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This paper presents an adaptive memetic algorithm based on a new neighbourhood structure (AMA) for solving the assembly job shop scheduling problem, with the aim of minimising the maximum completion time (makespan). To utilise the knowledge of problem, a theoretical analysis is conducted to explore the criteria for feasible and effective movement of operations under assembly constraints, and a multi-operation precise joint movement neighbourhood structure is proposed accordingly. In the AMA, to ensure the feasibility of solutions during the evolution process, a feasible encoding mechanism based on the constraint degree of operations is designed, a greedy active decoding method as well as feasible crossover operation based on independent operation chains are designed specifically for this encoding method. To avoid premature convergence of the population, a population update operator with diversity adaptive control is proposed. Finally, by comparing the results with five state-of-the-art algorithms, the superiority of AMA in terms of solution quality and stability is verified, particularly with the update of known optimal solutions for 11 instances. [ABSTRACT FROM AUTHOR]

Time-of-use (TOU) tariff has been implemented in the manufacturing industry to improve energy efficiency by regulating the electricity imbalance between supply and demand. Besides, not-all-machine (NAM) option is another way of energy-saving by using only a subset of all the available machines. This study investigates a robust identical parallel machine scheduling problem with a two-stage TOU tariff and NAM option. Only interval bounds on job processing times are known. The problem is first formulated into a min–max regret model to maximise the robustness. Based on problem properties, both an iterative relaxation-based exact algorithm and a memetic differential evolution-based heuristic are developed to solve the problem. Computational experiments on 240 randomly generated instances with up to 20 jobs are conducted to evaluate the performance of the developed methods. Besides, 900 large-sized randomly generated instances with up to 150 jobs are tested for sensitivity analysis and to identify managerial insights for achieving energy-efficient schedules. [ABSTRACT FROM AUTHOR]

Social network characterizes complex relationship among individuals, which further constitute numerous various communities based on some attributes. Furthermore, multiple communities may be of a hierarchical structure, and parts of them are overlapped with each other. Additionally, as time goes by, some individuals or relations in social networks may change, so that it has been challenging to detect hierarchical and overlapping community structures in dynamical social networks. Most previous methods were based on a two-stage strategy, which firstly determines a hierarchical structure for the concerned social network, and then detects overlapping communities at each hierarchical level. However, the detection in the second stage is often restricted to the hierarchical structure determined in the first stage. As a result, this work proposes a one-stage memetic algorithm (MA) for jointly detecting hierarchical and overlapping community structures in dynamic social networks, in which a number of quality evaluation functions and constraints of community capacity as well as number of hierarchical levels are considered to enhance quality of detection. The proposed MA improves the genetic algorithm with local search and immigrant schemes. By simulation, in comparison to previous methods, the proposed MA is shown to have better quality. By visual analysis, the detected community structures look more correctly and can be explained more reasonably. Furthermore, dynamic community structures in different scenarios are analyzed in detail. [ABSTRACT FROM AUTHOR]

Hostelry establishments face the challenge of devising a table and chair allocation for accommodating their customers on a daily basis. This problem scales significantly with the introduction of constraints, such as scenario obstacles or the requirement of a minimum distance separation. The TLP (Table Location Problem) and the CLP (Chair Location Problem) are NP-Hard complexity problems that aim to attain the optimal table and chair distribution for certain applications. Existing approaches to this problem fail to address both the TLP and CLP simultaneously, thus resulting in suboptimal solutions achieved by imposing optimization constraints. Therefore, in this paper, a sequential optimization methodology based on a GBLS MA (Gradient-Based Local Search Memetic Algorithm) optimizations is proposed for optimizing the table and chair disposition simultaneously while also considering scenario and distancing restrictions. The proposed methodology is then implemented into a realistic establishment, where different optimization strategies within the CLP are compared. Results prove the viability and flexibility of the proposed sequential optimization for complex hostelry scenarios. [ABSTRACT FROM AUTHOR]

