ), and reduce ULC , based on the NSGA-II algorithm. The optimal configuration of the structural parameters of the bionic tube was obtained, and the bionic tubes present better crashworthiness characteristics than initial structures under the axial loading. The findings of this study provide new insights into the transformation of natural biological inspiration into engineering applications for protective structures. [ABSTRACT FROM AUTHOR]

We consider a staff allocation problem at a sequential sorting facility. In this facility, staff need to be assigned to work areas, through which commodities flow sequentially to be processed. Assigning staff optimally involves a trade-off between several different objectives, such as minimising the overall number of workers, as well as having fewer changes in the staff levels over time. While optimising for these, many operational requirements need to be met, including minimum processing volumes, correct ordering/processing of the commodities, and not exceeding staff resource constraints. We develop a deterministic time-expanded network flow model to solve the staff allocation problem. The model addresses the problem at a more granular timescale and with more operational constraints than previously used models. We use a lexicographical approach to deal with the multiple objectives. To demonstrate the model's value, we apply it to a staff problem of a UK mail centre, showing that in the majority of cases, our model improves on current staffing practises on both objectives. We also show how the model performs in several different scenarios, including increasing total mail volumes and changing the proportion of letters and parcels to be sorted. [ABSTRACT FROM AUTHOR]

The conventional machining of the widely used nickel-based superalloy Inconel 625 is challenging since it exhibits high strength at elevated temperatures. Cryogenic machining is an effective cooling technique to extract the heat developed during machining and enhance the machining performance of superalloys by eliminating the high-temperature related machining obstacles. The maximum machining performance of cryogenic machining can be achieved by optimising the input parameters considered for the study. This work attempts to improve the cryogenic machining performance of Inconel 625 using grey relational analysis. Multiple responses that are having the most significance on the performance of the machining such as main cutting force, tool wear and surface roughness were considered to optimise the process. The experiments were designed on the basis of the orthogonal array. Taguchi's L9 design with three levels of cutting speed, feed and depth of cut was considered for conducting the experiments. A cutting speed of 60 m/min, feed of 0.05 mm/rev and depth of cut of 0.2 mm were obtained as the optimal parameters. The analysis of modes of wear and morphology of chips was also conducted using SEM imaging to understand the wear mechanisms and machining performance at the optimal level under cryogenic cooling. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive procedure for the parametric design and analysis of a underwater vehicle rotary docking skirt (RDS) structure. Different types of software were integrated to improve the design efficiency, and different techniques were used to establish an approximate model of the RDS structure with high fitting accuracy. Nondominated sorting genetic algorithm II (NSGA-II) was improved by dynamic adjustment of the population size according to the number of iterations and new crossover and mutation operations (i.e. INSGA-II), which was then applied to a multi-objective optimisation model of the RDS structure. A Pareto solution set was obtained, and a sensitivity analysis was performed on the design variables and objective functions of four representative optimisation schemes. These results can be used to provide a richer selection for designers to reference when designing RDS structures. Abbreviations: RDS; Rotary docking skirt; NSGA-II; Nondominated sorting genetic algorithm II [ABSTRACT FROM AUTHOR]

While time series forecasting models are generally trained by optimising certain forms of error, the end-user's forecasting needs in a multi-objective setting can be broader, and often mutually conflicting. A production manager may prioritise high product fill rates and low average inventory resulting from a forecast over just low error. The conflict among multiple objectives is notably worrisome in intermittent demand forecasting, where error-minimising approaches can devalue the practitioner's objectives. To address such forecasting problems, we propose an Adaptive Multi-objective Optimal Combination (AMOC) of forecasts which incorporates the end-user's preferences across multiple objectives. We demonstrate the use of AMOC in a real-life application of intermittent demand forecasting for optimising four distinct inventory management objectives using five specialised forecasting methods across single-period and multi-period inventory handling scenarios. Additionally, we conduct a comprehensive experiment on a subset of M5 competition data to exhibit the robustness of the AMOC using 13 diverse forecasting methods and four statistical objectives. [ABSTRACT FROM AUTHOR]

