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Wavefront shaping is an established solution to converge acoustic waves through complex media. Discrete phase sampling is inevitable in practice because of manufacture resolution. Limited phase sampling undermines focusing performance, which is hardly improved by increasing the number of phase sections. We propose that such loss can be alleviated when the modulator is designed directly on discrete phases. The feedback-based wavefront shaping with the genetic algorithm has been taken here. The performance of the optimized four-phase metalens catches up with the continuous one and is robust to variations in complex media. Experiments have confirmed its function. These results have potential applications in acoustic focusing and imaging through complex media. [ABSTRACT FROM AUTHOR]

Due to the severity of resource consumption and uncertainty in orders, new challenges have arisen for production scheduling in enterprises. This paper studies the scheduling problem of minimising the total weighted tardiness for jobs with fuzzy processing times and due dates on identical parallel machines with resource constraint. To address this research problem, we first propose methods to calculate the upper bound of resource consumption and the maximum number of machines that can be used, effectively reducing the search space and improving the algorithm's efficiency. Secondly, a local search algorithm based on job swapping is proposed to enhance the algorithm's performance. Then, repair algorithms based on job removal and job swapping are designed to repair infeasible solutions. Finally, we propose a fuzzy genetic algorithm (FGALS) to solve the problem based on the above elements. Through extensive simulation experiments, the effectiveness and efficiency of the FGALS algorithm are verified by comparing it with commercial solver Gurobi and several meta-heuristic algorithms. [ABSTRACT FROM AUTHOR]

Acoustic metasurfaces are widely used for noise attenuation due to their outstanding acoustic performance and subwavelength characteristics. The paper introduces a topology-optimized inverse design approach for broadband sound-absorbing metasurfaces, aiming to achieve efficient sound absorption performance across a wide frequency spectrum. By integrating genetic algorithms with the finite element method and driven by objectives such as the absorption frequency range and absorption coefficient, we can transcend the limitations of manual design and fully exploit the design potential of structures. Furthermore, the study investigates the sound absorption mechanism and thermal-viscous dissipation through finite element simulations and experimental validations. The research findings reveal that the proposed broadband sound-absorbing metasurfaces demonstrate excellent sound absorption capabilities across the designated frequency range, boasting an average sound absorption coefficient exceeding 85%. These findings offer fresh perspectives and methodologies for advancing the research and practical application of broadband sound-absorbing metasurfaces. [ABSTRACT FROM AUTHOR]

The rise of industry 4.0 has led to the implementation of new technology-based manufacturing standards (TMSs). This study proposes a new contract to support a manufacturer, Haier, in diffusing its home-country TMSs into host countries with technology gaps. The manufacturer conducts the diffusion by operating two-sided networks (i.e. consumer and supplier networks) and dual sourcing (i.e. placing orders from two suppliers in different countries). A procurement contract is designed to coordinate the two-sided networks; it defines a market-pricing and part-procurement problem to maximise the scale of customers and local suppliers, respectively. These suppliers decide whether to accept the contract after considering their production conditions and technology gaps. A part-supply contract is executed if the procurement contract is accepted. Surplus parts are procured from the manufacturer's home country if the local supply is insufficient. The coupling of procurement and supply contract (PSC) design is inherent, while a hierarchical collaborative optimisation (HCO) model based on Stackelberg game theory is formulated. The HCO model deploys a multi-objective bi-level programming decision structure to examine the PSC coupling. A nested non-dominated sorting genetic algorithm II is designed to address this HCO model, while a case study of refrigerator TMS diffusion is analysed for verification. [ABSTRACT FROM AUTHOR]

A supply and demand mismatch, or imbalance of the amount of supplies in the market, is always an issue and can happen all the time. Capacity sharing is an effective way to address this problem, and the capacity sharing platform facilitates the optimal matching between multiple capacity buyers and sellers. In the context of Industry 4.0, many industries are adopting intelligent algorithms to assist in decision-making. This paper presents an optimal or near-optimal matching algorithm to cope with a large volume of capacity-sharing problems. The fairness of the matching solution is captured by including three objectives from platform, sellers and buyers. In this paper, a 2-dimensional crossover and an order-first mutation are developed and employed with genetic algorithms (GA), including GA and NSGA-II. Additionally, a novel repair mechanism is proposed by considering various constraints to transform infeasible solutions into feasible ones. Two matching schemes are studied based on whether orders from buyers can be split or not. The results show that both algorithms based on traditional GA and NSGA-II are effective for different schemes. In addition, it is found that GA has better performance in the case of 'more sellers' and NSGA-II shows better performance in the 'more buyers' case. [ABSTRACT FROM AUTHOR]

The unreacted equation of state (EOS) for an unreacted explosive can provide fundamental information to understand any analytical model for the shock and initiation process. Based on the Hugoniot expression in Jones–Wilkins–Lee (JWL) form derived from the Mie–Grüneisen EOS and conservation equation across the shock wave, a three-point calibrating method to determine the JWL EOS parameters for unreacted explosives was developed using intelligent algorithms and shock Hugoniot relationship of the explosives considered. The calibration method proposed utilizes the back propagation neural network to predict the nonlinear system composed of different JWL parameter sets; the genetic algorithm is then used to find the optimal solution of the JWL parameter set. Unreacted JWL EOS parameters of eight typical explosives were calibrated using the calibrating method developed, and an excellent agreement can be observed between JWL EOS and experimental p–v curves for all eight explosives selected, indicating the high accuracy of the three-point calibrating method. However, the effectiveness of the three-point calibrating method was experimentally validated with the experimental data measured from the shock tests of the dihydroxylammonium 5,5′-bitetrazole-1,1′-dioxide (TKX-50)-based explosive, where the JWL p–v curve derived from the three-point calibrating method is in good agreement with the experimental curve. [ABSTRACT FROM AUTHOR]

