Re-optimization Strategy Research Articles

Leveraging innovization and transfer learning to optimize best management practices in large-scale watershed management

Recent research in evolutionary multi-objective optimization (EMO) highlights the concept of “Innovization”, which identifies essential patterns in high-quality, non-dominated solutions. This study introduces a novel method to pinpoint influential Best Management Practices (BMPs) in the Chesapeake Bay Watershed, optimizing the trade-off solution process. This approach, though innovative, demands considerable expertise and involves generating multiple solutions for expert analysis to detect commonly used BMPs. We devised three re-optimization strategies from these findings using an innovized BMP list, efficiently producing high-quality solutions. We also implemented transfer learning to adapt these strategies for new counties, demonstrating effectiveness in four West Virginia counties by reducing decision variables by 3% to 33% and achieving similar reductions in four additional counties. This showcases the potential of combining innovization with transfer learning to simplify complex optimization challenges, emphasizing its significant applicability in real-world settings.

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

Objective. 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 results. 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.

“I Just Had to Go With It Once I Got There”: Inequality, Housing, and School Re-optimization

Residential segregation by race and class is a durable form of inequality. Yet, we know less about how the unequal sorting of families into neighborhoods and schools occurs. Drawing on interviews with a diverse sample of 156 families, we examine whether residential and school decisions are connected and how they differ by household income. We find that, for higher-income families, residential decisions maintain and build on existing educational advantages, while lower-income parents churn between both houses and schools, doing the continuous work of compensating for unequal settings. Only the highest income—mostly White—parents report that they can combine their housing and school decisions and achieve satisfaction in both domains. In contrast, housing insecurity and unequal, racially-stratified geographies constrain less advantaged, primarily minority families to prioritize affordable shelter over school choice. When such trade-offs lead to inadequate educational experiences for their children, these families try to improve their children’s schools through re-optimization strategies, withdrawing and re-enrolling them into different schools. While some parents perceive that these changes benefit their children, such school transfers can also increase educational instability. More generally, the lack of quality schools in affordable neighborhoods burdens families by requiring compensatory strategies to resolve housing and educational shortcomings.

A fast and robust constraint-based online re-optimization approach for automated online adaptive intensity modulated proton therapy in head and neck cancer

Objective. In head-and-neck cancer intensity modulated proton therapy, adaptive radiotherapy is currently restricted to offline re-planning, mitigating the effect of slow changes in patient anatomies. Daily online adaptations can potentially improve dosimetry. Here, a new, fully automated online re-optimization strategy is presented. In a retrospective study, this online re-optimization approach was compared to our trigger-based offline re-planning (offlineTB re-planning) schedule, including extensive robustness analyses. Approach. The online re-optimization method employs automated multi-criterial re-optimization, using robust optimization with 1 mm setup-robustness settings (in contrast to 3 mm for offlineTB re-planning). Hard planning constraints and spot addition are used to enforce adequate target coverage, avoid prohibitively large maximum doses and minimize organ-at-risk doses. For 67 repeat-CTs from 15 patients, fraction doses of the two strategies were compared for the CTVs and organs-at-risk. Per repeat-CT, 10.000 fractions with different setup and range robustness settings were simulated using polynomial chaos expansion for fast and accurate dose calculations. Main results. For 14/67 repeat-CTs, offlineTB re-planning resulted in <50% probability of D 98% ≥ 95% of the prescribed dose (D pres) in one or both CTVs, which never happened with online re-optimization. With offlineTB re-planning, eight repeat-CTs had zero probability of obtaining D 98% ≥ 95%D pres for CTV7000, while the minimum probability with online re-optimization was 81%. Risks of xerostomia and dysphagia grade ≥ II were reduced by 3.5 ± 1.7 and 3.9 ± 2.8 percentage point [mean ± SD] (p < 10−5 for both). In online re-optimization, adjustment of spot configuration followed by spot-intensity re-optimization took 3.4 min on average. Significance. The fast online re-optimization strategy always prevented substantial losses of target coverage caused by day-to-day anatomical variations, as opposed to the clinical trigger-based offline re-planning schedule. On top of this, online re-optimization could be performed with smaller setup robustness settings, contributing to improved organs-at-risk sparing.

