Programming Model Research Articles

Optimizing Attended Home Delivery: Multiple recovery options and customer availability profiles to face synchronization failures

In the growing sector of Attended Home Delivery, unsynchronized deliveries between couriers and recipients affect both customers’ satisfaction and companies’ costs. Hence, reducing such failures improves companies’ service quality and logistics efficiency. To address this issue, we study an Attended Home Delivery Problem with Recovery Options (AHDPRO) in which customers specify their probability of being at home during different timeslots of the day and their preferred recovery option in case of a synchronization failure. The options include leaving the package in a predefined safe location, bringing it to a generic collection point, or scheduling a second delivery attempt. Each alternative involves different costs and, in most cases, additional operational decisions. The AHDPRO aims to complete all customer deliveries while minimizing overall routing times as well as the overall penalty due to the recovery actions implemented and weighted by the probability of a synchronization failure to occur. We propose a branch-and-cut algorithm, including valid inequalities and heuristic procedures, to solve a Mixed-Integer Linear Programming model based on an expanded graph. Using the developed method as a tool for evaluating costs and operations, we conduct an experimental campaign on scenarios adapted from the literature involving lexicographic-based optimization procedures able to address the multiple attributes of the solutions. The results obtained allow us to assess the impact of the different recovery options on the optimal solutions and their values. Additionally, the results yield several managerial insights for companies operating in the Attended Home Delivery sector, such as the timeslot length, perceived service quality, and other key operational factors contributing to efficient planning and improved customer satisfaction.

Optimizing peak shaving operation in hydro-dominated hybrid power systems with limited distributional information on renewable energy uncertainty

The increasing integration of renewable energy sources (RES) in power systems poses challenges for peak shaving operations due to RES uncertainty. However, it is difficult to obtain complete distributional information for uncertainty modeling. This study focuses on optimizing peak shaving in hydro-dominated hybrid power systems under such uncertainty. We utilize limited distributional information of RES forecast errors, specifically the first two moments, to build a moment ambiguity set. Employing distributionally robust chance-constrained programming (DRCCP), we develop a peak shaving model that quantifies the flexibility reserve of hydropower by risk level and the forecast errors. To enhance computational tractability, we apply the Chebyshev inequality to reformulate the moment-based DRCCP model into a mixed-integer linear programming model. Numerical simulations conducted on a provincial power grid in China validate the model's effectiveness. Key findings indicate that: (1) The model effectively leverages hydropower to provide ramping flexibility for peak shaving and quantifies the flexibility reserve needed for RES forecast errors. (2) This uncertainty modeling approach is more practical than probability distribution function-based methods, ensuring reliable peak shaving scheduling and reducing conservatism. (3) Decision-makers can adjust risk level to modify hydropower flexibility reserve, balancing robustness and conservatism of peak shaving scheduling.

Optimizing task assignment and routing operations with a heterogeneous fleet of unmanned aerial vehicles for emergency healthcare services

This paper studies the optimization of task assignment and pickup and delivery operations using a heterogeneous fleet of unmanned aerial vehicles (UAVs). We specifically address the distribution of emergency medical supplies, including medications, vaccines, and essential medical aid, as well as the collection of biological blood samples for testing and analysis. Unique challenges, such as supply shortages, time windows, and geographical considerations, are explicitly taken into account. The problem is first formulated as a mixed-integer linear programming model aimed at maximizing the total profit derived from the execution of a set of emergency healthcare pickup and delivery tasks. An enhanced Q-learning-based adaptive large neighborhood search (QALNS) is proposed for large-scale benchmark instances. QALNS exhibits a superior performance on benchmark instances. It also improves the quality of the solutions on average by 5.49% and 6.86% compared to the Gurobi solver and a state-of-the-art adaptive large neighborhood search algorithm, respectively. Sensitivity analyses are performed on critical factors contributing to the performance of the QALNS algorithm, such as the learning rate and the discount indicator. Finally, we provide managerial insights on the use of the fleet of UAVs and the design of the network.

