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Service-oriented manufacturing (SOM) is a new industrial model that combines manufacturing with service. To enhance the competitiveness of manufacturers in SOM systems, we investigate a coordinated production and delivery scheduling problem considering a third-party logistics (3PL) provider. The considered problem can be divided into two phases, including production phase and delivery phase. In the production phase, jobs are processed by multiple manufacturers located in different geographical zones. Jobs are with deteriorating processing time and set-up time. Several structural properties of the problem in the production phase are proposed, based on which a heuristic algorithm is designed to make decisions on job batching and batch sequencing. After being processed, the finished jobs are immediately delivered to a customer by manufacturers' own vehicles or the 3PL provider. A dynamic programming algorithm is developed to solve the problem in the delivery phase. The integrated scheduling problem is proved to be NP-hard. Hence, we develop a general variable neighbourhood search (GVNS) to solve the problem in a reasonable time. The basic variable neighbourhood descent (BVND) is used as a local search method in the GVNS. Finally, the computational experiments are conducted and the results verify the effectiveness and stability of the proposed approach. [ABSTRACT FROM AUTHOR]

Ship maintenance service optimisation is of great significance for improving the competitiveness of shipbuilding enterprises. In this paper, we investigate a ship maintenance service scheduling problem considering the deteriorating maintenance time, distributed maintenance tasks, and limited maintenance teams. The objective is to minimise the service span. First, we construct an initial mixed-integer programming model for the studied problem. Then, through the property analysis of the problem with a single maintenance team, an exact scheduling algorithm is proposed. In addition, the lower bound of the problem with multiple maintenance teams is derived. A scheduled rule is developed to obtain the lower bound for the problem. Based on the property analysis, the original mixed-integer programming model is simplified to an improved mathematical programming model. Since the studied problem is NP-hard, this paper proposes two heuristic algorithms and an integrated metaheuristic algorithm based on the variable neighbourhood search to obtain approximate optimal solutions in a reasonable time. In computational experiments, the two models can solve problems on small scale, while metaheuristics can find approximately optimal solutions in each problem category. Moreover, the computational results validate the performance of the proposed integrated metaheuristic in terms of convergence and stability. [ABSTRACT FROM AUTHOR]

This paper examines the balancing of assembly lines with multi-manned stations and a heterogeneous workforce. Both topics received considerable attention in the literature, but not in an integrated fashion. Combining these two characteristics gives rise to a highly combinatorial Multi-manned Assembly Line Worker Integration and Balancing Problem. When considering multi-manned stations, the already coupled decisions on assigning tasks to heterogeneous workers and workers to stations must be further linked with task scheduling assessments. We propose a Mixed-Integer Linear Programming model and develop two heuristic solution procedures, which tackle the problem with a hierarchical decomposition approach. Computational tests on a large dataset indicate that the proposed method can obtain good primal bounds in short computational times. We demonstrate that these results can be applied to the monolithic model either as a warm start or in a proximity search procedure to obtain synergistic gains with statistically significant differences. From a managerial perspective, we show that multi-manned stations can reduce the assembly line's length even in the presence of a heterogeneous workforce, which is crucial for many industries manufacturing large-size products. [ABSTRACT FROM AUTHOR]

The locomotive routing problem (LRP) considers the transportation of non-motorised containers called torpedo ladle cars (TPCs) within a steelworks. Locomotives are responsible for picking up and delivering TPCs to fulfil requests for various facilities, including steelmaking, ironmaking, heating, and repair facilities. This study introduces the many-to-many locomotive routing problem (M2MLRP), specifically tailored for the steel industry. M2MLRP was formulated using pattern- and routing-based models, and a logic-based Benders-decomposition-based matheuristic algorithm was developed. The pattern-based model successfully solved 20 small randomly generated instances, while the proposed matheuristic algorithm demonstrated the rapid generation of near-optimal solutions. Notably, unlike the pattern- and routing-based models, the proposed algorithm extended its capability to generate good feasible solutions for medium- and large-sized instances. Across all instances, the matheuristic algorithm outperformed both the routing-based model and an adaptive large neighbourhood search algorithm, showcasing its superior solution quality. This study provides a significant contribution to the field by being the first to address M2MLRP in the context of the steel industry. [ABSTRACT FROM AUTHOR]

The purpose of this article is to investigate a practical scheduling problem in which a group of elective surgical cases are scheduled over time, while considering their unpredictable durations and potential delays in the sterilisation of surgical instruments. The primary objectives were to schedule the maximum number of surgeries and decrease overtime for the surgical staff, as well as limit the number of instruments requiring emergency sterilisation. The study was conducted in collaboration with the University Hospital of Angers in France, which also contributed historical data for the experiments. We propose two robust mixed integer linear programming models, which are then solved iteratively through a rolling horizon approach, in which the objective functions are taken into account in lexicographic order. Experiments on randomly generated instances indicated which of the two approaches had better performance. Comparison of the results for a real-world scenario involving actual planning at the hospital indicated a greater than 69% decrease in overtime, and a minimum of 92% fewer stressful situations in the sterilising unit. [ABSTRACT FROM AUTHOR]

