Data-driven Optimization Approach Research Articles

Rightful Rewards: Refining Equity in Team Resource Allocation through a Data-Driven Optimization Approach

In group management, accurate assessment of individual performance is crucial for the fair allocation of resources such as bonuses. This paper explores the complexities of gauging each participant’s contribution in multi-participant projects, particularly through the lens of self-reporting—a method fraught with the challenges of under-reporting and over-reporting, which can skew resource allocation and undermine fairness. Addressing the limitations of current assessment methods, which often rely solely on self-reported data, this study proposes a novel equitable allocation policy that accounts for inherent biases in self-reporting. By developing a data-driven mathematical optimization model, we aim to more accurately align resource allocation with actual contributions, thus enhancing team efficiency and cohesion. Our computational experiments validate the proposed model’s effectiveness in achieving a more equitable allocation of resources, suggesting significant implications for management practices in team settings.

Data-Driven Heuristic Optimization for Complex Large-Scale Crude Oil Operation Scheduling

This paper addresses the challenging scheduling of crude oil operations (SCOO) problem, characterized by the intricate sequencing of activities involving discrete events and continuous variables. Given the NP-Hard nature of scheduling problems due to their combinatorial complexity, this study employs a data-driven optimization approach. Initially, historical operational data relevant to the SCOO are scrutinized; however, due to data limitations, small-scale instances are solved using a mathematical programming model to generate data. Subsequently, operational solution data are processed using the Apriori algorithm, a renowned data mining technique. The insights gained are translated into heuristic rules, laying the groundwork for a novel data-driven heuristic algorithm tailored for the SCOO problem. This algorithm is then applied to a 45-day scheduling scenario, demonstrating the efficacy of the proposed approach.

Designing a sustainable plastic bottle reverse logistics network: A data-driven optimization approach

Management of recovery options for plastic beverage containers involves some challenges. Most materials of used containers are recycled and are used to produce new products. The locations of collection facilities are the strategic decisions, affecting the amount collected and the total cost of a Reverse Logistics Network (RLN). In this study, a multi-objective (MO) optimization model is introduced to configure a plastic beverage container RLN, considering economic, environmental, and social objectives. This study also implements a scenario-based possibilistic approach to handle the uncertainty of the parameters. Furthermore, a data-driven fuzzy optimization framework is developed to consider the overlapping and multi-clustered characteristics of historical data samples. The application of the proposed method is demonstrated by considering a network in Vancouver, Canada. The numerical results reveal that the optimal configuration of the RLN resulting from the proposed MO model exhibits significant sensitivity to fluctuations in costs, demands, and the prioritization of the objective functions. Additionally, the proposed data-driven framework can incorporate decision makers' preferences when tuning the conservatism degree of uncertain parameters and the preference level of different objectives of the MO model. Moreover, the developed data-driven algorithm can reduce over-conservatism by 14% and guarantee the feasibility of optimal solutions compared to other data-driven strategies.

Economic operation of an agent-based virtual storage aggregated residential electric-heating loads in multiple electricity markets

The increasing penetration of intermittent renewable energy sources leads to a higher demand for flexible balancing service in the power system. Consequently, demand-side management techniques, such as utilizing flexible resources from residential electric-heating loads, provide a promising solution. However, due to various uncertainties such as stochastic weather and market prices, it is challenging to develop an economically efficient strategy. Hence, this paper proposes an economic operation optimization approach for residential electric-heating loads to develop optimal bidding and scheduling strategy in the multi-stage market. First, the dynamic characteristics of aggregated electric-heating loads are modeled as the virtual energy storage systems (VESS) to quantify the flexibility potential arising from the combination of building thermal inertia, user tolerance and thermal energy storage (TES). Subsequently, the joint energy and balancing service market is introduced to exploit the operating flexibility of VESS. A multi-agent deep reinforcement learning (MADRL) optimization framework is proposed to address the multi-objective problem at different decision frequency, which involves achieving lower economic costs while maintaining indoor comfort. Finally, we combine the DRL with imitation learning (IL) by introducing the behavior cloning mechanism to mitigate training process instability. It reduces the computational costs and eliminates the need for redundant additional structure. Comparative simulation experiments demonstrate the efficiency of data-driven optimization approach, which provides an adaptive, economic cost-effective and general solution for multi-type aggregators to activate their flexibility in dynamic markets.

