Problem Size Research Articles

Scatter Search for Optimal Sizing of a Hybrid Renewable Energy System for Scheduling Green Hydrogen Production

At present, energy demands are mainly covered by the use of fossil fuels. The process of fossil fuel production increases pollution from oil extraction, transport to processing centers, treatment to obtain lighter fractions, and delivery and use by the final consumers. Such polluting circumstances are aggravated in the case of accidents involving fossil fuels. They are also linked to speculative markets. As a result, the trend is towards the decarbonization of lifestyles in advanced societies. The present paper addresses the problem of the optimal sizing of a hybrid renewable energy system for scheduling green hydrogen production. A local system fully powered by renewable energies is designed to obtain hydrogen from seawater. In order to monetize excess energy, the grid connection of the system is considered under realistic energy market constraints, designing an hourly purchasing strategy. This crucial problem, which has not been taken into account in the literature, is solved by the specific dispatch strategy designed. Several optimization methods have been used to solve this problem; however, the scatter search method has not previously been employed. In this paper, the problem is faced with a novel implementation of this method. The implementation is competitive in terms of performance when compared to, on the one hand, the genetic algorithm and differential evolution methods, which are well-known state-of-the-art evolutionary algorithms, and, on the other hand, the optimal foraging algorithm (OFA), a more recent algorithm. Furthermore, scatter search outperformed all other methods in terms of computational cost. This is promising for real-world applications that require quick responses.

Study Of Unknown Dead Bodies Brought For Post Mortem In Tertiary Hospital

Background: Medico legal autopsies are conducted in India in two circumstances: cases where a person has died because of unnatural circumstances and where the body of the deceased is unidentified. The present study relates to the latter of the two aforementioned circumstances, i.e. where the dead body is not identified. Methodology: In present descriptive prospective study the size and magnitude of problems regarding unidentified bodies. Also the profile of unidentified dead bodies such as age, cause of death, post-mortem interval, manner of death, seasonal trends, area (urban or rural), place from where the body was recovered was studied Results: Total 1919 bodies were brought to us of which 169 cases were selected. The mean age of the patients was 44.82±14.83 years with male predominance. Most of the cases were identified on the basis of the clothes belonging and 16.57% bodies were brought from the rural areas. Majority of the unidentified bodies were found in the autumn season (28.99 %). Maximum unidentified dead bodies were discovered at religious places (n= 64, 37.87 %). In the present study, most deaths were natural, n=114 (67.46 %) and the majority of autopsies were performed between 4 and 6 days (n= 73, 43.20%) Regarding the cause of death according to police records in the present study, it was determined that there were the most cases of illness/ diseased condition (n=55). Conclusion: Unknown/unclaimed should be presented for autopsy forthwith without any delay so that decomposition and other artifacts do not set in and obscure the findings of the postmortem examination

DeepMulticut: Deep Learning of Multicut Problem for Neuron Segmentation From Electron Microscopy Volume.

Superpixel aggregation is a powerful tool for automated neuron segmentation from electron microscopy (EM) volume. However, existing graph partitioning methods for superpixel aggregation still involve two separate stages-model estimation and model solving, and therefore model error is inherent. To address this issue, we integrate the two stages and propose an end-to-end aggregation framework based on deep learning of the minimum cost multicut problem called DeepMulticut. The core challenge lies in differentiating the NP-hard multicut problem, whose constraint number is exponential in the problem size. With this in mind, we resort to relaxing the combinatorial solver-the greedy additive edge contraction (GAEC)-to a continuous Soft-GAEC algorithm, whose limit is shown to be the vanilla GAEC. Such relaxation thus allows the DeepMulticut to integrate edge cost estimators, Edge-CNNs, into a differentiable multicut optimization system and allows a decision-oriented loss to feed decision quality back to the Edge-CNNs for adaptive discriminative feature learning. Hence, the model estimators, Edge-CNNs, can be trained to improve partitioning decisions directly while beyond the NP-hardness. Also, we explain the rationale behind the DeepMulticut framework from the perspective of bi-level optimization. Extensive experiments on three public EM datasets demonstrate the effectiveness of the proposed DeepMulticut.