Lot-sizing and scheduling in a job shop environment is a fundamental problem that appears in many industrial settings. The problem is very complex, and solutions are often needed fast. Although many solution methods have been proposed, with increasingly better results, their computational times are not suitable for decision-makers who want solutions instantly. Therefore, we propose a novel greedy heuristic to efficiently generate production plans and schedules of good quality. The main innovation of our approach represents the incorporation of a simulation-based technique, which directly generates schedules while simultaneously determining lot sizes. By utilising priority rules, this unique feature enables us to address the complexity of job shop scheduling environments and ensures the feasibility of the resulting schedules. Using a selection of well-known rules from the literature, experiments on a variety of shop configurations and complexities showed that the proposed heuristic is able to obtain solutions with an average gap to Cplex of 4.12%. To further improve the proposed heuristic, a cooperative coevolutionary genetic programming-based hyper-heuristic has been developed. The average gap to Cplex was reduced up to 1.92%. These solutions are generated in a small fraction of a second, regardless of the size of the instance. [ABSTRACT FROM AUTHOR]

This paper seeks to optimise fleet management for companies that utilise both internal combustion engine vehicles (ICEVs) and electric vehicles by considering various costs, including fixed costs, variable costs, time window penalty costs, fuel costs, and electricity consumption costs. Additionally, the study examines the influence of four types of regulations, i.e. carbon cap, carbon tax, carbon trading, and carbon offsetting, on carbon emissions and fleet configuration. The optimisation model was solved using a Clarke-Wright savings heuristic algorithm followed by an improved adaptive genetic algorithm (IAGA), and a sensitivity analysis was conducted under different regulatory policies. The results show that all four types of regulations can effectively reduce fleet emissions. While carbon prices have a greater impact on carbon regulations than carbon quota, carbon trading was more effective under similar circumstances. Therefore, governments should implement appropriate regulatory strategies to reduce energy consumption and encourage enterprises to reduce their emissions. [ABSTRACT FROM AUTHOR]

Research on dynamic capabilities (DCs) has focused primarily on reactive DC deployment (i.e., opportunity discovery) with less attention being paid to proactive DC deployment (i.e., opportunity creation). Drawing on the opportunity creation perspective in entrepreneurship, we argue that proactive DC deployment is characterized by uncertainty, which makes deductive reasoning less effective and thus requires the use of heuristic reasoning. We also incorporate the evolutionary logic of variation, selection, and retention to theorize about the role of heuristic reasoning in the process of proactive DC deployment. Specifically, we integrate the concept of ecologically rational heuristic reasoning to answer two key questions: First, how does ecologically rational heuristic reasoning facilitate proactive DC deployment when the decision context is uncertain? Second, why is ecologically rational heuristic reasoning effective for proactive DC deployment? In summary, our paper advances the ongoing debate concerning DCs and the way in which heuristic reasoning assists a firm in deploying such capabilities. [ABSTRACT FROM AUTHOR]

The lot-sizing problem aims at determining the products to be produced and their quantities for each time period, which is a difficult problem in production planning. This problem becomes even more complicated when practical aspects such as limited production capacity, bill of materials, and item substitution are considered. In this paper, we study a new variant of the lot-sizing problem, called the multi-level capacitated lot-sizing problem with substitution and backorder. Unlike previous studies, this variant considers substitutions at both the product and component levels, which is based on the real needs of manufacturers to increase planning flexibility. Backorders are allowed, but should be delivered within a certain time limitation. We formulate this problem using a mathematical programming model. A matheuristic approach is proposed to solve the problem. This first generates an initial feasible solution using a relax-and-fix algorithm, and then improves it using a hybrid fix-and-optimise algorithm. The proposed algorithm is calibrated with a full factorial design of experiments, and its efficiency is well validated. Finally, through extensive numerical experiments, we analyse the properties of this new lot-sizing problem, such as the effect of substitution options, and the influence of backorder time limitation, and provide several useful managerial insights for manufacturing companies to save costs in production planning. [ABSTRACT FROM AUTHOR]

Nowadays, achieving higher levels of flexibility in manufacturing systems is necessary to maintain and enhance competitiveness. Accordingly, a new generation of production machine, namely Reconfigurable Machine Tool (RMT), has recently been introduced that can be effectively adapted to changes. Nevertheless, such systems are more worker-reliant, and neglecting workforce aspects results in suboptimal or even infeasible production schedules. In this regard, this study investigates an integrated lot-sizing and scheduling problem benefiting from RMTs under workforce constraints. First, a novel Mixed-Integer Linear Programming (MILP) model is provided to formulate the problem. Afterward, to confront the high complexity of the problem, an efficient Decomposition Heuristic (DH) empowered by a tailored feasibility cut is devised. A combination of MILP and Constraint Programming (CP) is employed in the DH to model the relevant master and sub-problems, respectively. Finally, the performance of the DH compared to the MILP model and an extended lower bound is evaluated. Moreover, the advantages of utilizing RMTs in the system are explored based on four defined key performance indicators. [ABSTRACT FROM AUTHOR]

This study deals with a closed-loop supply chain where inventory levels are controlled by an order-up-to inventory policy. The system under consideration is the cylinder-packaged gas supply chain of Air Liquide company, where empty cylinders used by customers are returned to be filled again by company plants. First, we examine the goodness of fit for demand distributions based on company real data. This enables us to better characterise demands pertaining to different classes of products. Then, we formulate the multi-echelon serial inventory model to be optimised and propose a heuristic to compute the target inventory levels that helps in achieving the desired customer service level while minimising the total inventory cost. The proposed heuristic is easy to implement in the field and gives results close those obtained using a simulation-optimisation approach that is more time-consuming. Finally, we perform a numerical analysis based on company real data and compare several methods that can be used to compute the target inventory levels by varying mainly two assumptions: parameters regarding demand distributions and metrics used to assess customer service levels. [ABSTRACT FROM AUTHOR]

