Programming Model Research Articles

Finance-based scheduling for projects in owners’ portfolios: MILP versus improved GA and PSO

PurposeIn the construction industry, cash flow issues can impact both contractors and owners. Although finance-based scheduling (FBS) models were developed to control contractors’ cash flow, researchers completely disregarded cash flow management for owners’ portfolios. Therefore, FBS is once again introduced in this study from the perspective of the owners of portfolios.Design/methodology/approachFor the FBS problem of portfolio owners (FBS-PO), a mixed integer linear program (MILP) model is developed. The purpose is to ensure that owners’ cash inflows are greater than cash outflows while minimizing the sum of weighted extensions of the projects in portfolios. Owing to the difficulties encountered in solving the proposed MILP model, genetic algorithm (GA) and particle swarm optimization (PSO) meta-heuristics are used. To ensure the feasibility of the solutions, a special serial schedule generation heuristic was developed in conjunction with the random key method for chromosome representation.FindingsThe GA produced higher-quality solutions compared to PSO, though it required more processing time. The GA has proven to be a far better choice than the MILP exact solver for bigger portfolios. Additionally, there is a linear correlation between the number of activities in portfolios and the amount of computational time required for the GA to converge.Originality/valueThis paper introduces the new research domain of FBS from the owners’ perspective (FBS-PO). In order to establish the FBS-PO as a stand-alone domain, a critical review of the FBS studies in the literature was conducted. In addition, the goals, characteristics, implementation and challenges of the FBS heuristics are benchmarked against those of the special serial schedule generation heuristic developed for the FBS-PO problem.

The single machine scheduling problem with bio-mathematical fatigue constraints

Despite the adverse impacts of occupational fatigue such as accidents and injuries in the manufacturing industry, it has not been systematically examined in the literature on production scheduling. In this paper, we integrate the classic bio-mathematical fatigue prediction model from the brain science literature into the simple single machine scheduling problem with sequence-dependent setup times. Then, we formulate the problem as a mixed-integer linear programming model and propose an adaptive large neighborhood search heuristic. The effectiveness of the heuristic is numerically validated through real cases. Finally, we argue that considering bio-mathematical fatigue prediction can lead to safer production schedules, notably reducing the fatigue working hours in our real case.

Coordinated operation of pumped hydro energy storage with reversible pump turbine and co-located battery energy storage system

Abstract This publication examines the coordinated operation of pumped hydro energy storage and battery energy storage systems to improve profitability. While pumped hydro energy storages offer high storage capacity but have slower response times, battery energy storage systems have lower capacity but faster response times. A hybrid system combining both can thus harness synergies. A mixed-integer linear programming model was developed to depict the coordinated use of both systems in the German market. The proposed approach is also applicable to other regional markets where energy and balancing services are traded in a similar manner. In this model, the pumped hydro energy storage operates in the spot market and provides automatic Frequency Restoration Reserve, while the battery energy storage systems supplies Frequency Containment Reserve. The model takes into account the costs caused by degradation effects in both storage types. The results show a 10.05 % increase in revenue through coordination compared to the independent operation of both storage systems. This added value can be achieved through more efficient use of the power capacity, particularly that of the battery energy storage systems, in coordinated operation.

Data Privacy Made Easy: Enhancing Applications with Homomorphic Encryption

Homomorphic encryption is a powerful privacy-preserving technology that is notoriously difficult to configure and use, even for experts. The key difficulties include restrictive programming models of homomorphic schemes and choosing suitable parameters for an application. In this tutorial, we outline methodologies to solve these issues and allow for conversion of any application to the encrypted domain using both leveled and fully homomorphic encryption. The first approach, called Walrus, is suitable for arithmetic-intensive applications with limited depth and applications with high throughput requirements. Walrus provides an intuitive programming interface and handles parameterization automatically by analyzing the application and gathering statistics such as homomorphic noise growth to derive a parameter set tuned specifically for the application. We provide an in-depth example of this approach in the form of a neural network inference as well as guidelines for using Walrus effectively. Conversely, the second approach (HELM) takes existing HDL designs and converts them to the encrypted domain for secure outsourcing on powerful cloud servers. Unlike Walrus, HELM supports FHE backends and is well-suited for complex applications. At a high level, HELM consumes netlists and is capable of performing logic gate operations homomorphically on encryptions of individual bits. HELM incorporates both CPU and GPU acceleration by taking advantage of the inherent parallelism provided by Boolean circuits. As a case study, we walk through the process of taking an off-the-shelf HDL design in the form of AES-128 decryption and running it in the encrypted domain with HELM.

