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23 pages, 529 KB

Open AccessArticle

Attributing Inventory Performance via Shapley-Based Counterfactual Decomposition

by Lu Xu

Computers 2026, 15(6), 375; https://doi.org/10.3390/computers15060375 - 8 Jun 2026

Viewed by 266

Abstract

Inventory systems are typically evaluated using aggregate performance metrics such as out-of-stock and average inventory. In supply chain management, it is important to understand the underlying reasons for a period’s performance—specifically, how previous inventory management decisions, such as order placement, lead to the [...] Read more.

Inventory systems are typically evaluated using aggregate performance metrics such as out-of-stock and average inventory. In supply chain management, it is important to understand the underlying reasons for a period’s performance—specifically, how previous inventory management decisions, such as order placement, lead to the result and what their contributions are. Traditional methods are often restrictive and cannot be applied to broader cases. This paper proposes a Shapley-based decomposition framework that attributes the realized performance gap between the observed inventory policy and optimized reference policy to individual decisions. A numerical experiment on a simulated finite-horizon periodic-review inventory system with stochastic demand and lead time is conducted to illustrate the basic idea of the method. Compared to traditional methods, the proposed approach directly explains a realized benchmark-relative performance difference and is applicable to integer-constrained, non-differentiable, and simulation-based inventory systems. It enables transparent inventory management performance evaluation and effective root-cause analysis. Full article

(This article belongs to the Special Issue Operations Research: Trends and Applications)

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33 pages, 1310 KB

Open AccessArticle

A Policy-Based Rough Optimization with Large Neighborhood Search for Carbon-Aware Flexible Job Shop Scheduling with Tardiness Penalty

by Saurabh Sanjay Singh and Deepak Gupta

Computers 2026, 15(5), 314; https://doi.org/10.3390/computers15050314 - 14 May 2026

Viewed by 614

Abstract

Sustainable manufacturing requires schedules that balance environmental responsibility with delivery reliability. This paper studies the Carbon-Aware Flexible Job Shop Scheduling Problem with Tardiness Penalty (CAFJSP-T), where total carbon emissions and total tardiness penalty are the primary objectives. We propose a Policy-based Rough Optimization [...] Read more.

Sustainable manufacturing requires schedules that balance environmental responsibility with delivery reliability. This paper studies the Carbon-Aware Flexible Job Shop Scheduling Problem with Tardiness Penalty (CAFJSP-T), where total carbon emissions and total tardiness penalty are the primary objectives. We propose a Policy-based Rough Optimization with a Large Neighborhood Search (Pro-LNS) framework integrating Proximal Policy Optimization (PPO) and adaptive Large Neighborhood Search (LNS). PPO constructs a feasible schedule by selecting operation-machine assignments from job-readiness, machine-availability, earliest-completion, and critical-path features. This policy-generated schedule provides a structurally informed incumbent, enabling LNS to avoid unguided search and focus destroy-and-repair refinement on high-impact operations. Both phases use the same normalized scalarized carbon-tardiness objective, which guides PPO rewards and LNS removal, reinsertion, and acceptance while preserving precedence, eligibility, and capacity constraints. Experiments on small, medium, and large workcenter benchmarks show strong due-date performance and controlled carbon emissions. Under equal objective weighting, Pro-LNS achieves a median optimality gap of 6.12% relative to the exact formulation, with all instances within 14%, while requiring 4.08 s on average and at most 10.51 s. Comparisons with PPO-only, Advantage Actor-Critic (A2C), Soft Actor-Critic (SAC), and Genetic Algorithm (GA) schedulers show that Pro-LNS attains the best weighted scalarized objective across representative instance-weight settings. Friedman and Holm-corrected Wilcoxon tests confirm significant improvements over all competitors, with average weighted-objective gains of 4.90%, 7.25%, 8.81%, and 9.51% over PPO-only, A2C, SAC, and GA, respectively. These results demonstrate that Pro-LNS is an effective and computationally practical hybrid approach for carbon-aware, tardiness-sensitive flexible job shop scheduling. Full article

(This article belongs to the Special Issue Operations Research: Trends and Applications)