Inspired by real-life applications, mainly in hand-intensive manufacturing, the incorporation of learning effects into scheduling problems has garnered attention in recent years. This paper deals with the flowshop scheduling problem with a learning effect, when minimising the makespan. Four approaches to model the learning effect, well-known in the literature, are considered. Mathematical models are providing for each case. A solver allows us to find the optimal solution in small problem instances, while a Simulated Annealing algorithm is proposed to deal with large problem instances. In the latter, the initial solution is obtained using the well-known Nawaz-Enscore-Ham algorithm, and two local search operators are evaluated. Computational experiments are carried out using benchmark datasets from the literature. The Simulated Annealing algorithm shows a better result for learning approaches with fast learning effects as compared to slow learning effects. Finally, for industrial decision makers, some insights about how the learning effect model might affect the makespan minimisation flowshop scheduling problem are presented. [ABSTRACT FROM AUTHOR]

A class of strong lower bounds on the solution value of a Linearised Integer Programming reformulation is introduced for the binary quadratic optimisation model to assign origin and destination nodes to strip and stack doors, resp., in a cross-dock infrastructure. The goal is to minimise the transportation cost of the commodities to be handled at the cross-dock. Strip- and stack-related decomposition submodels are developed by taking benefit of the Integer Linearisation Property that appears in the new model. A linear search heuristic is also provided for obtaining feasible solutions by exploiting the special structure of the problem. We present an extensive computational study on a testbed of 55 instances to show that the proposed joint scheme for lower bounding and feasible solution providing is very efficient. The results obtained by our proposal are similar to those obtained by CPLEX in the 50 instances taken from the literature, but usually the time is much smaller for the large instances. The comparison with CPLEX is particularly good for the new five largest instances, which make it very promising for its application in real-life cases. [ABSTRACT FROM AUTHOR]

In a companion paper (Date, K., and R. Nagi. [(2023) Optimal Placement of Multiple Finite-size Rectangular Facilities in an Existing Layout. International Journal of Production Research, THIS ISSUE]) we investigated a new problem of optimal placement of multiple finite-size rectangular facilities with known dimensions in the presence of existing rectangular facilities. We introduced the requisite theory to arrive at a solution by dividing the feasible region into sub-regions whose boundaries provide the candidates for the optimal placement. In this paper, we focus on developing optimal and heuristic solution methods for solving problem instances with various facility counts. To solve small instances with fewer facilities, we develop two optimal methods: (1) efficient explicit enumeration based on a tree state-space representation and (2) an implicit enumeration scheme that uses a flow-decomposition-based lower bound to reduce the search space and time. To solve industrial-sized instances, we have to resort to a family of construction and improvement heuristics given that the problem is NP-hard. The heuristic procedures perform well for non-pathological cases with an acceptable optimality gap. The main contribution of this paper is a range of optimal to heuristic methods that can be applied by the practitioner depending on their problem characteristics and desired efficiency. [ABSTRACT FROM AUTHOR]

Urged by the necessity to establish sustainable supply chains (SCs), this study focuses on exploring the closed-loop inventory routing problem (CIRP) for perishable food packed by multi-type returnable transport items (RTIs). The selling revenue of perishable food is dependent on food's remaining shelf life and the specific type of RTIs used for packaging. RTI selection decisions need to be jointly considered in the CIRP to weigh the potential benefits against associated costs. For this problem, we first develop an integer linear programme (ILP) to maximise the total profit of the holistic SC. Subsequently, we design a tailored kernel search (KS) matheuristic as an efficient solution. A real CIRP with multi-type RTIs for fresh strawberries is used to demonstrate the practicality of the ILP. For this case study, we perform extensive sensitivity analysis of the relevant parameters, extracting valuable managerial insights. Finally, experiments are conducted on 170 randomly generated instances. Computational results show that the proposed KS manages to achieve competitive solutions for instances with up to 10 retailers much more efficiently than CLPEX. For instances with up to 40 retailers, the KS algorithm significantly outperforms CPLEX in terms of solution quality, improving the obtained profit by 80.03% on average under the same computational time. [ABSTRACT FROM AUTHOR]

Autonomous vehicle storage and retrieval systems (AVS/RSs) are widely used in e-commerce warehouses due to their high throughput and flexibility. In such systems, storage and retrieval transactions are performed by lifts and vehicles. This paper focuses on the sequencing retrievals problem in an AVS/RS, which is an important problem for daily operations. We formulate this sequencing problem as a mixed-integer program to determine a retrieval sequence for the lift and the vehicles, one that minimises the makespan. A dynamic programming approach is proposed to solve the sequencing problem to optimality. However, the solution time of the dynamic programming method is exponentially increasing in the number of retrieval requests. To be more practical, we present a beam search heuristic that can solve large-sized instances in reasonable time. Computational experiments verify that near-optimal solutions can be found by the beam search heuristic. Compared to commonly used heuristics and straightforward heuristics, the beam search decreases the makespan by up to 15%. Finally, we analyse how vehicle modes impact the makespan, showing evidence that a small makespan can be achieved when considering a realistic mode of vehicles. [ABSTRACT FROM AUTHOR]

In this work, we study a variant of the team orienteering problem motivated by a real-world situation faced by a Mexican telecommunications company. The problem consists of the daily assignment and scheduling of service orders to crews, aiming to balance their wages, and considering the compatibility between service orders and crews. We present a mixed-integer linear formulation with two different metrics to achieve balanced scores and two valid inequalities leveraging the structure of the problem. Afterward, we propose a practical adaptive multi-start heuristic that integrates the learning mechanism of a reactive Greedy Randomized Adaptive Search Procedure. We test the performance of the models and the proposed algorithm on a benchmark of instances adapted from the literature and in a case study based on real data. The results confirm the effectiveness of the proposed algorithm to support the decision-making process. [ABSTRACT FROM AUTHOR]

