Objective Value Research Articles

A reference-point-method-based online proton treatment plan re-optimization strategy and a novel solution to planning constraint infeasibility problem.

Propose a highly automated treatment plan re-optimization strategy suitable for online adaptive proton therapy. The strategy includes a rapid re-optimization method that generates quality replans and a novel solution that efficiently addresses the planning constraint infeasibility issue that can significantly prolong the re-optimization process.

Approach: We propose a systematic reference point method (RPM) model that minimizes the l-infinity norm from the initial treatment plan in the daily objective space for online re-optimization. This model minimizes the largest objective value deviation among the objectives of the daily replan from their reference values, leading to a daily replan similar to the initial plan.
Whether a set of planning constraints is feasible with respect to the daily anatomy cannot be known before solving the corresponding optimization problem. The conventional trial-and-error-based relaxation process can cost a significant amount of time. To that end, we propose an optimization problem that first estimates the magnitude of daily violation of each planning constraint. Guided by the violation magnitude and clinical importance of the constraints, the constraints are then iteratively converted into objectives based on their priority until the infeasibility issue is solved.

Main result: The proposed RPM-based strategy generated replans similar to the offline manual replans within the online time requirement for six head and neck and four breast patients. The average target $D_{95}$ and relevant organ at risk sparing parameter differences between the RPM replans and clinical offline replans were -0.23, -1.62 Gy for head and neck cases and 0.29, -0.39 Gy for breast cases. The proposed constraint relaxation solution made the RPM problem feasible after one round of relaxation for all four patients who encountered the infeasibility issue.

Significance: We proposed a novel RPM-based re-optimization strategy and demonstrated its effectiveness on complex cases, regardless of whether constraint infeasibility is encountered.

Tailoring the wrap: intraoperative functional lumen imaging probe (FLIP) during hiatal hernia repair

Abstract Introduction The introduction of the functional lumen imaging probe (FLIP) has provided objective, real-time feedback on the geometric variations with each component of a hiatal hernia repair (HHR). The utility of this technology in altering intraoperative decision-making has been scarcely reported. Herein, we report a single-center series of intraoperative FLIP during HHR. Methods A retrospective review of electronic medical records between 2020 and 2022 was conducted and all patients undergoing non-recurrent HHR with FLIP were queried. Patient and hernia characteristics, intraoperative FLIP values and changes in decision-making, as well as early post-operative outcomes were reported. Both diameter and distensibility index (DI) were measured at 40 ml and 50 ml balloon inflation after hiatal dissection, after hiatal closure, and after fundoplication when indicated. Results Thirty-three patients met inclusion criteria. Mean age was 62 ± 14 years and mean BMI was 28 ± 6 kg/m2. The majority (53%) were type I hiatal hernias. The largest drop in DI occurred after hiatal closure, with minimal change seen after fundoplication (mean DI of 4.3 ± 2. after completion of HH dissection, vs 2.7 ± 1.2 after hiatal closure and 2.3 ± 1 after fundoplication when performed). In 13 (39%) of cases, FLIP values directly impacted intraoperative decision-making. Fundoplication was deferred in 4/13 (31%) patients, the wrap was loosened in 2/13 (15%); the type of fundoplication was altered to achieve adequate anti-reflux values in 2/13 (15%) patients, and in 1/13 (3%) the wrap was tightened. Conclusion FLIP measurements can be used intraoperatively to guide decision-making and alter management plan based on objective values. Long-term outcomes and further prospective studies are required to better delineate the value of this technology.