Site Reassignment for Mobile Outreach Teams: Investigating the Effectiveness of Decentralized Decision Making

Problem definition: United Nations Sustainable Development Goal (SDG) 3.8 states that health coverage should be universal by 2030. This is challenging in rural and poor areas. To address this challenge, mobile outreach teams of healthcare workers visit a fixed set of remote sites to provide healthcare services. Because of dynamics in demand and supply, once-rational site-to-team assignment decisions can become far from optimal over time. This paper considers the problem of reassigning sites to teams to maximize effectiveness. Solving this problem through a central planner does not fit the context: outreach teams commonly have a high degree of decision-making autonomy. We study a decentralized approach where subsets of teams collaborate in a series of team meetings to reassign sites. The key question for decision makers is whether and when such an approach is effective. Methodology/results: We propose a mixed-integer programming model for centralized site reassignment. We extend this model to represent the decentralized approach and develop a set of simple decision rules for this approach. We use empirical data from six country outreach programs of the nongovernmental organization MSI Reproductive Choices. Our results suggest that, when properly designed, decentralized decision making performs close to centralized decision making, and even outperforms it in the case of inaccurate information at the central level. The finding remains valid when demand and supply fluctuate, and is insensitive to the chosen objective. Managerial implications: Humanitarian organizations currently deploy mobile outreach teams to provide a wide variety of health services. We present several useful insights for decision makers in humanitarian organizations when making design choices, taking account of context. In particular, we show that decentralized site reassignment provides a feasible and effective alternative to centralized approaches in many contexts. Supplemental Material: The online supplement is available at https://doi.org/10.1287/msom.2021.0437 .

Revisiting the shuffle of generalized Feistel structure

The Generalized Feistel Structure (GFS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ exttt{GFS}$$\\end{document}) is one of the most widely used frameworks in symmetric cipher design. In FES 2010, Suzaki and Minematsu strengthened the cryptanalysis security of GFS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ exttt{GFS}$$\\end{document} by searching for shuffles with the best diffusion property. In ASIACRYPT 2018, Shi et al. suggested a set of shuffles, which makes GFS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ exttt{GFS}$$\\end{document} a better resistance against Demirci–Selcuk meet-in-the-middle cryptanalysis. Since these shuffles are different from the currently known good ones and also different from the shuffles used in TWINE\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ exttt{TWINE}$$\\end{document} and LBlock\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ exttt{LBlock}$$\\end{document}, our research focuses on a more comprehensive evaluation of GFS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ exttt{GFS}$$\\end{document} with different shuffles, including diffusion property of shuffle, differential, linear, impossible differential, zero-correlation linear, integral and Demirci–Selcuk meet-in-the-middle cryptanalysis, to find the best one. Such evaluations entail significant time consumption. Thus, we utilize Mixed Integral Linear Programming models and introduce an evaluate-and-filter strategy to achieve it efficiently. Our results verify that the shuffles discovered by Suzaki and Minematsu and those used in TWINE\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ exttt{TWINE}$$\\end{document} and LBlock\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ exttt{LBlock}$$\\end{document} are the best so far. We also find that the cryptanalysis resistances of GFS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ exttt{GFS}$$\\end{document} are not necessarily consistent. It is this finding that makes the necessity of our more comprehensive evaluation self-evident.

Train assignment and handling capacity arrangement in multi-yard railway container terminals: An enhanced adaptive large neighborhood search heuristic approach

Expanding terminal scale and constructing multiple railway handling yards have become popular strategies for leading railway container terminals globally to cope with the ever-increasing handling volume. Although such an approach greatly enhances the terminal’s productivity, it also intensifies handling operations and introduces additional inter-yard interactions, complicating terminal management. To address these challenges, this paper investigates an integrated optimization approach for multi-yard railway container terminals, which feature both rail-road and rail-rail container transshipment operations. The train assignment plan for each yard and the handling capacity arrangement for each train are jointly optimized while considering inter-yard container transits, workload allocation for yards, and safety requirements in train shunting. This problem is formulated as a nonlinear programming model, with the objective to minimize operational delay and service time for each incoming train, and to minimize workload differences and container transit volume among yards. To efficiently solve this problem, this research develops an enhanced adaptive large neighborhood search (EALNS) heuristic, which includes several customized operators and feasibility repair methods, and is further enhanced with a local search method and backtracking mechanism compared to the standard ALNS framework. Computational experiments with different data scales and problem settings demonstrate the superiority of the EALNS in terms of solution quality and stability compared with three other solution methods. Additionally, practical insights for terminal operations are drawn through detailed analysis of different infrastructure configurations, transshipment train characteristics, and unit cost settings.

