Search Results (138)

The metal–dielectric hybrid tetramer metasurface has received a lot of attention in the field of optical sensing owing to the excellent refractive index sensing performance. However, achieving simultaneous high-quality Q-factor, polarization insensitivity, multi-band tunability across visible to near-infrared spectra, and ultra-narrow linewidth is an urgent problem to be solved. To overcome this challenge, we proposed a metal–dielectric yurt tetramer metasurface. The finite-difference time-domain method was used to simulate the sensing properties. We explored the physical mechanism of different resonance modes, optimized the structure parameters of the metasurface, and investigated the influence of incident light and environmental parameters on the sensing properties. The results show that the proposed structure not only possesses a high Q-factor but also exhibits excellent wavelength tunability in the visible to near-infrared band and has polarization insensitivity. By skillfully introducing the structural size perturbation, the surface plasmon resonance mode and two Fano resonance modes are successfully excited at the wavelengths of 737.43 nm, 808.99 nm, and 939.50 nm. The light–matter interaction at the Fano resonance frequencies is highly enhanced so that a maximum refractive index sensitivity, figures of merit (FOM), and Q-factor of 500.94 nm/RIU, 491.12 RIU⁻¹, and 793.13 are obtained. The narrowest full width at half maximum (FWHM) is 1.02 nm, respectively. This work provides a theoretical basis for the realization of a high-performance metasurface refractive index sensor. Full article

Mass market adoption of EVs is critical for decreasing greenhouse gas emissions and dependence on fossil fuels. However, this transition faces significant challenges, particularly the limited availability of public charging infrastructure. Expanding charging stations and renewable integrated EV parking lots can accelerate the adoption of EVs by enhancing charging accessibility and sustainability. This paper introduces an integrated optimization framework to determine the optimal siting of a Residential Parking Lot (RPL), a Commercial Parking Lot (CPL), and an Industrial Fast Charging Station (IFCS) within the IEEE 33-bus distribution system. In addition, the optimal sizing of rooftop solar photovoltaic (SPV) systems on the RPL and CPL is addressed to enhance energy sustainability and reduce grid dependency. The framework aims to minimize overall power losses while considering long-term technical, economic, and environmental impacts. To solve the formulated multi-dimensional optimization problem, Horse Herd Optimization (HHO) is used. Comparative analyses on IEEE-33 bus demonstrate that HHO outperforms well-known optimization algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO) in achieving lower energy losses. Case studies show that installing a 400-kW rooftop PV system can reduce daily energy expenditures by up to 51.60%, while coordinated vehicle scheduling further decreases energy purchasing costs by 4.68%. The results underscore the significant technical, economic, and environmental benefits of optimally integrating EV charging infrastructure with renewable energy systems, contributing to more sustainable and resilient urban energy networks. Full article

In this study, we introduce a novel interdisciplinary framework that applies concepts from statistical physics, specifically lattice-gas models, to the classical order lot-sizing problem in supply chain management. Traditional methods often rely on heuristic or deterministic approaches, which may fail to capture the inherently probabilistic and dynamic nature of decision-making across multiple periods. Drawing on structural parallels between inventory decisions and adsorption phenomena in physical systems, we constructed a mapping that represented order placements as particles on a lattice, governed by an energy function analogous to thermodynamic potentials. This formulation allowed us to employ analytical tools from statistical mechanics to identify optimal ordering strategies via the minimization of a free energy functional. Our approach not only sheds new light on the structural characteristics of optimal planning but also introduces the concept of configurational entropy as a measure of decision variability and robustness. Numerical simulations and analytical approximations demonstrate the efficacy of the lattice gas model in capturing key features of the problem and suggest promising avenues for extending the framework to more complex settings, including multi-item systems and time-varying demand. This work represents a significant step toward bridging physical sciences with supply chain optimization, offering a robust theoretical foundation for both future research and practical applications. Full article