Tensor dictionary-based heterogeneous transfer learning to study emotion-related gender differences in brain

In practice, collecting auxiliary labeled data with same feature space from multiple domains is difficult. Thus, we focus on the heterogeneous transfer learning to address the problem of insufficient sample sizes in neuroimaging. Viewing subjects, time, and features as dimensions, brain activation and dynamic functional connectivity data can be treated as high-order heterogeneous data with heterogeneity arising from distinct feature space. To use the heterogeneous priori knowledge from the low-dimensional brain activation data to improve the classification performance of high-dimensional dynamic functional connectivity data, we propose a tensor dictionary-based heterogeneous transfer learning framework. It combines supervised tensor dictionary learning with heterogeneous transfer learning for enhance high-order heterogeneous knowledge sharing. The former can encode the underlying discriminative features in high-order data into dictionaries, while the latter can transfer heterogeneous knowledge encoded in dictionaries through feature transformation derived from mathematical relationship between domains. The primary focus of this paper is gender classification using fMRI data to identify emotion-related brain gender differences during adolescence. Additionally, experiments on simulated data and EEG data are included to demonstrate the generalizability of the proposed method. Experimental results indicate that incorporating prior knowledge significantly enhances classification performance. Further analysis of brain gender differences suggests that temporal variability in brain activity explains differences in emotion regulation strategies between genders. By adopting the heterogeneous knowledge sharing strategy, the proposed framework can capture the multifaceted characteristics of the brain, improve the generalization of the model, and reduce training costs. Understanding the gender specific neural mechanisms of emotional cognition helps to develop the gender-specific treatments for neurological diseases.

The Matrix‐Free Macro‐Element Hybridized Discontinuous Galerkin Method for Steady and Unsteady Compressible Flows

ABSTRACTThe macro‐element variant of the hybridized discontinuous Galerkin (HDG) method combines advantages of continuous and discontinuous finite element discretization. In this paper, we investigate the performance of the macro‐element HDG method for the analysis of compressible flow problems at moderate Reynolds numbers. To efficiently handle the corresponding large systems of equations, we explore several strategies at the solver level. On the one hand, we utilize a second‐layer static condensation approach that reduces the size of the local system matrix in each macro‐element and hence the factorization time of the local solver. On the other hand, we employ a multi‐level preconditioner based on the FGMRES solver for the global system that integrates well within a matrix‐free implementation. In addition, we integrate a standard diagonally implicit Runge–Kutta scheme for time integration. We test the matrix‐free macro‐element HDG method for compressible flow benchmarks, including Couette flow, flow past a sphere, and the Taylor–Green vortex. Our results show that unlike standard HDG, the macro‐element HDG method can operate efficiently for moderate polynomial degrees, as the local computational load can be flexibly increased via mesh refinement within a macro‐element. Our results also show that due to the balance of local and global operations, the reduction in degrees of freedom, and the reduction of the global problem size and the number of iterations for its solution, the macro‐element HDG method can be a competitive option for the analysis of compressible flow problems.

Fleet size problem for one-way electric carsharing services considering customers’ waiting tolerance and waiting stress

Fleet size problem for one-way electric carsharing services considering customers’ waiting tolerance and waiting stress

The Heterogeneous-Fleet Electric Vehicle Routing Problem with Nonlinear Charging Functions

This paper introduces the Heterogeneous-Fleet Electric Vehicle Routing Problem with Nonlinear Charging Functions (HEVRP-NL). This problem involves routing a heterogeneous fleet of electric vehicles, utilizing multiple charging modes, and accounting for time-dependent waiting time functions at charging stations. The problem is modeled using a path-based mixed-integer linear programming formulation. To solve this problem, we present an algorithmic framework that alternates between two components. The first component is an iterated local search algorithm with a problem-specific route evaluation function, which obtains local optimal solutions and generates a pool of high-quality routes. The second component is a set-partitioning model that combines a subset of routes from the pool, which is constructed based on reduced costs, into a feasible solution. We design HEVRP-NL benchmark instances based on the publicly available electric fleet size and mix vehicle routing problem instances, which are used to evaluate our methods. For small-scale HEVRP-NL instances, the proposed model can be employed in a general-purpose mixed integer programming solver to achieve optimal solutions or find good upper bounds. This exact approach serves as an evaluation of our heuristic algorithm’s ability to attain optimal solutions rapidly. Extensive computational results on large-scale HEVRP-NL instances illustrate the advantages of considering non-linear charging functions and show the impact of waiting time at the charging stations. Finally, we conduct experiments on 120 benchmark instances for the E-VRP-NL and 168 benchmark instances for the E-FSMFTW-PR, which are the special cases of our problem. The results indicate that our algorithm outperforms existing approaches from the literature and identifies 32 new best solutions for the E-VRP-NL and 33 new best solutions for the E-FSMFTW-PR, respectively.