A Clustering-Based Optimization Approach for Hospital Miscoding Correction

This paper addresses the problem of correcting medical coding errors with respect to some coding recommendations. The problem consists in clustering medical codings and determining for each cluster the set of features to correct in order to maximize the financial benefits subject to coding correction effort constraints. For this purpose, we model the coding recommendation as a disjunction of hypercubes and introduce the concept of correction sets. A mixed integer linear programming model is then proposed to assign medical codes to correction sets in order to maximize the financial benefits. The miscoding is then explained by characterizing optimal clusters with association rules and coding error distribution. A case study on patient stays associated with malnutrition-related ICD codes is presented, and the performance of the proposed methodology is assessed in regard to the current coding staff practice. A significant increase in health services reimbursement is achieved with a limited number of subjects’ features reviewed. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Medical miscoding has a significant negative impact on hospitals with a financial loss for under coding and a penalty for over coding. Whether a medical review is necessary for all descriptive features of a miscoded subject? Is it possible to reduce unnecessary medical reviews without compromising the goal of increasing hospital financial benefits? This article attempts to answer these questions with a data-driven optimization approach to determine a limited number of miscoding clusters and the set of features to review for each in order to best balance the financial benefits and the medical review workload. The application to a real-life case study leads to a significant increase in hospital fiscal revenue of nearly 6,992,489.69, while reviewing only a small number of descriptive features (5293 out of 22056 features, or 24% of features). Causes are also provided for each discovered coding error subtype to ameliorate medical coders’ coding practices. Furthermore, the proposed approach allows the decision-maker to balance the cost-benefit and the requirement of public health institutions (i.e., miscoding rate).

Data-Driven Optimization for Air Traffic Flow Management with Trajectory Preferences

In this paper, we present a novel data-driven optimization approach for trajectory-based air traffic flow management (ATFM). A key aspect of the proposed approach is the inclusion of airspace users’ trajectory preferences, which are computed from traffic data by combining clustering and classification techniques. Machine learning is also used to extract consistent trajectory options, whereas optimization is applied to resolve demand-capacity imbalances by means of a mathematical programming model that judiciously assigns a feasible four-dimensional trajectory and a possible ground delay to each flight. The methodology has been tested on instances extracted from the Eurocontrol data repository. With more than 32,000 flights considered, we solve the largest instances of the ATFM problem available in the literature in short computational times that are reasonable from the practical point of view. As a by-product, we highlight the trade-off between preferences and delays as well as the potential benefits. Indeed, computing efficient solutions to the problem facilitates a consensus between the network manager and airspace users. In view of the level of accuracy of the solutions and the excellent computational performance, we are optimistic that the proposed approach can make a significant contribution to the development of the next generation of air traffic flow management tools.

A data-driven optimization approach to baseball roster management

A data-driven optimization approach to baseball roster management

Active learning-driven control point optimization method for efficient modeling of complex stiffened curved shells

The mesh-mapping technique has recently garnered significant attention due to its ability to model stiffened curved shells accurately. The precision of the modeling depends on the number of control points, particularly for complex stiffened curved shells. However, using too many control points results in a time-consuming process of model training and mapping, thereby impeding the structural optimization efficiency. To overcome this challenge, this study proposes an active learning (AL) function that considers the mechanical response and uncertainty of stiffened curved shells. This function is applied to select control points of the radial basis function (RBF) model. The control points with the most substantial AL function value are added to the control point set, and the process is iterated until convergence is achieved. Additionally, the proposed method is compared to other AL strategies to confirm its high efficiency. Furthermore, a data-driven optimization approach, based on the deep neural network method, is employed to minimize the weight of the complex stiffened curved shells. Finally, two engineering examples are presented to verify the effectiveness of the proposed method, which reduces the modeling time by 83.87% and 66.29%, respectively, in comparison to using all control points in mesh-mapping. Additionally, the total time of modeling and optimization is decreased by 42.63% and 59.35%, respectively. In conclusion, the proposed control point optimization method based on active learning-driven for complex stiffened curved shells has substantial potential in efficient modeling, significantly reducing the time of mesh-mapping modeling, and improving engineering’s optimization efficiency.