Past experiences and memory significantly contribute to self-learning and improved decision-making. These can assist decision-makers in refining their strategies for better outcomes. Fractional calculus is a tool that captures a system’s memory or past experience through its repeating patterns. In the realm of uncertainty, neutrosophic set theory demonstrates greater suitability, as it independently assesses membership, non-membership, and indeterminacy. In this article, we aim to extend the theory further by introducing fractional calculus for neutrosophic-valued functions. The proposed method is applied to an economic lot-sizing problem. Numerical simulations of the lot-sizing model suggest that strong memory employment with a memory index of 0.1 can lead to an increase in average profit in memory-independent phenomena with a memory index of 1 by approximately 44% to 49%. Additionally, the neutrosophic environment yields superior profitability results compared to both precise and imprecise settings. The synergy of fractional-order dynamics and neutrosophic uncertainty modeling paves the way for enhanced decision-making in complex, ambiguous environments. Full article

Lot sizing and cutting stock problems are critical for manufacturing companies seeking to optimize resource utilization and minimize waste. This paper addresses the interconnected nature of these problems, often occurring sequentially in industries involving cut items or packaging. We propose a novel mixed integer linear programming (MILP) model that integrates the capacitated lot sizing problem with the one-dimensional cutting stock problem within a multi-level manufacturing framework. The cutting stock problem is addressed using an arc flow formulation. Our model aims to minimize setup, production, holding, and waste material costs while incorporating capacity constraints, setup requirements, inventory balance, and the use of various cutting machines. The effectiveness of our model is demonstrated through numerical experiments using a commercial optimization package. While the model efficiently generates optimal solutions for most scenarios, larger instances pose challenges within the specified time limits. Sensitivity analysis is conducted to evaluate the effect of changing essential parameters of the integrated problem on model performance and to provide managerial insights for real-life applications. Full article

In inventory management, storage capacity constraints complicate multi-item lot-sizing decisions. As the number of items increases, deciding how much of each item to order without exceeding capacity becomes more difficult. Dynamic programming works efficiently for a single item, but when capacity constraints are nearly minimal across multiple items, novel heuristics are required. However, previous heuristics have mainly focused on inventory bound constraints. Therefore, this paper introduces push and pull heuristics to solve the multi-item uncapacitated lot-sizing problem under near-minimal capacities. First, a dynamic programming approach based on a network flow model was used to generate the initial replenishment plan for the single-item lot-sizing problem. Next, under storage capacity constraints, the push operation moved the selected replenishment quantities from the current period to subsequent periods to meet all demand requirements. Finally, the pull operation shifted the selected replenishment quantities from the current period into earlier periods, ensuring that all demand requirements were satisfied. The results of the random experiment showed that the proposed heuristic generated solutions whose performance compared well with the optimal solution. This heuristic effectively solves all randomly generated instances representing worst-case conditions, ensuring robust operation under near-minimal storage. For large-scale problems under near-minimal storage capacity constraints, the proposed heuristic achieved only small optimality gaps while requiring less running time. However, small- and medium-scale problems can be solved optimally by a Mixed-Integer Programming (MIP) solver with minimal running time. Full article

This study addresses the multi-period, multi-item, single-stage capacitated lot sizing problem (CLSP) in a parallel machine environment with machine eligibility constraints under a make-to-order production policy. A mixed-integer linear programming (MILP) model is developed to minimize total operational costs, including production, overtime, extra shifts, inventory holding, and backorders. The make-to-order setting introduces additional complexity by requiring individualized customer orders, each with specific due dates and product combinations, to be scheduled under constrained capacity and setup requirements. The model’s performance is evaluated in the context of a real-world production planning problem faced by a manufacturer of cold-formed steel profiles. In this setting, parallel forming machines process galvanized sheets of cold-rolled steel into a variety of profiles. The MILP model is solved using open-source optimization tools, specifically the HiGHS solver. The results show that optimal solutions can be obtained within reasonable computational times. For more computationally demanding instances, a runtime limit of 300 s is shown to improve solution quality while maintaining efficiency. These findings confirm the viability and cost-effectiveness of free software for solving complex industrial scheduling problems. Moreover, experimental comparisons reveal that solution times and performance can be further improved by using commercial solvers such as CPLEX, highlighting the potential trade-off between cost and computational performance. Full article

Electric vehicles can serve as controllable loads, storing energy during off-peak periods and acting as generation units during peak periods or periods with high electricity prices. They function as distributed generation resources within distribution systems, requiring controlled charging and discharging of batteries. In this paper, we address the problem of the optimal allocation of parking lots within a distribution system to efficiently supply electric vehicle loads. The goal is to determine the best capacity and size of parking lots to meet peak hour demands while considering constraints on the permanent operation of the distribution system. Using the particle swarm optimization (PSO) algorithm, the study maximizes total benefits, taking into account network parameters, vehicle data, and market prices. Results show that installing parking lots could be economically profitable for distribution companies and could improve voltage profiles. Full article