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21 pages, 340 KB

Open AccessArticle

Pareto-Optimal Explainable Diagnosis Under Cost-Aware Parallel Reasoning

by Ana Chacón-Luna, Miguel Tupac-Yupanqui, Nicolás Márquez and Cristian Vidal-Silva

Computers 2026, 15(5), 265; https://doi.org/10.3390/computers15050265 - 23 Apr 2026

Viewed by 595

Abstract

Model-Based Diagnosis (MBD) is widely used to identify minimal conflicts and repair actions in constraint-based systems. Recent advances in parallel reasoning have significantly reduced runtime in large-scale models through speculative and multicore execution strategies. However, existing approaches primarily focus on computational efficiency and [...] Read more.

Model-Based Diagnosis (MBD) is widely used to identify minimal conflicts and repair actions in constraint-based systems. Recent advances in parallel reasoning have significantly reduced runtime in large-scale models through speculative and multicore execution strategies. However, existing approaches primarily focus on computational efficiency and implicitly assume that minimal diagnoses are inherently suitable explanations for human decision makers. In complex configuration environments, minimality does not necessarily imply interpretability, as diagnoses may involve structurally dispersed or semantically heterogeneous constraints. To address this limitation, this paper introduces a multi-objective explainability-aware framework for parallel MDB. Diagnosis selection is formulated as a Pareto optimization problem balancing total computational cost and a formally defined interpretability penalty. Interpretability is quantified using graph-based structural dispersion, semantic entropy, hierarchical complexity, and ambiguity metrics. The proposed E-ParetoDiag algorithm computes non-dominated diagnoses and identifies balanced knee-point solutions without modifying correctness guarantees of underlying diagnosis algorithms. Experimental evaluation on large-scale benchmark datasets demonstrates a measurable trade-off between runtime and interpretability, particularly in dense constraint systems. Comparative analysis against classical selection strategies shows that the proposed approach reduces structural dispersion by up to 18% while increasing computational cost by only 7%. Statistical validation confirms that these improvements are significant (p < 0.01) in medium- and high-density scenarios. The results indicate that aggressive parallelism may improve computational efficiency while increasing explanation complexity, highlighting the need for multi-objective selection strategies. Overall, the proposed framework extends scalable symbolic reasoning toward a human-centered diagnosis paradigm and establishes a principled foundation for explainability-aware optimization in constraint-based systems. Full article

(This article belongs to the Special Issue Operations Research: Trends and Applications)

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21 pages, 1299 KB

Open AccessArticle

Improving Financial Literacy Among Portuguese Youth: A Multicriteria Decision Analysis Using the Analytic Hierarchy Process

by Manuel Reis, Tiago Miguel, Paula Sarabando and Rogério Matias

Computers 2026, 15(4), 245; https://doi.org/10.3390/computers15040245 - 16 Apr 2026

Viewed by 706

Abstract

Financial literacy is critical for individual well-being and sustainable economic development, yet significant gaps remain among Portuguese young adults. Using a two-phase design, this study combines a diagnostic assessment and multi-criteria decision analysis to identify and prioritise effective financial education strategies. In Phase [...] Read more.

Financial literacy is critical for individual well-being and sustainable economic development, yet significant gaps remain among Portuguese young adults. Using a two-phase design, this study combines a diagnostic assessment and multi-criteria decision analysis to identify and prioritise effective financial education strategies. In Phase 1, a diagnostic questionnaire administered to 172 first-year university students revealed pronounced deficiencies in core financial concepts. Only 29.1% correctly answered a question on compound interest, and almost half were unable to understand the concept of inflation. Additionally, 62.8% reported low exposure to financial education during compulsory schooling, and 59.9% strongly agreed that it should be included in the mandatory curriculum, indicating both unmet need and strong receptiveness. Phase 2 employed the Analytic Hierarchy Process (AHP) to evaluate five educational alternatives across four criteria. Engagement and motivation (0.32) and knowledge acquisition (0.31) were prioritised over behavioural impact (0.22) and accessibility (0.15). Based on expert assessments weighted by student preferences, in-person courses emerged as the most effective strategy (0.42), substantially outperforming online courses (0.22), videos and digital content (0.14), books (0.13), and games (0.10). The findings point to the need for policy-driven integration of structured, educator-led financial education within formal curricula, supported by approaches that prioritise active engagement and knowledge acquisition over convenience, with digital tools serving as complements rather than replacements. Full article