The current literature mainly uses hub capacity or transport route selection to manage the congestion of emergency multimodal transport and pays less attention to transshipment scheduling. This paper proposes an integrated optimization problem of transport routes and transshipment sequences (ITRTSP) and constructs a hybrid flow shop scheduling model to describe it. Based on this model, a recursive method is proposed to calculate the minimum waiting times that cargoes consume in queues at hubs, given the transport routes and transshipment sequences. Furthermore, a memetic algorithm is designed with route selection as the outer layer and transshipment sequence selection as the inner layer for solving ITRTSP. Compared with existing achievements, the model and algorithms can quantify the dependency between transshipment sequence selection and emergency transport time in multimodal transport network settings. The model and algorithms are applied to solve some real-scale examples and compared with the first-come-first-served (FCFS) rule commonly used in the current literature. The results indicate that the makespan is reduced by up to approximately 4.2%, saving 33.68 h. These findings demonstrate that even with given hub capacities and transport routes, congestion can still be managed and the schedule optimized through transshipment scheduling, further improving emergency transport efficiency. [ABSTRACT FROM AUTHOR]

This article investigates the problem of distributed shape formation (DSF) in swarm robot systems. DSF involves each robot autonomously selecting and moving toward a target point to achieve a desired shape. A comprehensive mathematical model for DSF of swarm robots is developed, capturing the relationships among states, behaviors, and kinematics constraints. A novel memetic algorithm (MA) is proposed to generate and evolve behavior strategies that enable real-time decision-making for each robot. The proposed MA includes specifically designed behaviors to address subproblems within DSF, such as target selection conflict, collision, and deadlock. These behaviors, along with propositions about the states of robots, are utilized to construct strategies by a tree-based encoding scheme. An evaluation mechanism based on multiagent simulation is devised to evaluate the performance of strategies. Additionally, a repair mechanism is introduced to eliminate redundant or unreachable subtrees in a strategy. To further improve the performance of generated strategies, tree-based local search operators are employed to exploit the neighborhood of the best strategy found yet during the iteration. Experimental results and the Wilcoxon rank-sum test show that the strategies generated by the proposed MA outperform state-of-the-art algorithms, significantly reducing the completion time of DSF. [ABSTRACT FROM AUTHOR]

The distributed flowshop has been a hot topic in research in recent years. This paper considers a distributed permutation flowshop scheduling problem with an assemble machine, so-called the distributed assembly permutation flowshop scheduling problem (DAPFSP), with total tardiness criterion. We propose an effective memetic algorithm (EMA). Firstly, a constructive heuristic combining the well-known earliest due date rule and largest processing time rule is presented for producing a seed sequence. On this basis, an effective initialisation method is used to generate an initial population with a high level of quality and diversity. The EMA uses a new structure of a small iteration nested within a large iteration. Moreover, an improved crossover and mutation expand the solution space in a good direction. According to different operation situations and objects, we design four targeted and flexible local search methods. We evaluate the strategies of the EMA, compare and analyse it with seven efficient algorithms based on the 810 benchmark instances. The experimental results confirm the effectiveness and efficiency of the proposed EMA. [ABSTRACT FROM AUTHOR]

The heightened autonomy and robust adaptability inherent in a multi-robot system have proven pivotal in disaster search and rescue, agricultural irrigation, and environmental monitoring. This study addresses the coordination of multiple robots for the surveillance of various key target positions within an area. This involves the allocation of target positions among robots and the concurrent planning of routes for each robot. To tackle these challenges, we formulate a unified optimization model addressing both target allocation and route planning. Subsequently, we introduce an adaptive memetic algorithm featuring dual-level local search strategies. This algorithm operates independently among and within robots to effectively solve the optimization problem associated with surveillance. The proposed method’s efficacy is substantiated through comparative numerical experiments and simulated experiments involving diverse scales of robot teams and different target positions.

With the growth of online networks, understanding the intricate structure of communities has become vital. Traditional community detection algorithms, while effective to an extent, often fall short in complex systems. This study introduced a meta-heuristic approach for community detection that leveraged a memetic algorithm, combining genetic algorithms (GA) with the stochastic hill climbing (SHC) algorithm as a local optimization method to enhance modularity scores, which was a measure of the strength of community structure within a network. We conducted comprehensive experiments on five social network datasets (Zachary's Karate Club, Dolphin Social Network, Books About U.S. Politics, American College Football, and the Jazz Club Dataset). Also, we executed an ablation study based on modularity and convergence speed to determine the efficiency of local search. Our method outperformed other GA-based community detection methods, delivering higher maximum and average modularity scores, indicative of a superior detection of community structures. The effectiveness of local search was notable in its ability to accelerate convergence toward the global optimum. Our results not only demonstrated the algorithm's robustness across different network complexities but also underscored the significance of local search in achieving consistent and reliable modularity scores in community detection.