Convergence of the block Lanczos method for the trust‐region subproblem in the hard case

SummaryThe trust‐region subproblem (TRS) plays a vital role in numerical optimization, numerical linear algebra, and many other applications. It is known that the TRS may have multiple optimal solutions in the hard case. In [Carmon and Duchi, SIAM Rev., 62 (2020), pp. 395–436], a block Lanczos method was proposed to solve the TRS in the hard case, and the convergence of the optimal objective value was established. However, the convergence of the KKT error as well as that of the approximate solution are still unknown for this method. In this paper, we give a more detailed convergence analysis on the block Lanczos method for the TRS in the hard case. First, we improve the convergence speed of the approximate objective value. Second, we derive the speed of the KKT error tends to zero. Third, we establish the convergence of the approximation solution, and show theoretically that the projected TRS obtained from the block Lanczos method will be close to the hard case more and more as the block Lanczos process proceeds. Numerical experiments illustrate the effectiveness of our theoretical results.

Matheuristics for mixed-model assembly line balancing problem with fuzzy stochastic processing time

Our work aims to investigate methods for solving the mixed-model assembly line balancing problem (MALBP) under uncertainty with the objective of minimizing the number of workstations. Specifically, we model task processing time as fuzzy stochastic variables (FRVs) due to the inherent uncertainties and variations in the manufacturing environment. Additionally, we introduce a ranking method for FRVs and propose a mathematical model to address MALBP. The recently developed Red Fox Optimization (RFO) algorithm is also discretized for the first time to support solving this problem. Finally, matheuristic algorithms combine a metaheuristic such as the popular Genetic Algorithm (GA), Particle Swarm Optimization (PSO), or the Discretized Red Fox Optimization (DRFO) algorithm with the Mixed-Integer Programming (MIP) model to generate the best solution in a reasonable time. Our comparative results demonstrate that the GA-MIP combination outperforms the others in both objective value and computational time.

OPTIMISING THE LADDER RAISE TASK IN THE PHYSICAL APTITUDE TEST FOR FIRE-FIGHTERS

INTRODUCTION & AIM Identifying physically capable candidates is a crucial component of recruiting firefighters given the physical demands of the occupation. At the request of a local fire agency, a review of one test item within the physical aptitude testing protocol was undertaken with the aim of this pilot study to compare physical and perceptual performance between the ladder raise task and a suite of surrogate tests to determine suitability for use in recruitment screening. METHODS 23 participants (unskilled in firefighting) performed the criterion ladder task (‘Ladder’) and three surrogate tasks: stationary constant-tension overhead press (existing test item; ‘Reel’); barbell overhead walk/return (‘Barbell’); overhead modular ladder lift (Modular Under-Running Apparatus (‘MURA’)). The tests were segmented into six components relating to lifting and under-running the device. Task duration, task completion, perceptual and kinetic variables were measured. Pearson’s correlation, OLP regressions and paired t-tests assessed relationships between the Ladder and surrogate tasks. RESULTS The Reel most closely reflected the criterion task in component 1 (lifting) for both peak and average forces (dz =-0.11, p=0.920; dz=-0.11, p=0.532 respectively). The MURA and Barbell were most closely aligned to the Ladder in all other objective values with trivial-small differences evident. The MURA was most closely aligned with the Ladder in subjective measures for components-2/5 and components-3/4 including difficulty (dz= -0.41, p=0.133; dz=-0.32, p=0.250, respectively), fatigue (dz= -0.25, p=0.354; dz=-0.10, p=0.796, respectively) and task representation (7.2[1.6]; 7.4[1.6], respectively). CONCLUSION The currently used Reel task revealed low construct and content validity. The MURA was shown to be the most appropriate surrogate option for the local fire agency to further investigate for use within recruitment physical testing. Further investigation with a sample of career firefighters should be undertaken to verify face validity at the industry level.