This work proposed a fast inverse design method for microwave and infrared (IR) bi-stealth metamaterials based on the equivalent circuit model (ECM). Using this method, we designed a microwave and IR bi-stealth metamaterial by deploying a multilayered structure of the indium tin oxide (ITO) film based metasurface. First, the IR emissivity of the ITO film was calculated in the framework of the ECM. Then, an ITO metasurface was proposed to implement low IR emission and high microwave transmission simultaneously. Based on the ECM of the square patch, the ECM of the whole metamaterial was established at the microwave band. An inverse design program was built by incorporating the ECM with genetic algorithm (GA). Structure parameters of the metamaterial were optimized by GA to achieve the broadest microwave stealth bandwidth for the given thickness. Finally, the sample of the optimized bi-stealth metamaterial was prepared and tested. The calculated, simulated, and measured results are in good agreement, showing that such a metamaterial has an IR emissivity of 0.18 in the band from 3 to 14 μm and an efficient microwave stealth band from 4.8 to 17 GHz with a thickness of 4.9 mm. The proposed method will benefit the design and application of microwave and IR bi-stealth metamaterials. [ABSTRACT FROM AUTHOR]

This paper presents a novel solution approach for a variant of the job shop scheduling problem with machine unavailability due to both condition-based preventive maintenance and corrective maintenance following random breakdowns. We first provide an exact mathematical formulation of the problem under simplifying assumptions, namely that the number of breakdowns for each job position on each machine is known, the degradation rates are fixed, and the preventive and corrective maintenance durations are deterministic parameters. Moreover, to handle the more realistic case of stochastic machine degradation, random breakdowns, and uncertain maintenance durations, a simulation-optimisation algorithm is proposed. The real makespan function is first approximated using multiple surrogate measures, which are optimised through independent genetic algorithms. Then, the fittest solutions obtained from these surrogate measures are simulated, and the best among them is added to an elite list, which is included in the genetic algorithms' populations for the next iteration. Schedule robustness is ensured by using an objective function that consists of the weighted average of the expected makespan and its 90th percentile. Furthermore, to reduce the likelihood of falling into a local optimum, a stopping criterion based on simulated annealing is implemented. Numerical experimentation on extended benchmark instances confirmed the validity of the mathematical formulation and the favourable performance of the proposed simulation-optimisation algorithm in terms of computational time and solution quality. [ABSTRACT FROM AUTHOR]

A non-polarizable force field for lithium (Li+) and bis(trifluoromethanesulfonyl)imide (TFSI−) ions solvated in diglyme at around 0.2 mol fraction salt concentration was developed based on ab initio molecular dynamics (AIMD) simulations and a modified polymer consistent force field model. A force–torque matching based scheme, in conjunction with a genetic algorithm, was used to determine the Lennard-Jones (LJ) parameters of the ion–ion and ion–solvent interactions. This force field includes a partial charge scaling factor and a scaling factor for the 1–4 interactions. The resulting force field successfully reproduces the radial distribution function of the AIMD simulations and shows better agreement compared to the unmodified force field. The new force field was then used to simulate salt solutions with glymes of increasing chain lengths and different salt concentrations. The comparison of the MD simulations, using the new force field, with experimental data at different salt concentrations and AIMD simulations on equimolar concentrations of the triglyme system demonstrates the transferability of the force field parameters to longer glymes and higher salt concentrations. Furthermore, the force field appears to reproduce the features of the experimental x-ray structure factors, suggesting accuracy beyond the first solvation shell, for equimolar salt solutions using both triglyme and tetraglyme as the solvent. Overall, the new force field was found to accurately reproduce the molecular descriptions of LiTFSI-glyme systems not only at various salt concentrations but also with glymes of different chain lengths. Thus, the new force field provides a useful and accurate tool to perform in silico studies of this family of systems at the atomistic level. [ABSTRACT FROM AUTHOR]

Given the high demand for energy in the manufacturing industry and the increasing use of renewable but volatile energy sources, it becomes increasingly important to coordinate production and energy availability. With the help of incentive-based programmes, grid operators can incentivise consumers to adjust power demand in critical situations such that grid stability is not threatened. On the consumer side, energy-efficient scheduling models can be used to make energy consumption more flexible. This paper proposes a bi-objective job-shop scheduling problem with variable machine speeds that aims on minimising the total energy consumption and total weighted tardiness simultaneously. We use a genetic algorithm to solve the model and derive Pareto frontiers to analyse the trade-off between both conflicting objectives. We gain insights into how incentive-based programmes can be integrated into machine scheduling models and analyse the potential interdependencies and benefits that result from this integration. [ABSTRACT FROM AUTHOR]

Distributed manufacturing is gradually becoming the future trend. The fierce market competition makes manufacturing companies focus on productivity and product delivery. The hybrid flow shop scheduling problem (HFSP) is common in manufacturing. Considering the difference of machines at the same stage, the multi-objective distributed hybrid flow shop scheduling problem with unrelated parallel machines (MODHFSP-UPM) is studied with minimum makespan and total tardiness. An improved multi-population genetic algorithm (IMPGA) is proposed for MODHFSP-UPM. The neighbourhood structure is essential for meta-heuristic-based solving algorithms. The greedy job insertion inter-factory neighbourhoods and corresponding move evaluation method are designed to ensure the efficiency of local search. To enhance the optimisation ability and stability of IMPGA, sub-regional coevolution among multiple populations and re-initialisation procedure based on probability sampling are designed, respectively. In computational experiments, 120 instances (including the same proportion of medium and large-scale problems) are randomly generated. The IMPGA performs best in all indicators (spread, generational distance, and inverted generational distance), significantly outperforming existing efficient algorithms for MODHFSP-UPM. Finally, the proposed method effectively solves a polyester film manufacturing case, reducing the makespan and total tardiness by 40% and 60%, respectively. [ABSTRACT FROM AUTHOR]