A data-driven optimisation method for a class of problems with redundant variables and indefinite objective functions

In the realm of practical problem-solving, multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) are becoming increasingly prevalent. MOPRVIF involve determining the optimal decision variables that optimise multiple objectives, leveraging the relational data of a set of variables and multiple objectives. For these problems, this paper focuses on the following two issues: one is the demand for a unified computational model to solve this problem; the other is how to improve the algorithm's deep intelligent search capability. In this regard, this paper designs a dual data-driven multi-objective optimisation method. The method used consisted of four parts: elimination of redundant variables (ERV), objective function construction (OFC), selection evolution operator (SEO), and multi-objective evolutionary algorithm (MOEA). MOEA was the main focus of the method. ERV is data preparation and variable selection according to multiple objectives. OFC involves constructing the relationship model between variables and objectives, and a high-accuracy model is important for guaranteeing reliable results. Furthermore, SEO can adjust the evolution operator during a deep search. This is an important guarantee for deep, intelligent search. MOEA combined OFC and SEO to form the final solution algorithm—Dual Data Driven Multi-Objective Evolutionary Algorithm (DDMOEA). DDMOEA was explored using two different disciplinary problems of drug compound optimisation and wild blueberry cultivation and benchmarks were selected. The first two problem domains are distinct. The first problem is more complex than the second; however, both encompass redundant variables and indefinite objective functions. Benchmarks are utilised independently to gauge the profound intelligent search capability. The experiments affirm that the dual data-driven optimization approach proposed in this paper is effective, practical, and scalable.

On a Data-Driven Optimization Approach to the PID-Based Algorithmic Trading

This paper proposes an optimal trading algorithm based on a novel application of conventional control engineering (CE). We consider a fundamental CE concept, namely, the feedback control, and apply it to algorithmic trading (AT). The concrete feedback control strategy is designed in a form of the celebrated proportional–integral–derivative (PID) model. The highly fluctuating nature of the modern financial markets has led to the adoption of a model-free realization of the generic PID framework. The control theoretical methodology we propose is combined with the advanced statistics for the historical market data. We obtain a specific log-normal probability distribution function (pdf) associated with the specific quantities associated with the available stock data. The empirical log-normal pdf mentioned above enables the necessary PID gains optimization. For this aim, we apply the data-driven optimization approaches and consider the corresponding Monte Carlo solution procedure. The optimized PID trading algorithm we propose is also studied in the Fourier analysis framework. This equivalent frequency domain representation involves a new concept in financial engineering, namely, the “stock market energy” concept. For the evaluation, we implement the proposed PID optimal trading algorithm and develop a Python-based prototype software. We finally apply the corresponding prototype software to a data set from the Binance BTC/USDT (Bitcoin/Tether) stock market. The experimental result illustrates the implementability of the proposed optimal PID trading scheme and also shows the effectiveness of the proposed CE methods in the modern AT.

A diagnostic analytics model for managing post-disaster symptoms of depression and anxiety among students using a novel data-driven optimization approach

Prevalent mental disorders, such as depression and anxiety, commonly manifest in students throughout the transition to early adulthood. Mental illnesses can significantly impact students’ academic and social activities. An automatic or semiautomatic health monitoring approach is very effective for diagnosing depression and anxiety. This study aims to implement and scrutinize a data-driven optimization method for identifying and providing therapy to students with symptoms of depression and anxiety. The proposed method starts with data preprocessing and operating sentiment analysis to identify mentally disordered students. An ensemble learning classifier later divides students with symptoms into three categories based on their health condition: severe, moderate, and mild. A hyperparameter optimization approach is further adopted to improve the model’s performance. Finally, a rule-based dispatching system is implemented for scheduling therapy sessions. The proposed novel data-driven method is a post-disaster intelligent and reliable method that integrates three well-adopted techniques to address students’ depression and anxiety. The findings indicate that the conventional approach to monitoring depression among students previously detected only 7 to 15% of cases. However, the performance of the offered strategy revealed a confirmed rate of 44% of depressed and anxious students.