The dynamic pickup and delivery problem (DPDP) is essential in supply chain management and logistics. In this study, we consider a real-world DPDP from daily delivery scenarios of a company. In the problem, orders are generated randomly and released periodically. The orders should be completed as soon as possible to minimize the cost. We propose a novel memetic algorithm (MA) to address this problem. The proposed MA consists of a genetic algorithm and a local search strategy that periodically solves a static pickup and delivery problem when new orders are released. We have conducted extensive experiments on 64 real-world instances to assess the performance of our method. Three state-of-the-art algorithms are chosen as the baseline algorithms. Experimental results demonstrate the effectiveness of the MA in solving the real-world DPDP. [ABSTRACT FROM AUTHOR]

Abstract We present a new method for approximating two-body interatomic potentials from existing ab initio data based on representing the unknown function as an analytic continued fraction. In this study, our method was first inspired by a representation of the unknown potential as a Dirichlet polynomial, i.e., the partial sum of some terms of a Dirichlet series. Our method allows for a close and computationally efficient approximation of the ab initio data for the noble gases Xenon (Xe), Krypton (Kr), Argon (Ar), and Neon (Ne), which are proportional to $$r^{-6}$$ r - 6 and to a very simple $$depth=1$$ d e p t h = 1 truncated continued fraction with integer coefficients and depending on $$n^{-r}$$ n - r only, where n is a natural number (with $$n=13$$ n = 13 for Xe, $$n=16$$ n = 16 for Kr, $$n=17$$ n = 17 for Ar, and $$n=27$$ n = 27 for Neon). For Helium (He), the data is well approximated with a function having only one variable $$n^{-r}$$ n - r with $$n=31$$ n = 31 and a truncated continued fraction with $$depth=2$$ d e p t h = 2 (i.e., the third convergent of the expansion). Also, for He, we have found an interesting $$depth=0$$ d e p t h = 0 result, a Dirichlet polynomial of the form $$k_1 \, 6^{-r} + k_2 \, 48^{-r} + k_3 \, 72^{-r}$$ k 1 6 - r + k 2 48 - r + k 3 72 - r (with $$k_1, k_2, k_3$$ k 1 , k 2 , k 3 all integers), which provides a surprisingly good fit, not only in the attractive but also in the repulsive region. We also discuss lessons learned while facing the surprisingly challenging non-linear optimisation tasks in fitting these approximations and opportunities for parallelisation.

This paper considers the impact of setup time in production scheduling and proposes energy-aware distributed hybrid flow shop scheduling problem with sequence-dependent setup time (EADHFSP-ST) that simultaneously optimizes the makespan and the energy consumption. We develop a mixed integer linear programming model to describe this problem and present a two-stage adaptive memetic algorithm (TAMA) with a surprisingly popular mechanism. First, a hybrid initialization strategy is designed based on the two optimization objectives to ensure the convergence and diversity of solutions. Second, multiple population co-evolutionary approaches are proposed for global search to escape from traditional cross-randomization and to balance exploration and exploitation. Third, considering that the memetic algorithm (MA) framework is less efficient due to the randomness in the selection of local search operators, TAMA is proposed to balance the local and global searches. The first stage accumulates more experience for updating the surprisingly popular algorithm (SPA) model to guide the second stage operator selection and ensures population convergence. The second stage gets rid of local optimization and designs an elite archive to ensure population diversity. Fourth, five problem-specific operators are designed, and non-critical path deceleration and right-shift strategies are designed for energy efficiency. Finally, to evaluate the performance of the proposed algorithm, multiple experiments are performed on a benchmark with 45 instances. The experimental results show that the proposed TAMA can solve the problem effectively.

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the context of the economic globalization, there is an increased disruption risk in the supply chain network due to the outsourcing, complexity and uncertainty. At the same time, the disruption may propagate across the entire supply chain network because of the interdependence. With the resource constraints, appropriate recovery strategies which can minimize the impact of disruption propagation and effectively improve the supply chain network resilience have attracted a great deal of attention. In this paper, we first construct the disruption propagation model considering the recovery strategy based on the characteristics of the competitiveness, time delay and underload cascading failure in the supply chain network. This model uses the memetic algorithm to determine the set of recovery nodes among all disruption nodes, which can minimize the impact of disruption propagation. And then, the simulation analysis is conducted on the synthetic network and the real-world supply chain network. We compare the proposed recovery strategy with other strategies (according to the genetic algorithm, according to the descending order of the load of failure node, according to the ascending order of the load of failure node, according to the descending order of the node degree, according to the ascending order of the node degree) and provide decision-making reference against supply chain disruptions. [ABSTRACT FROM AUTHOR]