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This paper proposes a prediction-based product change recovery strategy for the SC (supply chain) under long-term disruptions. A real-world case composed of multi-period planning and dynamic customer demand is considered. First, to forecast dynamic customer demand, a data-based demand predictive method with feedback errors is designed. Second, to schedule procurement and production in advance, based on the predicted demand, the selection of the supply portfolio is transformed into a bi-objective mixed integer programming problem incorporating product change. Furthermore, goods allocation and customer order fulfillment strategy is also designed to finish the transportation of goods and delivery of customer orders. To systematically synthesise and address the problems aforementioned, a three-stage heuristic method is further developed. Finally, a case study is presented to substantiate the reliability of the proposed strategy via an actual SC model of Dongsheng Electronics Co., Ltd. Based on the results obtained after one month, the proposed disruption recovery strategy can reduce the unit product cost and improve the service level, which outperforms the original method adopted by Dongsheng. Additionally, sensitivity analysis of unit product change cost is conducted to reveal the effect of different unit product change costs on SC performance. [ABSTRACT FROM AUTHOR]

This paper considers the multi-vehicle production routeing problem with a maximum-level replenishment policy. This is a well-established problem within vendor managed inventory where production, inventory and routeing decisions are made simultaneously. We present a novel method to solve the problem that outperforms existing methods both in terms of solution gaps and the number of best-known solutions. The proposed matheuristic is tested on three different sets of benchmark instances consisting of 1218 instances and finds or improves the best-known solution for 632 of them. For the remaining instances, the matheuristic is less than 2.5% from the best-known solutions. The method is particularly proficient on large instances and is also efficient for the inventory routeing problem. The success of the method is largely due to its improvement phase where a novel path-flow-inspired mathematical model is introduced. Here, a route set obtained from the current solution is used and retailers can be simultaneously inserted and removed from a route, making the method flexible even when a small route set is used. In addition, we introduce a new production subproblem that approximates the costs of using a vehicle instead of approximating the costs of visiting a retailer, making it very fast to solve. [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]

This paper investigates an energy-efficient scheduling problem on unrelated parallel machines considering general position-based deterioration which arises from the labour-intensive textile industry. The actual processing time of a job is not only associated with the job and the machine but also with its position in the processing sequence. The objective is to minimise the total energy consumption with a bounded makespan. To address this problem, we first establish a mixed-integer linear programming (MILP) model. Afterwards, the initial model is improved by deriving lower and upper bounds on the makespan, and an upper bound on the number of jobs processed on each machine. We also develop an iterative heuristic embedded with a variable neighbourhood search procedure (IHVNS). The algorithm obtains initial solutions iteratively by solving assignment problems and then repairs and improves them with the VNS procedure. Computational results demonstrate that the improved model is up to 230 times faster than the original one. Moreover, the proposed heuristic yields excellent solutions with average gaps of less than 0.73% for large-scale instances. Especially, the results reveal that the IHVNS algorithm is more suitable than MILP models for solving large-scale problems with tight makespan restrictions. [ABSTRACT FROM AUTHOR]

In this paper, a practical relevant operational production planning problem in complex manufacturing systems is addressed. In this problem, lots are planned individually to provide a more detailed plan than approaches that only consider production quantities. A three-step approach, which is currently fully integrated and used in a Decision Support System, is then introduced. This work follows the one of Mhiri et al. [2018. "Heuristic Algorithm for a WIP Projection Problem at Finite Capacity in Semiconductor Manufacturing." IEEE Transactions on Semiconductor Manufacturing 31 (1): 62–75] who addressed this problem. We push the approach a step further by introducing new optimisation possibilities through new smoothing rules, whose performance is studied according to different indicators. Furthermore, we present the production planning process in which the decision support tool is embedded and how it bridges the gap between the upper and lower planning levels. [ABSTRACT FROM AUTHOR]

The proposed paper introduces a new optimization algorithm inspired by nature called the polar fox optimization algorithm (PFA). This algorithm addresses the herd life of polar foxes and especially their hunting method. The polar fox jumping strategy for hunting, which is performed through high hearing power, is mathematically formulated and implemented to perform optimization processes in a wide range of search spaces. The performance of the polar fox algorithm is tested with 14 classic benchmark functions. To provide a comprehensive comparison, all 14 test functions are expanded, shifted, rotated and combined for this test. For further testing, the recent CEC 2021 test's complex functions are studied in the unimodal, basic, hybrid and composition modes. Finally, the rate of convergence and computational time of PFA are also evaluated by several changes with other algorithms. Comparisons show that PFA has numerous benefits over other well-known meta-heuristic algorithms and determines the solutions with fewer control parameters. So it offers competitive and promising results. In addition, this research tests PFA performance with 6 different challenging engineering problems. Compared to the well-known meta-artist methods, the superiority of the PFA is observed from the experimental results of the proposed algorithm in real-world problem-solving. The source codes of the PFA are publicly available at https://github.com/ATR616/PFA. [ABSTRACT FROM AUTHOR]

