Delivery Time Constraints Research Articles

Communicating with Deaf Patients in the Clinical Environment: Lessons Learned from a Virtual Patient Panel

Background Communication and cultural differences predispose Deaf patients to suboptimal healthcare. Despite this disparity, health professionals have historically received little to no training in caring for Deaf patients. Patient panels are an effective tool in medical education to model communication strategies. Objective In this paper, we describe the design, implementation, and results of a virtual patient panel focused on communicating with Deaf patients in clinical contexts. We offer practical suggestions for incorporating similar educational interventions in health professions education to prepare trainees to effectively navigate these conversations with their patients. Methods The panel consisted of a one-hour question and answer discussion facilitated by the authors with Deaf patients and Certified Deaf Interpreters (CDI). The panel was presented to 271 second-year medical students at our institution in November of 2023. Following this discussion, students were encouraged to share one or two key takeaways from the session through a survey link. These results were analyzed using pile-sorting qualitative analysis to identify main themes. Results There were 73 respondents, with a response rate of 27%. After the panel, the most popular takeaway points from student reflections included communication (n=56, 77%) and access to care (n=47, 64%), followed by autonomy (n=17, 23%), the doctor-patient relationship (n=15, 21%), and culture (n=11, 15%). Based on this initiative, we identified and offer twelve tips for developing similar exercises. These tips are thematically presented under three groupings: Planning Considerations, Patient Panelist Considerations, In-Session Considerations, and Post-Session Considerations. Conclusion This patient panel was the first of its kind in our medical school curriculum. Important considerations in panel design and implementation should focus on delivery time constraints with live-interpreting and further exploring the role of trust and communication in the physician-patient relationship.

Dynamic Scheduling of Multi-agent Electromechanical Production Lines based on Iterative Algorithms

In response to the optimization scheduling problem in the dyeing production process, the author proposes a hierarchical scheduling method for dyeing vats based on genetic algorithm and multi-agent. In this method, a hierarchical scheduling algorithm is used to decompose production scheduling into static and dynamic strategies. The static strategy adopts a genetic algorithm that supports batch processing of multiple products, non equality of equipment, order delivery time, switching cost, and other constraints: Dynamic strategy is a coordinated dynamic optimization algorithm that uses multi-agent systems to support the running status of dye tanks based on static strategies. By solving the algorithm with multiple constraints and dynamic factors in the production process, the final result of the dyeing tank operation task is obtained. The simulation compared pure genetic algorithm with manual scheduling, and the results showed that the hierarchical dynamic scheduling strategy based on data-driven achieved the goal of optimizing the production scheduling of dyeing vats. The practical application results also demonstrate the feasibility of this method.

Evaluation of the Minimum Segment Width and Fluence Smoothing Tools for Intensity-modulated Techniques in Monaco Treatment Planning System.

This study aims to minimize monitor units (MUs) of intensity-modulated treatments in the Monaco treatment planning system while preserving plan quality by optimizing the "Minimum Segment Width" (MSW) and "Fluence Smoothing" parameters. We retrospectively analyzed 30 prostate, 30 gynecological, 15 breast cancer, 10 head and neck tumor, 11 radiosurgery, and 10 hypo-fractionated plans. Original prostate plans employed "Fluence Smoothing" = Off and were reoptimized with Low, Medium, and High settings. The remaining pathologies initially used MSW = 0.5 cm and were reoptimized with MSW = 1.0 cm. Plan quality, including total MU, delivery time, and dosimetric constraints, was statistically analyzed with a paired t-test. Prostate plans exhibited the highest MU variation when changing "Fluence Smoothing" from Off to High (average ΔMU = -5.1%; P < 0.001). However, a High setting may increase overall MU when MSW = 0.5 cm. Gynecological plans changed substantially when MSW increased from 0.5 cm to 1.0 cm (average ΔMU = -29%; P < 0.001). Organs at risk sparing and planning target volumes remained within 1.2% differences. Replanning other pathologies with MSW = 1.0 cm affected breast and head and neck tumor plans (average ΔMU = -168.38, average Δt = -11.74 s, and average ΔMU = -256.56, average Δt = -15.05 s, respectively; all with P < 0.004). Radiosurgery and hypofractioned highly modulated plans did not yield statistically significant results. In breast, pelvis, head and neck, and prostate plans, starting with MSW = 1.0 cm optimally reduces MU and treatment time without compromising plan quality. MSW has a greater impact on MU than the "Fluence Smoothing" parameter. Plans with high modulation might present divergent behavior, requiring a case-specific analysis with MSW values higher than 0.5 cm.