The advancement in wireless communication is fueling the growth of innovative antenna array designs toward cost-effective and performance-oriented solutions. This paper proposed unconventional methods to design antenna arrays for multi-pattern synthesis without using attenuators or phase shifters. A low-cost alternative is proposed with "Time-modulation"-based antenna array capable of electronic scanning and beam steering. Here, "Time" is utilized as a fourth-dimensional (4D) array parameter, and that is why "Time-modulated" arrays are also called as 4D antenna arrays. The idea is to control the high-speed switch attached with each antenna periodically to produce desirable current and phase tapering. This article expanded the "Time-modulation" concept to synthesize multiple radiation patterns like monopulse patterns, scanned beam patterns, shaped beam patterns, and cosecant-squared beam patterns for multifunctional radar systems. Suitable time schemes are developed to generate the narrowband sum–difference patterns useful for monopulse radars. Simultaneous scanned beam patterns are also proposed for narrowband communication. Furthermore, to address the wideband applications, shaped flat-top beam patterns and cosecant-squared beam patterns are also proposed. In this regard, 20- and 16-element "Time-modulated" linear array antennas are developed, and the parameters of the arrays are controlled by suitably designed objective functions with quasi-Newton method (QNM)-based memetic optimization method. For this purpose, first a well-known genetic algorithm is adopted to search the potential trust regions in the exploration stage and QNM is used for fine-tuning. Furthermore, the Broyden's good method-based direction-updating equation is used with QNM to improve the performance. [ABSTRACT FROM AUTHOR]

This paper introduces an integer linear program for a variant of the linear ordering problem. This considers, besides the pairwise preferences in the objective function as the linear ordering problem, positional preferences (weighted rank) in the objective. The objective function is mathematically supported, as the full integer linear program is motivated by the instant run-off voting method to aggregate individual preferences. The paper describes two meta-heuristics, iterated local search and Memetic algorithms to deal with large instances which are hard to solve to optimality. These results are compared with the objective value of the linear relaxation. The instances used are the ones available from the LOP library, and new real instances with preferences given by juries. [ABSTRACT FROM AUTHOR]

Portfolio optimization will apply the concept of diversification across asset classes, which means investing in a wide variety of asset types and classes for a risk-mitigation strategy. Portfolio optimization is a way to maximize net gains in a portfolio while minimizing risk. A portfolio means investing in a wide variety of asset types and classes for a risk-mitigation strategy by the investor. In this paper, factor analysis and cluster algorithm are used to screen stocks and an improved differential evolution algorithm for solving portfolio optimization model is proposed. By comprehensively analyzing the stock data with factor analysis and k-means clustering algorithm, it has found that important factors have important effect on stock price movement, and finally 10 stocks are selected with investment value. Besides, a Mean-Conditional Value at Risk (CVaR) model is constructed, which takes into account both the cost function and the diversification constraint. Finally, a second-order memetic differential evolution (SOMDE) algorithm is presented for solving the proposed model. The experiments show that the proposed SOMDE algorithm is valid for solving the Mean-CVaR model and that factor analysis for stock selection can benefit portfolio with higher return and less risk greatly. [ABSTRACT FROM AUTHOR]

In the current global cooperative production environment, modern industries are confronted with intricate production plans, demanding the adoption of contemporary production scheduling strategies. Within this context, distributed manufacturing has emerged as a prominent trend. Manufacturing enterprises, especially those engaged in activities like automotive mold production and welding, are facing a significant challenge in managing a significant amount of small-scale tasks characterized by short processing times. In this situation, it becomes imperative to consider the transportation time of jobs between machines. This paper simultaneously considers the transportation time of jobs between machines and the start-stop operation of the machines, which is the first time to our knowledge. An improved memetic algorithm (IMA) is proposed to solve the multi-objective distributed flexible job shop scheduling problem (MODFJSP) with the goal of minimizing maximum completion time and energy consumption. Then, a new multi-start simulated annealing algorithm is proposed and integrated into the IMA to improve the exploration ability and diversity of the algorithm. Furthermore, a new multiple-initialization rule is designed to enhance the quality of the initial population. Additionally, four improved variable neighborhood search strategies and two energy-saving strategies are designed to enhance the search ability and reduce energy consumption. To verify the effectiveness of the IMA, we conducted extensive testing and comprehensive evaluation on 20 instances. The results indicate that, when faced with the MODFJSP, the IMA can achieve better solutions in almost all instances, which is of great significance for the improvement of production scheduling in intelligent manufacturing.