Sequential learning based re-optimization approaches for less model-based dynamic pick-up routing problem

We address a lessmodel-based dynamic routing problem arising from home parcel pick-up service, where lessmodel-based means existing customers who dynamically request services independently following Poisson process with a stochastic rate. Overall, through an extended application of re-optimization (RO) strategy, a Markov decision process formulation and approximation dynamic programming- and Bayes' theorem-based solution approaches are proposed. Specifically, first a pool of basic policies corresponding to all possible values of the rate are developed offline via approximate value iteration. Then, Bayes' theorem-based sequential learning is designed that can sequentially update the belief about the rate's probability distribution over its possible values. Third, coupled with the updated belief, basic policies are collectively implemented in two different ways, resulting in RO approaches which involve constructions of two different online policies, i.e. a belief-weighted deterministic policy and a belief-based random policy, and their re-optimizations at decision epochs. In the numerical study, through comparison with model-based (i.e. using full knowledge of the rate) and model-free heuristics, our approaches are examined and valuable insights are obtained. Important insights include that (i) the belief-weighted deterministic policy outperforms the belief-based random policy, and further, (ii) the former is better than the latter at preserving the improvement resulting from the improved model-based policy.

Experimental impact of power re-optimization in a mesh network

We experimentally observe the signal-to-noise ratio (SNR) degradation of previously established services induced by loading new services in a network, and we mitigate this degradation by periodic power re-optimization via two different strategies: (1) a static strategy based on end-of-life parameters and (2) a dynamic strategy based on real-time monitoring to the current state of the network. We use a mesh network testbed of four nodes and five links with commercial equipment only. We observe up to 3.4 dB SNR degradation on the previously established services due to the loading of new services. Then we demonstrate an improvement of up to 3.2 dB in the network margin achieved by applying our proposed power re-optimization strategy.

A Chaotic Genetic Algorithm with Variable Neighborhood Search for Solving Time-Dependent Green VRPTW with Fuzzy Demand

Aiming at the time-dependent green vehicle routing problem with fuzzy demand, this paper comprehensively considers the dispatch costs, time window penalty costs, fuel costs, and the effects of vehicle travel speed, road gradient, and vehicle load on fuel consumption, a mixed integer programming model is formulated based on pre-optimization and re-optimization strategies. The traditional vehicle routing problems are modeled based on a symmetric graph. In this paper, considering the influence of time-dependent networks on route optimization, modeling is based on an asymmetric graph, which increases the complexity of the problem. In the pre-optimization stage, a pre-optimization scheme is generated based on the credibility measure theory; in the re-optimization stage, a new re-optimization strategy was used to deal with the service failure node In order to solve this problem, we developed a chaotic genetic algorithm with variable neighborhood search, pseudo-randomness of chaos was introduced to ensure the diversity of initial solutions, and adaptive neighborhood search times strategy and inferior solution acceptance mechanism were proposed to improve the performance of the algorithm. The numerical results show that the model and algorithm we proposed are effective.

Dynamic reconfiguration for TEG systems under heterogeneous temperature distribution via adaptive coordinated seeker