Abstract One of the most widely used fuzzy linear programming models is the multi‐objective interval type‐2 fuzzy linear programming (IT2FLP) model, which is of particular importance due to the simultaneous integration of multiple criteria and objectives in a single problem, the fuzzy nature of this type of problems, and thus, its closer similarity to real‐world problems. So far, many studies have been done for the IT2FLP problem with uncertainties of the vagueness type. However, not enough studies have been done regarding the multi‐objective interval type‐2 fuzzy linear programming (MOIT2FLP) problem with uncertainties of the vagueness type. As an innovation, this study investigates the MOIT2FLP problem with vagueness‐type uncertainties, which are represented by membership functions (MFs) in the problem. Depending on the localisation of vagueness in the problem, that is, vagueness in the objective function vector, vagueness in the technological coefficients, vagueness in the resources vector, and any possible combination of them, various problems may arise. Furthermore, to solve problems with MOIT2FLP, first, using the weighted sum method as an efficient and effective method, each of the MOIT2FLP problems is converted into a single‐objective problem. In this research, these types of problems are introduced, their MFs are stated, and different solution methods are suggested. For each of the proposed methods, the authors have provided an example and presented the results in the corresponding tables.

Abstract Hardware‐based sensing frameworks such as cooperative fuel research engines are conventionally used to monitor research octane number (RON) in the petroleum refining industry. Machine learning techniques are employed to predict the RON of integrated naphtha reforming and isomerisation processes. A dynamic Aspen HYSYS model was used to generate data by introducing artificial uncertainties in the range of ±5% in process conditions, such as temperature, flow rates, etc. The generated data was used to train support vector machines (SVM), Gaussian process regression (GPR), artificial neural networks (ANN), regression trees (RT), and ensemble trees (ET). Hyperparameter tuning was performed to enhance the prediction capabilities of GPR, ANN, SVM, ET and RT models. Performance analysis of the models indicates that GPR, ANN, and SVM with R2 values of 0.99, 0.978, and 0.979 and RMSE values of 0.108, 0.262, and 0.258, respectively performed better than the remaining models and had the prediction capability to capture the RON dependence on predictor variables. ET and RT had an R2 value of 0.94 and 0.89, respectively. The GPR model was used as a surrogate model for fitness function evaluations in two optimisation frameworks based on genetic algorithm and particle swarm method. Optimal parameter values found by the optimisation methodology increased the RON value by 3.52%. The proposed methodology of surrogate‐based optimisation will provide a platform for plant‐level implementation to realise the concept of industry 4.0 in the refinery.

ABSTRACTThis study introduces a comprehensive framework that integrates Multi-objective optimisation with Multiple-Criteria Decision Making (MCDM) techniques to enhance decision-making in sustainable machining processes. Through a systematic literature review, the framework is developed, incorporating sustainability indicators, experimental validation, and multi-objective optimisation algorithms to identify the optimal machining conditions. To address the challenge of subjective judgements and the multiple solutions generated by multi-objective optimisation, MCDM techniques are employed to select the best solution. By considering economic viability, environmental impact reduction, and social commitment, the proposed framework overcomes the limitations of traditional optimisation approaches. It provides an objective and holistic approach to decision-making, promoting sustainability in machining processes. The framework’s effectiveness and applicability are demonstrated through a case study, validating its potential for practical implementation and informed decision-making

In this paper, we propose a method for solving a real‐world timetabling problem at Mandarine Academy. The primary motivation for this work is to provide an automated professional course scheduling tool to replace the time‐consuming task of manually creating timetables that are constantly incorrect. Following a review of both scientific literature and company requirements, a mathematical model of the problem is provided, which includes 18 constraints (hard/soft) and five objectives, two of which are competing. We test a handful of multi‐objective evolutionary algorithms (MOEA's) starting with the non‐dominated sorting genetic algorithm (NSGA II and NSGA III), the multi‐objective evolutionary algorithm based on decomposition (MOEA/D), the indicator‐based evolutionary algorithm and finally the strength Pareto evolutionary algorithm. Two custom genetic operators (mutation and crossover) are proposed and compared to conventional operators (PMX and swap mutation). To obtain elite configurations, a tuning phase involving all of the aforementioned algorithms is carried out. Experiments were divided by problem size, with three to five objectives tested. Experiments include the use of real‐world data from the company's catalog. This dataset was made available to the scientific community to serve as a testing ground for professional course scheduling, an underexploited field of scheduling. We discuss findings, including a comparison of each algorithm's performance using various metrics, as well as convergence graphs and population evolution. [ABSTRACT FROM AUTHOR]