Data-Driven Optimization for Deposition with Degradable Tools

We present a data-driven optimization approach for robotic controlled deposition with a degradable tool. Existing methods make the assumption that the tool tip is not changing or is replaced frequently. Errors can accumulate over time as the tool wears away and this leads to poor performance in the case where the tool degradation is unaccounted for during deposition. In the proposed approach, we utilize visual and force feedback to update the unknown model parameters of our tool-tip. Subsequently, we solve a constrained finite time optimal control problem for tracking a reference deposition profile, where our robot plans with the learned tool degradation dynamics. We focus on a robotic drawing problem as an illustrative example. Using real-world experiments, we show that the error in target vs actual deposition decreases when learned degradation models are used in the control design.

Macronutrient Recommendations for Remission and Prevention of Diabetes in Asian Indians Based on a Data-Driven Optimization Model: The ICMR-INDIAB National Study.

To derive macronutrient recommendations for remission and prevention of type 2 diabetes (T2D) in Asian Indians using a data-driven optimization approach. Dietary, behavioral, and demographic assessments were performed on 18,090 adults participating in the nationally representative, population-based Indian Council of Medical Research-India Diabetes (ICMR-INDIAB) study. Fasting and 2-h postglucose challenge capillary blood glucose and glycosylated hemoglobin (HbA1c) were estimated. With HbA1c as the outcome, a linear regression model was first obtained for various glycemic categories: newly diagnosed diabetes (NDD), prediabetes (PD), and normal glucose tolerance (NGT). Macronutrient recommendations were formulated as a constrained quadratic programming problem (QPP) to compute optimal macronutrient compositions that would reduce the sum of the difference between the estimated HbA1c from the linear regression model and the targets for remission (6.4% for NDD and 5.6% for PD) and prevention of progression in T2D in PD and NGT groups. Four macronutrient recommendations (%E- Energy) emerged for 1) diabetes remission in NDD: carbohydrate, 49-54%; protein, 19-20%; and fat, 21-26%; 2) PD remission to NGT: carbohydrate, 50-56%; protein,18-20%; fat, 21-27%; 3 and 4) prevention of progression to T2D in PD and NGT: carbohydrate, 54-57% and 56-60%; protein, 16-20% and 14-17%, respectively; and fat 20-24% for PD and NGT. We recommend reduction in carbohydrates (%E) and an increase in protein (%E) for both T2D remission and for prevention of progression to T2D in PD and NGT groups. Our results underline the need for new dietary guidelines that recommend appropriate changes in macronutrient composition for reducing the burden due to diabetes in South Asia.

Data-Driven Optimization for Dynamic Shortest Path Problem Considering Traffic Safety

Traffic congestion is an inescapable problem that frustrates drivers in megacities. Although there is hardly a way to eliminate the congestion, it is possible to mitigate the impact through predictive methods. This paper develops a data-driven optimization approach for the dynamic shortest path problems (DSPP), considering traffic safety for urban navigations. The dynamic risk scores and travel times at different times and locations are estimated by the Safe Route Mapping (SRM) methodology and Long Short-Term Memory (LSTM) with Autoencoder, respectively, where possible variations in the future are considered. The DSPP is formulated as a mixed-integer linear programming problem under risk constraints to minimize the total travel cost, defined as the weighted sum of distance and travel time. To improve the efficiency of the DSPP, we design an improved tabu search with alternative initial-solution algorithms to accommodate various problem scales. Moreover, subgraph and self-adaptive insertion techniques are adopted as acceleration strategies to enhance computational efficiency further. Numerical experiments investigate the computational performance and the solution quality of our algorithm. The result shows satisfactory solution quality and computational efficiency with the proposed acceleration strategies compared to the CPLEX solver, a label-setting algorithm, and a state-of-the-art algorithm. Our algorithm can also compete with Google Maps regarding the travel cost in a real network in Manhattan, NY, USA, which is promising for Urban Navigations.