Drones are increasingly used for last-mile delivery due to their speed and cost-effectiveness. This study focuses on a novel locker-drone delivery system, where trucks transport parcels from the warehouse to lockers, and drones complete the final delivery. This system is ideal for community and intra-facility logistics. The research optimises the network design by determining the location of lockers, the number of drones at each locker, and the assignment of demands to lockers, minimising operating costs. Both single-parcel and multi-parcel capacity drones are examined. We build an optimisation model for each system, considering drone service capacity as a critical constraint. We design an algorithm combining average sample approximation and a genetic algorithm to address demand uncertainty. The algorithm's efficiency is validated through comparative analysis with Gurobi. Numerical experiments, using real and generated data, optimise the network design. Results show that the multi-capacity drone system requires fewer lockers and drones than the singlecapacity system. Although the single-capacity system yields lower drone delivery costs, it incurs higher truck delivery costs. Additionally, a comprehensive cost analysis compares the cost-efficiency of the locker-drone system with a conventional drone delivery system, revealing the cost-saving advantage of the locker-drone system. [ABSTRACT FROM AUTHOR]

Super-resolution fluorescence imaging has offered unprecedented insights and revolutionized our understanding of biology. In particular, localized plasmonic structured illumination microscopy (LPSIM) achieves video-rate super-resolution imaging with ∼50 nm spatial resolution by leveraging subdiffraction-limited nearfield patterns generated by plasmonic nanoantenna arrays. However, the conventional trial-and-error design process for LPSIM arrays is time-consuming and computationally intensive, limiting the exploration of optimal designs. Here, we propose a hybrid inverse design framework combining deep learning and genetic algorithms to refine LPSIM arrays. A population of designs is evaluated using a trained convolutional neural network, and a multiobjective optimization method optimizes them through iteration and evolution. Simulations demonstrate that the optimized LPSIM substrate surpasses traditional substrates, exhibiting higher reconstruction accuracy, robustness against noise, and increased tolerance for fewer measurements. This framework not only proves the efficacy of inverse design for tailoring LPSIM substrates but also opens avenues for exploring new plasmonic nanostructures in imaging applications.

Changes to components, known as engineering changes (ECs), rarely occur on their own. In fact, in complex assembly systems, most ECs are introduced in batches to ensure that changed components match. As a result, to implement ECs optimally, multiple component's stock must be considered until the change is executed on the EC effectivity date. This problem is known as the EC effectivity date optimisation problem, a variation of the general inventory control problem with deterministic and dynamic demand. As optimisation and monitoring of this problem is computationally expensive, research has suggested to investigate whether metaheuristics can provide adequate support. To fill this research gap, we present the results of a benchmark on basic metaheuristics for EC effectivity date optimisation. To do so, we have compared five common metaheuristics in their basic form (Ant Colony Optimisation, Genetic Algorithm, Particle Swarm Optimisation, Tabu Search, and Simulated Annealing) on a real-world test set. Of the tested algorithms the Genetic Algorithm identified most best solutions and returned good average results for the test cases. However, as its reliability was comparatively low, our research suggests a sequential application of the Genetic Algorithm and Tabu Search. [ABSTRACT FROM AUTHOR]

This paper provides a systematic review of the Genetic Algorithm (GA)s proposed to solve planning and scheduling problems in Engineer-To-Order (ETO) contexts. Our review focuses on how the key characteristics of ETO projects affect both the problem studied and the GA algorithmic features. Typical ETO projects consist of one-of-a-kind products with complex structures and uncertain designs. A deep analysis of the papers published between 2000 and 2022 enables identifying 10 main characteristics of ETO projects, six activity types, 10 decision types, eight groups of constraints, and 10 optimisation objectives. Our study shows that none of the reported papers integrates all 10 ETO characteristics. The less studied ETO characteristics are incorporating design and engineering information in the problem definition and the design uncertainty. Our review also identifies 10 recurrent encoding formats and emphasises the most frequently used genetic operators. We observed that most planning and scheduling problems consider objectives and decisions related to product customisation or supply chain configuration yielding multi-objective problems. Most multi-objective GAs use a weighted sum or are based on NSGAII. Diversity maintenance methods, adaptive and parameter tunning mechanisms, or hybridisation with machine learning models are still not used in this context. A systematic review of genetic algorithms dedicated to industrial planning and scheduling Analysis on how the characteristics of ETO projects impact the design of genetic representation and operators Recommendation on approaches employed to reach high-quality solutions [ABSTRACT FROM AUTHOR]

In the maintenance optimisation framework, grouping maintenance is a promising solution for maintenance planning of multi-component systems, in which maintenance activities are performed together to reduce maintenance costs. One of the most widely identified challenges in real applications of grouping maintenance is that it may disturb the maintenance workload balance (smoothness), causing many difficulties in production and/or labour scheduling and inventory management. In this study, we propose a joint optimisation approach for maintenance grouping and workload balancing to address the above challenge. First, a mathematical model of the joint optimisation problem was derived. A multi-objective grouping optimisation approach based on the Weighted Sum model and Genetic Algorithm was implemented to determine the Pareto-optimal grouping solution. The proposed approach was applied to a real case study of an automotive plant comprising 40 production lines with 1090 components. The results highlighted the advantages, effectiveness, and flexibility of the proposed maintenance approach in real-world applications. [ABSTRACT FROM AUTHOR]