Hostelry establishments face the challenge of devising a table and chair allocation for accommodating their customers on a daily basis. This problem scales significantly with the introduction of constraints, such as scenario obstacles or the requirement of a minimum distance separation. The TLP (Table Location Problem) and the CLP (Chair Location Problem) are NP-Hard complexity problems that aim to attain the optimal table and chair distribution for certain applications. Existing approaches to this problem fail to address both the TLP and CLP simultaneously, thus resulting in suboptimal solutions achieved by imposing optimization constraints. Therefore, in this paper, a sequential optimization methodology based on a GBLS MA (Gradient-Based Local Search Memetic Algorithm) optimizations is proposed for optimizing the table and chair disposition simultaneously while also considering scenario and distancing restrictions. The proposed methodology is then implemented into a realistic establishment, where different optimization strategies within the CLP are compared. Results prove the viability and flexibility of the proposed sequential optimization for complex hostelry scenarios.

The current literature mainly uses hub capacity or transport route selection to manage the congestion of emergency multimodal transport and pays less attention to transshipment scheduling. This paper proposes an integrated optimization problem of transport routes and transshipment sequences (ITRTSP) and constructs a hybrid flow shop scheduling model to describe it. Based on this model, a recursive method is proposed to calculate the minimum waiting times that cargoes consume in queues at hubs, given the transport routes and transshipment sequences. Furthermore, a memetic algorithm is designed with route selection as the outer layer and transshipment sequence selection as the inner layer for solving ITRTSP. Compared with existing achievements, the model and algorithms can quantify the dependency between transshipment sequence selection and emergency transport time in multimodal transport network settings. The model and algorithms are applied to solve some real-scale examples and compared with the first-come-first-served (FCFS) rule commonly used in the current literature. The results indicate that the makespan is reduced by up to approximately 4.2%, saving 33.68 h. These findings demonstrate that even with given hub capacities and transport routes, congestion can still be managed and the schedule optimized through transshipment scheduling, further improving emergency transport efficiency.

Estimating the shear strength of large in situ rock discontinuities is often required to assess the stability of rock masses. This estimation is, however, complicated by the well-known scale effect and the fact that the discontinuity surfaces are only partially accessible through traces. A new approach, referred to as the stochastic approach for discontinuity shear strength (StADSS), was recently presented to address these two points. This approach relies on a random field model and a semi-analytical shear strength model, the latter of which is referred to as the NDSS (Newcastle discontinuity shear strength) model. The NDSS model has to be implemented as a numerical code, and because the StADSS model is a Monte Carlo approach with hundreds if not thousands of simulations, the computational time to obtain a shear strength distribution is not negligible. The objective of this study is to find an efficient alternative to the NDSS model in the form of a continued fraction model that can predict the sheared area within a rough discontinuity subjected to direct shearing under constant normal stress as a function of the material strength, effective normal stress applied to the discontinuity and the standard deviation of asperity gradients (defined as the difference in elevation of two points of the surface over the horizontal distance between these points) of the surface. Using a 10/90 training/testing split of the dataset, a memetic algorithm-based truncated continued fraction regression (CFR) model was formulated. The distribution of CFR predictions was found to be very close to that of the dataset used for training. Then, the CFR model was tested against experimental data of the sheared area and shear strength (peak and residual) obtained from small (90 mm per 90 mm) and large (2 m per 2 m) specimens. It was found that 75% of the predictions fall within 20% of the experimental values. The continued fraction regression model can be used as an efficient alternative to the semi-analytical NDSS model, provided that it is used within the bounds of variables used to establish it. Highlights: A mathematical model was established to predict shear strength of rock joints The model is based on continued fraction regression (CFR) and memetic algorithm The CFR model was validated against data of peak and residual shear strength 75% of the CFR predictions fall within 20% of the experimental data The CFR model can be implemented as a spreadsheet with a low computational cost [ABSTRACT FROM AUTHOR]