• A solid desiccant cooling system is optimised using a response surface methodology. • Regression models were developed and showed commendable predictive capability. • Multi-objective optimisation was achieved by considering four key parameters. • Case studies demonstrated the system's applicability in Australian climates. Solid desiccant-assisted dew-point indirect evaporative cooling (SD-DPIEC) systems have gained considerable attention as a potential eco-friendly alternative to vapour-compression cooling systems in building cooling applications. However, one major drawback of these systems is their substantial water consumption during evaporative cooling. To tackle this issue, this study aims to improve the cooling efficiency and water utilisation of an SD-DPIEC system using response surface methodology (RSM). This research focuses on optimising four key parameters: supply air temperature, humidity ratio, water consumption rate and coefficient of performance (COP). The independent variables encompass the ambient temperature, relative humidity, regeneration temperature, and recirculation air ratio. Employing a multi-objective optimisation approach via the desirability function, the optimised SD-DPIEC system is subsequently tested in two prevalent weather patterns in Australia. The results demonstrated that the regression models derived from RSM exhibited commendable predictive capability, with the determination coefficient R 2 and Adequate Precision exceeding 0.97 and 40.46, respectively. The outcomes revealed that the system attained its optimal performance with a supply air temperature of 20.36 °C, humidity ratio of 12.56 g kg-1, a water consumption rate of 3.11 kg/hr, and COP of 2.03 under the ambient temperature of 33.79 °C, relative humidity of 68.48 %, regeneration temperature of 51.78 °C, and recirculation air ratio of 60 %. Based on the optimisation results, a case study was undertaken to evaluate the system's applicability in representative Australian climates. The results demonstrated that the system could uphold air conditions with the supply air temperature below 19 °C and humidity ratio below 11.51 g kg-1 under the studied Australian climates. [ABSTRACT FROM AUTHOR]

Recent natural or manmade hazards highlighted the pressing need for allocating enough financial resources for the enhancement of the resilience of infrastructure in our society. In this regard, bridges play a pivotal role in transportation networks, where any reduction in their performance can influence the functionality of the whole network. Bridge retrofitting is a common strategy for enhancing transportation network resilience. Composite reinforcement with different thicknesses and concrete seat extenders with different widths have been used to retrofit an existing curved bridge in Iran. A novel hybrid algorithm called NSGAII-SA was proposed and compared against a simple optimisation strategy, a traditional genetic algorithm solver (NSGA-II), and pure simulated annealing (SA). These optimisation strategies identify the optimal retrofit scenarios set in terms of maximum resilience index, minimum recovery time, and minimum retrofitting cost. For this problem, NSGAII-SA outperforms the traditional NSGA-II. The comparison shows that for this particular application, SA has the best overall performance. However, the proposed NSGAII-SA algorithm has satisfactory performance too, it is a clear improvement over NSGAII and it has the potential to be very useful and better suited than SA for problems with large design spaces and many local minima. [ABSTRACT FROM AUTHOR]

The diagonal flow fan, known for its innovative airflow guiding technique, presents a dual advantage of heightened efficiency and reduced noise levels. Despite garnering attention in energy production systems, research on optimizing outlet guide vanes for diagonal flow fan performance remains limited. This study introduces a novel machine learning approach, utilizing Opt LHD sampling instead of traditional simulations and experiments to accumulate ample sample data. The fusion of LS-SVM and improved GA-PSO is employed for multi-objective optimization of outlet guide vanes, aiming to simultaneously enhance fan performance and reduce energy consumption. Experimental assessments reveal the optimized fan's significant improvements, including a 106 Pa increase in total pressure, a 3.6 dB reduction in noise levels, and a remarkable 16.3% enhancement in total pressure efficiency. These results highlight the robustness of the machine learning approach in optimizing diagonal flow fan exit guide vanes, effectively addressing the research gap in this area. Additionally, numerical analysis of internal flow dynamics and acoustic properties pre and post-optimization uncovers the intrinsic mechanisms influencing the overall performance of the diagonal flow fan. [ABSTRACT FROM AUTHOR]