Human–robot collaborative technology maximises the advantages of the capabilities of humans and robots, and provides diverse operating scenarios for the remanufacturing industry. Accordingly, this paper proposes an innovative human–robot collaborative disassembly line balancing problem (HRC-DLBP). First, a mixed-integer programming (MIP) model is devised for the HRC-DLBP to minimise the number of workstations, smoothness index, and various costs. Second, a hybrid local search genetic algorithm (HLSGA) is developed to solve the proposed HRC-DLBP efficiently. According to the problem characteristics, a four-layer encoding and decoding strategy was constructed. The search mechanism of the local search operator was improved, and its search strategy was adjusted to suit the genetic algorithm structure better. Furthermore, the accuracy of the proposed MIP model and HLSGA is verified through two HRC-DLBP examples. Subsequently, three HRC-DLBP examples are used to prove that the HLSGA is superior to five other excellent algorithms. The case of the two-sided disassembly line problem reported in the literature is also solved using the HLSGA. The results are found to be significantly better than the reported outputs of the improved whale optimisation algorithm. Besides, HLSGA also outperforms the results reported in the literature in solving EOL state-oriented DLBP. Finally, the HLSGA is applied to a power battery disassembly problem, and several optimal allocation schemes are obtained. [ABSTRACT FROM AUTHOR]

In this work, we detail a novel application of inverse design and advanced manufacturing to rapidly develop and experimentally validate modifications to a shaped charge jet analog. The shaped charge jet analog comprises a copper liner, a high explosive (HE), and a silicone buffer. We apply a genetic algorithm to determine an optimal buffer design that can be placed between the liner and the HE that results in the largest possible change in jet velocity. The use of a genetic algorithm allows for discoveries of unintuitive, complex, yet optimal buffer designs. Experiments using the optimal design verified the effectiveness of the buffer and validated the machine learning approach to hydrodynamic design optimization. [ABSTRACT FROM AUTHOR]

Expanding the pool of stable halide perovskites with attractive optoelectronic properties is crucial to addressing current limitations in their performance as photovoltaic (PV) absorbers. In this article, we demonstrate how a high-throughput density functional theory (DFT) dataset of halide perovskite alloys can be used to train accurate surrogate models for property prediction and subsequently perform inverse design using genetic algorithm (GA). Our dataset consists of decomposition energies, bandgaps, and photovoltaic efficiencies of nearly 800 pure and mixed composition ABX3 compounds from both the GGA-PBE and HSE06 functionals, and are combined with ∼100 experimental data points collected from the literature. Multi-fidelity random forest regression models are trained on the DFT + experimental dataset for each property using descriptors that one-hot encode composition, phase, and fidelity, and additionally include well-known elemental or molecular properties of species at the A, B, and X sites. Rigorously optimized models are deployed for experiment-level prediction over >150 000 hypothetical compounds, leading to thousands of promising materials with low decomposition energy, band gap between 1 and 2 eV, and efficiency of >15%. Surrogate models are further combined with GA using an objective function to maintain chemical feasibility, minimize decomposition energy, maximize PV efficiency, and keep bandgap between 1 and 2 eV; thus, hundreds more optimal compositions and phases are discovered. We present an analysis of the screened and inverse-designed materials, visualize ternary phase diagrams generated for many systems of interest using machine learning predictions, and suggest strategies for further improvement and expansion in the future. [ABSTRACT FROM AUTHOR]

In the context of assembly lines, the term 'rest time' refers to those time of inactivity allowed to a worker after the completion of his/her tasks on a product. Rest times are often assigned to workers that perform ergonomically hazardous tasks, to prevent their occupational diseases. When rest times are assigned after balancing the line, the line becomes unbalanced and the ergonomic load of workers is reduced at the price of deteriorated line performance. In this paper, an approach able to assign rest times during the balancing procedure is presented. The approach consists of a genetic algorithm coupled with a discrete event simulator to evaluate the fitness function of individuals, which considers the performance, the ergonomic, and the cost aspects. The OCRA index has been utilised to assess the ergonomic risk of workers. Results show that the presented approach can significantly support firms in designing manual assembly lines: the possibility to assign rest times during the balancing procedure allows for increasing the quality of solutions in 103 out of 135 benchmark instances. The greatest improvements are obtained when the ergonomic load of tasks is relevant, by achieving good performances of the line with limited costs, thus defending companies' profitability. [ABSTRACT FROM AUTHOR]

High flexibility is an important feature of seru system that has received less attention. In this paper, we discuss how to do such flexible seru system formation, especially focusing on the strategic decision phase. We formulate the flexible seru system formation problem (FSFP) as a nonlinear programming model to evaluate flexibility performance in terms of flexibility–investment cost and flexibility–loss cost. To exactly obtain the optimal solution of the FSFP, we transform the nonlinear model into a linear one and solve it with Gurobi solver. For the large-scale problem, we proposed a parallel Master–Slave adaptive genetic algorithm (PMSA-GA) by transforming it into a two-stage stochastic programming model. The adaptive selection is used to improve the quality of solutions in PMSA-GA. To reduce the computational time, multiple populations of seru formation evolve in parallel with the assistance of the Master–Slave mechanism. Extensive experiments are tested to evaluate the performance of the proposed model and algorithm, and the effect of cost parameters on the system performance is discussed. The results show that the FSFP model takes the property of dynamic demand into account and is more suitable for dynamic demand environments than the task-oriented seru formation (TOSF) strategy from the previous literature. [ABSTRACT FROM AUTHOR]

This work extends the energy-efficient job shop scheduling problem with transport resources by considering speed adjustable resources of two types, namely: the machines where the jobs are processed on and the vehicles that transport the jobs around the shop-floor. Therefore, the problem being considered involves determining, simultaneously, the processing speed of each production operation, the sequence of the production operations for each machine, the allocation of the transport tasks to vehicles, the travelling speed of each task for the empty and for the loaded legs, and the sequence of the transport tasks for each vehicle. Among the possible solutions, we are interested in those providing trade-offs between makespan and total energy consumption (Pareto solutions). To that end, we develop and solve a bi-objective mixed-integer linear programming model. In addition, due to problem complexity we also propose a multi-objective biased random key genetic algorithm that simultaneously evolves several populations. The computational experiments performed have show it to be effective and efficient, even in the presence of larger problem instances. Finally, we provide extensive time and energy trade-off analysis (Pareto front) to infer the advantages of considering speed adjustable machines and speed adjustable vehicles and provide general insights for the managers dealing with such a complex problem. [ABSTRACT FROM AUTHOR]