The flexible movement of racks in the mobile-rack picking system (MRPS) significantly improves the picking efficiency of e-commerce orders with the characteristics of "one order multi–items" and creates a challenging problem of how to place racks in the warehouse. This is because the placement of each rack in the MRPS directly influences the distance that racks need to be moved during order picking, which in turn affects the order picking efficiency. To handle the rack location optimization problem (RLOP), this work introduces a novel idea and methodology, taking into account the heat degree and the relevance degree of racks, to enhance the efficiency of rack placements in the MRPS. Specifically, a two-stage solution strategy is implemented. In stage 1, an integer programming model (Model 1) is developed to determine the heat and relevance degree of racks, and it can be solved quickly by the Gurobi. Stage 2 entails developing a bi-objective integer programming model (Model 2) with the objective to minimize the travel distances of robots in both heavy load and no-load conditions, using the rack heat and relevance degree as inputs. In light of the challenge of decision coupling and the vast solution space in stage 2, we innovatively propose two lower bounds by slacking off the distance between storage locations. A matheuristic algorithm based on Benders decomposition (MABBD) is designed, which utilizes Benders-related rules to reconstruct Model 2, introduces an enhanced cut and an improved optimal cut with RLOP characteristics, and designs the warm start strategy and the master variable fixed strategy. Given the substantial size of real-life problems, the Memetic algorithm (MA) is specifically devised to address them. Instances of varying sizes are also employed to validate the science and efficacy of the model and algorithm. [ABSTRACT FROM AUTHOR]

Networks in the real world are dynamic and evolving. The most critical process in networks is to determine the structure of the community, based on which we can detect hidden communities in a complex network. The design of strong network structures is of great importance, meaning that a system must maintain its function in the face of attacks and failures and have a strong community structure. In this paper, we proposed the robust memetic algorithm and used the idea to optimize the detection of dynamic communities in complex networks called RDMA_NET (Robust Dynamic Memetic Algorithm). In this method, we work on dynamic data that affects the two main parts of the initial population value and the calculation of the evaluation function of each population, and there is no need to determine the number of communities in advance. We used two sets of real-world networks and the LFR dataset. The results show that our proposed method, RDMA_Net, can find a better solution than modern approaches and provide near-optimal performance in search of network topologies with a strong community structure. [ABSTRACT FROM AUTHOR]

Quantifying gas solubility in oil under reservoir conditions is essential in reservoir engineering. Several laboratory techniques have been suggested to quantify it; nevertheless, these procedures are characterized by their demanding time requirements and high costs. Estimating the solution gas-oil ratio relies significantly on applying mathematical and artificial intelligence models. The research commenced by collecting 757 datasets from published articles. Subsequently, the data was partitioned into two distinct categories: training and test data. Following this data preparation phase, artificial intelligence models, including Histogram Gradient Boosting Regression (HGBoost), Adaptive Boosting (AdaBoost), Support Vector Regression (SVR), and the Adaptive Neuro-Fuzzy Inference System (ANFIS), were employed to model the solution gas-oil ratio. Additionally, the Memetic algorithm (MA) was employed to recompute the coefficients of the Petrosky and Farshad model, initially developed with 81 data points, using an expanded dataset of 605 training instances. Subsequently, model accuracy and performance were assessed through statistical and visual analyses. The results indicate that the HGBoost model performs best, achieving an overall R2 of 0.9886 and an overall RMSE of 41.29 scf/STB. Notably, among the mathematical models, the modified Petrosky and Farshad model stands out with an overall R2 of 0.9078 and an overall RMSE of 114.49 scf/ STB. The Taylor diagram and violin plot both indicate that the HGBoost model and the modified Petrosky and Farshad model outperform other machine learning models and mathematical models, respectively. These results demonstrate the accuracy of the models described in this study. [ABSTRACT FROM AUTHOR]