A Model for Vehicle Routing Problem under Returns and Emission Consideration in B2C E-Commerce Logistics

Purpose: With the widespread use of the Internet, electronic commerce allows customers to purchase products from the virtual stores of businesses instead of physical stores. This study addressed a mixed-integer linear programming model for a vehicle routing problem under returns and emission considerations in B2C e-commerce logistics. Methodology: This study proposes a mathematical model to solve a variant of the vehicle routing problem. The objective is to minimize the fuel consumption cost, penalty cost for unmet demand of returned items, and fixed cost for operating a vehicle. A clustering-based solution algorithm has been introduced to solve large-sized instances within reasonable solution times. Findings: The numerical analysis for the base case shows that the suggested model can assist decision-makers in coordinating forward distribution and reverse collection decisions within the context of sustainable e-commerce logistics. The result of the adjusted model for minimizing emission shows that a reduction of nearly 17% in total emission amount can be achieved, however, the adjusted model postpones all demand for the collection of returned items. Furthermore, the cluster-based solution approach causes a considerable decrease in solution time while providing promising solutions. Originality: This study represents a contribution to the existing literature on the subject by considering: i) emission to determine effects on the vehicle routing problem, ii) the postponement of the collection of returned items due to the limited delivery time, iii) proposing the clustering-based solution approach to tackle with larger-sized problems.

Optimization of Rubber Sheet Rolling Machine Parameters using a Taguchi-based TOPSIS Linear Programming Model

The Multi-Response Optimization (MRO) problem is a critical aspect of the engineering design, particularly in improving process efficiency and product quality. This study focuses on optimizing the parameters for a rubber sheet rolling machine, a vital component of Thailand's natural rubber industry. The objective is to enhance its operational efficiency and product consistency by addressing key criteria, such as production time and rubber sheet thickness. A novel approach integrating the Taguchi method and the Technique for Order Preference by Similarity to Ideal Solution Linear Programming (TOPSIS-LP) model is proposed. The Taguchi method systematically designs experiments, while the Preference by Similarity to Ideal Solution (TOPSIS) model consolidates multiple performance indicators into a single optimal solution. Optimal roller gaps of 4.5 mm, 3.0 mm, 2.0 mm, and 0.1 mm for the first, second, third, and fourth roller pairs, were, respectively, identified. The results demonstrated a reduction in rubber sheet thickness to 2.06 mm (5.94% improvement) and production time to 9.71 seconds per sheet (1.33% improvement) compared to the original settings. The qualitative analysis confirmed the robustness and reliability of the optimized parameters, achieving consistent results across various evaluation methods. This study presents a significant advancement in the MRO problem, offering a robust framework applicable to similar challenges in industrial settings. The findings provide a foundation for future automation and optimization efforts, driving sustainable improvements in the manufacturing efficiency and product quality.

Application of a Physics-Informed Convolutional Neural Network for Monitoring the Temperature Fields in High-Temperature Gas Reactors