Simultaneous allocation of service times and buffer capacities in manufacturing systems in a random environment is a NP-hard combinatorial optimisation problem. This paper presents a sophisticated simulation-based optimisation approach for the design of unreliable production lines to maximise the production rate. The proposed method allows for a global search using a Genetic Algorithm (GA), which is coupled with Finite Perturbation Analysis (FPA) as a local search technique. Traditional techniques based on perturbation analysis optimise decision variables of the same nature (e.g. service time only, buffer capacity only), whereas the proposed technique simultaneously provides an allocation of service times and buffer capacities. One of the main focuses of this paper is the investigation of the persistence or absence of the buffer and service rate allocation patterns which are among the most essential insights that come from designing production lines. The results show the superiority of the combined GA-FPA approach regarding GA and FPA in terms of solution quality and convergence behaviour. Moreover, considering instances ranging from 3 to 100 machines, our numerical experiments are in line with the literature for small instances (as similar allocation patterns are identified in our work), but important differences are highlighted for medium/large instances. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of scheduling jobs on identical machines for minimising the maximum completion time (makespan). Each job requires a sequence-independent setup time, which represents the time needed to prepare the machines for job execution. Then, we introduce a dynamic programme to solve the case with two machines, and show that this problem admits a fully polynomial time approximation scheme. For the case of m machines, we propose heuristics and an adapted genetic algorithm. Some numerical experiments are done to evaluate the proposed algorithms. [ABSTRACT FROM AUTHOR]

Scheduling is an important decision-making problem in production planning and the resulting decisions have a direct impact on reducing waste, including energy and idle capacity. Batch scheduling problems occur in various industries from automotive to food and energy. This paper introduces the parallel p-batch scheduling problem with batch delivery, content-dependent loading/unloading times and energy-aware objective function. The problem has been motivated by a real system used for freezing products in a food processing company. A mixed-integer linear programming model (MILP) has been developed and explained through a numerical example. As it is not practical to solve large-size instances via a mathematical model, the discrete differential evolution algorithm has been improved (iDDE) and hybridised with the genetic algorithm (GA). A release-oriented vector generation procedure and a heuristic batch formation mechanism have been developed to efficiently solve the problem. The performance of the proposed approach (iDDEGA) has been compared with CPLEX, iDDE and GA through a comprehensive computational study. A case study was conducted based on real data collected from the freezing process of the company, which also verified the practical use and advantages of the proposed methodology. [ABSTRACT FROM AUTHOR]

Due to the incoherent superposition of various color light transmission matrices (TMs) in space, it has always been a challenge to measure the multi-color light TMs directly or indirectly in the field of wavefront shaping. In this paper, a hybrid algorithm for indirectly measuring the TMs of multi-color light is proposed. The prominent feature of this algorithm is that the spatial and frequency domain information of the speckle patterns is simultaneously input into the multi-input fully connected neural network. Compared with previous spatial data-driven methods, our method can better learn the mapping relationships hidden behind the color samples, making the measurement of the TMs of multi-color light through the scattering media more accurate. Experimental results show that the enhancement factor of multi-point multi-color light focusing using the hybrid algorithm is improved by about 27% compared with the genetic algorithm, and the number of iterations is reduced by about 47%. The hybrid algorithm presents a new idea for multi-color light focusing, which is of great significance for wavefront shaping in complex optical fields. [ABSTRACT FROM AUTHOR]

With the developments of the electronic industry, efforts to achieve high DC insulating reliability are underway to improve the mean time to failure (MTTF) of multilayer ceramic capacitors (MLCCs). However, there are few studies on appropriate measuring methods to evaluate their different reliability characteristics quantificationally. This study measured different reliability characteristics of BaTiO 3 -based multilayer ceramic capacitors sintered at three different temperatures with highly accelerated life tests (HALTs). Kaplan–Meier survival analysis, two-parameter Weibull fitting, and a new method using a stud genetic algorithm were used to calculate their MTTF in HALT conditions, and the comparison among them was carried out to select a proper fitting way. With the appropriate method, their MTTF at the rated condition was predicted and compared. Their conduction modes were also differentiated to explain their aging sensitivities to temperature and voltage. As a result, a proper way to evaluate the sample's reliability characteristics with a bathtub curve was established, and the best sintering temperature for capacitance that is also the best for reliability at the rated condition was confirmed. Meanwhile, different conducting models of MLCCs were also proved to be related to their insulation stability. [ABSTRACT FROM AUTHOR]

We study a problem from a real-world application, in which a daily set of orders must be processed following two stages, consisting of preparing perishable products on benches and allocating them to conveyors to be packed in disposable trays. Daily decisions must be made regarding the number and start time of working shifts, the number of workers and their allocation to machines, and the scheduling of orders in a two-stage flexible flow shop environment. The flow shop environment of the studied problem is common in many industries of perishable products, making the problem very general. The problem involves a number of operational constraints, and three objective functions that are minimised in a lexicographic way. To solve the problem, we implement a constructive heuristic and embed it within three metaheuristics: a Random multi-start algorithm (MR), a Biased random key genetic algorithm (BRKGA), and a Variable neighbourhood search (VNS) based one. We perform computational experiments over a set of realistic instances, and present a lower bound obtained from a constraint programming model for the scheduling counterpart. The results of the experiments show that the BRKGA is the most effective in practice for the integrated problem of workforce allocation and scheduling. [ABSTRACT FROM AUTHOR]