A thermoelectric generation (TEG) system has the weakness of relatively low thermoelectric conversion efficiency caused by heterogeneous temperature distribution (HgTD). Dynamic reconfiguration is an effective technique to improve its overall energy efficiency under HgTD. Nevertheless, numerous combinations of electrical switches make dynamic reconfiguration a complex combinatorial optimization problem. This paper aims to design a novel adaptive coordinated seeker (ACS) based on an optimal configuration strategy for large-scale TEG systems with series–parallel connected modules under HgTDs. To properly balance global exploration and local exploitation, ACS is based on ‘divide-and-conquer’ parallel computing, which synthetically coordinates the local searching capability of tabu search (TS) and the global searching capability of a pelican optimization algorithm (POA) during iterations. In addition, an equivalent re-optimization strategy for a reconfiguration solution obtained by meta-heuristic algorithms (MhAs) is proposed to reduce redundant switching actions caused by the randomness of MhAs. Two case studies are carried out to assess the feasibility and superiority of ACS in comparison with the artificial bee colony algorithm, ant colony optimization, genetic algorithm, particle swarm optimization, simulated annealing algorithm, TS, and POA. Simulation results indicate that ACS can realize fast and stable dynamic reconfiguration of a TEG system under HgTDs. In addition, RTLAB platform-based hardware-in-the-loop experiments are carried out to further validate the hardware implementation feasibility.

Integrated aircraft tail assignment and cargo routing problem with through cargo consideration

Traditionally, freight airlines assign individual aircraft to each flight and sequentially determine the cargo itineraries for each order. This may limit the option of cargo itineraries, and lead to mismatches between cargo demands and freighter capacities. To overcome this limitation, we introduce the concepts of through cargo connection and short through cargo connection. A through cargo connection requires cargoes to stay on the same aircraft between two connected flights, and a short through cargo connection is a through connection that needs less time than the standard cargo transshipment time. By considering the through cargo connections and short through cargo connections, we can generate the complete set of all possible cargo itineraries, so as to improve the overall schedule efficiency and robustness, and reduce the cargo ground handling cost. In this paper, two mathematical models (the connection- and string-based models) are proposed to solve the aircraft tail assignment problem (TAP) and cargo routing problem (CRP) simultaneously with through cargo considerations. We developed a column generation framework to solve the LP relaxation of the string-based model, and a diving heuristic with reoptimization strategy to find a good IP solution in a reasonable time. The proposed solution methods are tested using industrial cases from the largest freight airline in China. The computational experiments revealed that the integrated models could bring up to 5.04%∼10.31% profit growth, and the through cargo savings are tripled compared with the baseline solutions. By analyzing the computational performance of two models, we recommend that airlines can apply connection-based model only if the problems are not very large and the computational time is sufficient; otherwise, the string-based model is more robust in terms of problem scales and computational time.

MRI-Guided Online Adaptive Stereotactic Body Radiation Therapy of Liver and Pancreas Tumors on an MR-Linac System.

Simple SummaryThe hybrid magnetic resonance imaging and medical linear accelerator (MR-Linac) systems are expected to revolutionize radiation therapy, uniquely offering high quality soft-tissue contrast and fast imaging to facilitate the online re-planning and guidance of the treatment delivery. While the clinical procedures for stereotactic body radiotherapy are well-established for conventional linacs (with their strengths and weaknesses), they still require significant development and refinement for the MR-Linac systems. Adjustment of fractionation schemes including clinical goals, patient selection, organ motion management, treatment session length, staff logistics, and overall complexity of the online re-planning sessions are key factors that drive the adoption of MR-Linac technologies. In this report, we present the clinical implementation of an MRI-guided radiation therapy workflow, which was used to treat 16 upper gastro-intestinal cancer patients on a 1.5 T MR-Linac platform. The workflow was proven to be feasible for a wide range of clinical scenarios, and the overall treatment session time was significantly reduced as tasks were optimized and the clinical team gradually gained expertise.Purpose: To describe a comprehensive workflow for MRI-guided online adaptive stereotactic body radiation therapy (SBRT) specific to upper gastrointestinal cancer patients with abdominal compression on a 1.5T MR-Linac system. Additionally, we discuss the workflow’s clinical feasibility and early experience in the case of 16 liver and pancreas patients. Methods: Eleven patients with liver cancer and five patients with pancreas cancer were treated with online adaptive MRI-guidance under abdominal compression. Two liver patients received single-fraction treatments; the remainder plus all pancreas cancer patients received five fractions. A total of 65 treatment sessions were investigated to provide analytics relevant to the online adaptive processes. The quantification of target and organ motion as well as definition and validation of internal target volume (ITV) margins were performed via multi-contrast imaging provided by three different 2D cine sequences. The plan generation was driven by full re-optimization strategies and using T2-weighted 3D image series acquired by means of a respiratory-triggered exhale phase or a time-averaged imaging protocol. As a pre-requisite for the clinical development of the procedure, the image quality was thoroughly investigated via phantom measurements and numerical simulations specific to upper abdominal sites. The delivery of the online adaptive treatments was facilitated by real-time monitoring with 2D cine imaging. Results: Liver 1-fraction and 5-fraction online adaptive session time were on average 80 and 67.5 min, respectively. The total session length varied between 70–90 min for a single fraction and 55–90 min for five fractions. The pancreas sessions were 54–85 min long with an average session time of 68.2 min. Target visualization on the 2D cine image data varied per patient, with at least one of the 2D cine sequences providing sufficient contrast to confidently identify its location and confirm reproducibility of ITV margins. The mean/range of absolute and relative dose values for all treatment sessions evaluated with ArcCheck were 90.6/80.9–96.1% and 99/95.4–100%, respectively. Conclusion: MR-guidance is feasible for liver and pancreas tumors when abdominal compression is used to reduce organ motion, improve imaging quality, and achieve a robust intra- and inter-fraction patient setup. However, the treatment length is significantly longer than for the conventional linac, and patient compliance is paramount for the successful completion of the treatment. Opportunities for reducing the online adaptive session time should be explored. As the next steps, dose-of-the-day and dose accumulation analysis and tools are needed to enhance the workflow and to help further refine the online re-planning processes.