Recently, the Honey Badger Algorithm (HBA) was proposed as a metaheuristic algorithm. Honey badger hunting behaviour inspired the development of this algorithm. In the exploitation phase, HBA performs poorly and stagnates at the local best solution. On the other hand, the sand cat swarm optimization (SCSO) is a very competitive algorithm compared to other common metaheuristic algorithms since it has outstanding performance in the exploitation phase. Hence, the purpose of this paper is to hybridize HBA with SCSO so that the SCSO can overcome deficiencies of the HBA to improve the quality of the solution. The SCSO can effectively exploit optimal solutions. For the research conducted in this paper, a hybrid metaheuristic algorithm called HBASCSO was developed. The proposed approach was evaluated against challenging CEC benchmark instances taken from CEC2015, CEC2017, and CEC2019 benchmark suites The HBASCSO is also evaluated concerning the original HBA, SCSO, as well as several other recently proposed algorithms. To demonstrate that the proposed method performs significantly better than other competitive algorithms, 30 independent runs of each algorithm were evaluated to determine the best, worst, mean, and standard deviation of fitness functions. In addition, the Wilcoxon rank-sum test is used as a non-parametric comparison, and it has been found that the proposed algorithm outperforms other algorithms. Hence, the HBASCSO achieves an optimum solution that is better than the original algorithms. [ABSTRACT FROM AUTHOR]

The mandibular condyle is a rounded bony projection with an upper biconvex, oval surface in the axial plane. Its form differs significantly among different individuals and age groups. This study aims to address the variability in mandibular condyle morphology, which can be indicative of Temporomandibular Joint disorders (TMD). Given the clinical importance of accurate condyle characterization, we developed a novel detection method leveraging deep learning and feature selection technologies. This method explicitly employs the YOLOv8 network to initially identify the region of interest (ROI) in digital panoramic images. Subsequently, the MobileViT system extracts detailed features from these regions. We introduced a modified Mountaineering Team-Based Optimization Algorithm to refine the feature selection process, which efficiently isolates the most relevant features from the extracted set. Our experimental design involved a robust dataset of 3000 digital panoramic images, classified into four distinct morphological types: round, pointed, angled, and flat. We assessed the performance of our developed method through various metrics, focusing on its ability to detect and describe the morphology of the condyle. The results demonstrate a high capability of the model, achieving an accuracy of 81.5% in binary classification and 83.5% in multi-classification scenarios. [ABSTRACT FROM AUTHOR]

With the increased construction reservoirs, hydropower systems are becoming larger and more complex, which brings challenges of optimal operation of large-scale reservoirs to improve the power generation. To address this efficiently, we propose an aggregation-decomposition method based on cascade reservoir drawdown rule. Based on a two-stage method, we analyze the monotonicity of power generation increment of cascade reservoirs and propose the drawdown rule, which we used to guide the drawdown order of cascade reservoirs. On this basis, we propose an aggregation-decomposition coupling drawdown rule and progressive optimal algorithm (ADDR-POA) method of large-scale reservoirs. To confirm the viability of the proposed approach, we selected 29 series–parallel-mixed reservoirs in the upper Yangtze River Basin in China as the study subjects and optimized them with the goal of maximizing the total power generation. Results show that compared to conventional mathematical optimization method and heuristic algorithm, ADDR-POA can effectively express the compensation effect between reservoirs and has a good performance in improving the total power generation of the basin and reducing iteration times, which presents a novel approach for solving the problem of drawdown operation of large-scale reservoirs. [ABSTRACT FROM AUTHOR]

Renewable energy sources–based power systems are increasing rapidly every year with higher chances of destabilization and low power quality due to harmonics, subharmonics, and interharmonics. The elimination of the root causes of these harmonics is difficult because harmonics are generated at different levels due to various electronic components. However, the accurate estimation of power signal harmonic can be done and eliminated with a counter signal. Therefore, in this paper, the evolutionary mating heuristic is implied to accurately estimate the parameters of power signal harmonics. The proposed heuristic was tested under multiple scenarios to accurately estimate the phase and amplitude parameters of power signal harmonics at various noise levels. The tuning of the evolutionary mating heuristic is carried out for encountering predator probability and crossover probability Epp <0.8, Cp <0.9. The simulation analysis depicts that the best fitness achieved by the proposed heuristic for noise levels (no) 104, 78, 52, 26, and 13 dB are 2.8169e−11, 9.6969e−09, 3.7703e−06, 1.3501e−03, and 2.8624e−02, respectively, for iteration I = 1249. The comparative analysis shows that the proposed heuristic performance is significantly better than other heuristics. [ABSTRACT FROM AUTHOR]

Random walks find extensive applications across various complex network domains, including embedding generation and link prediction. Despite the widespread utilization of random walks, the precise impact of distinct biases on embedding generation from sequence data and their subsequent effects on link prediction remain elusive. We conduct a comparative analysis of several random walk strategies, including the true self-avoiding random walk and the traditional random walk. We also analyze walks biased towards node degree and those with inverse node degree bias. Diverse adaptations of the node2vec algorithm to induce distinct exploratory behaviors were also investigated. Our empirical findings demonstrate that despite the varied behaviors inherent in these embeddings, only slight performance differences manifest in the context of link prediction. This implies the resilient recovery of network structure, regardless of the specific walk heuristic employed to traverse the network. Consequently, the results suggest that data generated from sequences governed by unknown mechanisms can be successfully reconstructed. [ABSTRACT FROM AUTHOR]