(This article belongs to the Special Issue Operations Research: Trends and Applications)

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18 pages, 802 KB

Open AccessArticle

Adaptive Sequence-Based Heuristic for Two-Dimensional Guillotine Cutting and Packing Problems

by Óscar Oliveira and Dorabela Gamboa

Computers 2026, 15(4), 216; https://doi.org/10.3390/computers15040216 - 1 Apr 2026

Viewed by 788

Abstract

This paper proposes adaptive sequence-based heuristics for solving rectangular two-dimensional guillotine Cutting and Packing Problems (CPPs). These problems are essential in various industrial sectors, aiming to maximise resource utilisation by selecting profitable item subsets or minimise waste by using the fewest possible identical [...] Read more.

This paper proposes adaptive sequence-based heuristics for solving rectangular two-dimensional guillotine Cutting and Packing Problems (CPPs). These problems are essential in various industrial sectors, aiming to maximise resource utilisation by selecting profitable item subsets or minimise waste by using the fewest possible identical large objects. The core methodology is grounded in the principle that if a specific item sequence generates a high-quality solution, incremental adjustments to that sequence can yield even better outcomes. By iteratively refining item ordering through the BubbleSearch method, the heuristics balance search intensification with the diversification of the solution space. Extensive computational experiments were conducted on benchmark datasets, including SET1, ATP, and CLASS, across multiple problem variants such as the Single Stock-Size Cutting Stock Problem (SSSCSP) and the Single Large Object Placement Problem (SLOPP). The results confirm that these heuristics and their extension with path relinking consistently deliver optimal or near-optimal solutions. These heuristics achieve high performance in computational times that are significantly shorter than existing state-of-the-art methods, demonstrating their robustness, flexibility, and suitability for software transferability and real-world industrial adoption. Full article

(This article belongs to the Special Issue Operations Research: Trends and Applications)

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45 pages, 3443 KB

Open AccessArticle

Novel Hybrid Nature-Inspired Metaheuristic Algorithm for Global and Engineering Design Optimization

by Hasan Kanaker, Osama Al Sayaydeh, Essam Alhroob, Nader Abdel Karim, Sami Smadi and Nurul Halimatul Asmak Ismail

Computers 2026, 15(4), 211; https://doi.org/10.3390/computers15040211 - 27 Mar 2026

Cited by 3 | Viewed by 1239

Abstract

Metaheuristic algorithms have become indispensable for solving high-dimensional, non-convex, and constrained optimization problems arising in science and engineering. However, no single method can simultaneously provide strong global exploration, accurate local exploitation, and robust performance across diverse problem classes. This paper proposes JADEFLO, a [...] Read more.

Metaheuristic algorithms have become indispensable for solving high-dimensional, non-convex, and constrained optimization problems arising in science and engineering. However, no single method can simultaneously provide strong global exploration, accurate local exploitation, and robust performance across diverse problem classes. This paper proposes JADEFLO, a new hybrid nature-inspired metaheuristic that couples Adaptive Differential Evolution with Optional External Archive (JADE) and Frilled Lizard Optimization (FLO) in a two-stage search framework. In the first stage, JADE drives global exploration using p-best mutation, an external archive, and adaptive control of the mutation factor and crossover rate to maintain population diversity. In the second stage, FLO performs intensive local refinement by mimicking the hunting and tree-climbing behaviors of frilled lizards through dedicated exploration and exploitation moves. The resulting algorithm has linear time complexity with respect to the population size, dimensionality, and number of iterations. JADEFLO is evaluated on the IEEE CEC 2022 single-objective benchmark suite (F1–F12) and three constrained engineering design problems (Pressure Vessel, tension/compression spring, and speed reducer), using 30 independent runs and comparisons against more than thirty state-of-the-art metaheuristics, including GA, PSO, DE variants, GWO, WOA, MFO, and FLO. The results show that JADEFLO attains the best overall rank on the CEC functions, delivers faster convergence and higher accuracy on most test cases, and matches or improves the best-known designs with markedly reduced variance. These findings indicate that JADEFLO is a promising general-purpose optimizer and a flexible foundation for future extensions to multi-objective and large-scale optimization. Full article