This work presents current advances in applying a physics-informed convolutional neural network (CNN) to evaluate temperature distributions in advanced reactors. Our goal is to demonstrate that the CNN can reconstruct temperature fields within the solid region of a prismatic fuel assembly in a high-temperature gas reactor (HTGR) with sensor data available in only a few cooling channels. Before that, we showcase the superior performance of the physics-informed CNN in comparison to a purely data-driven multilayer perceptron (MLP), considering a canonical heated channel setup. This analysis shows the advantages of our approach and justifies its choice. The datasets employed here are obtained upon numerical simulations performed with codes under the Nuclear Energy Advanced Modeling and Simulation program. This work is important, as industry experience indicates that the assembly material in HTGR concepts is prone to large thermal-mechanical loads nearing operational limits. This makes it crucial to characterize peak temperatures and their distributions near hot spots. Modern thermocouples are unreliable in these types of harsh environments because of the high neutron fluxes and elevated temperatures involved. The CNN-based field reconstruction represents an attractive solution, enabling sensor arrays in less aggressive locations and augmenting indirect predictions for less accessible regions. The results show that the CNN reduces prediction errors by orders of magnitude in comparison to the MLP, considering the simple yet well-representative heated channel case. In the case of the HTGR fuel assembly, the CNN can successfully reconstruct temperature fields over various cooling regimes. Furthermore, we also explore the algorithm’s ability to detect abnormalities. Interestingly, the CNN proves it has the capacity to detect blockage in one of the noninstrumented cooling channels.

A hierarchical spatial and temporal optimisation of the air-high speed rail intermodal network

Spatial and temporal coordination of air-high speed rail (HSR) intermodal networks is important to reduce emission, improve service, enhance efficiency, and reduce costs in the provision of air-HSR integration. This paper constructs a hierarchical optimisation model that first considers a spatial scope to solve the problem of route allocation and frequency choice which minimises total environmental, operational and passenger cost with a Mixed-Integer Linear Programming (MILP) model, based on a demand estimation for passenger trips between city pairs. Then, the second hierarchical level of the model considers a temporal scope to maximise connection opportunities between the resulting air and HSR networks using time windows to adjust frequencies with a Prescriptive Integer Quadratic Programming (PIQP) model. An application to a network of 40 cities in mainland China with both air and HSR transport service shows that the total emission of the network can be reduced by 22 %. Comparative analyses show that optimising for passenger costs favours increased air travel on medium- and long-haul routes, while an emissions-focused approach encourages a shift toward HSR for short and medium distances. Sensitivity analyses on carbon pricing further highlight the potential of gradual price adjustments to incentivise lower-emission modes without requiring additional HSR infrastructure.

Product tracing or component tracing? Blockchain adoption in a two-echelon supply chain management

Consumer awareness of product authenticity and carbon footprint tracing are among the most remarkable reasons for adopting blockchain in the supply chain in today’s world. However, the research literature has not yet examined specific ways to adopt blockchain in the supply chain. This study aims to develop a decision support tool to deal with the adoption of blockchain technology to design a two-echelon supply chain. In this regard, four specific cases for integrating supply chain and blockchain are developed based on types of tracing and block generation authority. In product tracing, green products are investigated throughout the supply chain, while in component tracing, green products are examined between the components of the supply chain. As it is necessary to record and verify the supply chain information by authorities in the blockchain network, in this work, such authorities are taken into account for both links and members of the supply chain. As far as we know, this is the first attempt to classify the various methods of adopting blockchain in Green Supply Chain Management (GSCM) and propose mathematical optimization models related to them. In this line, four Mixed-Integer Linear Programming (MILP) models with the aim of minimizing the costs related to the physical supply chain and blockchain deployment are developed for the integration of the supply chain with blockchain technology. They are treated by the Branch and Efficiency (B&E) algorithm and Simultaneous Data Envelopment Analysis (SDEA) model considering common (cost and service) and innovative (blockchain) criteria. The results showed that link-based and component tracing models are cost-effective. In addition, the cost objective function of green product tracing is more sensitive to the number of blocks than that of component tracing. Eventually, the study provides great opportunities for decision-makers and managers to understand how to adopt blockchain in terms of supply chain network characteristics, cost, transparency, and service.

A Military-Civilian Training Partnership for Army Nurses.