Among the growth and development of businesses, managing financial flows and increasing its effectiveness is an important achievement that causes value stability, efficiency of systems and procedures throughout the chains of Business. Therefore, paying attention to uncontrollable and controllable variables that lead to value creation is inevitable. The purpose of this study is to study the impact of social and biological factors (production and recycling of polymer parts) of environment on the value creation of the company along the supply chain. Also the key role of financial ratios has also been considered in this regard. The present research is of quantitative and applied types and is based on mathematical modeling. It is the result of a combination of genetic algorithms and Simulated Annealing. The financial analysis parameters of the model include current ratios, debt to equity, Instantaneous ratio, net profit margin, cash ratio and rate of return. The analysis of the results shows that considering financial goals and indicators leads to improved profitability and by removing financial indicators from the model, profitability is reduced. Therefore, this is means that the environmental and social performance of the supply chain is improved. Companies can pay special attention to social issues and environmental factors along with their profitability Increase their economic value. Profitability can also be improved by exposing social responsibilities and the mission of environmental protection. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Internet of Things (IoT), which has had a revolutionary influence on human existence, has become a topic of significant attention among the scientific and industrial communities. Smart healthcare, smart cities, smart devices, smart industry, smart grid, and smart cities are just a handful of the many IoT ideas that have altered human life due to the rapid progress of this IoT technology. Security issues involving IoT devices have come up as a significant issue in recent years with special emphasis on the healthcare sector. This increased emphasis is mostly due to the exposure of serious vulnerabilities in IoT security with recent hacking activities. There is significant proof that conventional methods of protecting networks are effective. Still, the use of conventional security protocols for protection of IoT gadgets and networks from hacking is not feasible due to the constrained resources associated with IoT devices and the distinct characteristics observed in IoT protocols. To improve the privacy of the IoT, researchers will need a unique collection of resources, techniques, and datasets in IoT field. To address the earlier described issues, CatBoost is an innovative ensemble approach that combines many tree techniques and optimizes for performance. This model aims to accurately and automatically detect instances of assaults and anomalies in IoT sensors within the healthcare domain. For the successful creation of a security-based model, the hyperparameters are tuned with self-adaptive memetic firefly algorithm (SAMFA) optimization. The primary advantages of this study include (i) The development of an improved ensemble learning CatBoost model-based security system for IoT healthcare network intrusion detection, (ii) the SAMFA optimization method has been implemented for determining the ideal set of hyperparameters for the CatBoost algorithm, and (iii) Assessing the model's performance with a novel dataset of real-life observations (IoT Healthcare Security Dataset). The suggested model outperforms several previous state-of-the-art techniques, with experimental findings indicating outstanding anomaly identification accuracy of 99.99%. [ABSTRACT FROM AUTHOR]

Despite their many advantages over other communication links, radio over fiber (RoF) links also suffer from nonlinearity. This limits the dynamic range performance of the RoF link. Linearization techniques like digital predistortion can be explored for mitigating this nonlinearity. A hybrid optimization algorithm called hybrid Memetic algorithm, which uses the benefits of both Tabu search algorithm and Memetic algorithm, is used for parameter estimation of the digital predistorter. Simulation results demonstrate that the hybrid Memetic algorithm offers very promising outcomes. [ABSTRACT FROM AUTHOR]

Cold-chain logistics presents significant challenges to sustainable development in terms of economics, society, and the environment. To address this issue, this paper focuses on solving a real-world problem—the multipath location-routing problem-based cold-chain logistics (MPLRPCCL), which considers soft time windows and heterogeneous fleets. The mathematical model aims to optimize the total logistics costs, including fuel consumption, carbon emissions, cargo deterioration, penalties, operating depots, and renting fleets. To effectively solve this problem, the paper proposes three versions of a memetic algorithm (MA) that incorporate control modules for monitoring the MA's performance and improving its global optimization performance. Computational evaluations were conducted, and the results show that the proposed algorithms are effective and outperform the classic MA framework. Furthermore, the proposed model and algorithms were applied to real-world cases in Hangzhou city, China, demonstrating their practical applicability. Overall, this paper offers a promising approach to tackling the complex challenges posed by cold-chain logistics and contributes to the sustainable development of this important field. [ABSTRACT FROM AUTHOR]

A low-carbon economic scheduling method based on a Q-learning-based multiobjective memetic algorithm (Q-MOMA) is proposed to improve the economy of cogeneration system scheduling and reduce carbon emission. First, the model incorporates a carbon capture device, a heat storage device, and a demand response mechanism to enhance the system's flexibility and wind power consumption. In addition, the Q-MOMA algorithm combines global and local search and uses a Q-learning algorithm to dynamically adjust the crossover and mutation probabilities to improve the algorithm's searchability. Finally, the fuzzy membership function method is used to make a multiobjective decision, which balances the economy and low carbon of the system, and a compromise scheduling scheme is given. The effectiveness of the proposed model and solution method is verified through the simulation calculation of the improved system and compared with the simulation results of various optimization algorithms. The simulation results show that the proposed model can improve the wind power consumption space and the system's economy and reduce carbon emissions. The Q-MOMA algorithm has a relatively better optimization ability in the low-carbon economic scheduling of the cogeneration system. [ABSTRACT FROM AUTHOR]