We present a collection of simulations of the Edwards–Anderson lattice spin glass at T = 0 to elucidate the nature of low-energy excitations over a range of dimensions that reach from physically realizable systems to the mean-field limit. Using heuristic methods, we sample the ground states of instances to determine their energies while eliciting excitations through manipulating boundary conditions. We exploit the universality of the phase diagram of bond-diluted lattices to make such a study in higher dimensions computationally feasible. As a result, we obtain a variety of accurate exponents for domain wall stiffness and finite-size corrections, which allow us to examine their dimensional behavior and their connection with predictions from mean-field theory. We also provide an experimentally testable prediction for the thermal-to-percolative crossover exponent in dilute lattice Ising spin glasses. [ABSTRACT FROM AUTHOR]

This paper reviews the integration of Q‐learning with meta‐heuristic algorithms (QLMA) over the last 20 years, highlighting its success in solving complex optimization problems. We focus on key aspects of QLMA, including parameter adaptation, operator selection, and balancing global exploration with local exploitation. QLMA has become a leading solution in industries like energy, power systems, and engineering, addressing a range of mathematical challenges. Looking forward, we suggest further exploration of meta‐heuristic integration, transfer learning strategies, and techniques to reduce state space. This article is categorized under:Technologies > Computational IntelligenceTechnologies > Artificial Intelligence [ABSTRACT FROM AUTHOR]

This paper presents a heuristic tool aiming at bringing the multiple dimensions of interdisciplinarity (ID) and transdisciplinarity (TD) into direct conversation for researchers, funders, and policymakers. These societal actors have divergent conceptions, definitions, and practices of ID and TD that can be fruitfully put into dialogue to prosecute successful projects and programs. We anchor our study on the concept of "knowledge regime" and its three components (ideologies and myths, shared beliefs and practices, and imaginaries and values) to develop a comprehensive view of the heterogeneous understandings of ID and TD that goes beyond the cognitive dimension. Founded on a qualitative methodology, we designed a heuristic tool to disentangle this heterogeneity and bridge the different understandings in a comparable way. Through a semi-structured dialogue, users of the tool discuss ten questions that guide reflections on understandings of ID and TD used in projects, funding programs, and policy processes and their implications to reveal differences and increase mutual understanding. The findings offer details on the tool and systematize insights from those users who tested it in different contexts. We conclude by discussing the contribution this heuristic tool makes when considering ID and TD as knowledge regimes in the scientific and policy domains. [ABSTRACT FROM AUTHOR]

Introduction: Research in various areas indicates that expert judgment can be highly inconsistent. However, expert judgment is indispensable in many contexts. In medical education, experts often function as examiners in rater-based assessments. Here, disagreement between examiners can have far-reaching consequences. The literature suggests that inconsistencies in ratings depend on the level of performance a to-be-evaluated candidate shows. This possibility has not been addressed deliberately and with appropriate statistical methods. By adopting the theoretical lens of ecological rationality, we evaluate if easily implementable strategies can enhance decision making in real-world assessment contexts. Methods: We address two objectives. First, we investigate the dependence of rater-consistency on performance levels. We recorded videos of mock-exams and had examiners (N=10) evaluate four students' performances and compare inconsistencies in performance ratings between examiner-pairs using a bootstrapping procedure. Our second objective is to provide an approach that aids decision making by implementing simple heuristics. Results: We found that discrepancies were largely a function of the level of performance the candidates showed. Lower performances were rated more inconsistently than excellent performances. Furthermore, our analyses indicated that the use of simple heuristics might improve decisions in examiner pairs. Discussion: Inconsistencies in performance judgments continue to be a matter of concern, and we provide empirical evidence for them to be related to candidate performance. We discuss implications for research and the advantages of adopting the perspective of ecological rationality. We point to directions both for further research and for development of assessment practices. [ABSTRACT FROM AUTHOR]

The performance of multivariate calibration models ŷ = f(x) for the prediction of a numerical property y from a set of x‐variables depends on the type of scaling of the x‐variables. Common scaling methods are autoscaling (dividing the centered x by its standard deviation s) and Pareto scaling (dividing the centered x by sP with P = 0.5). The adjusted Pareto scaling presented here varies the exponent P between 0 (no scaling) and 1 (autoscaling) with the aim of obtaining an optimum prediction performance for ŷ. Related scaling methods based on the variable spread are range scaling and vast scaling; while level scaling is based on the location (central value) of the variable. These scaling methods and robust versions are compared for models created by partial least‐squares (PLS) regression. The applied strategy repeated double cross validation (rdCV) evaluates the model performance for test set objects and considers its variability. Results with three data sets from chemistry show: (a) the efficacy of the different scaling methods depends on the data structure; (b) optimization of the Pareto exponent P is recommended; (c) range scaling or vast scaling may be better than adjusted Pareto scaling; (d) in general a heuristic search for the best scaling method is advisable. Overall, the consideration of different variants of scaling allow for a flexible adjustment of the variable contributions to the calibration model. [ABSTRACT FROM AUTHOR]

The 'good-enough' processing account argues that, given the parallel activation of two parsing routes—algorithmic and heuristic parsing, the processor prefers heuristics over algorithms when unfolding incoming input. Literature on L2 'good-enough' processing conjoins with this argument, also claiming that various factors may modulate how the L2 processor adjusts its way to heuristic or algorithmic parsing. The present study investigates how L2 learners with contrastive L1 backgrounds (Czech; English) achieve 'good-enough' comprehension in Korean, a popular L2 target but understudied for this topic. We focus on morphological causative and suffixal passive constructions, which differ in terms of the alignment between thematic roles and case-marking and the interpretive computation that verbal morphology invites. Participants joined acceptability judgement and self-paced reading tasks, with manipulation of word order (verb-final vs. verb-initial). Results from these tasks suggest two aspects of L2 comprehension. First, L1 and L2 comprehension do not qualitatively differ regarding 'good-enough' processing: the L2 processor utilises both parsing routes to reduce the burden of work at hand at the earliest opportunity. Second, the divergence of L1 and L2 processing behaviours during comprehension may originate from various factors surrounding L2 learners (e.g., L2 usage, L1–L2 interface, task types), anchoring the noisy representations of L2 knowledge. [ABSTRACT FROM AUTHOR]