In 2018, the U.S. Army Surgeon General created the Army Medical Department Military-Civilian Trauma Team Training (AMCT3) program to enhance the clinical proficiency of medical personnel serving on Army trauma teams called forward resuscitative surgical detachments (FRSDs). FRSDs provide resuscitative and surgical care to wounded patients in the deployed environment until they can be medically evacuated to a higher level of care. Through AMCT3, FRSD personnel work at civilian trauma centers while not deployed in order to maintain their combat casualty care competency. This article describes an innovative nursing-specific AMCT3 program model in which Army nurses serving on an FRSD are embedded within a level 1 trauma center for a 3-year assignment. The goal of the program, which was established at Vanderbilt University Medical Center (VUMC) in Nashville, Tennessee, is to improve participants' clinical proficiency and skills in preparation for the next major military conflict, with the aim of reducing preventable battlefield deaths. The VUMC Military Affairs Committee, in consultation with Army medical leaders, developed a unique 3-part model for a nursing partnership program at VUMC. This model includes separate tracks for critical care and emergency nurses. The nurses receive training in their specialty, cross-training in the opposite track, and other professional development opportunities. A critical care nurse and an emergency nurse were assigned to the program in January 2022 and September 2022, respectively. Between January 2022 and June 2023, the critical care nurse completed all the required individual critical task lists-specialty-specific clinical skills Army medical personnel must be competent in before deployment-except for 1, obtaining intra-abdominal pressure. The VUMC AMCT3 nursing partnership training model ensures that Army nurses are highly prepared to function as part of the FRSD and to provide superior combat casualty care in a resource-limited environment.

Optimizing a link-based travel incentive scheme integrating personal carbon trading for low-carbon commuting.

The pressing need to reduce greenhouse gas emissions and optimize traffic demand underlines the importance of effective travel demand management. Previous studies have explored budget-based and aggregated incentive programs, which diminish a heavy financial burden on governments and tend to be limited in contributing to effective behavior change in practice due to budget issues. This study proposes a personal carbon trading travel incentive (PCTTI) mechanism, to encourage private car commuters using low-carbon travel routes. The revenue obtained from the sale of carbon emission reductions, resulting from changes in commuter routes, serves as a partial budget for the incentives under PCTTI. To determine the optimal incentives, we developed an incentive scheme optimization model based on a bi-level programing model. Numerical analysis reveals the substantial potential of PCTTI to reduce carbon emissions and travel costs in the road traffic system, but also highlights the sensitivity of these results to the carbon trading price and the commuters' value of time. Specifically, the effectiveness of PCTTI diminishes when the carbon price drops below $6.494 per ton or when commuters' value of time exceeds $3.99 per hour. These results indicate that the PCTTI mechanism offers a scalable and economically sustainable approach to enhance travel demand management and achieve environmental benefits.

Optimizing the Supply Chain for Recycling Electric Vehicle NMC Batteries

The rapid growth of electric vehicle production has led to increased waste batteries that can no longer be used. This increase causes environmental and economic challenges. Lithium-ion battery waste harms the environment as it contains toxic and flammable chemicals. New raw materials need to be procured economically due to the need for more infrastructure and a circular economy. Therefore, the solution to overcome the impact of the accumulation of lithium battery waste is to recycle the battery. Recycling end-of-life batteries is necessary to mitigate material supply risks, reduce demand for new materials, and mitigate harmful environmental and health impacts. This study aims to provide a conceptual model for the supply chain network design of electric vehicles' Nickel Manganese Cobalt (NMC) battery recycling process. We developed a mathematical model to determine the allocation of multi-product recycling products from multi-suppliers and other related entities such as manufacturers and landfills over multiple periods. The analysis method utilizes techno-economic investment feasibility analysis and load distance method. The problem in the recycling process supply chain network is formulated in a Mixed Integer Linear Programming (MILP) model. The MILP optimization results show that the proposed model produces a globally optimal solution for allocating NMC batteries. The application of this study is to provide a solution to the treatment of waste batteries from electric vehicle end-users in Java Island, Indonesia. In addition, it can develop economic opportunities in the waste battery recycling business in the electric vehicle industry. It is building a contribution to a sustainable electric vehicle battery management system by reducing the dependence on demand for new materials from mining and analyzing the sustainability of the NMC electric vehicle battery recycling process.