Reduction in presynaptic dopamine transporter may be associated with future problematic delusion

ObjectivesPsychosis, especially in delusions, greatly impairs the quality of life of patients with Parkinson’s disease (PD) and their caregivers. Few objective risk indicators of the association between psychosis and clinical features has been reported. It is unclear whether the reduction in DAT binding represents the underlying mechanism of delusion or its association. There are no long-term data on the objective prognostic value of DAT binding for delusions. We investigated whether DAT binding at baseline can be a prognostic risk factor for future development of PD delusions. Materialsand Methods: We reviewed the detailed clinical chart of patients with PD without a history of psychosis who underwent [123I]FP-CIT SPECT during the disease. The endpoint was defined as when the delusions occurred during the 5 years after the examination of [123I]FP-CIT SPECT. Specific binding ratio (SBR) values were calculated. ResultsSixty-one patients with PD were included in the analysis, and 11 patients had delusions within 5 years of [123I] FP-CIT SPECT. The average (p = 0.004), minimum (p = 0.004), maximum (p = 0.001), right-sided (p = 0.002), and left-sided (p = 0.003) SBRs in the striatum were significantly smaller in patients with delusions than in patients without delusions. Each difference of each SBR was significantly smaller than those without delusions after adjusting after controlling for age, gender, disease severity, timing of [123I]FP-CIT SPECT, anti-parkinsonian medications, hospitalization, administering more or newly anti-parkinsonian drugs, and receiving DBS or LCIG. ConclusionsPD delusions is still problematic, and lowering DAT binding may be helpful for predicting future delusions, regardless of the timing of [123I]FP-CIT SPECT.

Evaluation of the prognostic impact of pathologic tumor regression grade on patients with colorectal cancer after preoperative chemoradiotherapy.

The prognostic value of the pathological response to preoperative chemoradiotherapy (CRT) in rectal cancer (RC) remains unknown. We aimed to assess the predictive value of the response to CRT that was derived from an evaluation of the histological findings (whole-section vs. representative-section sampling) and attempted to determine an objective cut-off value for the tumor regression grade (TRG). We examined the association of the TRG with the outcomes (recurrence-free survival [RFS] and overall survival [OS]) of 78 patients with RC. Patients with RC treated with preoperative CRT were divided into development (30 cases) and validation (48 cases) cohorts. The TRG was classified as grades I (Ia, Ib), II, and III. The cut-off value was determined by receiver operating characteristic (ROC) curve analysis. The TRG determined from whole-section sampling versus representative-section sampling was more strongly correlated with patient survival. We found that in both cohorts, patients with a cut-off value of <73% had a poor prognosis. Finally, the cut-off value was found to be an independent predictive factor in both univariate and multivariate analysis. The TRG that was used to evaluate patients with RC who underwent preoperative CRT was an independent prognostic factor for outcome.

Kapasitas Pemerintah Desa Mekarsari Kecamatan Selaawi Dalam Mewujudkan Desa Sadar Hukum

Legal awareness plays an essential role in achieving legal values and goals. This awareness becomes the basis for realizing the values of legal certainty, usefulness, justice and legal objectives for general prosperity. The Regulation of the Minister of Law and Human Rights of the Republic of Indonesia stipulates that public legal awareness includes understanding and compliance with legal norms. The Legal Awareness Subdistrict/Village Program (DSH), which began in the 1980s, aims to increase residents' legal awareness through outreach by the Ministry of Law and Human Rights. In an effort to increase legal awareness, the government formed Legally Aware Families (Kadarkum) and Legally Aware Villages/Subdistricts. The Legal Awareness Family Competition, as held in several regions in Indonesia, involves the community in increasing their understanding of the law through various competitions. This research describes the capacity of the Mekarsari Village Government in realizing a Legally Aware Village, involving aspects of strategic leadership, organizational structure, human resources, financial management, infrastructure and program management. Despite facing limitations, Mekarsari Village has succeeded in creating an environment that prioritizes legal understanding and has become an example for other villages in strengthening legal awareness at the local level.