Multi-variety and small-batch production mode enables manufacturing industries to expeditiously satisfy customers' personalised demands, where a large amount of identical jobs can be split into several sublots, and be processed by reconfigurable machines with multiple machining technics. However, such highly flexible manufacturing environments bring some intractable problems to the production scheduling. Mathematical programming and meta-heuristic methods become less efficient when a scheduling problem contains both discrete and continuous optimisation attributes. Therefore, matheuristic, which combines advantages of the two methodologies, is regarded as a promising solution. This paper investigates a flexible job shop scheduling problem with lot-streaming and machine reconfigurations (FJSP-LSMR) for the total weighted tardiness minimisation. First, a monolithic mixed integer linear programming (MILP) model is established for the FJSP-LSMR. Afterwards, a matheuristic method with a variable neighbourhood search component (MH-VNS) is developed to address the problem. The MH-VNS adopts the classical genetic algorithm (GA) as the framework, and introduces two MILP-based lot-streaming optimisation strategies, LSO1 and LSO2, to improve lot-sizing plans with varying degrees. Four groups of instances are extended from the well-known Fdata benchmark to evaluate the performance of proposed MILP model, LSO1 and LSO2 components, and MH-VNS. Numerical experimental results suggest that LSO1 and LSO2 are efficient in different scenarios, and the proposed MH-VNS can well balance the solution quality and computational costs for reasonably integrating the GA- and MILP-based local search strategies. In addition, a complicated FJSP-LSMR case is abstracted from a real-world shop floor for processing large-sized structural parts to further validate the MH-VNS. [ABSTRACT FROM AUTHOR]

In order to regulate work in process (WIP) to the desired value in the job shop production control system, capacity adjustment as an effective and efficient measure, which is typically achieved by flexible staffs and working time. In this paper, instead of traditional labour-oriented approaches, we consider a machinery-based capacity adjustment via reconfigurable machine tools (RMTs) to compensate for unpredictable events. To this end, we employ model predictive control (MPC) in combination with genetic algorithm (GA) to explicitly consider complex reconfiguration strategies and address the related integer assignment optimisation problems. To further reduce energy consumption and avoid frequent and unnecessary reconfigurations while keeping a certain level of performance, we adopt an event-triggered MPC scheme with the proposed 'Double-layer event-triggering conditions'. Through extensively illustrated simulations, we demonstrate the effectiveness and plug-and-play availability of the proposed method for a six-workstation four-product job shop system and compare it to a state-of-the-art method. [ABSTRACT FROM AUTHOR]

Utilization of prediction and optimization techniques in machining operations assists with the decision-making required for machining parameter selection, directly impacting process outcomes including the surface roughness of the workpiece. Often these methods exclude the consideration of tool wear and critical information contained within sensor data collected during the cutting process. This study enhances the application of a hybrid physics-based and machine learning predictive framework for Ra that incorporates tool wear information via a focused investigation of computer numerical control (CNC) turning AISI 316, followed by a comparative analysis of metaheuristic optimizers to determine the optimal machining parameters. The experimental results include an analysis on the influence of the machining parameters, flank wear, and total cutting distance of the tool on the surface roughness. The proposed prediction model was improved for AISI 316 from a previous study conducted by the authors. The model achieved a root-mean-square error (RMSE) of 0.108 μm and testing results indicated that 87% of predictions fell within limits set by the ASME B46.1-2019 standard. Genetic algorithm (GA), particle swarm optimization (PSO), and simulated annealing (SA) are compared using the modified prediction model as the objective function. Despite GA producing the lowest minimum Ra for constrained and unconstrained testing cases, SA generated the highest accuracy during validation testing, achieving an error of 4.54% for a constrained scenario. The outcomes of this study strengthen the hybrid prediction framework proposed by the authors and reinforce the value the optimization process provides to machining operators in the manufacturing industry. [ABSTRACT FROM AUTHOR]

Considering the extensive applications of turning and facing operations in mechanical engineering manufacturing, the energy consumption of machining equipment has emerged as a significant concern in the manufacturing industries, such as aerospace and automotive. This article focuses on establishing a predictive model and optimizing energy consumption for facing and turning operations on two types of steel, namely, AISI 1038 and AISI 4142, using two different cutting tools. The experiments were planned using Taguchi's L16 orthogonal array, and the coefficients of the predictive model were determined through a linear regression approach. Additionally, a genetic algorithm (GA) with two distinct selection techniques was employed to optimize the three main variables: cutting depth, cutting speed, and feed rate, all aimed at reducing energy consumption. The results of this study show that the selection method used in GA significantly affects convergence toward the optimal solution, while the choice of cutting tool has a considerable impact on energy usage. Moreover, the variation in the effect of cutting parameters on machining energy consumption is analyzed to determine which parameters contribute most to energy savings. The results underscore the importance for manufacturers of using advanced predictive and optimization tools, offering them a competitive edge by achieving economic, environmental, and performance-related benefits. [ABSTRACT FROM AUTHOR]

In this paper, a multi-objective prediction and optimization integration method combining response surface method (RSM) and non-dominated sorting genetic algorithm-II (NSGA-II) was proposed to improve the overhead low-power pulsed laser-MAG (metal active gas) hybrid welding quality. A four-factor with a five-level experiment matrix considering the weld current(I), weld speed(V), laser power(P), and assembly clearance(C) was established based on te central composite design method. The relationship between weld parameters and back weld width (BW) and weld reinforcement (WR) was approximated by RSM. The significance and adequacies were validated by the analysis of variance and validation experiments, and the average error of validation experiments between predict and actual value is less than 5%, indicating that the model exhibits high predictive accuracy. The individual effect of a single parameter and the interaction of multiple parameters on BW and WR were studied, and the weld current and assembly clearance are the most significant parameters influencing BW and WR, respectively. NSGA-II was used for multi-objective optimization taking the constructed RSM models as objective functions and generating the Pareto-optimal front composed of optimal solutions. Finally, the verification experiments show that the optimal solutions of different fronts can obtain the desired welding morphology without any defect. [ABSTRACT FROM AUTHOR]

