Reliable Problem Research Articles

Research on mobile energy storage scheduling strategy for emergency power supply protection of post-disaster isolated loads

Aiming at the problem of insufficient power supply capacity of isolated loads in oceanic islands, a concept based on mobile energy storage and power conservation is proposed in this paper. Firstly, the energy flow and traffic flow characteristics of mobile energy storage are quantitatively analyzed, and an accurate mathematical model is established. Furthermore, the time-space distribution characteristics of load are carefully considered, and the Voronoi diagram and Tyson polygon method are introduced to determine cell differentiation. On this basis, combined with the power demand of load nodes and the energy storage characteristics of mobile energy storage vehicles, the evaluation indicators of cell energy and relative surplus energy of cells are designed, and the agent-cellular automata algorithm is used to schedule the mobile energy storage. The simulation results show that the power supply mode based on mobile energy storage can effectively improve the reliability of isolated loads. This paper provides a new perspective for solving the reliable power supply problem of isolated loads.

Optimization of reliable vehicle routing problem for medical waste collection with time windows in stochastic transportation networks

The collection and transportation of medical waste (MW) is critical because of environmental and public safety implications. However, the variability in travel times of waste transport vehicles, influenced by various factors, complicates this process. This article proposes a multi-objective optimization model to enhance travel time reliability (TTR) while minimizing vehicle transportation costs and penalties for time window violations. Given the NP-hard nature of the problem, a genetic algorithm enhanced by an improved harmony search genetic algorithm (IHSGA) is utilized. The model and algorithm are evaluated in N City, China. The results highlight the significance of TTR in optimizing MW collection operations. Sensitivity analysis of key parameters validates the model’s robustness. Furthermore, comparative analysis demonstrates the superiority of IHSGA over traditional methods. These findings underscore the importance of incorporating additional costs associated with travel time variability in decision making, and advocate for strategies to mitigate such uncertainties.

TSDRL: A three-stage deep reinforcement learning method for reliable collaboration of manufacturing service towards mass personalized production

With the development of intelligent manufacturing driven by new-generation information technology, manufacturing service collaborative chains (MSCC) based on Industrial Internet Platforms have become the main way to cope with mass personalized production. To adapt the dynamics and variability of personalized demands, manufacturing services are required to be frequently reorganized. This leads to dynamic changes in manufacturing service availability and ambiguous reliable collaboration mechanisms, making it challenging for existing methods to consistently maintain stable MSCC. Therefore, this paper proposes a three-stage deep reinforcement learning method (TSDRL) to improve the reliability of MSCC while guaranteeing the satisfaction of personalized demands. The method comprehensively considers the dynamic changes of manufacturing services and service reliability, and solves the reliable MSCC recommendation problem. Specifically, in stage one, high-quality services are selected through DRL state. In stage two, the improved NSGA-III algorithm is used to optimize the MSCC. In stage three, the BP+GA algorithm is used to mine user preferences and recommend high-satisfaction MSCC. By leveraging the exploratory process of DRL, cyclic optimization of MSCC is performed. Through comprehensive analysis and experimental research, it is shown that the method of this paper is robust and practical in dynamic environment.

Reliable lifelong planning A*: Technique for re-optimizing reliable shortest paths when travel time distribution updating

Re-optimization technique is an efficient approach for solving the shortest path problem in dynamic deterministic networks, where link travel times are updated in real-time. However, existing re-optimization techniques, built on the assumption that link travel times are deterministic, cannot be used to solve reliable shortest path problems in real road networks with noticeable levels of travel time uncertainties. This study proposes a novel re-optimization technique, named reliable lifelong planning A* (RLPA*), for re-optimizing reliable shortest path finding results in dynamic stochastic networks, where link travel time distributions are updated in real-time. The proposed RLPA* technique can efficiently determine the optimal solution in dynamic stochastic networks by reusing path search results produced in the previous time instance. The proposed RLPA* technique is further utilized to solve the K reliable shortest paths problem, which is regarded as a series of reliable shortest path searches in a dynamic stochastic network. To validate the proposed algorithms, a comprehensive case study using real traffic data is conducted. The case study results demonstrated that the proposed algorithms significantly outperform the corresponding state-of-the-art algorithms on all testing networks.