Input Regularization for Integer Optimal Control in BV with Applications to Control of Poroelastic and Poroviscoelastic Systems

We revisit a class of integer optimal control problems for which a trust-region method has been proposed and analyzed in arXiv:2106.13453v3 [math.OC]. While the algorithm proposed in arXiv:2106.13453v3 [math.OC] successfully solves the class of optimization problems under consideration, its convergence analysis requires restrictive regularity assumptions. There are many examples of integer optimal control problems involving partial differential equations where these regularity assumptions are not satisfied. In this article we provide a way to bypass the restrictive regularity assumptions by introducing an additional partial regularization of the control inputs by means of mollification and proving a $\Gamma$-convergence-type result when the support parameter of the mollification is driven to zero. We highlight the applicability of this theory in the case of fluid flows through deformable porous media equations that arise in biomechanics. We show that the regularity assumptions are violated in the case of poro-visco-elastic systems, and thus one needs to use the regularization of the control input introduced in this article. Associated numerical results show that while the homotopy can help to find better objective values and points of lower instationarity, the practical performance of the algorithm without the input regularization may be on par with the homotopy.

Iterative MILP algorithm to find alternate solutions in linear programming models

AbstractWe address in this paper linear programming (LP) models in which it is desired to find a finite set of alternate optima. An LP may have multiple alternate solutions with the same objective value or with increasing objective values. For many real life applications, it can be interesting to have a pool of solutions to compare what operations should be executed and what is the cost/benefit of doing it. To obtain a specified number of these alternate solutions in the increasing order of objective values, we propose an iterative MILP algorithm in which we successively add integer cuts on inactive constraints. We demonstrate the application and effectiveness of this algorithm on a 2 dimensional LP and on small and large supply chain problems. The proposed iterative MILP algorithm provides an effective approach for finding a specified number of alternate optima in LP models, which provides a useful tool in a variety of applications as for instance in supply chain optimization problems.

Irrigation Distribution Network Design Parameters and Their Influence on Sustainability Management

In 2030, the world population will exceed 8.5 billion, increasing the challenges to satisfy basic needs for food, shelter, water, and/or energy. Irrigation plays a vital role in productive and sustainable agriculture. In the current context, it is determined not only by water availability but also by optimal management. Several authors have attempted to measure the performance of irrigation networks through various approaches in terms of technical indicators. To improve the sustainability in the pipe sizing of the pressurised irrigation networks, 25 different models were evaluated to discuss the advantages and disadvantages to consider in future methodologies to size water systems, which guarantee the network operation but contribute to improving the sustainability. They enable water managers to use them as tools to reduce a complex evaluation of the performance of a system, and focusing on better management of resources and sustainability indicators for agricultural ecosystems are clear and objective values.

A hybrid multi-objective solution approach for a reliable truck-drone routing problem integrated with pickup and delivery services

ABSTRACT Humanitarian logistics aims to reduce the arrival time of relief goods and optimize services in disaster-affected regions. This paper introduces a multi-objective hybrid truck-drone routing problem with pickup and delivery services, considering reliability (MHTDRP-PD-R) to enhance relief operations within humanitarian logistics. The model is formulated as a mixed-integer linear programming problem with the simultaneous goals of minimizing transportation time and maximizing reliability through routing and inventory decisions made by multiple trucks and drones. An adaptive large neighborhood search (ALNS) algorithm, coupled with heuristic technique, is developed to tackle the problem. The ALNS algorithm refines initial solutions through destroy and repair operators, integrated with multi-objective optimization methods to create a unified objective function. Small and large-scale test problems are then employed to evaluate solution quality using multi-objective optimization. The calculation results show the superiority of the developed ALNS integrated with the ε-constraint method regarding the objective value and solution time.

Adaptive Quick Sliding Mode Reaching Law and Disturbance Observer for Robust PMSM Control Systems

The permanent magnet synchronous motor (PMSM) has been of interest to eco-friendly industries on account of its advantages such as high performance, efficiency, and precision control. However, perturbations due to PMSM parameter uncertainty, load torque, and external disturbance interfere with the construction of PMSM precision control systems. Therefore, a robust control system is needed to avoid unnecessary system movement caused by perturbations. In this paper, sliding mode control (SMC) is adopted to implement a robust control system for the PMSM. In order to reduce the reaching time from the initial system state to the sliding surface and the chattering phenomenon that can cause the system to malfunction, the adaptive quick sliding mode reaching law based on an exponential function and power equation is proposed. Although the SMC is robust to disturbance and parameter uncertainty, unexpected disturbances can destabilize the system. To estimate the unmatched disturbance in a short time, the second-order fast terminal sliding mode observer (SFTSMO) is proposed. The results show that the motor control system based on the proposed method has a fast convergence speed to an objective value, position tracking performance, and robustness.