Cross-Region Courier Displacement for On-Demand Delivery With Multi-Agent Reinforcement Learning

On-demand delivery has become prevailing for people to order meals and groceries online, especially during the pandemic. It is essential to dispatch massive orders to limited couriers to satisfy on-demand delivery users, especially during peak hours. Existing studies mainly focus on order dispatching within a region, and they are challenging to be applied to the cross-region courier displacement problem due to (i) unique practical factors, including regional spatial-temporal demand-supply dynamics and strict delivery time constraints, and (ii) the large-scale setting and high-dimensional decision space given massive couriers in on-demand delivery. To address these challenges, in this work, we propose an efficient cross-region courier displacement framework, i.e., Courier Displacement Reinforcement Learning (short for CDRL) based on centralized multi-agent actor-critic, which first design the actor-critic network with a time-varying displacement intensity control module to capture demand-supply dynamics and utilize the centralized training and decentralized execution multi-agent framework to address the large-scale coordination. One-month real-world order records collected from one of the biggest on-demand delivery services in the world are utilized to show the performance of our design. The extensive results show that our method offers a 47.97% of increase in balancing supply and demand and reduces idle ride time by 24.62% simultaneously.

RISKY BEHAVIOURS AMONG DELIVERY RIDERS AT SIGNALIZED INTERSECTIONS IN MALAYSIA

The increasing number of food delivery riders, known as P-hailing riders, has increased the number of traffic crashes involving this group. The high demand for online orders and delivery time constraints causes the delivery riders to engage in risky riding behaviours during delivery. Thus, there is a need to study this alarming problem before it becomes more serious. This study aims to determine factors influencing risky behaviours among delivery riders at signalized intersections. A roadside observation was conducted in the month of October 2022 at Subang Jaya, Selangor. A total of 19,803 delivery riders were observed for four days observations at two signalized T- and two cross-signalized intersections. A bivariate analysis (logistic regression) was applied to determine the relationship between seven risky behaviours (unfastened helmet, not wearing shoes, incomplete set of side mirrors, red light running, mobile phone use, stopping after stop lines, and abrupt lane change) with five explanatory variables including the day of the week, time of the day, weather condition, type of intersection and approach road. The result shows that red light running, and mobile phone use is more likely to occur during the weekend. Clear weather increases risky behaviours such as red light running, using a mobile phone, and abrupt lane changes. The findings of this study are useful as input to the related authorities in developing programs to decrease the number of crashes involving food delivery riders.

Order Acceptance and Scheduling Model Based on Improved Genetic Algorithm

As the manufacturing industry’s industrial structure continues to evolve, many enterprises have shifted from traditional make-to-store modes (MTS) to make-to-order modes (MTO). This paper focuses on MTO manufacturing enterprises’ order acceptance and scheduling problem, namely, how to exclude low-margin orders under capacity constraints and schedule them. We establish a hypothetical model with the goal of maximizing corporate profits, taking into account production capacity constraints and delivery time constraints. To solve the model, we propose a heuristic solution method based on genetic algorithms. The feasibility of the method is verified through randomly generated examples. The research results demonstrate that our proposed solution method can quickly solve large-scale order decision-making problems and has practical significance as a guiding tool.

P‐103: Multi Path Control System for AGV Based on Digital Twin in Display Module Process

Increasing display process complexity, Automated Guided Vehicle becomes more important and the delivery time constraint is getting shorter. The complicated process causes traffic congestion and deadlocks between AGVs. Preventing these issues, this paper suggests digital twin technology that allows AGV to choose the best route among various paths. This research validates the AGV simulation for the display plant. The result shows AGV average delivery time was shortened by 14.1% compared to the traditional algorithm. In addition, the AGV capacity rate can be reduced by 21.5% and the number of AGVs can be reduced by 10.1%.