Fast Adaptive Temperature-Based Re-Optimization Strategies for On-Line Hot Spot Suppression during Locoregional Hyperthermia.

Experience-based adjustments in phase-amplitude settings are applied to suppress treatment limiting hot spots that occur during locoregional hyperthermia for pelvic tumors. Treatment planning could help to further optimize treatments. The aim of this research was to develop temperature-based re-optimization strategies and compare the predicted effectiveness with clinically applied protocol/experience-based steering. This study evaluated 22 hot spot suppressions in 16 cervical cancer patients (mean age 67 ± 13 year). As a first step, all potential hot spot locations were represented by a spherical region, with a user-specified diameter. For fast and robust calculations, the hot spot temperature was represented by a user-specified percentage of the voxels with the largest heating potential (HPP). Re-optimization maximized tumor T90, with constraints to suppress the hot spot and avoid any significant increase in other regions. Potential hot spot region diameter and HPP were varied and objective functions with and without penalty terms to prevent and minimize temperature increase at other potential hot spot locations were evaluated. Predicted effectiveness was compared with clinically applied steering results. All strategies showed effective hot spot suppression, without affecting tumor temperatures, similar to clinical steering. To avoid the risk of inducing new hot spots, HPP should not exceed 10%. Adding a penalty term to the objective function to minimize the temperature increase at other potential hot spot locations was most effective. Re-optimization times were typically ~10 s. Fast on-line re-optimization to suppress treatment limiting hot spots seems feasible to match effectiveness of ~30 years clinical experience and will be further evaluated in a clinical setting.

Optimization of Task Scheduling and Dynamic Service Strategy for Multi-UAV-Enabled Mobile-Edge Computing System

In this study, we introduce a multi-unmanned aerial vehicle (multi-UAV) enabled mobile edge computing (MEC) system, with UAVs as the computing server for the task offloading of ground users. The energy consumption for ground users is minimized by jointly optimizing the UAV task scheduling, bit allocation, and UAV trajectory in a unified framework. To accomplish such goal, we propose a two-layer optimization strategy, where the upper layer optimizes the UAV task scheduling based on a dynamic programming-based bidding optimization method, while the lower one solves the bit allocation and UAV trajectory. In particular, the lower layer is decoupled into several subproblems to reduce the computational complexity, which can be easily solved using an alternating direction method of multipliers. However, the UAV trajectories optimized by solving the decoupled subproblems may lead to path conflicts. As such, we further propose a re-optimization strategy to eliminate such conflicts. Experimental results demonstrate that the proposed strategy achieves a favorable performance than those of greedy and random strategies in terms of total user energy consumption, the trajectory conflicts can be eliminated effectively, and the UAV trajectory can satisfy the safety constraints.