As the waste products have a variety of connection structure characteristics, the energy consumed in tool switching in the disassembly process is considered to better comprehensively optimise the energy consumption index. A mixed-integer non-linear programming (MINLP) model of multi-objective partial disassembly line balancing problem (PDLBP) is constructed to minimise four optimisation objectives which are the number of workstations, workstation load, number of the tools are switched, and energy consumption. Based on the characteristics of PDLBP, we constructed an energy consumption matrix of tool switching and proposed a multi-objective improved social spider algorithm (ISSA). The random movement and mask change operations of ISSA were improved, and the artificial spiders were added to enhance the global optimisation capabilities of ISSA. ISSA was applied to optimise two typical benchmark instances, which have different scales, respectively. And the computational results were compared with several algorithms of existing literature to verify the superiority of ISSA. Finally, ISSA was applied to a partial disassembly instance of a printer, which considered the energy consumed in tool switching. Then, multiple better disassembly schemes were provided for decision-makers. [ABSTRACT FROM AUTHOR]

Solving facility layout problems aims at identifying the optimal plan for placing a number of facilities or departments on a shop floor considering multiple optimisation criteria and constraints. Recently, the unequal area facility layout problem (UA-FLP) has drawn more attention as it is closer to real industrial scenarios. To address this problem, the typical UA-FLP in an air-conditioner production shop floor is analysed, and then a modified non-dominated sorting genetic algorithm (NSGA-II) is developed to identify the optimal layout plan considering the material handling cost (MHC) and the closeness rating score (CRS). NSGA-II is a stable algorithm for engineering applications and has an encoding structure of chromosomes that can express facility layout expediently. Besides that, the two objectives (MHC and CRS) that are conflicting might be another reason for adopting NSGA-II. During the process, the crossover and mutation operators of NSGA-II are modified based on the non-overlapping method, which reduces the time cost for eliminating unsuitable layout plans. The modified NSGA-II is compared with two related algorithms, and the results show that it has better performance on the UA-FLP with a large number of departments. [ABSTRACT FROM AUTHOR]

The article presents a procedure for assessing and selecting the shape of the ring spinner spindle head that is optimum for yarn manufacturing using the balloonless system, considering multiple criteria. This procedure consists of two stages: the Pareto-optimal method and the distance function method. Twelve spindle head variants were designed and manufactured of 100Cr6 bearing steel composed of C (0.93–1.05%), Si (0.15–0.35%), Mn (0.25–0.45%) along with aluminium alloys EN AW-6060 (AlSi1MgMn) containing Si (0.70–1.3%), Mg (0.6–1.2%), Mn (0.4–1.0%) and EN AW-6060 (AlMgSi) containing Si (0.30–0.60%) and Mg (0.35–0.60%). The shapes of the heads were developed on the basis of the design solutions used previously by the leading ring-spinning machine and spinning spindle manufacturers. Four design variants of spindle heads, made of AlMgSi wrought aluminium alloy, have a unique shape that enables shaping their teeth (notches) by extrusion on a hydraulic press. The assessment criteria were production cost per unit, head weight, maximum surface hardness HV0.1, and four yarn technological quality criteria: maximum yarn tension values during the formation of the base, body and head of the package, and the tension amplitude. The assessment values for the criteria obtained through calculations and measurements were normalized. Then, a set of Pareto-optimal solutions was determined using seven criteria. Due to the fact that this set consisted of six variants, the distance function was used to select the best one. The best design variant of the head is the one corresponding to the lowest distance function value. In our case, the variant is a unique shape, made of AlMgSi alloy, with teeth thickened on the outer diameter of the head, ensuring the lowest yarn tension value at the height during the formation of the package.

Skylight is an effective strategy for maximising daylight penetration while minimising electrical lighting energy demand in buildings. However, in tropical climate regions, skylight can be problematic due to the risk of excessive sunlight. This study aimed to optimise skylight design parameters using multi-objective optimisation (MOO) approach through a case study of a low-rise building with office rooms configured to surround a skylight in the tropical climate of Yogyakarta, Indonesia. It was conducted using computational modelling and simulation with RadianceIES tools in IES-VE 2019 software. The parameters examined included skylight shape, opening area, and thickness, while the performance indicators were spatial daylight autonomy (sDA300/50%), average daylight factor (DFave), and annual sunlight exposure (ASE1000,250). The sensitivity analysis showed that skylight opening area significantly influenced daylight performance. Moreover, the optimum design, drawn from the objective function f and Pareto frontiers, was a rounded trapezium skylight with an opening area of 897 m2, achieving sDA300/50%–ASE1000,250 = 35%, DFave = 0.9%, and mean distance to the utopia point of 64.1%. These results could serve as a guide for architects and engineers in designing skylight for typical buildings.