Two-stage stochastic programming with robust constraints for the logistics network post-disruption response strategy optimization

Logistics networks (LNs) are essential for the transportation and distribution of goods or services from suppliers to consumers. However, LNs with complex structures are more vulnerable to disruptions due to natural disasters and accidents. To address the LN post-disruption response strategy optimization problem, this study proposes a novel two-stage stochastic programming model with robust delivery time constraints. The proposed model jointly optimizes the new-line-opening and rerouting decisions in the face of uncertain transport demands and transportation times. To enhance the robustness of the response strategy obtained, the conditional value at risk (CVaR) criterion is utilized to reduce the operational risk, and robust constraints based on the scenario-based uncertainty sets are proposed to guarantee the delivery time requirement. An equivalent tractable mixed-integer linear programming reformulation is further derived by linearizing the CVaR objective function and dualizing the infinite number of robust constraints into finite ones. A case study based on the practical operations of the JD LN is conducted to validate the practical significance of the proposed model. A comparison with the rerouting strategy and two benchmark models demonstrates the superiority of the proposed model in terms of operational cost, delivery time, and loading rate.

Problem of Groupage Cargo Routing in the Multicommodity Transport Network with Given Tariffs and Delivery Time Constraints

Problem of Groupage Cargo Routing in the Multicommodity Transport Network with Given Tariffs and Delivery Time Constraints

Green Scheduling of Identical Parallel Machines with Release Date, Delivery Time and No-Idle Machine Constraints

Global warming and climate change are threatening life on earth. These changes are due to human activities resulting in the emission of greenhouse gases. This is caused by intensive industrial activities and excessive fuel energy consumption. Recently, the scheduling of production systems has been judged to be an effective way to reduce energy consumption. This is the field of green scheduling, which aims to allocate jobs to machines in order to minimize total costs, with a focus on the sustainable use of energy. Several studies have investigated parallel-machine shops, with a special focus on reducing and minimizing the total consumed energy. Few studies explicitly include the idle energy of parallel machines, which is the energy consumed when the machines are idle. In addition, very few studies have considered the elimination of idle machine times as an efficient way to reduce the total consumed energy. This is the no-idle machine constraint, which is the green aspect of the research. In this context, this paper addresses the green parallel-machine scheduling problem, including release dates, delivery times, and no-idle machines, with the objective of minimizing the maximum completion time. This problem is of practical interest since it is encountered in several industry processes, such as the steel and automobile industries. A mixed-integer linear programming (MILP) model is proposed for use in obtaining exact solutions for small-sized instances. Due to the NP-hardness of the studied problem, and encouraged by the successful adaptation of metaheuristics for green scheduling problems, three genetic algorithms (GAs) using three different crossover operators and a simulated annealing algorithm (SA) were developed for large-sized problems. A new family of lower bounds is proposed. This was intended for the evaluation of the performance of the proposed algorithms over the average percent of relative deviation (ARPD). In addition, the green aspect was evaluated over the percentage of saved energy, while eliminating the idle-machine times. An extensive experimental study was carried out on a benchmark of test problems with up to 200 jobs and eight machines. This experimental study showed that one GA variant dominated the other proposed procedures. Furthermore, the obtained numerical results provide strong evidence that the proposed GA variant outperformed the existing procedures from the literature. The experimental study also showed that the adoption of the no-idle machine time constraints made it possible to reduce the total consumed energy by 29.57%, while the makespan (cost) increased by only 0.12%.