Summary: Relay selection plays a crucial role in enhancing the performance of wireless networks particularly in the context of cognitive radio (CR) systems with energy harvesters. In this paper, we propose a novel approach, namely, CGAPSO Shapley, for the best relay selection while simultaneously optimizing the parameters of signal‐to‐interference‐plus‐noise ratio (SINR), throughput, and outage probability. The CGAPSO Shapley algorithm combines the Shapley value, a cooperative game theory concept, with cellular genetic algorithm particle swarm optimization (CGAPSO) to achieve effective and efficient optimization of relay selection. The CGAPSO framework provides a hybrid structure that integrates cellular genetic algorithm (CGA) and particle swarm optimization (PSO), enabling simultaneous evolution of the population and particles within cells. The incorporation of the Shapley value and the hybrid CGAPSO framework enables effective exploration of the solution space and provides decision‐makers with comprehensive insights for relay selection. By utilizing the Shapley value, we assign weights to the relay nodes based on their contributions to the overall optimization objectives, considering their CR capabilities and energy harvesting capabilities. Some benchmark test functions are used to compare the hybrid algorithm with both the standard CGAPSO, Particle swarm optimization gravitational search algorithm (PSOGSA) and PSO algorithms in evolving best solution. The results show the hybrid algorithm possesses a better capability to escape from local optimums with faster convergence than the standard algorithms. The novel CGAPSO Shapley approach achieves an outage probability of 0.323324, marking a significant improvement of 60% over the outage probability achieved with conventional approach. [ABSTRACT FROM AUTHOR]

The accurate acquisition and prediction of the chlorophyll content in peanuts can provide reference for scientifically sound planting and management of peanut crops. In this study, peanut canopy leaves analysed during the flowering and pegging period were taken as the research object, and the spectral data collected by a portable hyperspectral spectrometer were used as the data source to study the inversion model of peanut leaf soil and plant analyser development (SPAD). To address the issues of randomness, the need for multiple iterations, and strong subjectivity in threshold selection in the initial variable set of the random frog (RF) algorithm, an improved RF algorithm was developed based on the sparse group lasso tool. By analysing and comparing the results of variable extraction of the improved RF algorithm and six characteristic wavelength screening algorithms, three algorithms were selected for pairwise coupling. These six algorithms were the correlation coefficient method (CC), successive projections algorithm, iteratively retains informative variables (IRIV), uninformative variable elimination (UVE), competitive adaptive reweighed sampling, and a genetic algorithm. The results show that: (1) the results of CC, UVE, and IRIV algorithms are better in a single algorithm experiment; and (2) in the coupled algorithms experiment, CC-UVE, CC-IRIV, and UVE-IRIV methods can effectively reduce dimensions, and the model has a certain level of stability. Among them, the UVE-IRIV-Extreme gradient boosting model had the highest accuracy, R2 = 0.652, RMSE = 1.867, and the variable compression rate was as high as 97.84%. The research results can provide a theoretical basis for rapid and accurate monitoring of the SPAD values of peanut leaves. [ABSTRACT FROM AUTHOR]

• Analyzed key factors in real-world satellite constellation tracking of time-sensitive moving targets. • Introduced PDA-GA, employing dual adaptive and parallel mechanisms to enhance genetic algorithm efficiency. • Demonstrated the effectiveness of our approach through rigorous experiments in simulated scenarios. The evolution of satellite surveillance technology, bolstered by advanced onboard intelligent systems and enhanced attitude maneuver capabilities, has thrust mission scheduling and execution into the spotlight as a prominent and dynamic research field in recent years. As the demand intensifies for mission scheduling and execution to transition from static ground targets to time-sensitive moving targets, conventional scheduling methods often fall short of delivering satisfactory results for continuously tracking these dynamic targets with constellation. The paper introduces a rapid yet efficacious satellite constellation task assignment method, termed the Parallel Dual Adaptive Genetic Algorithm (PDA-GA), for the task assignment of multiple moving target tracking. Specifically, the dual adaptive mechanism isolates the genetic algorithm's sensitivity to parameters, while the parallel mechanism increases the evolutionary process's computation speed by deploying complex computations to the GPU. Based on the meticulous analysis of the relevant factors that need to be considered in real tracking scenarios, the proposed PDA-GA can improve the search quality and efficiency of the task assignment solution. We conduct an extensive array of contrast and ablation experiments to showcase the performance and efficiency of PDA-GA in conjunction with autonomous attitude control algorithms across five simulated tracking scenarios. Furthermore, to enable high-fidelity simulation of tracking scenarios, we introduce the Constellation Target Tracking Environment (CTTE), which is equipped with a physics engine and algorithms for multi-satellite task assignment and single-satellite attitude control. This endeavor lays a foundation for future research endeavors focused on autonomous tracking of multiple time-sensitive moving targets within large-scale constellation. [ABSTRACT FROM AUTHOR]

A wide range of applications of unsaturated hydraulic conductivity is well known in geotechnical, hydrological, and agricultural engineering fields. The standard prediction models for hydraulic conductivity function overlook the complexity of soil pore structure and employ a simplistic approach based on the bundle of capillary tubes. This study proposes an alternative approach employing pore network models calibrated to match soil water retention data to predict the hysteretic hydraulic conductivity function of granular soils. A novel approach to constructing a multidirectional pore network built on an irregular lattice with variable coordination numbers is presented for the realistic representation of soil voids. The geometric and topological parameters of the pore network model are optimized using the genetic algorithm, and adequate pore‐scale processes (piston‐like advance and corner flow during drainage and piston‐like advance, pore body filling, and snap‐off during imbibition) are modeled to get reasonable predictions of hysteretic hydraulic conductivity functions over the entire suction range of granular soils. Comparisons between the pore network model results, standard physically based models, and measured data for a variety of granular soils show that the proposed pore network has a superior performance over other models and compares favorably to the experimental data. [ABSTRACT FROM AUTHOR]