A typical wind system captures wind energy and converts it into electricity, which is then converted to DC for battery storage using an AC/DC converter; an inverter then supplies AC electricity at the grid frequency. However, this solution involves losses in electronic components and incurs costs and environmental impacts associated with battery storage. To address these issues, a different wind system layout configuration is considered, where the energy storage duties are assumed by a hydro storage system employing a reversible hydraulic pump (referred to as a Pump as Turbine). This solution utilises an elevated reservoir connected to the Pump as Turbine to compensate for fluctuations in wind and load; this approach offers lower costs, a longer lifespan, reduced maintenance, and a smaller waste management cost. This study focuses on a comprehensive sensitivity analysis of the new wind system power layout, considering multiple objectives. Specifically, the analysis targets the net change in the mass of water (potential energy) stored in the pumped hydro system, the captured wind energy, and the torque provided in hydraulic turbine mode. On the other hand, the design variables are represented by the Pump as Turbine-specific speed, the hydraulic system gearbox ratio, and the pump head. To assess how solutions are affected by random changes in wind speed and external load, the sensitivity analysis considers the multi-objective optimisation problem showing for both the wind speed and the external load a stochastic contribution. [ABSTRACT FROM AUTHOR]

The management of high-volume (HV) waste poses a persistent challenge in sustainable materials management and represents an untapped opportunity in circular economy models. This study proposes a conceptual decision-making framework to operationalise a novel circular economy strategy for HV waste, involving temporary storage to facilitate nature-based secondary resource recovery. Using an illustrative case study of a candidate HV waste (legacy mining waste), we apply a robust multi-objective spatial optimisation approach at a national scale, employing an exact solution approach. Our methodology integrates mixed-integer linear programming to evaluate the economic viability, social benefits, and impacts of climate change uncertainties on nature-based solutions (NbS) implementation across diverse scenarios. The results demonstrate that NbS can enhance economic feasibility by incorporating carbon sequestration and employment benefits while demonstrating resilience against climate change projections to ensure long-term sustainability. The findings suggest that although NbS can improve the circular economy of HV nationally, it is essential to assess additional ecosystem services and address multiple uncertainties for effective macro-level sustainability assessment of HV management. This study offers a robust decision-making framework for policymakers and stakeholders to plan and implement nature-based circular economy strategies for HV waste streams at a national level while effectively managing long-term planning uncertainties. [ABSTRACT FROM AUTHOR]

A set of non-linear optimisation algorithms are combined with a finite element simulation code to analyse the energy absorption and elastic stress wave propagation problem in multilayer/multimaterial armour systems under ballistic impacts. An Abaqus Python script is used to simulate the ballistic event and to generate the variables and post-processing outputs necessary for the integration with the optimisation algorithms. A number of modelling strategies are considered and three optimisation algorithms are used: Particle Swarm Optimisation (PSO), Genetic Algorithm (GA) and Simulated Annealing (SA). The performance and efficiency of each algorithm are assessed through four benchmark tests with different levels of complexity. A multi-objective optimisation procedure is proposed that uses the most efficient algorithm based on every single-objective formulation, variables and constraints from the benchmark tests, resulting in a highly non-linear problem. The proposed optimisation methods successfully achieve the study purposes both in the simulation of generic ballistic impacts and in the quality of the optimised solutions, demonstrating the potential for this type of optimisation method on terminal ballistic applications, serving as a standpoint for further studies into higher energy impacts and material non-linearities. [ABSTRACT FROM AUTHOR]

It is crucial to understand and meet the multi-dimensional affective image needs of users for product form in a user demand-oriented product development model. Multi-objective evolutionary algorithms based on decomposition will be introduced into the field of kansei engineering to carry out research on product form optimisation design based on multi-objective evolutionary algorithms. A constrained multi-objective discrete optimisation model was established using the kansei engineering prediction model constructed through machine learning techniques as the objective function, and a reference vector guided evolutionary algorithm was used to solve it. The superiority of this method was verified by comparing it with other commonly used solving methods in this field. Combining entropy weight method and TOPSIS, select the optimisation design scheme that best meets the multi-dimensional affective needs of users from the obtained pareto set. Taking the train as an example, the proposed method was explained. The results indicate that the optimisation scheme obtained by this method can achieve the improvement and optimisation of product form in multiple affective dimensions. Meanwhile, a comparative study on the applicability of multi-objective evolutionary algorithms in the form optimisation problem of different affective dimensions is carried out to provide reference and suggestions for subsequent product design research. [ABSTRACT FROM AUTHOR]