Integrated proactive-reactive tool for dynamic scheduling of parallel machine operations

This study introduces an innovative scheduling tool for dynamic identical parallel machine environments, leveraging a non-preemptive overlapping load adjustment methodology. Central to this tool is a dual-phase proactive-reactive dynamic scheduling mechanism. Initially, in the proactive phase, the tool utilizes an earliest loading strategy to map out a range of viable scheduling alternatives, capitalizing on job slack times without cementing any schedules prematurely. This is followed by a reactive phase, wherein the tool dynamically adjusts to real-time disturbances via a user-controlled module, thereby crafting adaptable schedules while ensuring continuous feasibility. Enhanced by real-time graphical displays, the tool offers end-users a holistic view of the scheduling process and the implications of unexpected disruptions, aiding in informed decision-making. Extensive computational experiments encompassing 600 instances of varying sizes - small, medium, and large - underscore the tool’s efficiency. These experiments reveal the generation of a wide spectrum of scheduling alternatives, ranging on average from 1676 for small-sized instances to as many as 298,210 for large-sized ones. Notably, the average maximum completion time (Cmax) closely mirrors the results obtained from the Cplex optimizer, with an average deviation of merely 1.07%, 0.48%, and 12.23% for small, medium, and large instances, respectively. Moreover, despite the creation of up to 300,000 potential scheduling solutions for larger instances, the tool consistently yields a low average number of tardy jobs; 1.28, 1.87, and 2.07 for the respective problem sizes, which clearly highlights its effectiveness. The outcomes conclusively demonstrate the tool’s flexibility and efficiency, offering end-users a multitude of superior scheduling options in the presence of unforeseen disruptions that characterize today’s operating environment.

Escape: an optimization method based on crowd evacuation behaviors

Meta-heuristic algorithms, particularly those based on swarm intelligence, are highly effective for solving black-box optimization problems. However, maintaining a balance between exploration and exploitation within these algorithms remains a significant challenge. This paper introduces a useful algorithm, called Escape or Escape Algorithm (ESC), inspired by crowd evacuation behavior, to solve real-world cases and benchmark problems. The ESC algorithm simulates the behavior of crowds during the evacuation, where the population is divided into calm, herding, and panic groups during the exploration phase, reflecting different levels of decision-making and emotional states. Calm individuals guide the crowd toward safety, herding individuals imitate others in less secure areas, and panic individuals make volatile decisions in the most dangerous zones. As the algorithm transitions into the exploitation phase, the population converges toward optimal solutions, akin to finding the safest exit. The effectiveness of the ESC algorithm is validated on two adjustable problem size test suites, CEC 2017 and CEC 2022. ESC ranked first in the 10-dimensional, 30-dimensional tests of CEC 2017, and the 10-dimensional and 20-dimensional tests of CEC 2022, and second in the 50-dimensional and 100-dimensional tests of CEC 2017. Additionally, ESC performed exceptionally well, ranking first in the engineering problems of pressure vessel design, tension/compression spring design, and rolling element bearing design, as well as in two 3D UAV path planning problems, demonstrating its efficiency in solving real-world complex problems, particularly complex problems like 3D UAV path planning. Compared with 12 other high-performance, classical, and advanced algorithms, ESC exhibited superior performance in complex optimization problems. The source codes of ESC algorithm will be shared at https://aliasgharheidari.com/ESC.html and other websites.

Comparative analysis of direct search methods for material design optimization

This paper contributes to the field of optimization for periodic composite material design by systematically evaluating the performance of four direct search algorithms: greedy, simulated annealing (SA), genetic algorithms (GA), and particle swarm optimization (PSO). The central goal is to determine the optimal arrangement of two material domains inside a representative volume element (RVE) while respecting the constraint that half volume of the domains is composed of a specific material, with the effective shear modulus as the objective function. The main contribution of this work is the detailed comparison of these algorithms in terms of success rates and computational efficiency, providing critical insights into their suitability for different problem scales. For a small-scale problem (16 cells), PSO achieves a 100% success rate, while SA demonstrates over 90% success but at a higher computational cost. The greedy algorithm, despite its simplicity, achieves a 72% success rate with the lowest computational effort, requiring an average of 256 objective function evaluations. GA, however, exhibits the lowest success rate at 52% and requires the highest average number of evaluations (5003.2). Furthermore, the paper extends the analysis to a more complex problem (36 cells), where the adaptability and efficiency of the algorithms are tested. This investigation highlights the trade-offs between algorithmic complexity, success rate, and computational cost, offering practical guidelines for selecting suitable optimization methods based on problem size and characteristics.