This study investigates the truck–cargo matching problem in less-than-truckload (LTL) logistics hubs, focusing on optimizing the three-dimensional loading of goods onto standardized pallets and assigning these loaded pallets to a fleet of heterogeneous vehicles. A two-stage hybrid heuristic algorithm is proposed to solve this complex logistics challenge. In the first stage, a tree search algorithm based on residual space is developed to determine the optimal layout for the 3D loading of cargo onto pallets. In the second stage, a heuristic online truck–cargo matching algorithm is introduced to allocate loaded pallets to trucks while optimizing the number of trucks used and minimizing transportation costs. The algorithm operates within a rolling time horizon, allowing it to dynamically handle real-time order arrivals and time window constraints. Numerical experiments demonstrate that the proposed method achieves high pallet loading efficiency (close to 90%), near-optimal truck utilization (nearly 95%), and significant cost reductions, making it a practical solution for real-world LTL logistics operations. [ABSTRACT FROM AUTHOR]

In order to solve the problem of autonomous vehicles driving safely on the road while searching for optimal paths to avoid traffic congestion and obstruction, this paper establishes an optimal path planning model based on image feature extraction. The model consists of four parts: the selection of key turning points, interpolation between turning points, definition of the evaluation function, and global optimal path search. First, based on the map information provided by the network, an image feature detection algorithm was proposed, and the key turning points of the map route were selected and stored in an open table. Then, based on mechanical analysis theory, the cubic interpolation between two key turning points of the optimal path was defined. This allows the car to drive along a continuous curve with a certain arc radius, enabling it to drive smoothly, effectively avoid obstacles, and ensure safety. Then, an evaluation function is defined as needed, and different evaluation functions are specified for different road sections to avoid excessive local deviations in planning and to reduce the impact of known obstacles on the path. Finally, the cost of the search path is calculated based on the evaluation function. According to the principle of minimum cost, a globally optimal heuristic search algorithm is proposed to find the globally optimal path. Experimental results show that the proposed global optimal path planning algorithm has excellent performance, with an average accuracy of 96.71% and a search speed of 2.78 ms. The model not only provides accurate path planning and fast path selection capabilities but also has strong robustness against interference. [ABSTRACT FROM AUTHOR]

Many drones work together in an ad hoc manner to form flying ad hoc networks. While these networks have opened up new possibilities for a wide range of applications like the commercial, and residential, they have also presented several problems, including high-speed nodes, limited density, and abrupt dynamic topology. Therefore, routing is a complex problem in such networks. The optimized link state routing protocol served as an inspiration for this plan. This article proposes a delay-conscious routing protocol for flying ad hoc networks and offers a new method for calculating the link lifespan among two unmanned aerial vehicles based on factors such as their distance apart, relative speed, and the direction in which they travel. An approach is presented in which the emperor penguin colony algorithm is used to select multi-point relay nodes. A node's ability to serve as a multi-point relay node is based on its remaining energy, link lifespan, neighboring degree, and eagerness. In sum up, the suggested approach generates paths between nodes taking energy and lifespan into account. The performance evaluation of the proposed routing is done against ML-OLSR and MP-OLSR. At a minimum, a 15% and 32% increase in latency and energy consumption were achieved by implementing the proposed technique. [ABSTRACT FROM AUTHOR]

Tensor decompositions have proven to be effective in analyzing the structure of multidimensional data. However, most of these methods require a key parameter: the number of desired components. In the case of the CANDECOMP/PARAFAC decomposition (CPD), the ideal value for the number of components is known as the canonical rank and greatly affects the quality of the decomposition results. Existing methods use heuristics or Bayesian methods to estimate this value by repeatedly calculating the CPD, making them extremely computationally expensive. In this work, we propose FRAPPE, the first method to estimate the canonical rank of a tensor without having to compute the CPD. This method is the result of two key ideas. First, it is much cheaper to generate synthetic data with known rank compared to computing the CPD. Second, we can greatly improve the generalization ability and speed of our model by generating synthetic data that matches a given input tensor in terms of size and sparsity. We can then train a specialized single-use regression model on a synthetic set of tensors engineered to match a given input tensor and use that to estimate the canonical rank of the tensor—all without computing the expensive CPD. FRAPPE is over 24 × faster than the best-performing baseline, and exhibits a 10 % improvement in MAPE on a synthetic dataset. It also performs as well as or better than the baselines on real-world datasets. [ABSTRACT FROM AUTHOR]