Routing and Scheduling in Multigraphs With Time Constraints—A Memetic Approach for Airport Ground Movement

Routing and scheduling problems with increasingly realistic modelling approaches often entail the consideration of multiple objectives, time constraints, and modelling the system as a multigraph. This detailed modelling approach has increased computational complexity and may also lead to violation of the additivity property of the costs. In the worst scenario, increased complexity makes the problem intractable for exact algorithms. Even when the problem is solvable, exact algorithms may not provide solutions within the given time budget, and the found solutions are not guaranteed to be optimal due to the additivity property violation. Approximate solution methods become more suitable in this case. This paper focuses on one particular real-world application, the Airport Ground Movement Problem, where both time constraints and parallel arcs are involved. We introduce a novel Memetic Algorithm for Routing in Multigraphs with Time constraints (MARMT) and present a comprehensive study of its different variants based on diverse genetic representation methods. We propose a local search operator that enhances search efficiency and effectiveness. MARMT is tested on real data based on two airports of different sizes. Our results show that MARMT does not suffer from the non-additivity property problem as it outperforms the state-of-the-art exact algorithm when allowed to converge. When a time budget of 10 seconds is imposed on MARMT, it is able to provide solutions with quality comparable (within 1-5% degradation) to the ones given by the exact algorithm with respect to the aggregated objective values. MARMT can be adapted for other applications, such as train operations.

A Training-Free Neural Architecture Search Algorithm Based on Search Economics

Motivated by the observation that most neural architecture search (NAS) methods are time-consuming because a “training process” is required to evaluate each searched neural architecture, this paper presents an efficient NAS algorithm based on a promising metaheuristic algorithm named search economics (SE) and a new training-free estimator to evaluate the searched neural architectures for not only obtaining a good neural architecture but also accelerating the computation time. The basic idea of the proposed NAS algorithm is to use the so-called expected value of each region in the search space to guide the search so that it will focus on searching high potential regions instead of solutions with high objective values in particular regions. To evaluate the performance of the proposed algorithm, we compare it with state-of-the-art non-training-free and training-free NAS methods. Experimental results show that the proposed algorithm is capable of finding a result that is similar to or better than those found by most non-training-free NAS algorithms compared in this study but taking only a tiny portion of the computation time.

A sample average approximation-based heuristic for the stochastic production routing problem

AbstractThe Production Routing Problem under demand uncertainty is an integrated problem containing production, inventory, and distribution decisions. At the planning level, the aim is to meet retailers demand, when only the demand distribution is known in advance, while minimizing the corresponding costs. In this study, a two-stage formulation is presented in which the routing can be adjusted at short notice. In the first stage, only production decisions are made, while delivery and inventory quantities and retailer visit schedules are determined in the second stage. To handle a large number of scenarios, two solution methods based on Sample Average Approximation are introduced. Furthermore, the impact of the routing quality is explored by applying a simple heuristic and an effective metaheuristic on the routing part. It is shown that, on average, the simple heuristic within an adjustable Sample Average Approximation approach provides better objective function values than the metaheuristic within a non-adjustable approach. Also all solution approaches outperform an expected value based approach in terms of runtime and objective function value.