(This article belongs to the Special Issue Operations Research: Trends and Applications)

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29 pages, 2558 KB

Open AccessArticle

IDN-MOTSCC: Integration of Deep Neural Network with Hybrid Meta-Heuristic Model for Multi-Objective Task Scheduling in Cloud Computing

by Mohit Kumar, Rama Kant, Brijesh Kumar Gupta, Azhar Shadab, Ashwani Kumar and Krishna Kant

Computers 2026, 15(1), 57; https://doi.org/10.3390/computers15010057 - 14 Jan 2026

Cited by 2 | Viewed by 1132

Abstract

Cloud computing covers a wide range of practical applications and diverse domains, yet resource scheduling and task scheduling remain significant challenges. To address this, different task scheduling algorithms are implemented across various computing systems to allocate tasks to machines, thereby enhancing performance through [...] Read more.

Cloud computing covers a wide range of practical applications and diverse domains, yet resource scheduling and task scheduling remain significant challenges. To address this, different task scheduling algorithms are implemented across various computing systems to allocate tasks to machines, thereby enhancing performance through data mapping. To meet these challenges, a novel task scheduling model is proposed using a hybrid meta-heuristic integration with a deep learning approach. We employed this novel task scheduling model to integrate deep learning with an optimized DNN, fine-tuned using improved grey wolf–horse herd optimization, with the aim of optimizing cloud-based task allocation and overcoming makespan constraints. Initially, a user initiates a task or request within the cloud environment. Then, these tasks are assigned to Virtual Machines (VMs). Since the scheduling algorithm is constrained by the makespan objective, an optimized Deep Neural Network (DNN) model is developed to perform optimal task scheduling. Random solutions are provided to the optimized DNN, where the hidden neuron count is tuned optimally by the proposed Improved Grey Wolf–Horse Herd Optimization (IGW-HHO) algorithm. The proposed IGW-HHO algorithm is derived from both conventional Grey Wolf Optimization (GWO) and Horse Herd Optimization (HHO). The optimal solutions are acquired from the optimized DNN and processed by the proposed algorithm to efficiently allocate tasks to VMs. The experimental results are validated using various error measures and convergence analysis. The proposed DNN-IGW-HHO model achieved a lower cost function compared to other optimization methods, with a reduction of 1% compared to PSO, 3.5% compared to WOA, 2.7% compared to GWO, and 0.7% compared to HHO. The proposed task scheduling model achieved the minimal Mean Absolute Error (MAE), with performance improvements of 31% over PSO, 20.16% over WOA, 41.72% over GWO, and 9.11% over HHO. Full article

(This article belongs to the Special Issue Operations Research: Trends and Applications)

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37 pages, 1525 KB

Open AccessArticle

Contribution-Driven Task Design: Multi-Task Optimization Algorithm for Large-Scale Constrained Multi-Objective Problems

by Huai Li and Tianyu Liu

Computers 2026, 15(1), 31; https://doi.org/10.3390/computers15010031 - 6 Jan 2026

Viewed by 613

Abstract

Large-scale constrained multi-objective optimization problems (LSCMOPs) are highly challenging due to the need to optimize multiple conflicting objectives under complex constraints within a vast search space. To address this challenge, this paper proposes a multi-task optimization algorithm based on contribution-driven task design (MTO-CDTD). [...] Read more.