Driven by the growing interest of the scientific community and the proliferation of research in this field, cranial implants have seen significant advancements in recent years regarding design techniques, structural optimisation, appropriate material selection and fixation system method. Custom implants not only enhance aesthetics and functionality, but are also crucial for achieving proper biological integration and optimal blood irrigation, critical aspects in bone regeneration and tissue health. This research aims to optimize the properties of implants designed from triply periodic minimal surface structures. The gyroid architecture is employed for its balance between mechanical and biological properties. Experimental samples were designed varying three parameters of the surface model: cell size, isovalue and shape factor. Computational simulation tools were used for determining the relationship between those parameters and the response variables: the surface area, permeability, porosity and Young modulus. These tools include computer aided design, finite element method and computational fluid dynamics. With the simulated values, the corresponding regression models were fitted. Using the NSGA-II, a multi-objective optimisation was carried out, finding the Pareto set which includes surface area and permeability as targets, and fulfil the constraints related with the porosity and Young modulus. From these non-dominated solutions, the most convenient for a given application was chosen, and an optimal implant was designed, from a patient computed tomography scan. An implant prototype was additively manufactured for validating the proposed approach. [ABSTRACT FROM AUTHOR]

Surrogate-Assisted Evolutionary Optimisation algorithms are a specialized brand of optimisers developed to undertake problems with computationally expensive fitness functions. These algorithms work by building a cheap approximation or model of the exact function and using it in the evaluation of solutions within the optimisation process. This use of modelling techniques within optimisation, while offers a practical reduction in function calls, brings along with it some additional questions. This paper starts with a description of the key elements of surrogate-assisted evolutionary optimisation algorithms as they are outlined throughout the literature, and then, proceeds to rearrange these elements using a novel blueprint of the field. The proposed blueprint can be used to represent any surrogate-assisted evolutionary algorithm in a way that illustrates its principles and components in a non-vague manner. In addition, a survey of the most prominent works in the field is conducted using this novel blueprint. Finally, a number of challenges and perspectives are listed before the paper is concluded. [ABSTRACT FROM AUTHOR]

The article proposes a multi-objective optimisation method based on Triply Periodic Minimal Surface (TPMS) lattice structures to meet the requirements of stiffness and energy absorption capacity. A bilinear material model is used to predict mechanical properties of lattice elements for optimisation with higher efficiency and accuracy. Considering the multi objective characteristics of optimisation, the Hybrid Cellular Automaton method (HCA) is modified by introducing equivalent loads and weight parameter, without multiple dynamic analyses. To further improve the structural performances, the Conformal Lattice Structures (CLS) are filled in the boundaries of structures based on the CLS construction technique where a strategy of inverse-mapping is proposed. Furthermore, a post-processing method is raised to eliminate the discontinuity of elements' connection areas for manufacting. The simulation results indicate that the optimised structures achieve a combination of stiffness and energy absorption capacity, and the specimen is manufactured by 3D printing, exposing the feasibility. [ABSTRACT FROM AUTHOR]

This article investigates heating options for poultry houses (or sheds) in order to meet their specific indoor air temperature requirements, with case studies conducted across Australia under conditions similar to those encountered worldwide. Hybrid geothermal–solar (PV)–gas heating systems with various configurations are proposed to minimise the lifecycle costs and GHG emissions of poultry shed heating, which involves six seven-week cycles per year. The baseload heating demand is satisfied using ground-source heat pumps (GSHPs), with solar photovoltaic panels generating the electricity needed. LPG burners satisfy the remaining heating demand. Integrating these systems with GSHPs aims to minimise the overall installation costs of the heating system. The primary focus is to curtail the costs and GHG emissions of poultry shed heating with these hybrid systems, considering three different electricity offsetting scenarios. It is found that a considerable reduction in the lifecycle cost (up to 55%) and GHG emissions (up to 50%) can be achieved when hybrid systems are used for heating. The Pareto front solutions for the systems are also determined. By comparing the Pareto front solutions for various scenarios, it is found that the shave factor, a measure of the GSHP proportion of the overall system, significantly influences the lifecycle cost, while the size and utilisation of the solar PV panels significantly affect the lifecycle GHG emissions. [ABSTRACT FROM AUTHOR]