Stochastic Local Search for the Direct Aperture Optimisation Problem

Intensity modulated radiation therapy (IMRT) is a widely applied cancer treatment technique that aims at effectively deliver radiation to cancerous cells while sparing surrounding healthy tissues. To this end, the radiation coming from a linear accelerator is modulated using a physical device called multi-leaf collimator.Traditionally, a sequential approach is applied to generate treatment plans in IMRT. In this approach, the optimal intensities for each beam angle are computed (Fluence Map Optimisation, FMO), and then a sequencing problem is solved to obtain the collimator setup (aperture shapes) required to achieve the intensities computed for the FMO problem. The sequencing step is critical as the treatment plans computed to solve the FMO problem are not clinically acceptable. Unfortunately, treatment plans obtained by the sequencing step are severely impaired w.r.t. those obtained in the FMO step. One approach that aims to deal with the problem described above is the Direct Aperture Optimisation (DAO) approach. The DAO problem aims at simultaneously determining deliverable aperture shapes and a set of radiation intensities. This approach considers both physical and delivery time constraints, allowing to generate clinically acceptable treatment plans.In this paper, we propose a stochastic local search algorithm to solve the DAO problem. Our approach alternates the search between two neighbourhood definitions; the first focused on the fluence map (intensities) and the second on the aperture shapes. We analyse the algorithmic components, parameters, and overall results obtained by our algorithm on a set of clinical prostate cancer cases. We compare the obtained treatment plans to the ones obtained by the traditional two-step approach. Results show that our algorithm is able to find deliverable treatment plans without major impairments in treatment plans quality.

Crowd Sourcing Dynamic Pickup &amp; Delivery Problem considering Task Buffering and Drivers’ Rejection -Application of Multi-agent Reinforcement Learning-

In the last decade, dynamic and pickup delivery problem with crowd sourcing has been focused on as a means of securing employment opportunities in the field of last mile delivery. However, only a few studies consider both the driver's refusal right and the buffering strategy. This paper aims at improving the performance involving both of the above. We propose a driver-task matching algorithm that complies with the delivery time constraints using multi-agent reinforcement learning. Numerical experiments on the model show that the proposed MARL method could be more effective than the FIFO and the RANK allocation methods

A general algorithm for distributed treatments of multiple brain metastases.

Stereotactic radiosurgery (SRS) has become a primary treatment for multiple brain metastases (BM) but may require distribution of BMs over several sessions to make delivery time and radiation toxicity manageable. Contrasting to equal fraction dose in conventional fractionation, distributed SRS delivers full dose to a subset of BMs in each session while avoiding adjacent BMs in the same session to reduce toxicity from overlapping radiation. However, current clinical treatment planning for distributed SRS relies on manual BM assignment, which can be tedious and error prone. This work describes a novel approach to automate the distribution of BM in the Gamma Knife (GK) clinical workflow. We represent each BM as an electrostatic field of the same polarity that exerts repulsive forces on other BMs in the same session. This representation naturally leads to separation of close BMs into different sessions to lower the potential energy. Indeed, the BM distribution problem can be formulated as minimization of the total potential energy from all treatment sessions subject to delivery time constraints in mixed-integer quadratic programming (MIQP). We retrospectively studied eight clinical GK cases of multiple BM and compared the automated MIQP solution with clinically used BM distribution to demonstrate the efficacy of the proposed approach. With the problem size equal to the number of BMs times the number of sessions, this MIQP can be solved in a minute on a personal workstation. The MIQP solution effectively separated BMs for a given number of treatment sessions and evened out the delivery time distribution among sessions. Compared to the clinically used manual BM distributions in paired t-test for a similar range of delivery time variation, the automated BM distributions had lower energy objectives (range of decrease: [11% 89%]; median: 25%; ), more uniformly distributed treatment volumes (range of decrease for the normalized standard deviation of volume distribution: [0.02 0.95]; median: 0.16; ), more scattered BMs in each treatment session (range of increase for the mean minimum BM distance: [0 14]mm; median: 6mm; ), and lower overall (range of decrease: [0.0 1.6]cc; median: 0.2cc; ). Moreover, without distribution, that is, with all BMs treated in the same session, was substantially larger compared to both manual and automated BM distributions; the increase ranged from 0.1 to 16.6cc with a median of 1.3cc. The proposed approach models the clinical practice and provides an efficient solution for optimal selection of BM subsets for distributed SRS. Further evaluations are underway to establish this approach as a tool for improving clinical workflow and to facilitate systematic study on the benefits of distributed SRS treatments.