The generalized quadratic assignment problem (GQAP) involves assigning a set of facilities to a set of locations such that the sum of the assignment and transportation costs is minimized. Unlike the traditional one-to-one assignment problem, the GQAP is a many-to-one assignment problem. That is, multiple facilities can be assigned to each location without exceeding the capacity of the location. This research was motivated by the problem of assigning multiple facilities (e.g., machines or equipment) to locations at manufacturing plants. Another well-known application of the GQAP includes the assignment of facilities (i.e., containers) to locations (i.e., storage areas) in container yards. This paper presents simple but very effective approximation algorithms for solving real-world, large-size GQAP instances quickly without spending a lot of time setting the algorithm parameters, since there are few parameters to set. More specifically, a construction algorithm is used to generate an initial solution for the proposed problem, and the initial solution is improved using a simulated annealing algorithm. The performance of the proposed algorithms is tested with respect to solution quality and computation time on a set of test problems available in the literature. The results show the effectiveness of the proposed algorithms. Full article

Background/Objectives: One of the first-line disease-modifying treatments of multiple sclerosis (MS) is Glatiramer Acetate (GA), which requires daily or three-times-weekly subcutaneous injections. Disease progression, while slowed, still occurs with time. Increasing the impact of the treatment while decreasing the frequency of injections would be ideal. The mechanism of action of GA remains undefined. We developed an alternate approach, KGYY₆, whose mechanism of action targets the CD40 receptor with promising results in an Experimental Autoimmune Encephalomyelitis (EAE) model. Methods: GA and a CD40-targeting peptide, KGYY₆, were formulated as slow-release particles used to treat EAE in C57BL/6 mice. Results: Compared to liquid formulations, the particle formulations vastly improved drug efficacy in both cases, which would be advantageous in treating MS. GA is a combination of randomly generated peptides, in the size range of 5000–9000 Da, using the amino acids E, A, Y, and K. This approach introduces batch differences that impacts efficacy, a persistent problem with GA. KGYY₆ is generated in a controlled process and has a motif, K-YY, which could be generated when manufacturing GA. When testing two different lots of GA or KGYY₆, the latter performed equally well across lots, while GA did not. Conclusions: Slow-release formulations of both GA and KGYY₆ vastly improve the efficacy of both, and KGYY₆ is more consistent in efficacy across different lots. Full article

The Single-Source Shortest Paths (SSSP) graph problem is a fundamental computation. This study attempted to characterize concretely the energy behaviors of the two primary methods to solve it, the Bellman–Ford and Dijkstra algorithms. The very different interactions of the algorithms with the hardware may have significant implications for energy. The study was motivated by the multidisciplinary nature of the problem. Gaining better insights should help vital applications in many domains. The work used reliable embedded sensors in an HPC-class CPU to collect empirical data for a wide range of sizes for two graph cases: complete as an upper-bound case and moderately dense. The findings confirmed that Dijkstra’s algorithm is drastically more energy efficient, as expected from its decisive time complexity advantage. In terms of power draw, however, Bellman–Ford had an advantage for sizes that fit in the upper parts of the memory hierarchy (up to 2.36 W on average), with a region of near parity in both power draw and total energy budgets. This result correlated with the interaction of lighter logic and graph footprint in memory with the Level 2 cache. It should be significant for applications that rely on solving a lot of small instances since Bellman–Ford is more general and is easier to implement. It also suggests implications for the design and parallelization of the algorithms when efficiency in power draw is in mind. Full article