Matching tabular data to a knowledge graph (KG) is critical for understanding the semantic column types, column relationships, and entities of a table. Existing matching approaches rely heavily on core columns that represent primary subject entities on which other columns in the table depend. However, discovering these core columns before understanding the table's semantics is challenging. Most prior works use heuristic rules, such as the leftmost column, to discover a single core column, while an insightful discovery of the core column set that accurately captures the dependencies between columns is often overlooked. To address these challenges, we introduce Dependency-aware Core Column Set Discovery (DaCo), an iterative method that uses a novel rough matching strategy to identify both inter-column dependencies and the core column set. Additionally, DaCo can be seamlessly integrated with pre-trained language models, as proposed in the optimization module. Unlike other methods, DaCo does not require labeled data or contextual information, making it suitable for real-world scenarios. In addition, it can identify multiple core columns within a table, which is common in real-world tables. We conduct experiments on six datasets, including five datasets with single core columns and one dataset with multiple core columns. Our experimental results show that DaCo outperforms existing core column set detection methods, further improving the effectiveness of table understanding tasks. [ABSTRACT FROM AUTHOR]

Sheep were among the first animals domesticated by humans, and to this day, small ruminants are primarily raised for their meat, milk, and wool. This study evaluated the goodness of fit for growth curve models using observed age and weight data from crossbred lambs of various breeds based on the mean values between paired breeds. We employed a hybrid metaheuristic algorithm, combining a simulated annealing (SA) algorithm and a genetic algorithm (GA) called SAGAC, to determine the optimal parameter values for growth models, ensuring the best alignment between simulated and observed curves. The goodness of fit and model accuracy was assessed using the coefficient of determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). Errors were measured by comparing the criteria differences between simulated and observed data. Thirty crossbreed combinations were simulated, considering the average weight. Analysis of the observed and simulated growth curves indicated that specific crossbreeding scenarios produced promising results. This simulation approach is believed to assist geneticists in predicting potential crossbreeding outcomes, thereby saving time and financial resources in field research. [ABSTRACT FROM AUTHOR]

We consider a feasibility determination problem via simulation with stochastic binary outcomes, in which the design space can be either discrete or continuous, and outcomes can be predicted through a functional relationship that depends on linear combinations of design variables. The goal is to identify all the feasible designs with means (i.e., probabilities) no smaller than a threshold. A logistic model is used to capture the relationship between the probability and design variables. Traditional binary rewards often conceal the numbers of correct and false determinations, thereby being inefficient in large and continuous design spaces. We thus propose a similarity measure to smooth binary rewards. Then, a sampling policy that optimizes a so-called Similarity Differential (SD) is developed. Under some mild conditions, we show that the SD policy is capable of identifying all the feasible designs as the sampling budget goes to infinity. Two approximate versions of the SD policy are developed to sequentially determine the sampling decisions in large and continuous design spaces. Extensive numerical experiments are conducted to demonstrate the superior performance of our SD policy, document computational savings, and reveal underlying sampling behaviors. Alternatively, we provide a simple but effective heuristic that can be easily used by practitioners. [ABSTRACT FROM AUTHOR]

The software requirement selection problem endeavors to ascertain the optimal set of software requirements with the dual objectives of minimizing software cost and maximizing customer satisfaction. The intricate nature of this problem stems from the interdependencies among individual software requirements, rendering it a complicated NP-hard problem. In this paper, we introduce a novel multi-objective optimization algorithm christened the Quantum -Inspired Multi-Objective African Vulture Optimization Algorithm with Hierarchical Structures (QMO_HSAVOA), where hierarchical structure and in-quantum computation ideas are introduced to improve the performance of the algorithm in QMO_HSAVOA. To gauge the efficacy of QMO_HSAVOA in tackling the software requirement selection problem, we empirically apply it to the problem, orchestrating three distinct simulation experiments. The ensuing evaluation of QMO_HSAVOA's performance is conducted with meticulous scrutiny through the application of Friedman's statistical test to the experimental outcomes. These results decisively demonstrate that the proposed QMO_HSAVOA not only delivers exceptionally competitive outcomes but also outshines alternative algorithms. This finding provision is an innovative and highly efficient solution for addressing the software requirement selection problem. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the AAPF* algorithm, an innovative approach that synergistically integrates the A-star search algorithm (A*) with the artificial potential field (APF) method. This algorithm is designed to enhance safety and ensure smoother global path planning for autonomous vehicles, particularly addressing vehicle cornering constraints. Initially, for augmenting the safety of autonomous vehicle, we implement an obstacle expansion strategy with a factor of 2 units, enhancing environmental adaptability. The study then delves into the classical A* algorithm, examining its core principles and characteristics, leading to the development of novel heuristic functions and search strategies that address the limitations inherent in the classic A* algorithm. Subsequently, we explore the APF algorithm, recognized for its excellence in obstacle avoidance in path planning. The paper culminates in the amalgamation of the APF's repulsive field concept with the improved A* algorithm, crafting a comprehensive global planning algorithm tailored for autonomous vehicle path planning schemes. Experiments conducted in a simulated environment model validate the AAPF* algorithm's efficacy in improving both the safety and smoothness, demonstrating its potential for real-world applications. [ABSTRACT FROM AUTHOR]

This work presents a novel Hybrid meta-heuristic that combines the grey wolf optimizer (GWO), particle swarm optimization (PSO), and crow search method (CSA). Initially, our approach integrated the two techniques GWO and PSO in the initialization then followed by CSA to explore and selection is investigated. Our approach has been examined using an 18 benchmark function, a single-link manipulator(SLM), a single-link flexible manipulator (SLFM), and a double-link manipulator (DLM). We compared our method against the CSA, GWO, PSO, DE, GEO, JS, and WSO algorithms throughout our assessments. Our simulation findings in MATLAB 2014 demonstrate that our hybrid strategy effectively combines the three algorithms and outperforms all comparative approaches. In addition, the results demonstrate that our method converges to more optimum solutions with fewer iterations. [ABSTRACT FROM AUTHOR]