A Q-learning-based algorithm for the block relocation problem

The Block Relocation Problem (BRP), also known as the Container Relocation Problem, is a challenging combinatorial optimization problem in block stacking systems and has many applications in real-world scenarios such as logistics and manufacturing industry. The BRP is about finding the optimal way to retrieve blocks from a storage area with the objective of minimizing the number of relocations. The BRPs have been studied for a long time, and have been solved primarily using conventional optimization techniques, including mathematical programming models, as well as both exact and heuristic algorithms. For the first time, this paper tackles the problem using a reinforcement learning method. We focus on one of the major variants of the BRP—the restricted BRP with duplicate priorities (RBRP-dup). We first model the RBRP-dup as a Markov decision process and then propose a Q-learning-based algorithm to solve the problem. The Q-learning-based algorithm contains two phases. In the learning phase, two innovative mechanisms: an optimal rule-integrated behaviour policy and a heuristic-based dynamic initialization method, are incorporated into the Q-learning model to reduce the size of the state-action space and accelerate convergence. In the optimization phase, the insights obtained in the learning phase are combined with a heuristic algorithm to improve decision-making. The performance of our proposed method is evaluated against the state-of-the-art exact algorithm and a commonly used heuristic algorithm based on benchmark instances from the literature. The computational experiments demonstrate the superiority of our proposed method regarding solution quality in large and complex instances.

Bi-level programming model for military supply chain coordination with contracts based on particle swarm optimization algorithm

Bi-level programming model for military supply chain coordination with contracts based on particle swarm optimization algorithm

Optimizing a multimodal hub and spoke network for vaccine distribution using regional vulnerability index during healthcare emergencies

Purpose A vaccination strategy to cover the susceptible population is key to containing the spread of any virus during a healthcare emergency. This study quantifies the susceptibility of a region based on initial infection rates to prioritize optimal vaccine distribution strategies. The authors propose a metric, the regional vulnerability index (RVI), that identifies the degree of susceptibility/vulnerability of a region to virus infections for strategically locating hubs for vaccine storage and distribution. Design/methodology/approach A two-phase methodology is used to address this problem. Phase 1 uses a modified Susceptible-Infected-Recovered (SIR) model, ModSIR, to estimate the RVI. Phase 2 leverages this index to model a P-Center problem, prioritizing vulnerable regions through a Mixed Integer Quadratically Constrained Programming model, along with three variations that incorporate the RVI. Findings Results indicate a weighting scheme based on the population-to-RVI ratio fosters fair distribution and equitable coverage of vulnerable regions. Comparisons with the public distribution strategy outlined by the Government of India reveal similar zonal segregations. Additionally, the network generated by our model outperforms the actual distribution network, corroborated by network metrics such as degree centrality, weighted degree centrality and closeness centrality. Originality/value This research presents a novel approach to prioritizing vaccine distribution during pandemics by applying epidemiological predictions to an integer-programming framework, optimizing COVID-19 vaccine allocation based on historical infection data. The study highlights the importance of strategic planning in public health response to effectively manage resources in emergencies.

A Simplex Model of Long Pathways in the Brain Related to the Minimalist Program in Linguistics

Marcolli, Chomsky, and Berwick described the minimalist program, proposed by Chomsky in generative linguistics, as an algebra of binary trees in an analogy of quantum physics on Feynman diagrams. In this paper, we proposed another model of the minimalist program based on simplicial Hodge theory by taking the relevant brain neural network into account. We focused on a long directed pathway connecting distant areas in the brain, and took the (abstract) simplex spanning the locations on the terminal area, which the signals going through the pathway can reach. The identity of each signal is represented by the symmetry of the corresponding face, consisting of locations receiving the signal simultaneously. Then, we showed that this model fits the minimalist program. Further, we calculated the spectrum and eigenspaces of the Hodge Laplacian in important cases and found their surprising rationality. According to this rationality, we could draw pictures of syntactic relations based only on the calculation without using linguistic knowledge. In addition, though word order depends on what language is used, and thus has nothing to do with the minimalist program, planar word arrangements are still possible and within the scope of our model.