In case of using sales transaction and products availability data, discovering a preference list of customers (named customer types) is a challenging topic due to the lack of data. This process is inferred as demand estimation because by knowing the preference of each customer and available products in a time period in the future, we can estimate the demand of each product in case of arriving pre-known customer types. In this paper, we proposed an approach to find the frequency of each data point (solution) in a search space, helping us to apply a better local search in an evolutionary algorithm. Here, we proposed a memetic algorithm to discover customer types with the help of found frequency data. Afterward, we used five real hotel datasets to implement our memetic algorithm and finally compared this implementation with the two pre-proposed approaches, one evolutionary approach (genetic), and one mathematical approach (rank based). Evaluation proceeds in two phases in both of which, memetic shows a better performance than the other two approaches owing to using local search done with help of frequency data. [ABSTRACT FROM AUTHOR]

In this work, a methodology for the optimal design and operation of reactive extrusion processes in twin‐screw extruders is presented. As a process model, a very accurate mechanistic finite volume twin‐screw extruder model is used which is adapted to the reactive extrusion case. In the model, in contrast to other models, the pressure is described as a differential state in order to have a consistent set of equations. The model is compared to other models and advantages and disadvantages with respect to accuracy and computation time are discussed. For optimization, a memetic optimization algorithm (MA) that consists of a combination of a local optimization for the continuous operating parameters and an evolutionary algorithm (EA) for the global optimization of the discrete decision variables is employed to overcome the drawback of the longer model simulation time compared to simpler models. A reduction of optimization time by 90% for the MA compared to the EA was observed. The presented modeling and optimization methodology is validated on the widely investigated reactive extrusion process for ε‐Caprolactone. The methodology is very flexible and the underlying model has good prediction characteristics, thus it is applicable to a wide range of reactive extrusion processes of industrial interest. Highlights: Simulation and optimization of reactive extrusion.Improved mechanistic twin‐screw extruder model.Novel approaches for pressure and residence time modeling.Efficient combination of local and global optimization in a memetic algorithm.Benchmarking of the optimization results and performance with literature data. [ABSTRACT FROM AUTHOR]

Service composition is a core issue of cloud manufacturing (CMfg) to integrate distributed manufacturing services for customised manufacturing tasks. Existing studies focus on the quality of service (QoS) in composition by assuming that each service is independent with each other. However, the correlation between services determines whether a composition is feasible in practice and is a primary factor of its QoS. This paper considers two typical correlations, composability-oriented correlation and quality-oriented correlation. The composability-oriented correlation is modelled as a group of constraints to decide whether a solution is feasible. The influence of the quality-oriented correlation between two services on the overall QoS of a composition is quantified by a discount percentage based on their correlation degrees. A mathematical model of correlation-aware service composition is then established. To solve this problem, a many-objective memetic algorithm termed HypE-C (Hypervolume Estimation Algorithm for Multiobjective Optimisation involving Correlation) is designed. Three correlation-based local search strategies are established in the frame of HypE (Hypervolume Estimation Algorithm for Multiobjective Optimisation) to achieve better trade-off among multiple conflicting QoS criteria. Experiments demonstrate the effectiveness of the proposed algorithm HypE-C compared with five many-objective algorithms on eliminating infeasible search space and providing high QoS service composition solutions. [ABSTRACT FROM AUTHOR]

The Multi-Skill Resource-Constrained Project Scheduling Problem (MS-RCPSP) is an NP-Hard problem that involves scheduling activities while accounting for resource and technical constraints. This paper aims to present a novel hybrid algorithm called MEMINV, which combines the Memetic algorithm with the Inverse method to tackle the MS-RCPSP problem. The proposed algorithm utilizes the inverse method to identify local extremes and then relocates the population to explore new solution spaces for further evolution. The MEMINV algorithm is evaluated on the iMOPSE benchmark dataset, and the results demonstrate that it outperforms. The solution of the MS-RCPSP problem using the MEMINV algorithm is a schedule that can be used for intelligent production planning in various industrial production fields instead of manual planning.