The edge-cloud computing continuum effectively uses fog and cloud servers to meet the quality of service (QoS) requirements of tasks when edge devices cannot meet those requirements. This paper focuses on the workflow offloading problem in edge-cloud computing and formulates this problem as a nonlinear mathematical programming model. The objective function is to minimize the monetary cost of executing a workflow while satisfying constraints related to data dependency among tasks and QoS requirements, including security and deadlines. Additionally, it presents a genetic algorithm for the workflow offloading problem to find near-optimal solutions with the cost minimization objective. The performance of the proposed mathematical model and genetic algorithm is evaluated on several real-world workflows. Experimental results demonstrate that the proposed genetic algorithm can find admissible solutions comparable to the mathematical model and outperforms particle swarm optimization, bee life algorithm, and a hybrid heuristic-genetic algorithm in terms of workflow execution costs. [ABSTRACT FROM AUTHOR]

Machine learning techniques are potent tools to predict structural failures and forecast future failure patterns. They can provide insights that are not immediately recognizable to human operators, and hence, enhance the maintenance decision-making process, leading to better safety and longevity of structures. This research employed a genetic algorithm based on an identification technique to estimate the location and depth of cracks in circular arches. The arches were made of homogeneous material and had a rectangular cross-section. The study adopted two different procedures for detecting cracks in the circular arches. The first method used natural frequency contours represented in a three-dimensional plot to identify the location and depth of cracks by intersecting a set of natural frequency contours. The second procedure utilized the regression method and an optimization technique to minimize the cost function objective function and determine the crack parameters. The influences of crack location and depth on the vibrational behavior of cracked arches were presented and discussed. Similarly, the results indicate a high level of consistency between the two techniques in predicting both crack size and location. [ABSTRACT FROM AUTHOR]

Uniform magnetic field coils are widely used as electromagnetic equipment in industrial, medical, and research applications, with Helmholtz coils being a common configuration. For applications requiring a relatively high magnetic field (∼mT), Helmholtz coils typically feature a large coil cross-section. However, this characteristic makes them unsuitable for describing the magnetic field generated by a current loop model during the design process. In this work, we model the magnetic field of a large cross-section Helmholtz coil system, often referred to as a thick-walled Helmholtz coil. By employing a genetic algorithm, we transform the design problem of a Helmholtz coil into a constrained optimization problem. Subsequently, we propose a method for reverse designing a Helmholtz coil based on constraints on the target magnetic field. Finite element simulations verify the accuracy of the established magnetic field calculation model in describing the magnetic field generated by the thick-walled Helmholtz coil. Moreover, the designed Helmholtz coil effectively meets the design constraints and objectives. This method addresses the issue of significant errors in calculating the magnetic field and its uniformity resulting from the cross-sectional effect during the design of thick-walled Helmholtz coils. Furthermore, it satisfies the constraints for coil operating time and lightweight design. [ABSTRACT FROM AUTHOR]

ABSTRACT The time domain is the most widely chosen alternative in mechanical fatigue tests to completely represent random events. However, the main disadvantages of this approach are the long time required to complete the tests and the high cost of the complex equipment. On the other hand, fatigue test in the frequency domain is a potential alternative to overcome these disadvantages. That is, it requires less sophisticated equipment, because it uses only simple actuators instead of servo actuators. Moreover, the duration of the fatigue test is reduced, because the total length of the event is replaced by constant repetitions and constant damage. The objective of this work is to propose and develop a methodology that uses the results of structural fatigue as a starting point, and through an optimization problem solved by the genetic algorithm, it determines which loads/displacements caused them in the frequency domain. The purpose is to assist future developments in fatigue tests, supplanting tests in the time domain without losing the correlation in results. [ABSTRACT FROM AUTHOR]

Tensile properties of multifilament polyester woven fabrics are of great importance for their end uses such as parachutes, sails, tents, sleeping bags, filters and surgical textiles. The filament fineness, weave type and weave density have a great influence on the tensile properties of these fabrics. In this study, artificial intelligence (AI) models such as artificial neural networks (ANN), fuzzy logic (FL) and genetic algorithms (GA) were developed to forecast breaking strength and breaking elongation values of multifilament polyester woven fabrics. The fabric samples used in the study have three different microfilament finenesses and two different conventional filament finenesses with plain, twill and satin weave types. By applying four different weft density values, totally 60 woven fabric samples were obtained in the experimental design. The regression coefficient values ( R 2 ) between actual and predicted results were obtained as 0.80, 0.90 and 0.92 with ANN, FL and ANN–GA hybrid methods, respectively. The mean absolute percentage error (MAPE) was lower than 6% for all AI techniques used in this study. As a conclusion, it was proved that the breaking strength and breaking elongation properties of multifilament polyester woven fabrics can be forecasted with high accuracy rates by AI techniques. [ABSTRACT FROM AUTHOR]

Low-impact development (LID) practices are critical for mitigating urban stormwater runoff and alleviating flood risks. The strategic placement of LID facilities is paramount to optimizing their efficacy within urban landscapes. This study conducts a comprehensive bibliometric analysis of LID-related literature over the past decade, utilizing data visualization tools to elucidate key disciplines, publication trends, and the prevalence of various optimization algorithms. We delve into the application of multi-objective optimization (MOO) algorithms in LID facility layout, examining their practical applications, theoretical underpinnings, and case studies. The paper also scrutinizes the strengths and limitations of these algorithms, proposing future research trajectories that leverage MOO to enhance LID's role in urban stormwater management. [ABSTRACT FROM AUTHOR]