Reliable truck-drone routing with dynamic synchronization: A high-dimensional network programming approach

Within the context of burgeoning multimodal transportation systems, the emerging integration of drones and trucks is shaping the landscape of last-mile delivery. In this service-centric industry, uncertainty in travel time challenges the stability of delivery systems, highlighting the need for a more reliable and effective methodology. To tackle this challenge, we propose a reliable truck-drone collaborative routing problem enabling dynamic synchronization between trucks and drones in this paper. An integer programming model based on a space–time-state network has been developed to minimize the total travel time as well as their associated variabilities. Furthermore, to overcome the computational challenges for large-scale scenarios, a multi-dimensional-based hierarchical decomposition has been developed to identify a high-quality solution effectively. Numerical experimental results based on an illustrative network and a real-world network demonstrate the effectiveness of the introduced algorithm. Our proposed methodology advances operational planning for truck-drone collaborative delivery by appropriately navigating unavoidable variations of travel time, shedding light on the broad development prospects of the multimodal transportation system.

Sample intelligence-based progressive hedging algorithms for the stochastic capacitated reliable facility location problem

Selecting facility locations requires significant investment to anticipate and prepare for disruptive events like earthquakes, floods, or labor strikes. In practice, location choices account for facility capacities, which often cannot change during disruptions. When a facility fails, demand transfers to others only if spare capacity exists. Thus, capacitated reliable facility location problems (CRFLP) under uncertainty are more complex than uncapacitated versions. To manage uncertainty and decide effectively, stochastic programming (SP) methods are often employed. Two commonly used SP methods are approximation methods, i.e., Sample Average Approximation (SAA), and decomposition methods, i.e., Progressive Hedging Algorithm (PHA). SAA needs large sample sizes for performance guarantee and turn into computationally intractable. On the other hand, PHA, as an exact method for convex problems, suffers from the need to iteratively solve numerous sub-problems which are computationally costly. In this paper, we developed two novel algorithms integrating SAA and PHA for solving the CRFLP under uncertainty. The developed methods are innovative in that they blend the complementary aspects of PHA and SAA in terms of exactness and computational efficiency, respectively. Further, the developed methods are practical in that they allow the specialist to adjust the tradeoff between the exactness and speed of attaining a solution. We present the effectiveness of the developed integrated approaches, Sampling Based Progressive Hedging Algorithm (SBPHA) and Discarding SBPHA (d-SBPHA), over the pure strategies (i.e. SAA). The validation of the methods is demonstrated through two-stage stochastic CRFLP. Promising results are attained for CRFLP, and the method has great potential to be generalized for SP problems.

Development of test instruments to measure problem-solving and science literacy of grade XI students on ion equilibrium material in salt hydrolysis

Low science literacy in students will have an impact on students' ability to solve problems. To improve it, it is necessary to develop an assessment instrument in the form of a science literacy instrument. This study aims to develop valid, reliable problem solving and science literacy assessment instruments for students. The material chosen is ion equilibrium in salt solution. The research was conducted in high, medium and low ability schools namely SMA Negeri 1 Pekanbaru, SMA Negeri 12 Pekanbaru and SMA Negeri 15 Pekanbaru. There are 16 questions out of 20 questions that are constructively valid with a correlation value> 0.36. The questions developed have met the reliability requirements with a Cronbach alpha coefficient value greater than 0.6, reaching a value of 0.874. The level of difficulty of questions between 0.3 -0.7 is in the good enough category and differentiating power ≥ 0.3. Based on two trials, namely one-on-one trials and limited trials, the same results were obtained that the assessment instruments developed for students had very good readability aspects (85%), good adequacy aspects (64%) and assessment instruments developed for teachers had very good feasibility aspects (91%). There is a significant difference in the ability of students in high schools with medium and low ability schools.