Large-Scale Parameter Estimation for Crystal Structure Prediction. Part 1: Dataset, Methodology, and Implementation.

Crystal structure prediction (CSP) seeks to identify all thermodynamically accessible solid forms of a given compound and, crucially, to establish the relative thermodynamic stability between different polymorphs. The conventional hierarchical CSP workflow suggests that no single energy model can fulfill the needs of all stages in the workflow, and energy models across a spectrum of fidelities and computational costs are required. Hybrid ab initio/empirical force-field (HAIEFF) models have demonstrated a good balance of these two factors, but the force-field component presents a major bottleneck for model accuracy. Existing parameter estimation tools for fitting this empirical component are inefficient and have severe limitations on the manageable problem size. This, combined with a lack of reliable reference data for parameter fitting, has resulted in development in the force-field component of HAIEFF models having mostly stagnated. In this work, we address these barriers to progress. First, we introduce a curated database of 755 organic crystal structures, obtained using high quality, solid-state DFT-D calculations, which provide a complete set of geometry and energy data. Comparisons to various theoretical and experimental data sources indicate that this database provides suitable diversity for parameter fitting. In tandem, we also put forward a new parameter estimation algorithm implemented as the CrystalEstimator program. Our tests demonstrate that CrystalEstimator is capable of efficiently handling large-scale parameter estimation problems, simultaneously fitting as many as 62 model parameters based on data from 445 structures. This problem size far exceeds any previously reported works related to CSP force-field parametrization. These developments form a strong foundation for all future work involving parameter estimation of transferable or tailor-made force-fields for HAIEFF models. This ultimately opens the way for significant improvements in the accuracy achieved by the HAIEFF models.

Sizing large-scale industrial heat pump for heat recovery from treated municipal sewage in coal-fired district heating system

Electrification of district heating and deep integration of sectors of national economies are fundamental elements of the future smart energy systems. This paper discusses the problem of optimal sizing of large-scale high-temperature heat pumps using treated sewage water as a heat source in a coal-fired district heating system. The study presents an approach to modelling of heat pump system that enables techno-economic analysis for investment decision making. Such analysis is enabled by a black-box-type identification model of the selected industrial heat pump. The model was developed based on the data generated by physical modelling of the heat pump using Ebsilon Professional software. In addition, it is proposed that the heat pump system is integrated with a dedicated photovoltaic power plant. The case study takes into consideration site-specific technical, economic, ecological, and legal constraints, weather conditions, hydraulic performance of the heat-ing network, and variability of loads within the sewage and the district heating systems. The results revealed that the proposed modelling approach is effective regarding multiple simulations and system optimisation. In addition, it was found that large-scale heat pump projects can be technically feasible and profitable if the heat pump is appropriately sized and operated. In the given case, the optimum size of the heat pump for a city of around 180 000 inhabitants is around 12 MW under maximum winter load.

Effects of electric vehicles on energy sharing for optimal sizing of solar PV and battery energy storage

Energy sharing for homes with electric vehicles (EVs) enhances sustainability by optimizing energy usage, reducing peak demand, and integrating renewable energy sources, thereby lowering costs and improving energy resilience. This study investigates the effects of EVs on optimal sizing problem of solar photovoltaic (SPV) and battery energy storage system (BESS) for grid-tied homes which participate in energy sharing schemes. In this paper, it is assumed that the energy is shared between two homes: home-1 as the prosumer which has an EV and intends to buy SPV and BESS, and house-2 which is a consumer. The optimization problem is formulated to achieve the minimum cost of electricity (COE) for home-1 and to reduce the COE for home-2 while taking consideration of the design constraints over the project lifespan. A rule-based energy management system is developed for different sets of configurations to compare the economic and operational results. The optimization is done by incorporating realistic annual data of the irradiance, temperature, load, and uncertainties of EV. The developed optimization technique is general in nature and can be used for any grid tied homes willing to share the electricity. Sensitivity analyses on costs of SPV-BESS, home energy demand, and grid export constraints are provided. Uncertainty analyses investigates the price of energy sharing and solar PV generation. The impact of various EV models with their respective battery capacity is also analyzed. The results show that the proposed energy-sharing methodology reduces the COE for prosumer and consumer by 1.2 ¢/kWh and 3.6 ¢/kWh, respectively.