A multi-objective production scheduling model and dynamic dispatching rules for unrelated parallel machines with sequence-dependent set-up times

This study presents a production scheduling for unrelated parallel machines with machine and job sequence-dependent setup times. The system performance measures to minimize include makespan, total tardiness, and number of tardy jobs. The aim of the study is to develop a solution methodology that can solve the problem for the large scale. First, the problem is formulated as a mixed-integer linear programming model. The augmented ɛ-constraint method is applied to find Pareto solutions for small problem instances. The purpose is to demonstrate that Pareto solutions, which balance the trade-offs among three measures of performance, can be found for these instances. Dispatching rule-based heuristics is developed to solve large problem instances. The heuristics feature three dispatching rules that are designed to handle the dependent setup time. In addition, these rules are combined into six variants using a time-based rule-switching mechanism. The heuristics are tested with 18 problem instances, containing 244 to 298 jobs, in two demand scenarios derived from the monthly demand data from an industrial user. Under each demand scenario, a set of heuristics that provides the best performance with respect to the three measures is identified. The heuristics include combinations of the shortest completion time and due date-based rules. Finally, a multi-criteria decision-making analysis is performed to determine the conditions specified by the weight given to each measure, with which one heuristic is preferred over the others.

Economically viable reshoring of supply chains under ripple effect

This paper presents stochastic mixed integer linear programming models for economically viable supply chain reshoring under the ripple effect, that is, for the integrated optimization of supply chain reshoring and supply chain viability. The problem objective is to select supply chain facilities for relocation and backup suppliers for recovery supplies to simultaneously minimize cost, maximize service and enhance viability. If the primary supplier is not selected for reshoring, the viability can alternatively be improved by recovery supplies from the backup supplier. The problem is formulated as a multi-portfolio stochastic optimization. The portfolio of suppliers/plants for reshoring is optimized along with an alternative recovery portfolio of backup supplies. This study indicates that reshoring decisions directly affect supply chain viability and have positive impact on both its average and worst-case performance. The more risk-aversive is viable reshoring (the confidence level greater than 0.90), the greater is also improvement of supply chain business-as-usual resilience. If increase of expected customer service is more important than cost reduction, supplier reshoring is preferable to backup sourcing. The findings also indicate that cost- and service-optimal reshoring decisions are getting closer when the government subsidy for reshoring significantly increases such that the reduced reshoring costs and domestic costs do not exceed the achieved reduction of lost sales and backup sourcing. Then both conflicting objectives can be simultaneously achieved, which is a highly desirable solution of the economically viable reshoring of supply chain. Numerical examples based on a real-world industrial supply chain prove practical usefulness of the proposed stochastic approach and its high computational efficiency.

Distributionally robust single machine scheduling with release and due dates over Wasserstein balls

Single machine scheduling aims at determining the job sequence with the best desired performance, and provides the basic building block for more advanced scheduling problems. In the present study, a single machine scheduling model with uncertain processing time is considered by incorporating the job release time and due date. The job processing time follows unknown probability distribution, and can be estimated via the historical data. To model the uncertainty, the processing time distribution is defined over a Wasserstein ball ambiguity set, which covers all feasible probability distributions within the confidence radius of the empirical distribution, known as the center of the ball. Then a data-driven distributionally robust scheduling model is constructed with individual chance constraints. In particular, two equivalent reformulations are derived with respect to the ℓ1-norm and ℓ2-norm metrics of the Wasserstein ball, namely, a mixed-integer linear programming and a mixed-integer second order cone programming model, respectively. To accelerate the solving of large-scale instances, a tailored constraint generation algorithm is introduced. In the numerical analysis, the proposed distributionally robust scheduling approach is compared with the state-of-the-art methods in terms of out-of-sample performance.

A new branch-and-cut approach for integrated planning in additive manufacturing

In recent years, there has been considerable interest in the transformative potential of additive manufacturing (AM) since it allows for producing highly customizable and complex components while reducing lead times and costs. The rise of AM for traditional and new business models enforces the need for efficient planning procedures for AM facilities. In this area, the assignment and sequencing of components to be built by an AM machine, also called a 3D printer, is a complex challenge combining two combinatorial problems: The first decision involves the grouping of parts into production batches, akin to the well-known bin packing problem. Subsequently, the second problem pertains to the scheduling of these batches onto the available machines, which corresponds to a parallel machine scheduling problem. For minimizing makespan, this paper proposes a new branch-and-cut algorithm for integrated planning for unrelated parallel machines. The algorithm is based on combinatorial Benders decomposition: The scheduling problem is considered in the master problem, while the feasibility of an obtained solution with respect to the packing problem is checked in the sub-problem. Current state-of-the-art techniques are extended to solve the orthogonal packing with rotation and used to speed up the solution of the sub-problem. Extensive computational tests on existing and new benchmark instances show the algorithm’s superior performance, improving the makespan by 18.7% on average, with improvements reaching up to 97.6% for large problems compared to an existing integrated mixed-integer programming model.