During routine rail maintenance, rail grinding of sharp curves is challenging. In particular, setting up the grinding pattern is essential. A rail-grinding pattern optimisation method is proposed based on the mathematical relationship between the grinding pattern parameters and geometric parameters of the rail grinding profile. In this method, the design variables and constraints are reasonably selected, and a multi-objective optimisation function considering the wheel-rail contact stress, wheel-rail lateral force, and rail grinding amount is established. The optimisation model is solved using a chaotic microvariation adaptive genetic algorithm. The optimisation method is applied to the repairable grinding of a worn rail in a metro curve section. The results demonstrate that using this method can realise an ideal grinding pattern with the expected grinding effect in addition to high accuracy and high optimisation speed. The wheel-rail contact characteristics, wear characteristics, and train dynamic performance are significantly improved after rail grinding; this is significant for conducting grinding operations and improving the train's curve-passing ability. Therefore, this study provides theoretical guidance for formulating rail-grinding patterns. [ABSTRACT FROM AUTHOR]

The optimisation of electric vehicle battery boxes while preserving their structural performance presents a formidable challenge. Many studies typically involve fewer than 10 design variables in their optimisation processes, a deviation from the reality of battery box design scenarios. The present study, for the first time, attempts to use sensitivity analysis to screen the design variables and achieve an efficient optimisation design with a large number of original design variables. Specifically, the sensitivity analysis method was proposed to screen a certain number of optimisation variables, reducing the computational complexity while ensuring the efficiency of the optimisation process. A combination of the Generalised Regression Neural Network (GRNN) and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) was employed to construct surrogate models and solve the optimisation problem. The optimisation model integrates these techniques to balance structural performance and weight reduction. The optimisation results demonstrate a significant reduction in battery box weight while maintaining structural integrity. Therefore, the proposed approach in this study provides important insights for achieving high-efficiency multi-objective optimisation of battery box structures. [ABSTRACT FROM AUTHOR]

Skylight is an effective strategy for maximising daylight penetration while minimising electrical lighting energy demand in buildings. However, in tropical climate regions, skylight can be problematic due to the risk of excessive sunlight. This study aimed to optimise skylight design parameters using multi-objective optimisation (MOO) approach through a case study of a low-rise building with office rooms configured to surround a skylight in the tropical climate of Yogyakarta, Indonesia. It was conducted using computational modelling and simulation with RadianceIES tools in IES-VE 2019 software. The parameters examined included skylight shape, opening area, and thickness, while the performance indicators were spatial daylight autonomy (sDA300/50%), average daylight factor (DFave), and annual sunlight exposure (ASE1000,250). The sensitivity analysis showed that skylight opening area significantly influenced daylight performance. Moreover, the optimum design, drawn from the objective function f and Pareto frontiers, was a rounded trapezium skylight with an opening area of 897 m2, achieving sDA300/50%-ASE1000,250 = 35%, DFave = 0.9%, and mean distance to the utopia point of 64.1%. These results could serve as a guide for architects and engineers in designing skylight for typical buildings. [ABSTRACT FROM AUTHOR]

The global manufacturing sector is currently subject to Time-of-Use energy pricing, compelling them to avoid peak demand. Overuse of energy by industries can lead to the utilisation of thermal power plants, which can have a severe influence on the environment. Energy-efficient production scheduling can help manufacturers to reduce energy costs. This paper focuses on the energy efficiency and production output of a flexible manufacturing system (FMS) consisting of five machines and operates based on sequence-dependent scheduling. A centralised energy storage system is considered in the model to improve the voltage profile during peak load hours and lower peak load demand. A simple and effective strategy is proposed for peak load shaving via real-time scheduling of the FMS. The scheduling problem is treated as a multi-objective issue and tackled using a whale optimisation algorithm. The optimisation results demonstrate the effectiveness of the proposed model in reducing peak demand from the energy supplier side to zero by utilising the energy storage system while also maintaining an acceptable makespan. The model can help energy-intensive industries reduce peak demand by moving load to an energy storage system. By adopting these measures, industrial facilities can reduce their carbon footprint and contribute to the global effort to mitigate climate change. Highlights: The study focuses on a flexible manufacturing system's energy efficiency and production output. The study uses a centralised energy storage system to improve the voltage profile. The whale optimisation algorithm is used to solve the multi-objective problem. [ABSTRACT FROM AUTHOR]