The market share of electric vehicles (EVs) is growing rapidly. However, given the huge demand for parking and charging of electric vehicles, supporting facilities generally have problems such as insufficient quantity, low utilization efficiency, and mismatch between supply and demand. In this study, based on the actual EV operation data, we propose a driver travel-charging demand prediction method and a fuzzy bi-objective optimization method for location and size planning of charging parking lots (CPLs) based on existing parking facilities, aiming to reduce the charging waiting time of EV users while ensuring the maximal profit of CPL operators. First, the Monte Carlo method is used to construct a driver travel-charging behavior chain and a user spatiotemporal activity transfer model. Then, a user charging decision-making method based on fuzzy logic inference is proposed, which uses the fuzzy membership degree of influencing factors to calculate the charging probability of users at each road node. The travel and charging behavior of large-scale users are then simulated to predict the spatiotemporal distribution of charging demand. Finally, taking the predicted charging demand distribution as an input and the number of CPLs and charging parking spaces as constraints, a bi-objective optimization model for simultaneous location and size planning of CPLs is constructed, and solved using the fuzzy genetic algorithm. The results from a case study indicate that the planning scheme generated from the proposed methods not only reduces the travelling and waiting time of EV users for charging in most of the time, but also controls the upper limit of the number of charging piles to save construction costs and increase the total profit. The research results can provide theoretical support and decision-making reference for the planning of electric vehicle charging facilities and the intelligent management of charging parking lots. Full article

Carbon emission reduction is an important issue for sustainable development. The logistics industry is a key area for carbon emission reduction. The modern logistics supply chain includes logistics parks (fourth-party logistics, 4PL) and small, medium and micro logistics enterprises settled in the parks (third-party logistics, 3PL). Facing the pressure of the need for sustainable development, 3PL enterprises need to invest a lot of money in green transformation. However, 3PL enterprises are subject to serious financial constraints. In order to break the capital constraints, 3PL enterprises can raise funds from banks or from 4PL financing. Under the carbon quota policy, the government can regulate the 4PL or the 3PL. Therefore, this paper uses the Stackelberg game model to build a green financing strategy model of small and medium-sized logistics enterprises considering different supervision methods under carbon quotas, explores the optimal emission reduction decision-making process of small and medium-sized logistics enterprises, and provides solutions to the financing problems of small and medium-sized logistics enterprises in realizing sustainable development. The study found that the decisions of enterprises under different governmental supervision methods are affected by carbon quotas, and the government’s supervision of 3PL is more conducive to carbon emission reduction; in this scenario, the 4PL financing strategy is more likely to be adopted compared with bank financing, because 4PL charge lower service fees, thus encouraging 3PL to increase their low-carbon investment. Finally, this paper puts forward two different carbon emission supervision methods and considers the green financing services of 4PL; this provides references for government supervision and the sustainable development strategies of logistics enterprises. Full article

The capacitated multi-item lot-sizing problem, referred to as the CLSP, is to determine the lot sizes of products in each period in a given planning horizon of finite periods, meeting the product demands and resource limits in each period, and to minimize the total cost, consisting of the production, inventory holding, and setup costs. CLSPs are often encountered in industry production settings and they are considered NP-hard. In this paper, we propose a Lagrange relaxation (LR) approach for their solution. This approach relaxes the capacity constraints to the objective function and thus decomposes the CLSP into several uncapacitated single-item problems, each of which can be easily solved by dynamic programming. Feasible solutions are achieved by solving the resulting transportation problems and a fixup heuristic. The Lagrange multipliers in the relaxed problem are updated by using subgradient optimization. The experimental results show that the LR approach explores high-quality solutions and has better applicability compared with other commonly used solution approaches in the literature. Full article

Aiming at the problems of the underwater existence of some targets with relatively small size, low contrast, and a lot of surrounding interference information, which lead to a high leakage rate and low recognition accuracy, a new improved YOLOv7 underwater target detection algorithm is proposed. First, the original YOLOv7 anchor frame information is updated by the K-Means algorithm to generate anchor frame sizes and ratios suitable for the underwater target dataset; second, we use the PConv (Partial Convolution) module instead of part of the standard convolution in the multi-scale feature fusion module to reduce the amount of computation and number of parameters, thus improving the detection speed; then, the existing CIou loss function is improved with the ShapeIou_NWD loss function, and the new loss function allows the model to learn more feature information during the training process; finally, we introduce the SimAM attention mechanism after the multi-scale feature fusion module to increase attention to the small feature information, which improves the detection accuracy. This method achieves an average accuracy of 85.7% on the marine organisms dataset, and the detection speed reaches 122.9 frames/s, which reduces the number of parameters by 21% and the amount of computation by 26% compared with the original YOLOv7 algorithm. The experimental results show that the improved algorithm has a great improvement in detection speed and accuracy. Full article