Proximal Policy Optimization with Population-based Variable Neighborhood Search Algorithm for Coordinating Photo-Etching and Acid-Etching Processes in Sustainable Storage Chip Manufacturing

In the complex process of manufacturing storage chips, the photo-etching and acid-etching stages play a crucial role, significantly affecting energy consumption and environmental impact. This paper introduces a novel Bi-Level Programming Model for Storage Chip Manufacturing (BLPM-SCM) aimed at optimizing the coordination between these two stages. The upper-level model focuses on minimizing the time it takes to complete wafer production, while the lower-level model seeks to reduce the number of acid-etching tanks used, thereby balancing production efficiency with resource utilization. To address the inherent complexity of the bi-level model, we present a hybrid meta-heuristic algorithm that combines Proximal Policy Optimization (PPO) with a Population-based Variable Neighborhood Search (PVNS) method. The PPO-PVNS algorithm enhances the intensification phase by adaptively selecting shaking and local search strategies, while PVNS supports the diversification phase, ensuring comprehensive exploration of the search space through iterative updates of the solution population. Extensive numerical experiments demonstrate the algorithm's superior performance and generalization capabilities in optimizing the manufacturing process. It significantly improves the coordination between the photo-etching and acid-etching stages, achieving dual optimization of energy consumption and environmental benefits. Furthermore, this study provides valuable insights and decision-making tools for industry practitioners, offering innovative solutions for scheduling optimization in the semiconductor sector and promoting more sustainable and efficient production practices.

Mixed-integer programming models and heuristic algorithms for the maximum value dynamic network flow scheduling problem

Various applications in contested logistics and infrastructure restoration require dynamic flow solutions characterized by a schedule of network flows consecutively transmitted over a sequence of successive periods. For these schedules, we assume flows transmit via arcs during periods while flows reside at nodes from one period to the next. Within this context, we introduce the Maximum Value Dynamic Network Flow Problem (MVDFP) in which we seek to maximize the cumulative value of a non-simultaneous network flow schedule that accumulates node value whenever some minimum amount of flow resides at a node between periods. For solving the MVDFP, we first introduce a large mixed-integer program (MIP). As this MIP can become computationally-expensive for large networks, we present a trio of computationally-effective, easy to implement heuristic approaches that solve a series of smaller, more manageable MIPs. These heuristic approaches typically determine high-quality solutions significantly faster than the MIP obtains an optimal solution by dividing the full network flow schedule into a sequence of consecutive shorter network flow subschedules. In many cases, at least one of our heuristic approaches produces an optimal solution in a fraction of the MIP’s computational time. We present extensive computational results to highlight our heuristics’ efficacy, discuss for what instances each approach may be most applicable, and detail future research avenues.

Profit-oriented balancing of two-sided disassembly lines with resource-dependent task times

PurposeAs a result of environmentally conscious production requirements in the world, the concept of disassembly has been a focus of interest by researchers and practitioners over the last two decades. Disassembly is an important process in circular economy to recover and reuse of parts and materials. End-of-life and large-sized products such as minibuses and trucks may be disassembled on two-sided lines. The ability of using both right and left sides of two-sided lines may increase line efficiency and reduce space requirements across the line. This paper aims to address a two-sided disassembly line balancing problem (TSDLBP), which deals with assigning disassembly tasks, various equipments and assistants to the workstations to maximize total net recovery profit of the line.Design/methodology/approachA detailed explanation of the TSDLBP is first presented in the paper. A new 0–1 integer linear programming model is then proposed for the TSDLBP, aiming at maximizing total net recovery profit from disassembly of products. A set of test problems is generated, and an experimental analysis is conducted to make a comparison between traditional one-sided disassembly lines (TOSDL) and two-sided disassembly lines by means of performance improvement rate.FindingsOptimal results are obtained in 132 (81.48%) out of 162 the TOSDL balancing problems, while 92 (56.79%) out of 162 the TSDLBP using the proposed model. Total net recovery profits are compared on 88 problems for which optimal solutions are obtained in both the TOSDL and the TSDLBP. Results showed that implementing two-sided disassembly lines provides 29.18% increment in total net recovery profit compared to the TOSDL. Furthermore, the effects of different parameter levels on the net recovery profit are analyzed using two-way analysis of variance. According to the results, implementing two-sided disassembly line configuration increases total net recovery profit of the line significantly compared to traditional disassembly line configuration.Originality/valueThe use of disassembly lines has become essential because of increasing consumption that results in a huge number of end-of-life products in the world. Two-sided disassembly lines may be preferred for dismantling large-sized products due to their high disassembly capacity and fewer space requirements. This paper proposes a new mathematical model for disassembly line balancing problem. The proposed model differs from the existing models by means of efficiently assigning limited disassembly resources as well as assigning disassembly tasks to the workstations to maximize total net recovery profit of the production system. The model allows decision-makers to consider several resource limitations when balancing their disassembly lines. The paper also provides a comprehensive experimental study to compare traditional and two-sided disassembly lines by means of profitability of disassembly processes.