Optimisation‐based time slot assignment and synchronisation for TDMA MAC in industrial wireless sensor network

Wireless sensor network in the industrial environment [industrial wireless sensor network (IWSN)] has data delivery time constraint. Due to the dynamic routing and transmission collision, the data delivery time is unpredictable. The authors utilised time division multiple access (TDMA) MAC to avoid data collision and to provide bounded transmission delay. Moreover, a linear programming model is proposed to construct the TDMA schedules, which is focused on spatial reuse and fixed routing in IWSN. The objective function of the model is to minimise the time slot usage to increase the overall network bandwidth. Finally, both simulated annealing algorithm and particle swarm optimisation are applied to approximate the optimal solution of time slot usage.

Solving CCVRPTW using biased random key genetic algorithm (BRKGA) with multiple parent

This research addresses a Capacitated Vehicle Routing Problem with Time Windows (CCVRPTW) which is a Vehicle Routing Problem (VRP) with fleet capacity and delivery time constraints. In the research, a Biased Random Key Genetic Algorithm (BRKGA) is proposed to solve the problem. The standard BRKGA is improved by modifying the crossover process accommodating Multiple-Parent. The proposed BRKGA is coded using the Matlab programming language. A case of soft drink distribution is used to evaluate the performance of the algorithm. From the research, it can be concluded that (1) The proposed BRKGA is better than the heuristic method proposed by previous research for dealing with the same case; (2) the Multiple-Parent BRKGA can further improve the general BRKGA; (3) The Multiple-Parent BRKGA with third parent selected randomly from NON-ELITE group results in a better solution than that of selected randomly from the whole population.

Risk reduction for distribution of the perishable rescue items; A possibilistic programming approach

The expedient transportation of relief supplies plays an undeniable role in minimizing human suffering and maximizing the survival rate in disaster-affected areas. Particularly during the 2009 Black Saturday bushfires in Australia, an investigation by the Victorian Bushfires Royal Commission revealed that resources such as medical teams and medical supplies were poorly coordinated during the initial response phase. Therefore, the aim of this study is to develop a mixed integer programming model to support tactical decision making in allocating emergency relief resources in the context of the Black Saturday bushfires. The proposed model uses historical data to determine the rescue vehicles' delivery loads and schedules based on vehicle capacity utilization, the supply of relief items and strict delivery time windows. Furthermore, a possibilistic programming approach has been employed to minimize the transportation disruption risk under uncertainty in the parameters and solve the model in a complex and unpredictable environment. To evaluate the reliability of the model, various sensitivity analyses have been applied while considering the priority level of the defined objectives. The results show that it would be possible to efficiently manage this emergency distribution context, even if one or two resources have very restricted delivery time constraints. However, disruption risk and priorities to the decision makers prove to impact resource utilization. The modeling outputs will be useful in the development of emergency plans and distribution coordination strategies to enhance rapid response to emergency relief distribution in disaster zones.

Joint Optimization of Production Planning and Capacity Adjustment for Assembly System

Manufacturing companies are confronted with increasing market volatility, which is characterized by the increasing of product variants and decreasing of production volume of each variant to meet customers’ demands. To stay competitive and to fulfill quickly changing market needs, manufacturing companies have to cope with rapidly changing customer demands concerning product types, quantities and delivery dates and have the ability to enable a fast modification and system-change of future manufacturing systems. Production planning and control must compensate the resulting fluctuations in capacity demand. This paper deals with a problem of joint optimization of production planning and capacity adjustment based on products specifications, delivery time constraints and reconfigurable machines capabilities for assembly systems. At the production planning level, the production planning problem consists in a multi-product capacitated lot-sizing problem. At the capacity adjustment level, the machine can be reconfigured to meet the changing needs in terms of capacity and functionality. In this context, the same machine can be modified in order to perform different tasks depending on the offered axes of motion in each configuration and the availability of tools. The main objective of this paper is to determine simultaneously the economic production quantity of each product variants and the optimal capacity of assembly systems to ensure the adherence to delivery dates. A stochastic mathematical model is developed and solved using a simulation optimization approach based on the response surface methodology. The obtained results show clearly strong interactions between production quantity, delivery time constraints and capacity of assembly line which confirm the necessity of jointly considering production planning and capacity adjustment in an integrated model.