Large-scale constrained multi-objective optimization problems (LSCMOPs) are highly challenging due to the need to optimize multiple conflicting objectives under complex constraints within a vast search space. To address this challenge, this paper proposes a multi-task optimization algorithm based on contribution-driven task design (MTO-CDTD). The algorithm constructs a multi-task optimization framework comprising one original task and multiple auxiliary tasks. Guided by an optimal contribution objective assignment strategy, each auxiliary task optimizes a subset of decision variables that contribute most to a specific objective function. A contribution-guided initialization strategy is then employed to generate high-quality initial populations for the auxiliary tasks. Furthermore, a knowledge transfer strategy based on multi-population collaboration is developed to integrate optimization information from the auxiliary tasks, thereby effectively guiding the original task in searching the large-scale decision space. Extensive experiments on three benchmark test suites—LIRCMOP, CF, and ZXH_CF—with 100, 500, and 1000 decision variables demonstrate that the proposed MTO-CDTD algorithm achieves significant advantages in solving complex LSCMOPs. Full article

(This article belongs to the Special Issue Operations Research: Trends and Applications)

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17 pages, 1887 KB

Open AccessArticle

Mechanical Optimizations with Variable Mesh Size, Using Differential Evolution Algorithm

by David Robledo-Jimenez, Carlos Gustavo Manriquez-Padilla, Arturo Yosimar Jaen Cuellar, Angel Perez-Cruz and Juan Jose Saucedo-Dorantes

Computers 2026, 15(1), 29; https://doi.org/10.3390/computers15010029 - 6 Jan 2026

Viewed by 499

Abstract

Structural problems are a common topic among several optimization works; with the use of finite element analysis (FEA), the aim of these works is to improve the mechanical behavior of the distinct elements or bodies involved in these optimization problems. However, the impact [...] Read more.

Structural problems are a common topic among several optimization works; with the use of finite element analysis (FEA), the aim of these works is to improve the mechanical behavior of the distinct elements or bodies involved in these optimization problems. However, the impact of the meshing discretization on the outcome of the optimization process has not been studied in previous works. The present work investigates the effect of mesh element size on the mechanical optimization of two cases of study; the first one is about a modal optimization on a cantilever beam, and the second one is about a cellular beam, where the aim is to reduce the weight of the beam under static load. In these two optimization problems, variables commonly used in the literature were employed, while additionally including the mesh size as an extra variable. The computational framework is implemented on MATLAB R2022a, and the modal and weight optimizations are carried out through APDL (ANSYS Parametric Design Language) executed in batch mode. The results demonstrate that the consideration of the mesh size element can improve the computational time that is required to perform this mechanical optimization, achieving a 96% percentage of time reduction instead of making the analysis with the finest element size (in case 1) and a 90 percent time reduction for the second case of study. Full article

(This article belongs to the Special Issue Operations Research: Trends and Applications)

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30 pages, 2162 KB

Open AccessArticle

Decision Support for Cargo Pickup and Delivery Under Uncertainty: A Combined Agent-Based Simulation and Optimization Approach

by Renan Paula Ramos Moreno, Rui Borges Lopes, Ana Luísa Ramos, José Vasconcelos Ferreira, Diogo Correia and Igor Eduardo Santos de Melo

Computers 2025, 14(11), 462; https://doi.org/10.3390/computers14110462 - 25 Oct 2025

Cited by 1 | Viewed by 1665

Abstract

This article introduces an innovative hybrid methodology that integrates deterministic Mixed-Integer Linear Programming optimization with stochastic Agent-Based Simulation to address the PDP-TW. The approach is applied to real-world operational data from a luggage-handling company in Lisbon, covering 158 service requests from January 2025. [...] Read more.

This article introduces an innovative hybrid methodology that integrates deterministic Mixed-Integer Linear Programming optimization with stochastic Agent-Based Simulation to address the PDP-TW. The approach is applied to real-world operational data from a luggage-handling company in Lisbon, covering 158 service requests from January 2025. The MILP model generates optimal routing and task allocation plans, which are subsequently stress-tested under realistic uncertainties, such as variability in travel and service times, using ABS implemented in AnyLogic. The framework is iterative: violations of temporal or capacity constraints identified during the simulation are fed back into the optimization model, enabling successive adjustments until robust and feasible solutions are achieved for real-world scenarios. Additionally, the study incorporates transshipment scenarios, evaluating the impact of using warehouses as temporary hubs for order redistribution. Results include a comparative analysis between deterministic and stochastic models regarding operational efficiency, time window adherence, reduction in travel distances, and potential decreases in CO₂ emissions. This work provides a contribution to the literature by proposing a practical and robust decision-support framework aligned with contemporary demands for sustainability and efficiency in urban logistics, overcoming the limitations of purely deterministic approaches by explicitly reflecting real-world uncertainties. Full article