Application of a novel metaheuristic algorithm inspired by connected banking system in truss size and layout optimum design problems and optimization problems

Optimum design of truss structures can be a challenging and difficult field of study specially if an optimum design problem is comprised of continuous and discrete decision variables such as in truss size and layout optimization problems. Usually metaheuristic approaches are selected to solve these kind of problems and there are many different metaheuristic methods that have been used in dealing with truss optimization problems, which provide optimum solutions for those problems. However, among these various proposed metaheuristics, it is very seldom to face and find a method that is based on technologies such as computer networks. This paper aims to utilize a metaheuristic algorithm which is based on some principals of connected banking systems, which is called the Connected Banking System Optimizer(CBSO) algorithm. The performance of the CBSO is tested against three benchmark truss size/layout optimum design problems namely 15, 18 and 25 bar truss structures. Besides the truss size/layout problems, CEC-BC-2017 test functions with ten dimensions and also three other constrained optimum design problems are also investigated to examine the performance of the CBSO in dealing with these standard unconstrained and constrained test functions. The results of the CBSO optimum designs are presented and compared with some of the available studies in the literature. Statistical analyses are done to have a fair evaluation of the CBSO algorithm’s efficiency and its ability in dealing with the benchmark truss size/layout optimization problems. The CBSO outperforms its rivals and presents lighter weight truss structures. There is no need for configuration of extra parameters in the CBSO algorithm when solving the examined benchmark truss structures, which makes it a robust parameter-free optimization algorithm.

Nested benders decomposition for a deterministic biomass feedstock logistics problem

AbstractIn this paper, we address a biomass feedstock logistics problem to supply biomass from production fields to satellite storage locations (SSLs) and from there to bioenergy plants (BePs) and then to a biorefinery. It entails a new problem feature of routing load-out equipment sets among the SSLs to perform loading/unloading of biomass and/or its pre-processing operations. The ownership of the loading equipment is a very capital-intensive link of the ethanol production supply chain, which when loaded onto trucks and routed along the logistics chain significantly brings down the ethanol production costs. This will make ethanol a cost-competitive alternative to fossil fuels, lead to sustainable use of fossil fuels and add to the overall relevance of the bioenergy sector. In this regard, the objective of our problem is to minimize the total cost incurred due to the ownership of equipment sets, fixed setups, and land rental cost, as well as the cost of transporting biomass from the fields to the BePs and biocrude oil from the BePs to the refinery. A mixed-integer mathematical model of the problem is presented, and a nested Benders decomposition-based solution approach is developed which involves decomposing this large problem into three stages. Stage 1 deals with the selection of fields, BePs, and SSLs, and assignment of fields to the SSLs. The remaining model consists of multiple Capacitated Vehicle Routing Problems (CVRPs) that are separable over individual BePs. For each BeP, the CVRP is further decomposed into Stage 2 and Stage 3 sub-problems where the Stage 2 problem is an allocation problem that assigns SSLs to tours associated to each BeP, and the Stage 3 problem is a variant of Traveling Salesman Problem (TSP) that determines the sequence in which equipment is routed over the predesignated set of SSLs for each tour. These sub-problems are integer programs rather than linear programs. First novelty of our proposed approach is to effectively handle the integrality of variables arising due to the consideration of the routing of load-out equipment. Second is solution methodology and in the use of proposed multi-cut version of optimality cuts that capture the solution value at an integer solution for the sub-problems. These cuts aid in faster convergence and are shown to be stronger than those proposed in the literature. The applicability of the proposed methodology is demonstrated by applying it to a real-life problem that utilizes available GIS data for the catchment area of regions around Gretna and Bedford in Virginia. We then solved a set of varying problem size instances using the state-of-the-art CPLEX® Branch-and-Bound and Benders Strategy methods. The CPLEX® algorithms struggled to solve instances even 10 times smaller than the real-life problem size instances; with MIP optimality gaps ranging from 5.85% to 82.79% in the allowed time limit of 10,000 s. On the other hand, our proposed nested Benders decomposition algorithm was able to achieve faster convergence and provided optimal solutions for all the considered problem instances with an average CPU run-time of around 3,700 s. This validates the efficacy and superiority of our solution approach. Lastly, we summarize our work and point out some interesting potential future research opportunities.