Measuring carbon emission performance in China's energy market: Evidence from improved non-radial directional distance function data envelopment analysis

The most complex challenge facing the energy market is identifying effective solutions to reduce CO2 emissions (CEs) and enhance environmental performance (EP). Coal production within the power sector is the primary source of these emissions. In this study, we developed a novel linear programming model that accounts for undesirable outputs to assess the EP of 15 power enterprises in eastern China from 2016 to 2020. In addition, we employed a global non-radial Malmquist-Luenberger productivity index (GNML) to analyse the mechanisms influencing changes in efficiency among these enterprises. Our findings indicate that, while the EP of the power industry in eastern China improved, it remains at a relatively low level and exhibits instability. Moreover, technological efficiency (TE) and scale efficiency (SE) play a significant role in determining production efficiency within the sector. Therefore, it is essential for industry managers to implement standardized production management regulations, enhance technological development and scale investments, and strengthen control over unintended emissions that could facilitate energy transition.

An interesting wall-crossing: failure of the wall-crossing/MMP correspondence

We introduce a novel wall-crossing phenomenon in the space of Bridgeland stability conditions: a wall in the stability space of canonical genus 4 curves that induces non-Q-factorial singularities and hence, it cannot be detected as an operation in the Minimal Model Program of the corresponding moduli space, unlike the case for many surfaces. More precisely, we give an example of a wall-crossing in Db(P3)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{D}^{b}(\\mathbb {P}^{3})$$\\end{document} such that the wall induces a small contraction of the moduli space of stable objects associated to one of the adjacent chambers, but a divisorial contraction to the other. This significantly complicates the overall picture in this correspondence to applications of stability conditions to algebraic geometry. The full wall-crossing for canonical genus four curves and the geometry are considered in the published paper (Rezaee in Proc LMS 128(1):e12577, 2024); this article is devoted to describe a particularly interesting wall among the walls in Rezaee (Proc LMS 128(1):e12577, 2024) in full details to explain the novel phenomenon.

A Comprehensive Modeling on MoS2 Interface and Defect Engineering in CZTS Thin Film Solar Cells

This work uses the SCAPS-1D modeling program to examine the effects of faults in the molybdenum Disulfide (MoS2) layer and the MoS2 interface on the electrical performance of CZTS solar cells. To get an ideal energy gap (Eg) of 1.3 eV and a carrier concentration (CC) of 1014 cm⁻³, the research attempts to optimize the CZTS absorber layer. By maintaining a consistent doping level with 1016 cm-3 ≤ CC and an Eg within 1.6 eV < Eg ≤ 1.8 eV for the MoS2 film, the work also investigates the possibility of increased efficiency in CZTS/MoS2 devices. The study results indicate that open circuit voltage (VOC) and Efficiency (Eta-η) parameters are improved by a p-type MoS2 interface, indicating a promising development for CZTS solar systems. Nonetheless, n-type MoS2 suggests a compromise with a reduction in the fill factor. The study emphasizes the stability benefits of a p-type MoS2 interface as well as the importance of surface recombination velocity. The study also considers phase transitions that occur during the manufacture of devices, highlighting the intrinsic n-type character of MoS2 and the importance of experimental methods in CZTS device optimization. After analyzing the effects of defects on carrier density, depletion width, and quantum efficiency, the study concludes that enhancing the performance of CZTS solar cells requires an acceptor-type interface with p-type MoS2. With recombination resistances of 443.92 Ω cm2, 1530.33 Ω cm2, 81.54 Ω cm2, and 93.82 Ω cm2, the SCAPS model shows zero series resistance at a particular site for the fundamental, optimized design using n-MoS2 and p-MoS2. In the end, the work sheds light on the possibilities for additional experimental studies to advance the technology of CZTS solar cells.