(This article belongs to the Special Issue Operations Research: Trends and Applications)

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39 pages, 7020 KB

Open AccessArticle

Improved Multi-Faceted Sine Cosine Algorithm for Optimization and Electricity Load Forecasting

by Stephen O. Oladipo, Udochukwu B. Akuru and Abraham O. Amole

Computers 2025, 14(10), 444; https://doi.org/10.3390/computers14100444 - 17 Oct 2025

Cited by 2 | Viewed by 1436

Abstract

The sine cosine algorithm (SCA) is a population-based stochastic optimization method that updates the position of each search agent using the oscillating properties of the sine and cosine functions to balance exploration and exploitation. While flexible and widely applied, the SCA often suffers [...] Read more.

The sine cosine algorithm (SCA) is a population-based stochastic optimization method that updates the position of each search agent using the oscillating properties of the sine and cosine functions to balance exploration and exploitation. While flexible and widely applied, the SCA often suffers from premature convergence and getting trapped in local optima due to weak exploration–exploitation balance. To overcome these issues, this study proposes a multi-faceted SCA (MFSCA) incorporating several improvements. The initial population is generated using dynamic opposition (DO) to increase diversity and global search capability. Chaotic logistic maps generate random coefficients to enhance exploration, while an elite-learning strategy allows agents to learn from multiple top-performing solutions. Adaptive parameters, including inertia weight, jumping rate, and local search strength, are applied to guide the search more effectively. In addition, Lévy flights and adaptive Gaussian local search with elitist selection strengthen exploration and exploitation, while reinitialization of stagnating agents maintains diversity. The developed MFSCA was tested against 23 benchmark optimization functions and assessed using the Wilcoxon rank-sum and Friedman rank tests. Results showed that MFSCA outperformed the original SCA and other variants. To further validate its applicability, this study developed a fuzzy c-means MFSCA-based adaptive neuro-fuzzy inference system to forecast energy consumption in student residences, using student apartments at a university in South Africa as a case study. The MFSCA-ANFIS achieved superior performance with respect to RMSE (1.9374), MAD (1.5483), MAE (1.5457), CVRMSE (42.8463), and SD (1.9373). These results highlight MFSCA’s effectiveness as a robust optimizer for both general optimization tasks and energy management applications. Full article

(This article belongs to the Special Issue Operations Research: Trends and Applications)

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46 pages, 2758 KB

Open AccessArticle

Swallow Search Algorithm (SWSO): A Swarm Intelligence Optimization Approach Inspired by Swallow Bird Behavior

by Farah Sami Khoshaba, Shahab Wahhab Kareem and Roojwan Sc Hawezi

Computers 2025, 14(9), 345; https://doi.org/10.3390/computers14090345 - 22 Aug 2025

Cited by 4 | Viewed by 2331

Abstract

Swarm Intelligence (SI) algorithms were applied widely in solving complex optimization problems because they are simple, flexible, and efficient. The current paper proposes a new SI algorithm, which is based on the bird-like actions of swallows, which have highly synchronized behaviors of foraging [...] Read more.

Swarm Intelligence (SI) algorithms were applied widely in solving complex optimization problems because they are simple, flexible, and efficient. The current paper proposes a new SI algorithm, which is based on the bird-like actions of swallows, which have highly synchronized behaviors of foraging and migration. The optimization algorithm (SWSO) makes use of these behaviors to boost the ability of exploration and exploitation in the optimization process. Unlike other birds, swallows are known to be so precise when performing fast directional alterations and making intricate aerial acrobatics during foraging. Moreover, the flight patterns of swallows are very efficient; they have extensive capabilities to transition between flapping and gliding with ease to save energy over long distances during migration. This allows instantaneous changes of wing shape variations to optimize performance in any number of flying conditions. The model used by the SWSO algorithm combines these biologically inspired flight dynamics into a new computational model that is aimed at enhancing search performance in rugged terrain. The design of the algorithm simulates the swallow’s social behavior and energy-saving behavior, converting it into exploration, exploitation, control mechanisms, and convergence control. In order to verify its effectiveness, (SWSO) is applied to many benchmark problems, such as unimodal, multimodal, fixed-dimension functions, and a benchmark CEC2019, which consists of some of the most widely used benchmark functions. Comparative tests are conducted against more than 30 metaheuristic algorithms that are regarded as state-of-the-art, developed so far, including PSO, MFO, WOA, GWO, and GA, among others. The measures of performance included best fitness, rate of convergence, robustness, and statistical significance. Moreover, the use of (SWSO) in solving real-life engineering design problems is used to prove (SWSO)’s practicality and generality. The results confirm that the proposed algorithm offers a competitive and reliable solution methodology, making it a valuable addition to the field of swarm-based optimization. Full article