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.

A correct-by-construction approach for development of reliable planning problems

Abstract The automated planning community has developed a defacto standard planning language called PDDL. Using the PDDL tools, the reliability of PDDL descriptions can only be posterior examined. However, the Event-B method supports a rich refinement technique that is mathematically proven. Indeed, the Event-B method relies on a first-order predicates language and sets (including mathematical functions and relations) to model the data and on a simple action language to model the treatments. A model described in Event-B includes static modeling elements (sets, constants, axioms and theorems) and dynamic modeling elements (variables, invariant properties and events). Moreover, the Event-B method allows the step-by-step correct construction of Event-B models. To specify and solve the planning problems, a development process based on the combination of Event-B and PDDL is proposed. Our development process favors the obtaining of reliable PDDL description from an ultimate Event-B model using our Event-B2PDDL Eclipse plugin. Our process is successfully experimented on the sliding puzzle game.

A Reliability-Based Network Equilibrium Model with Electric Vehicles and Gasoline Vehicles

With the popularity of electric vehicles, they have become an indispensable part of traffic flow on the road network. This paper presents a reliability-based network equilibrium model to realise the traffic flow pattern prediction on the road network with electric vehicles and gasoline vehicles, which incorporates travel time reliability, electric vehicles’ driving range and recharge requirement. The mathematical expression of reliable path travel time is derived, and the reliability-based network equilibrium model is formulated as a variational inequality problem. Then a multi-criterion labelling algorithm is proposed to solve the reliable shortest path problem, and a column-generation-based method of the successive average algorithm is proposed to solve the reliability-based network equilibrium model. The applicability and efficiency of the proposed model and algorithm are verified on the Nguyen-Dupuis network and the real road network of Sioux Falls City. The proposed model and algorithm can be extended to other road networks and help traffic managers analyse traffic conditions and make sustainable traffic policies.

Reliable and flexible supplier selection problem: a genetic algorithm inspired simulation approach

Reliable and flexible supplier selection problem: a genetic algorithm inspired simulation approach

Reliable multiple allocation hub location problem under disruptions

Reliable multiple allocation hub location problem under disruptions

Max-type reliability in uncertain post-disaster networks through the lens of sensitivity and stability analysis

The functionality of infrastructures, particularly in densely populated areas, is greatly impacted by natural disasters, resulting in uncertain networks. Thus, it is important for crisis management professionals and computer-based systems for transportation networks (such as expert systems) to utilize trustworthy data and robust computational methodologies when addressing convoluted decision-making predicaments concerning the design of transportation networks and optimal routes. This study aims to evaluate the vulnerability of paths in post-disaster transportation networks, with the aim of facilitating rescue operations and ensuring the safe delivery of supplies to affected regions. To investigate the problem of links' tolerances in uncertain networks and the resiliency and reliability of paths, an uncertainty theory-based model that employs minmax optimization with a bottleneck objective function is used. The model addresses the uncertain maximum reliable paths problem, which takes into account uncertain risk variables associated with links. Rather than using conventional methods for calculating the deterministic tolerances of a single element in combinatorial optimization, this study introduces a generalization of stability analysis based on tolerances while the perturbations in a group of links are involved. The analysis defines set tolerances that specify the minimum and maximum values that a designated group of links could simultaneously fluctuate while maintaining the optimality of the max-type reliable paths. The study shows that set tolerances can be considered as well-defined and proposes computational methods to calculate or bound such quantities - which were previously unresearched and difficult to measure. The model and methods are demonstrated to be both theoretically and numerically efficient by applying them to four subnetworks from our case study. In conclusion, this study provides a comprehensive approach to addressing uncertainty in reliability problems in networks, with potential applications in various fields.