Sequential Resource Planning Decisions in an Epidemic Based on an Innovative Spread Model

Making rapid decisions in intervention resource planning is crucial for mitigating morbidity, mortality, and costs to the societies during epidemic outbreaks. This study presents a data-driven optimization approach for multiperiod resources planning, based on a sequential decision framework, considering up-to-date information and uncertainty in the spread of epidemics. In this method, a new <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$SEI^{3}H^{2}RD$ </tex-math></inline-formula> spread model is constructed to generate the most potential scenarios of an epidemic, based on all the historical information, and risk-averse stochastic programming was proposed to arrive at an optimal resource planning solution. The data-based numerical experiments demonstrate that our approach could control the epidemic by reducing the infected cases and deaths with similar or fewer resources than in the reality. In addition, we also find that the risk-averse design of the objective was able to take a steadier approach to resource planning helped avoid large fluctuations in resource allocations compared to a risk-neutral design. The other insight obtained from these experiments was that a moderate decision interval along with a planning horizon, which is slightly larger than the decision interval, would be a good choice for the sequential planning problem. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In the event of an outbreak of a new infectious disease, the uncertainty of the epidemic parameters limits the ability of the model to provide accurate predictions. However, decision-makers cannot wait for more information to alleviate this uncertainty and must immediately make decisions to control the epidemic. This article proposes a sequential decision-making approach with an innovative <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$SEI^{3}H^{2}RD$ </tex-math></inline-formula> spread model and a risk-averse scenario-based stochastic programming to help decision-makers arrive at real-time decisions. We illustrate the performance of this approach using the COVID-19 outbreak in Wuhan. The results show that our model predictions closely fit the real outbreak data and prove that our decision approach could reduce the cost of using the available resources and achieve the goal of controlling the epidemic. The proposed modeling framework can be adopted to study other infectious diseases and provide tangible policy recommendations for controlling outbreaks of such diseases.

Economic real-time energy trading services for electric vehicles with uncertain mobility demand

Real-time energy trading services have recently been proposed to facilitate the transition from vehicles with combustion engines to e-vehicles. Such services involve e-vehicle owners and a provider of energy trading skills, where the latter manages e-vehicle (dis-)charging at real-time energy prices. We propose and evaluate a data-driven optimization approach for the operational management of services that address e-vehicle owners with uncertain mobility demand and with a solar generator. We formulate the management problem as a sequential decision problem, and propose to solve it by integrating a lookahead policy with a safety energy buffering approach. We study by computer simulation the economic sustainability and the practical viability of the services under different degrees of mobility demand uncertainty, and for different e-vehicle owner types. We assess the negative impact of the uncertainty on both financial value and mobility demand satisfaction of the services. We show that our optimization approach with time-dependent energy buffering enables high mobility demand satisfaction while achieving, even in the case of extreme mobility demand uncertainty, on average at least 92% of the financial value that can be achieved under quasi-deterministic mobility demand. We show that combining our approach with departure time flexibility is sufficient to reach perfect mobility demand satisfaction and a further increase of the financial value.

Parametric data-driven optimization approach on plasmonic based ring resonator

In this paper, a plasmonic based ring resonator is being investigated. The metallic portion of plasmonic waveguide is chosen to be silver while the insulating material is air. The structure is numerically analyzed through Finite Difference in Time Domain (FDTD) method. To accurately model the relationship between the design parameter (radius of the ring) and the resonance condition of the resonator, a Machine Learning (ML) approach is applied. The ML model is trained on a dataset and tested for obtained results. The simulated results show that the proposed model is able to achieve the accuracy of prediction. The data driven optimization technique is being used for finding out the appropriate resonance condition of ring resonator. The proposed model is able to achieve good prediction accuracy (>90 %). The predicted result can be utilized in designing desired plasmonic devices like sensors, splitter, optical gates etc.