Assessing in-vitro models for microglial development and fetal programming: a critical review

This review evaluates in-vitro models for studying how maternal influences during pregnancy impact the development of offspring microglia, the immune cells of the central nervous system. The models examined include primary microglia cultures, microglia cell lines, iPSC-derived microglia, PBMC-induced microglia-like cells, 3D brain organoids derived from iPSCs, and Hofbauer cells. Each model is assessed for its ability to replicate the in-vivo environment of the developing brain, with a focus on their strengths, limitations, and practical challenges. Key factors such as scalability, genetic and epigenetic fidelity, and physiological relevance are highlighted. Microglia cell lines are highly scalable but lack genetic and epigenetic fidelity. iPSC-derived microglia provide moderate physiological relevance and patient-specific genetic insights but face operational and epigenetic challenges inherent to reprogramming. 3D brain organoids, derived from iPSCs, offer an advanced platform for studying complex neurodevelopmental processes but require extensive resources and technical expertise. Hofbauer cells, which are fetal macrophages located in the placenta and share a common developmental origin with microglia, are uniquely exposed to prenatal maternal factors and, depending on fetal barrier maturation, exhibit variable epigenetic fidelity. This makes them particularly useful for exploring the impact of maternal influences on fetal programming of microglial development. The review concludes that no single model comprehensively captures all aspects of maternal influences on microglial development, but it offers guidance on selecting the most appropriate model based on specific research objectives and experimental constraints.

Supplier Selection Model Considering Sustainable and Resilience Aspects for Mining Industry

Supplier selection plays a pivotal role in the mining industry, forming a key component of the supply chain management. It has been established that the integration of sustainability and resilience into this process can significantly enhance the industry’s ability to withstand economic, environmental, and social shocks. Despite a large body of literature investigating supplier selection, there is a notable gap in research specifically addressing the incorporation of sustainability and resilience criteria in the mining industry. The objective of this research is to bridge this knowledge gap and contribute to the understanding of sustainable and resilient supplier selection in the mining industry. A constructive research approach was employed, identifying both practical and theoretical problems and proposing a construction—a mathematical model. This model was developed in collaboration with industry key actors, ensuring its practical applicability and validity. The main result of this research is an optimization mathematical programming model that allows practitioners to evaluate and select suppliers considering both sustainability and resilience criteria. The model facilitates a comprehensive assessment of suppliers, incorporating a wide range of factors beyond cost, including environmental impact, social responsibility, and the ability to maintain supply under various potential disruptions.

Charging Scheduling of Electric Vehicles Considering Uncertain Arrival Times and Time-of-Use Price

To advance sustainable transportation solutions, this work investigates an electric vehicle charging scheduling problem under the uncertainty of vehicle arrival times. Given a set of appointed electric vehicles, the objective of the considered problem is to explore charging strategies that minimize the total charging cost for the charging station. To address this problem, this work first establishes a mixed-integer programming model. Then, an enhanced sample average approximation approach alongside two versions of distribution-free approaches are applied to solve the studied problem. Additionally, this study introduces a BP neural network-enhanced distribution-free approach to efficiently resolve the problem. Finally, numerical experiments are conducted to demonstrate the effectiveness of the proposed approaches.

Integration of hyperplastic models in strain space

This paper presents a simple and flexible integration scheme designed to capture the elastic-plastic behaviour of materials directly from a free energy function, a dissipation function, and kinematic constraints, typically formulated in strain space. As such, it eliminates the need to establish expressions for yield functions and/or plastic flow potentials. This is beneficial as analytical transformation of complex dissipation functions, particularly when combined with realistic kinematic constraints, often involves mathematical complexities. However, such a transformation is unnecessary as all essential information is already embedded within the various elements of the framework, i.e. the free energy potential, dissipation function and kinematic constraint expressions. Moreover, the scheme can be utilized to numerically visualise yield surfaces. The method is successfully applied to a family of sand and clay models featuring different dissipation functions, kinematic constraints (dilatancy rules) and friction mobilisation. These applications include showcasing models that lack analytical transformation, as well as models where analytical transformation to the yield and potential surfaces is possible, allowing for comparison. Through numerical simulation of several drained and undrained element tests, and the generation of yield surfaces, the efficacy of the proposed integration scheme is demonstrated. Furthermore, the proposed scheme is used to implement the Matsuoka-Nakai model in a finite element program to demonstrate its applicability to boundary value problems.