A New Approach for Vehicle Routing with Stochastic Demand: Combining Route Assignment with Process Flexibility

We propose a new approach for the vehicle routing problem with stochastic demands for the case in which customer demands are revealed before vehicles are dispatched. Our approach combines ideas from vehicle routing and manufacturing process flexibility to propose overlapped routing strategies with customer sharing. We characterize the asymptotic performance of the overlapped routing strategies under probabilistic analysis. Using the characterization, we demonstrate that our overlapped routing strategies perform close to the theoretical lower-bound derived from the reoptimization strategy, and significantly outperforms the routing strategy without overlapped routes. The effectiveness of the proposed overlapped routing strategies in non-asymptotic regimes is further verified through numerical analysis.

A modified descent method-based heuristic for binary quadratic knapsack problems with conflict graphs

The knapsack problem arises in a variety of real world applications, including flexible manufacturing systems, railway stations, hydrological studies and others. In this paper, we propose a descent method-based heuristic for tackling a special knapsack problem: the binary quadratic knapsack with conflict graphs. The proposed method combines (i) an intensification search with a descent method for enhancing the accuracy of the solutions and (ii) a diversification strategy which is used for enlarging the search space. The method uses degrading and re-optimization strategies in order to reach a series of diversified solutions. The performance of the proposed method is evaluated on benchmark instances taken from the literature, where its achieved results are compared to those reached by both GLPK solver and the best method available in the literature. The method seems very competitive, where it is able to achieve 37 new lower bounds.

Combinatorial optimal control of semilinear elliptic PDEs

Optimal control problems (OCPs) containing both integrality and partial differential equation (PDE) constraints are very challenging in practice. The most wide-spread solution approach is to first discretize the problem, which results in huge and typically nonconvex mixed-integer optimization problems that can be solved to proven optimality only in very small dimensions. In this paper, we propose a novel outer approximation approach to efficiently solve such OCPs in the case of certain semilinear elliptic PDEs with static integer controls over arbitrary combinatorial structures, where we assume the nonlinear part of the PDE to be non-decreasing and convex. The basic idea is to decompose the OCP into an integer linear programming (ILP) master problem and a subproblem for calculating linear cutting planes. These cutting planes rely on the pointwise concavity or submodularity of the PDE solution with respect to the control variables. The decomposition allows us to use standard solution techniques for ILPs as well as for PDEs. We further benefit from reoptimization strategies for the PDE solution due to the iterative structure of the algorithm. Experimental results show that the new approach is capable of solving the combinatorial OCP of a semilinear Poisson equation with up to 180 binary controls to global optimality within a 5 h time limit. In the case of the screened Poisson equation, which yields semi-infinite integer linear programs, problems with as many as 1400 binary controls are solved.

Re-optimization strategy for truck crane lift-path planning

Abstract When a truck crane lifts a load, the most important considerations are the safety and efficiency of the lift path; therefore, the objectives in planning a lift path should be the minimization of both the path length and number of operations. Most proposed methods for lift-path planning simply focused on the optimization of the path length. As a result, a planned path may include a large number of operations that makes the path complicated and degrades the safety and efficiency of the lift process. This paper presents a method to optimize the number of operations for a lift path with an optimized length for a truck crane by making the most efficient use of the crane characteristics. First, a conventional search algorithm is used to optimize the length of a lift path. Thereafter, a new operating-path optimization method is presented to reduce the number of points on the planned path based on construction of simple spaces and fundamental operating paths. Simulations produced satisfactory results, demonstrating that this method reduces the number of operations on a short lift path, thereby improving the safety and efficiency.