Study on Optimizing Inventory to Minimize Waste in the Fresh Produce Supply Chain

Abstract: The increasing demand for fresh produce, particularly greens, presents unique challenges in inventory management due to their short shelf life and susceptibility to spoilage. Inefficient management often results in significant waste, leading to financial losses for retailers and increased environmental burdens from the disposal of spoiled goods. Despite various strategies being employed in the agri-food sector, gaps remain in understanding how to optimize inventory levels to minimize waste at the retails level while maintaining high service levels for customers. This research aims to develop an inventory management model tailored to fresh produce that can mitigate these issues by balancing stock availability and minimizing wastage, particularly in large retail shops. To address the research problem, a mixed-integer linear programming (MILP) model was developed, focusing on three service levels—90%, 92%, and 95%—to evaluate the trade-offs between customer satisfaction and total inventory costs. The model incorporates key constraints, including stock balance, shelf-life limitations, and demand satisfaction. The model was validated using actual data, including stock, sales, and wastage records for the month of August, 2024. The objective function aimed to minimize total costs, encompassing holding costs, wastage, and lost revenue. The findings indicate that higher service levels, while reducing stockouts, significantly increase costs due to excess inventory and waste. The most cost-effective solution was observed at the 90% service level, which balanced waste reduction and customer satisfaction. The study also emphasizes the importance of integrating total cost reduction and minimize wastage. Future work should focus on incorporating consumer behavior patterns and extending the model to multi-echelon inventory systems, which include both distribution centers and retail outlets, to capture a more holistic view of the supply chain.

Development of the Early Childhood Health Education Program Model Using Picture Books Storytelling and IoT Smart Healthcare Devices

Development of the Early Childhood Health Education Program Model Using Picture Books Storytelling and IoT Smart Healthcare Devices

A Survey on Heterogeneous CPU–GPU Architectures and Simulators

ABSTRACTHeterogeneous architectures are vastly used in various high performance computing systems from IoT‐based embedded architectures to edge and cloud systems. Although heterogeneous architectures with cooperation of CPUs and GPUs and unified address space are increasingly used, there are still a lot of open questions and challenges regarding the design of these architectures. For evaluation, validation and exploration of next generation of heterogeneous CPU–GPU architectures, it is essential to use unified heterogeneous simulators for analyzing the execution of CPU–GPU workloads. This article presents a systematic review on challenges of heterogeneous CPU–GPU architectures with covering a diverse set of literatures on each challenge. The main considered challenges are shared resource management, network interconnections, task scheduling, energy consumption, and programming model. In addition, in this article, the state‐of‐the‐art of heterogeneous CPU–GPU simulation platforms is reviewed. The structure and characteristics of five cycle‐accurate heterogeneous CPU–GPU simulators are described and compared. We perform comprehensive discussions on the methodologies and challenges of designing high performance heterogeneous architectures. Moreover, for developing efficient heterogeneous CPU–GPU simulators, some recommendations are presented.

The effectiveness of the physical education curriculum following the Sports Club model on the physical development of high school students in Vietnam

Background and Study Aim. The physical development of high school students is often hindered by the limitations of traditional physical education programs, which may not fully engage students or promote balanced physical growth. In light of these concerns, the need to explore alternative educational models that can enhance students' physical development has become evident. The purpose of this article is to evaluate the potential effectiveness of a physical education curriculum following the Sports Club model in improving the physical development of high school students. Material and Methods. A total of 84 female 10th-grade students participated in the study. They were divided into two groups: an experimental group of 42 students following the Sports Club model and a control group of 42 students following the current physical education curriculum. The experiment lasted for 17 weeks. Both groups completed initial and final physical fitness tests. These tests included the 30-second sit-up test (to assess core strength), the 30-meter sprint test (to measure speed), the 4 × 10-meter shuttle run test (to evaluate agility), the standing long jump test (to measure explosive leg power), and the 5-minute running field test (to assess endurance). Results. The female students in the experimental group demonstrated greater physical development compared to the control group in all five assessment tests. The experimental group showed the most significant improvement in the 30-second sit-up test, with a growth rate of 26.71%. The lowest growth was observed in the 5-minute running field test, at 9.92%. In contrast, the control group showed its highest improvement in the 30-second sit-up test, with a growth rate of 9.44%. However, there was minimal to no improvement in the 4 × 10-meter shuttle run and the 5-minute running field test for the control group. Conclusions. The Sports Club model improved students' physical fitness more effectively compared to the current curriculum, offering greater benefits in all tested areas. It is recommended that teachers further integrate and refine the implementation of the Sports Club model in future physical education programs to enhance teaching effectiveness.