Solar powered cascade system for sustainable deep-freezing and power generation - exergoeconomic evaluation and multi-objective optimization for tropical regions

The global initiative to decarbonize the refrigeration sector aims to provide cooling energy while reducing CO2 emissions. In India, the majority of urban industrial refrigeration systems are powered by fossil fuel based electricity, which increases consumption of fossil fuel and it contributes to global warming. To address this issue, two unique solar thermal operated cooling configurations, the Simple Cascade System (SCS) and the Recuperative Cascade System (RCS), were designed to fulfil urban industrial cooling energy demands. The surplus electricity generated by these systems is transferred to the on-site grid. Ethane, n-butane, and R1233zd(E) were selected as working fluids due to their low global warming potential (GWP). Among them, the RCS system with R1233zd(E) demonstrated the highest energy and exergy performance, benefiting from its superior thermodynamic properties and effective utilization of internal heat. In New Delhi, the R1233zd(E) operated RCS system achieved the highest cooling and electricity outputs at 54 kW and 20 kW, respectively. Although the R1233zd(E) operated RCS system incurred higher costs, the performance improvement outweighed the increase in cost. To assess the system sustainability in terms of exergy utilisation, a new indicator sustainability index (SI) was developed and established with values of 1.1. Increasing the evaporator temperature enhanced the system performance but also raised total plant cost, while raising the system condenser temperature had the opposite effect. The multi-objective optimization assessment for RCS configuration indicated an optimal objective value of an energy utilization ratio (EUR) of 0.16, an exergy efficiency of 8.7 %, and a capital cost of 20 $/h.

Flame transfer function shaping for robust thermoacoustic systems: Application to a kinematic flame model

This work presents a data-driven optimization technique for the optimization of the flame response within a low-order acoustic network model. The methodology exploits the Nyquist criterion to compute a measure of thermoacoustic robustness at targeted frequencies, which serves as an objective value for the optimized system. The method is demonstrated using a simple Rijke tube model coupled with a [Formula: see text]-equation solver to model flame dynamics. The approach is shown to efficiently increase the stability margin of the system by modifying the flame transfer function. The methodology is applied to two examples, based on which possible scenarios are discussed and the potential and limitations associated with the practical implementation of the method are analyzed.

A Framework for Data-Driven Explainability in Mathematical Optimization

Advancements in mathematical programming have made it possible to efficiently tackle large-scale real-world problems that were deemed intractable just a few decades ago. However, provably optimal solutions may not be accepted due to the perception of optimization software as a black box. Although well understood by scientists, this lacks easy accessibility for practitioners. Hence, we advocate for introducing the explainability of a solution as another evaluation criterion, next to its objective value, which enables us to find trade-off solutions between these two criteria. Explainability is attained by comparing against (not necessarily optimal) solutions that were implemented in similar situations in the past. Thus, solutions are preferred that exhibit similar features. Although we prove that already in simple cases the explainable model is NP-hard, we characterize relevant polynomially solvable cases such as the explainable shortest path problem. Our numerical experiments on both artificial as well as real-world road networks show the resulting Pareto front. It turns out that the cost of enforcing explainability can be very small.

DOGE-Train: Discrete Optimization on GPU with End-to-End Training

We present a fast, scalable, data-driven approach for solving relaxations of 0-1 integer linear programs. We use a combination of graph neural networks (GNN) and a Lagrange decomposition based algorithm. We make the latter differentiable for end-to-end training and use GNNs to predict its algorithmic parameters. This allows to retain the algorithm's theoretical properties including dual feasibility and guaranteed non-decrease in the lower bound while improving it via training. We overcome suboptimal fixed points of the basic solver by additional non-parametric GNN update steps maintaining dual feasibility. For training we use an unsupervised loss. We train on smaller problems and test on larger ones showing strong generalization performance with a GNN comprising only around 10k parameters. Our solver achieves significantly faster performance and better dual objectives than its non-learned version, achieving close to optimal objective values of LP relaxations of very large structured prediction problems and on selected combinatorial ones. In particular, we achieve better objective values than specialized approximate solvers for specific problem classes while retaining their efficiency. Our solver has better any-time performance over a large time period compared to a commercial solver.