The re-optimization strategy of multi-layer hybrid building’s cooling and heating load soft sensing technology research based on temperature interval and hierarchical modeling techniques

To avoid expanding existing fossil power plants, building the energy performance management system is an effect method to cope with a high growth demand of electricity in China's urbanization process. The accuracy thermal load prediction is the first step to build an energy performance management system which has important significance for energy saving, decreasing air pollution and high-efficiency operation of HVAC system.This paper discusses the use of hybrid intelligent approaches and re-optimization strategy, a multi-layer hybrid model (APNN) has been proposed by hybridizing an auto-regressive model with exogenous inputs (ARX) and a particle swarm optimization neural network (PSO-NN) to make a good use of the comprehensive information of the meteorological data and historical data. For the purposes of improving prediction precision generalization ability of the multi-layer hybrid model, temperature interval and hierarchical modeling techniques were used. According to the re-optimization strategy, there are two improvements of the previous proposed APNN model, which are based on temperature interval and hierarchical modeling by solar radiation intensity. Compared with the basic prediction models, validation results show that accuracy of the optimized models are greatly improved. What’s more, the optimization of multi-layer hybrid building’s cooling and heating load soft sensing technology enhance learning and generalization capability of the basic APNN model.

Reoptimization strategies for a dynamic vehicle routing problem with mixed backhauls

We investigate a dynamic routing problem that seeks to plan, in the most efficient way, dynamic pickup requests that arrive in real-time while a predefined plan of serving delivery requests is being executed. Delivery and pick up requests may be served in an arbitrary mixed order. We refer to this problem as the dynamic vehicle routing problem with mixed backhauls DVRPMB. We address DVRPMB through iterative reoptimization. In addition to defining the reoptimization model, we drill down to significant aspects concerning the reoptimization process; that is, i how to reoptimize, ii when to reoptimize, and iii what part of the new plan to communicate to the drivers. On some of these aspects, we establish theoretical insights. To solve the reoptimization problem, we propose a branch-and-price approach and a novel insertion heuristic which is based on column generation and provides near optimal solutions in an efficient manner. We use extensive experimentation to test the proposed methods and analyze the related reoptimization policies. Based on the results obtained, we propose reoptimization guidelines under various operational settings. © 2014 Wiley Periodicals, Inc. NETWORKS, Vol. 643, 214-231 2014

Modeling of a simultaneous saccharification and fermentation process for ethanol production from lignocellulosic wastes by kluyveromyces marxianus

This paper presents the modeling of the main dynamics of a Simultaneous Saccharification and Fermentation (SSF) process using lignocellulosic wastes as substrate. SSF experiments were carried out using the yeast Kluyveromyces marxianus as the inoculum and oil palm wastes as the substrate, in order to obtain glucose and ethanol concentration data. The experimental data were used for the parameter identification and model validation. The resulting model predictsthe dynamic behavior of glucose and ethanol concentrations very closely. Performing a sensitivity analysis, parameters which have a higher effect in the modelpredictions are recognized, so the model can be re-optimized in particular cases with low computational requirements. The re-optimization strategy improves the model capacity to predict the dynamics of the SSF process.

Paired cooperative reoptimization strategy for the vehicle routing problem with stochastic demands

In this paper, we develop a paired cooperative reoptimization (PCR) strategy to solve the vehicle routing problem with stochastic demands (VRPSD). The strategy can realize reoptimization policy under cooperation between a pair of vehicles, and it can be applied in the multivehicle situation. The PCR repeatedly triggers communication and partitioning to update the vehicle assignments given real-time customer demands. We present a bilevel Markov decision process to model the coordination of a pair of vehicles under the PCR strategy. We also propose a heuristic that dynamically alters the visiting sequence and the vehicle assignment given updated information. We compare our approach with a recent cooperation strategy in the literature. The results reveal that our PCR strategy performs better, with a cost saving of around 20–30%. Moreover, embedding communication can save an average of 1.22%, and applying our partitioning method rather than an alternative can save an average of 3.96%.