(This article belongs to the Special Issue Operations Research: Trends and Applications)

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16 pages, 3704 KB

Open AccessEditor’s ChoiceArticle

Optimization of Scene and Material Parameters for the Generation of Synthetic Training Datasets for Machine Learning-Based Object Segmentation

by Malte Nagel, Kolja Hedrich, Nils Melchert, Lennart Hinz and Eduard Reithmeier

Computers 2025, 14(8), 341; https://doi.org/10.3390/computers14080341 - 21 Aug 2025

Viewed by 1276

Abstract

Synthetic training data is often essential for neural-network-based segmentation when real datasets are difficult or impossible to obtain. Conventional synthetic data generation relies on manually selecting scene and material parameters. This can lead to poor performance because the optimal parameters are often non-intuitive [...] Read more.

Synthetic training data is often essential for neural-network-based segmentation when real datasets are difficult or impossible to obtain. Conventional synthetic data generation relies on manually selecting scene and material parameters. This can lead to poor performance because the optimal parameters are often non-intuitive and depend heavily on the specific use case and on the objects to be segmented. This study proposes a novel, automated optimization pipeline to improve the quality of synthetic datasets for specific object segmentation tasks. Synthetic datasets are generated by varying material and scene parameters with the BlenderProc framework. These parameters are optimized with the Optuna framework to maximize the average precision achieved by models trained on this data and validated using a small real dataset. After initial single-parameter studies and subsequent multidimensional optimization, optimal scene and material parameters are identified for each object. The results demonstrate the potential of this optimization pipeline to produce synthetic training datasets that enhance neural network performance for specific segmentation tasks, offering insights into the critical role of scene design and material selection in synthetic data generation. Full article

(This article belongs to the Special Issue Operations Research: Trends and Applications)

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Review

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22 pages, 1566 KB

Open AccessReview

Multi-Objective Evolutionary Algorithms in Waste Disposal Systems: A Comprehensive Review of Applications, Case Studies, and Future Directions

by Saad Talal Alharbi

Computers 2025, 14(8), 316; https://doi.org/10.3390/computers14080316 - 4 Aug 2025

Viewed by 2482

Abstract

Multi-objective evolutionary algorithms (MOEAs) have emerged as powerful optimization tools for addressing the complex, often conflicting goals present in modern waste disposal systems. This review explores recent advances and practical applications of MOEAs in key areas, including waste collection routing, waste-to-energy (WTE) systems, [...] Read more.

Multi-objective evolutionary algorithms (MOEAs) have emerged as powerful optimization tools for addressing the complex, often conflicting goals present in modern waste disposal systems. This review explores recent advances and practical applications of MOEAs in key areas, including waste collection routing, waste-to-energy (WTE) systems, and facility location and allocation. Real-world case studies from cities like Braga, Lisbon, Uppsala, and Cyprus demonstrate how MOEAs can enhance operational efficiency, boost energy recovery, and reduce environmental impacts. While these algorithms offer significant advantages, challenges remain in computational complexity, adapting to dynamic environments, and integrating with emerging technologies. Future research directions highlight the potential of combining MOEAs with machine learning and real-time data to create more flexible and responsive waste management strategies. By leveraging these advancements, MOEAs can play a pivotal role in developing sustainable, efficient, and adaptive waste disposal systems capable of meeting the growing demands of urbanization and stricter environmental regulations. Full article

(This article belongs to the Special Issue Operations Research: Trends and Applications)

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