Construction of reflective thinking: A field independent student in numerical problems

In educational settings, reflective thinking is often overlooked, with an excessive emphasis on final answers, resulting in students needing more ability to evaluate and reconstruct their problem-solving processes. The ability for reflective thinking is required by students in solving problems, including numerical problems. This study uses a qualitative approach to focus on field-independent students' numerical problem-solving processes. The data collection technique begins by administering the Group Embedded Figures Test (GEFT), a valid and reliable numeracy problem instrument, and conducting in-depth interviews. Two students with similar initial mathematical abilities and field-independent cognitive styles were selected as research subjects. Findings reveal that these students face challenges such as lengthy problem descriptions and a lack of confidence but gradually develop strategies, emphasizing repeated problem analysis, concept interconnections, and error awareness. Researcher-provided scaffolding facilitates critical reflection, enabling the construction of new ideas. These results have practical implications for teachers, suggesting the need to design lessons that cater to diverse cognitive styles, providing more complex problems to field-independent students to enhance their problem-solving skills.

Reliability-aware swarm based multi-objective optimization for controller placement in distributed SDN architecture

The deployment of distributed multi-controllers for Software-Defined Networking (SDN) architecture is an emerging solution to improve network scalability and management. However, the network control failure affects the dynamic resource allocation in distributed networks resulting in network disruption and low resilience. Thus, we consider the control plane fault tolerance for cost-effective and accurate controller location models during control plane failures. This fault-tolerance strategy has been applied to distributed SDN control architecture, which allows each switch to migrate to next controller to enhance network performance. In this paper, the Reliable and Dynamic Mapping-based Controller Placement (RDMCP) problem in distributed architecture is framed as an optimization problem to improve the system reliability, quality, and availability. By considering the bound constraints, a heuristic state-of-the-art Controller Placement Problem (CPP) algorithm is used to address the optimal assignment and reassignment of switches to nearby controllers other than their regular controllers. The algorithm identifies the optimal controller location, minimum number of controllers, and the expected assignment costs after failure at the lowest effective cost. A metaheuristic Particle Swarm Optimization (PSO) algorithm was combined with RDMCP to form a hybrid approach that improves objective function optimization in terms of reliability and cost-effectiveness. The effectiveness of our hybrid RDMCP-PSO was then evaluated using extensive experiments and compared with other baseline algorithms. The findings demonstrate that the proposed hybrid technique significantly increases the network performance regarding the controller number and load balancing of the standalone heuristic CPP algorithm.

RuleDRL: Reliability-Aware SFC Provisioning With Bounded Approximations in Dynamic Environments

As a key enabling technology for 5G, network function virtualization abstracts services into software-based service function chains (SFCs), facilitating mission-critical services with high-reliability requirements. However, it is challenging to cost-effectively provide reliable SFCs in dynamic environments due to delayed rewards caused by future SFC requests, limited infrastructure resources, and heterogeneity in hardware and software reliability. Although deep reinforcement learning (DRL) can effectively capture delayed rewards in dynamic environments, its trial-and-error exploration in a vast solution space with massive infeasible solutions may lead to frequent constraint violations and traps in poor local optima. To address these challenges, we propose a RuleDRL algorithm that combines the capability of DRL to capture delayed rewards and the strength of rule-based schemes to explore high-quality solutions without violating constraints. Specifically, we first formulate the reliable SFC provision problem as an integer nonlinear programming problem, which is proven to be NP-hard. Then, we jointly design DRL and rule-based schemes that are coupled to make the final decision and establish a bounded approximation ratio in general cases. Extensive trace-driven simulations show that RuleDRL can save the total cost by up to 65.67% and improve the SFC acceptance ratio by up to 82%, compared to the state-of-the-art solution.