A novel binomial-based fuzzy type-2 approach for topology and size optimization of skeletal structures

The current work introduces a new probability-based fuzzy type-2 decision mechanism to adjust the optimization process during the simultaneous size and topology optimization of the Skeletal structural systems. For the probabilistic part a binomial module is developed that feeds the fuzzy mechanism by forecasting success probability for future topological actions. The proposed fuzzy decision mechanism permanently monitors the optimization process and attends to dynamically tune the balance between size and topology actions. The presented strategy, by reducing the number of ineffective iterations, significantly enhances the efficiency of the optimization process. Since the proposed decision mechanism is designed as an auxiliary separate module it can be integrated with different optimization methods. Accordingly, in this study, it is integrated with four different optimization algorithms and applied to solve distinct size and topology problems. To comprehensively evaluate the effect of the proposed strategy a new performance index is defined and employed. The acquired results demonstrate that the proposed decision mechanism considerably enhances the search performance of the algorithms on handling the structural size and topology optimization problems.

Visual software defect prediction method based on improved recurrent criss-cross residual network

PurposeThis study aims to solve the problems of large training sample size, low data sample quality, low efficiency of the currently used classical model, high computational complexity of the existing concern mechanism, and high graphics processing unit (GPU) occupancy in the current visualization software defect prediction, proposing a method for software defect prediction termed recurrent criss-cross attention for weighted activation functions of recurrent SE-ResNet (RCCA-WRSR). First, following code visualization, the activation functions of the SE-ResNet model are replaced with a weighted combination of Relu and Elu to enhance model convergence. Additionally, an SE module is added before it to filter feature information, eliminating low-weight features to generate an improved residual network model, WRSR. To focus more on contextual information and establish connections between a pixel and those not in the same cross-path, the visualized red as integer, green as integer, blue as integer images are inputted into a model incorporating a fused RCCA module for defect prediction.Design/methodology/approachSoftware defect prediction based on code visualization is a new software defect prediction technology, which mainly realizes the defect prediction of code by visualizing code as image, and then applying attention mechanism to extract the features of image. However, the challenges of current visualization software defect prediction mainly include the large training sample size and low sample quality of the data, and the classical models used today are not efficient, and the existing attention mechanisms have high computational complexity and high GPU occupancy.FindingsExperimental evaluation using ten open-source Java data sets from PROMISE and five existing methods demonstrates that the proposed approach achieves an F-measure value of 0.637 in predicting 16 cross-version projects, representing a 6.1% improvement.Originality/valueRCCA-WRSR is a new visual software defect prediction based on recurrent criss-cross attention and improved residual network. This method effectively enhances the performance of software defect prediction.

Scaling whole-chip QAOA for higher-order ising spin glass models on heavy-hex graphs

We show that the quantum approximate optimization algorithm (QAOA) for higher-order, random coefficient, heavy-hex compatible spin glass Ising models has strong parameter concentration across problem sizes from 16 up to 127 qubits for p = 1 up to p = 5, which allows for computationally efficient parameter transfer of QAOA angles. Matrix product state (MPS) simulation is used to compute noise-free QAOA performance. Hardware-compatible short-depth QAOA circuits are executed on ensembles of 100 higher-order Ising models on noisy IBM quantum superconducting processors with 16, 27, and 127 qubits using QAOA angles learned from a single 16-qubit instance using the JuliQAOA tool. We show that the best quantum processors find lower energy solutions up to p = 2 or p = 3, and find mean energies that are about a factor of two off from the noise-free distribution. We show that p = 1 QAOA energy landscapes remain very similar as the problem size increases using NISQ hardware gridsearches with up to a 414 qubit processor.

Integrated multi-manned disassembly line balancing problem with reverse supply chain design strategies by considering lot sizing

ABSTRACT Due to strict governmental regulations, manufacturers have increasingly focused on recovering end-of-life (EoL) products through disassembly lines, aiming to achieve economic benefits while addressing the environmental aspects of supply chain sustainability. Disassembly line balancing (DLB) has become a key challenge for enterprises seeking to perform environmentally conscious manufacturing as part of a closed-loop supply chain policy. Therefore, designing a sustainable closed-loop supply chain requires analyzing interrelated problems by focusing on strategic-, tactical-, and operational-level decisions simultaneously. This study introduces an integrated problem that addresses multi-manned DLB, lot sizing, and reverse supply chain (RSC) design problems. To mathematically represent the problem, a generic optimization model is developed to minimize overall costs, including station opening, inventory holding, shortage, transportation, and collection center/facility opening costs. This study addresses several research questions to explore the impact of distribution strategies (centralized and decentralized) on multi-manned DLB and lot size decisions within the context of reverse supply chain design.

Commentary on translational impact: Applying the bilingual problem size effect to cognitive assessment.

Commentary on translational impact: Applying the bilingual problem size effect to cognitive assessment.