Fast data-driven learning of parallel MRI sampling patterns for large scale problems

In this study, a fast data-driven optimization approach, named bias-accelerated subset selection (BASS), is proposed for learning efficacious sampling patterns (SPs) with the purpose of reducing scan time in large-dimensional parallel MRI. BASS is applicable when Cartesian fully-sampled k-space measurements of specific anatomy are available for training and the reconstruction method for undersampled measurements is specified; such information is used to define the efficacy of any SP for recovering the values at the non-sampled k-space points. BASS produces a sequence of SPs with the aim of finding one of a specified size with (near) optimal efficacy. BASS was tested with five reconstruction methods for parallel MRI based on low-rankness and sparsity that allow a free choice of the SP. Three datasets were used for testing, two of high-resolution brain images (text {T}_{2}-weighted images and, respectively, text {T}_{1rho }-weighted images) and another of knee images for quantitative mapping of the cartilage. The proposed approach has low computational cost and fast convergence; in the tested cases it obtained SPs up to 50 times faster than the currently best greedy approach. Reconstruction quality increased by up to 45% over that provided by variable density and Poisson disk SPs, for the same scan time. Optionally, the scan time can be nearly halved without loss of reconstruction quality. Quantitative MRI and prospective accelerated MRI results show improvements. Compared with greedy approaches, BASS rapidly learns effective SPs for various reconstruction methods, using larger SPs and larger datasets; enabling better selection of sampling-reconstruction pairs for specific MRI problems.

Emergency response facility location in transportation networks: A literature review

Emergency response activity relies on transportation networks. Emergency facility location interacts with transportation networks clearly. This review is aimed to provide a combined framework for emergency facility location in transportation networks. The article reveals emergency response activities research clusters, issues, and objectives according to keywords co-occurrence analysis. Four classes of spatial separation models in transportation networks, including distance, routing, accessibility, and travel time are introduced. The stochastic and time-dependent characteristics of travel time are described. Travel time estimation and prediction method, travel time under emergency vehicle preemption, transportation network equilibrium method, and travel time in degradable networks are demonstrated. The emergency facilities location models interact with transportation networks, involving location-routing model, location models embedded with accessibility, location models embedded with travel time, and location models employing mathematical program with equilibrium constraints are reviewed. We then point out the-state-of-art challenges: ilities-oriented, evolution landscape and sequential decision modelling, data-driven optimization approach, and machine learning-based algorithms. • Emergency response facility location problem in transportation networks is reviewed for taxonomy purpose. • Travel time estimation and forecast methods in various scenarios are summarized. • Emergency facility location models in transportation networks are summarized and categorized. • Ilities-oriented, sequential decision modeling, data-driven optimization, and machine learning algorithm are to be studied.

Data-driven optimization for last-mile delivery

This paper considers how an online food delivery platform can improve last-mile delivery services’ performance using multi-source data. The delivery time is one critical but uncertain factor for online platforms that also regarded as the main challenges in order assignment and routing service. To tackle this challenge, we propose a data-driven optimization approach that combines machine learning techniques with capacitated vehicle routing optimization. Machine learning methods can provide more accurate predictions and have received increasing attention in the operations research field. However, different from the traditional predict-then-optimize paradigm, we use a new smart predict-then-optimize framework, whose prediction objective is constructed by decision error instead of prediction error when implementing machine learning. Using this type of prediction, we can obtain a more accurate decision in the following optimization step. Efficient mini-batching gradient and heuristic algorithms are designed to solve the joint order assignment and routing problem of last-mile delivery service. Besides, this paper considers the mutual effect between routing decision and deliverytime, and provides the corresponding solution algorithm. In addition, this paper conducts a computational study and finds that the proposed method’s performance has an approximate 5% improvement compared with other methods.