Twinned Residual Auto-Encoder (TRAE)—A new DL architecture for denoising super-resolution and task-aware feature learning from COVID-19 CT images

The detection of the COronaVIrus Disease 2019 (COVID-19) from Computed Tomography (CT) scans has become a very important task in modern medical diagnosis. Unfortunately, typical resolutions of state-of-the-art CT scans are still not adequate for reliable and accurate automatic detection of COVID-19 disease. Motivated by this consideration, in this paper, we propose a novel architecture that jointly affords the Single-Image Super-Resolution (SISR) and the reliable classification problems from Low Resolution (LR) and noisy CT scans. Specifically, the proposed architecture is based on a couple of Twinned Residual Auto-Encoders (TRAE), which exploits the feature vectors and the SR images recovered by a Master AE for performing transfer learning and then improves the training of a "twinned" Follower AE. In addition, we also develop a Task-Aware (TA) version of the basic TRAE architecture, namely the TA-TRAE, which further utilizes the set of feature vectors generated by the Follower AE for the joint training of an additional auxiliary classifier, so to perform automated medical diagnosis on the basis of the available LR input images without human support. Experimental results and comparisons with a number of state-of-the-art CNN/GAN/CycleGAN benchmark SISR architectures, performed by considering , , and super-resolution (i.e., upscaling) factors, support the effectiveness of the proposed TRAE/TA-TRAE architectures. In particular, the detection accuracy attained by the proposed architectures outperforms the corresponding ones of the implemented CNN, GAN and CycleGAN baselines up to 9.0%, 6.5%, and 6.0% at upscaling factors as high as .

DRL Router: Distributional Reinforcement Learning-Based Router for Reliable Shortest Path Problems

This article studies reliable shortest path (RSP) problems in stochastic transportation networks. The term reliability in the RSP literature has many definitions, e.g., 1) maximal stochastic on-time arrival probability, 2) minimal travel time with a high-confidence constraint, 3) minimal mean and standard deviation combination, and 4) minimal expected disutility. To the best of our knowledge, almost all state-of-the-art RSP solutions are designed to target one specific RSP objective, and it is very difficult, if not impossible, to adapt them to other RSP objectives. To bridge the gap, this article develops a distributional reinforcement learning (DRL)-based algorithm, namely, DRL-Router, which serves as a universal solution to the four aforementioned RSP problems. DRL-Router employs the DRL method to approximate the full travel time distribution of a given routing policy and then makes improvements with respect to the user-defined RSP objective through a generalized policy iteration scheme. DRL-Router is 1) universal, i.e., it is applicable to a variety of RSP objectives; 2) model free, i.e., it does not rely on well calibrated travel time distribution models; 3) it is adaptive with navigation objective changes; and 4) fast, i.e., it performs real-time decision making. Extensive experimental results and comparisons with baseline algorithms in various transportation networks justify both the accuracy and efficiency of DRL-Router.

Radar: Reliable Resource Scheduling for Composable/Disaggregated Data Centers

Hardware disaggregation decouples resources (e.g., processors and memory) from monolithic servers, potentially improving service reliability. However, from another perspective, directly exposing resource modules to a shared network may adversely affect service reliability. In this paper, we study a reliable resource allocation problem in disaggregated DCs (DDCs), considering network impact and different disaggregation scales. We provide a mixed-integer linear programming formulation and a resource allocation framework named Radar for this problem. Numerical results demonstrate that the benefits of hardware disaggregation may be adversely affected by an imperfect network. It also shows that both the hardware backup and a proposed migration-based restoration can be applied to overcome this potential adverse effect.

Two-stage stochastic facility location problem with disruptions and restricted shortages

In this study, we consider a reliable facility location problem where an uncertain disruptive event might affect the demand of the nodes, the allocation costs, and the available capacities at the facilities. We assume that the decision maker decides the locations and the types (size and reliability levels) of the facilities under uncertainty, while the allocation and shortage decisions are made in response to the realizations of the uncertain parameters. Motivated from a humanitarian facility location problem, we enforce an upper bound on the risk of shortages under any scenario using the popular risk measure conditional value-at-risk (CVaR). We formulate the problem as a two-stage stochastic programming model, and solve it using the L-Shaped method accelerated with additional enhancements. Our computational experiments show the value of our modeling scheme, and the effectiveness of our solution method.