Paddle-based rotating-shield brachytherapy.

The authors present a novel paddle-based rotating-shield brachytherapy (P-RSBT) method, whose radiation-attenuating shields are formed with a multileaf collimator (MLC), consisting of retractable paddles, to achieve intensity modulation in high-dose-rate brachytherapy. Five cervical cancer patients using an intrauterine tandem applicator were considered to assess the potential benefit of the P-RSBT method. The P-RSBT source used was a 50 kV electronic brachytherapy source (Xoft Axxent™). The paddles can be retracted independently to form multiple emission windows around the source for radiation delivery. The MLC was assumed to be rotatable. P-RSBT treatment plans were generated using the asymmetric dose-volume optimization with smoothness control method [Liu et al., Med. Phys. 41(11), 111709 (11pp.) (2014)] with a delivery time constraint, different paddle sizes, and different rotation strides. The number of treatment fractions (fx) was assumed to be five. As brachytherapy is delivered as a boost for cervical cancer, the dose distribution for each case includes the dose from external beam radiotherapy as well, which is 45 Gy in 25 fx. The high-risk clinical target volume (HR-CTV) doses were escalated until the minimum dose to the hottest 2 cm(3) (D(2cm(3)) of either the rectum, sigmoid colon, or bladder reached their tolerance doses of 75, 75, and 90 Gy3, respectively, expressed as equivalent doses in 2 Gy fractions (EQD2 with α/β = 3 Gy). P-RSBT outperformed the two other RSBT delivery techniques, single-shield RSBT (S-RSBT) and dynamic-shield RSBT (D-RSBT), with a properly selected paddle size. If the paddle size was angled at 60°, the average D90 increases for the delivery plans by P-RSBT on the five cases, compared to S-RSBT, were 2.2, 8.3, 12.6, 11.9, and 9.1 Gy10, respectively, with delivery times of 10, 15, 20, 25, and 30 min/fx. The increases in HR-CTV D90, compared to D-RSBT, were 16.6, 12.9, 7.2, 3.7, and 1.7 Gy10, respectively. P-RSBT HR-CTV D90-values were insensitive to the paddle size for paddles angled at less than 60°. Increasing the paddle angle from 5° to 60° resulted in only a 0.6 Gy10 decrease in HR-CTV D90 on average for five cases when the delivery times were set to 15 min/fx. The HR-CTV D90 decreased to 2.5 and 11.9 Gy10 with paddle angles of 90° and 120°, respectively. P-RSBT produces treatment plans that are dosimetrically and temporally superior to those of S-RSBT and D-RSBT, although P-RSBT systems may be more mechanically challenging to develop than S-RSBT or D-RSBT. A P-RSBT implementation with 4-6 shield paddles would be sufficient to outperform S-RSBT and D-RSBT if delivery times are constrained to less than 15 min/fx.

Asymmetric dose-volume optimization with smoothness control for rotating-shield brachytherapy.

It is important to reduce fluence map complexity in rotating-shield brachytherapy (RSBT) inverse planning to improve delivery efficiency while maintaining plan quality. This study proposes an efficient and effective RSBT dose optimization method which enables to produce smooth fluence maps. Five cervical cancer patients each with a high-risk clinical-target-volume (HR-CTV) larger than 40 cm(3) were considered as the test cases. The RSBT source was a partially shielded electronic brachytherapy source (Xoft Axxent™). The anchor RSBT plans generated by the asymmetric dose-volume optimization with smoothness control (ADOS) method were compared against those produced by the dose-surface optimization (DSO) method and inverse-planning with simulated annealing (IPSA). Either L1-norm or L2-norm was used to measure the smoothness of a fluence map in the proposed ADOS method as one weighted term of the objective function. Uniform dwell-time scaling was applied to all plans such that HR-CTV D90 was maximized without violating the D2cc tolerances of the rectum, bladder, and sigmoid colon. The quality of the anchor plans was measured with HR-CTV D90 of the anchor plans. Single-shielded RSBT [(S-RSBT), RSBT with single, fix sized delivery window] and dynamic-sheilded RSBT [(D-RSBT), RSBT with dynamically varying sized delivery window] delivery plans generated based on the anchor plans were also measured, with delivery time constraints of 10, 20, and 30 min/fraction (fx). The average HR-CTV D90 values of the anchor plans achieved by the ADOS, DSO, and IPSA methods were 111.5, 94.2, and 107.4 Gy, respectively, where the weighting parameter β used in ADOS with L2-norm was set to be 100. By using S-RSBT sequencing and 20 min/fx delivery time, the corresponding D90 values were 88.8, 81.9, and 83.4 Gy; while using D-RSBT sequencing with 20 min/fx delivery time, the corresponding D90 values were 91.4, 88.3, and 78.9 Gy, respectively. The average optimization times for ADOS, DSO, and IPSA were, respectively, 77, 4, and 1800 s. By using L1-norm instead of L2-norm in the ADOS method, the optimization time was increased by 20 s, while the D90 was reduced by 6.8 Gy on average. ADOS-L1 was found to be more sensitive to the weighting parameter than ADOS-L2. If β was increased to 10 000, the D90 drop with ADOS-L1 was 38 Gy, while the drop with ADOS-L2 is 13 Gy. The ADOS method had a reasonable optimization time cost, while achieving comparable RSBT dose plans as the IPSA method, which is of much higher time complexity. Compared to the DSO and IPSA methods, ADOS is able to generate anchor plans which are more suitable for RSBT delivery while preserving the high quality of the original plans. Compared to ADOS-L1, ADOS-L2 is able to achieve better quality of anchor plans more efficiently.

Dynamic rotating‐shield brachytherapy

To present dynamic rotating shield brachytherapy (D-RSBT), a novel form of high-dose-rate brachytherapy (HDR-BT) with electronic brachytherapy source, where the radiation shield is capable of changing emission angles during the radiation delivery process. A D-RSBT system uses two layers of independently rotating tungsten alloy shields, each with a 180° azimuthal emission angle. The D-RSBT planning is separated into two stages: anchor plan optimization and optimal sequencing. In the anchor plan optimization, anchor plans are generated by maximizing the D90 for the high-risk clinical-tumor-volume (HR-CTV) assuming a fixed azimuthal emission angle of 11.25°. In the optimal sequencing, treatment plans that most closely approximate the anchor plans under the delivery-time constraint will be efficiently computed. Treatment plans for five cervical cancer patients were generated for D-RSBT, single-shield RSBT (S-RSBT), and (192)Ir-based intracavitary brachytherapy with supplementary interstitial brachytherapy (IS + ICBT) assuming five treatment fractions. External beam radiotherapy doses of 45 Gy in 25 fractions of 1.8 Gy each were accounted for. The high-risk clinical target volume (HR-CTV) doses were escalated such that the D2cc of the rectum, sigmoid colon, or bladder reached its tolerance equivalent dose in 2 Gy fractions (EQD2 with α∕β = 3 Gy) of 75 Gy, 75 Gy, or 90 Gy, respectively. For the patients considered, IS + ICBT had an average total dwell time of 5.7 minutes∕fraction (min∕fx) assuming a 10 Ci(192)Ir source, and the average HR-CTV D90 was 78.9 Gy. In order to match the HR-CTV D90 of IS + ICBT, D-RSBT required an average of 10.1 min∕fx more delivery time, and S-RSBT required 6.7 min∕fx more. If an additional 20 min∕fx of delivery time is allowed beyond that of the IS + ICBT case, D-RSBT and S-RSBT increased the HR-CTV D90 above IS + ICBT by an average of 16.3 Gy and 9.1 Gy, respectively. For cervical cancer patients, D-RSBT can boost HR-CTV D90 over IS + ICBT and S-RSBT without violating the tolerance doses to the bladder, rectum, or sigmoid. The D90 improvements from D-RSBT depend on the patient, the delivery time budget, and the applicator structure.