Reconfiguration Cost Research Articles

Graduation-inspired manufacturing system and synchronization mechanism for hybrid assembly cell lines

This paper presents an innovative flexible assembly system – hybrid assembly cell lines (HACL) to adapt to small batch, high product variety and dynamic demands. HACL provides specially designed trolleys for assembly operations, materials and tools to form the workstations flexibly and quickly at low reconfiguration cost. To conduct accurate and effective control for this complex assembly system, this paper presents a modified Graduation-inspired Manufacturing System (GMS), a recently proposed new card-based control system. GMS for HACL designs three kinds of tickets and the ticketing mechanism to absorb the uncertainties of HACL. Job tickets are for workload control and synchronization of part deliveries; setup tickets are for cell line formation and synchronization of resources of tools and machines; operation tickets are for production execution and synchronization of workers and operations in cell lines. GMS for HACL develops Internet of Things-enabled (IoT-enabled) architecture for managing the smart tickets to achieve automatic and intelligent interoperability between GMS and the physical workstations of HACL. This work designs synchronization mechanism based on specific model and genetic algorithm to synchronize manufacturing resources under GMS for HACL

A configuration optimization approach for reconfigurable manufacturing system based on column-generation combined with graph neural network

Reconfigurable manufacturing systems (RMS) offer the potential to improve systemic responsiveness and flexibility to better cope with dynamic environments. However, the inherent modularity of RMS and dynamic environments pose challenges in optimising system configurations. To address this issue, a two-stage stochastic programming model is established to minimise configuration cost, reconfiguration cost, expected inventory and back-order cost. To efficiently handle a large number of variables, a set-covering model is obtained by using Danzig-Wolfe (DW) decomposition along with its corresponding pricing subproblem. This paper proposes a solution algorithm based on the column generation framework, which can quickly obtain a good feasible solution. To further improve the algorithm performance for larger instances, a column selection method is introduced to identify additional columns that have the potential to reduce the objective function value of the integer solution during the column generation iterations. These columns are then added to the set-covering model. The process of column selection is accelerated by employing the Graph Neural Network (GNN) algorithm. Furthermore, GNN trained on data from small instances can be directly applied to larger instances as well. The effectiveness of the proposed model and algorithm is verified by numerical experiments.

Reconfigurable stochastic neurons based on strain engineered low barrier nanomagnets

Stochastic neurons are efficient hardware accelerators for solving a large variety of combinatorial optimization problems. ‘Binary’ stochastic neurons (BSN) are those whose states fluctuate randomly between two levels +1 and −1, with the probability of being in either level determined by an external bias. ‘Analog’ stochastic neurons (ASNs), in contrast, can assume any state between the two levels randomly (hence ‘analog’) and can perform analog signal processing. They may be leveraged for such tasks as temporal sequence learning, processing and prediction. Both BSNs and ASNs can be used to build efficient and scalable neural networks. Both can be implemented with low (potential energy) barrier nanomagnets (LBMs) whose random magnetization orientations encode the binary or analog state variables. The difference between them is that the potential energy barrier in a BSN LBM, albeit low, is much higher than that in an ASN LBM. As a result, a BSN LBM has a clear double well potential profile, which makes its magnetization orientation assume one of two orientations at any time, resulting in the binary behavior. ASN nanomagnets, on the other hand, hardly have any energy barrier at all and hence lack the double well feature. That makes their magnetizations fluctuate in an analog fashion. Hence, one can reconfigure an ASN to a BSN, and vice-versa, by simply raising and lowering the energy barrier. If the LBM is magnetostrictive, then this can be done with local (electrically generated) strain. Such a reconfiguration capability heralds a powerful field programmable architecture for a p-computer whereby hardware for very different functionalities such as combinatorial optimization and temporal sequence learning can be integrated in the same substrate in the same processing run. This is somewhat reminiscent of heterogeneous integration, except this is integration of functionalities or computational fabrics rather than components. The energy cost of reconfiguration is miniscule. There are also other applications of strain mediated barrier control that do not involve reconfiguring a BSN to an ASN or vice versa, e.g. adaptive annealing in energy minimization computing (Boltzmann or Ising machines), emulating memory hierarchy in a dynamically reconfigurable fashion, and control over belief uncertainty in analog stochastic neurons. Here, we present a study of strain engineered barrier control in unconventional computing.

Robust design and reconfiguration planning of mixed-model assembly lines under uncertain evolutions of product family

Assembly lines commonly run for dozens of years before being decommissioned. As product families may evolve several times per year by following the needs of sales and marketing, process engineers reconfigure the lines several dozens of times throughout their life cycle. If the line is not flexible enough, these reconfigurations may be costly, and they can lead to poor efficiency. The present work investigates the possibility of designing a line while accounting for product evolution throughout the life cycle of the line. The evolution of the product family is unknown and we consider a robust optimisation approach. We study a mixed-model assembly line, where each station contains a worker/robot and its equipment. The line produces different product models from the same family, and a reconfiguration occurs when a new product model replaces one of the current variants in the product family. Reconfiguration re-arranges resources and equipment pieces, and it can re-assign some tasks. In this study, we formulate a novel Mixed-Integer Linear Programming (MILP) that minimizes the total cost of the initial design and future reconfigurations of the line over some future product family evolution for the worst case. We consider the worst-case among different scenarios that represent possible production requirements of the new product model. An adversarial approach is also developed to solve large-size instances. We perform computational experiments on the benchmark data from the literature. The results show the proposed adversarial approach performs well, and the proposed robust model significantly reduces the design and reconfiguration costs when compared to the classical approach that designs and reconfigures by accounting only for the current product family.

SFC active reconfiguration based on user mobility and resource demand prediction in dynamic IoT-MEC networks.

To achieve secure, reliable, and scalable traffic delivery, request streams in mobile Internet of Things (IoT) networks supporting Multi-access Edge Computing (MEC) typically need to pass through a service function chain (SFC) consisting of an ordered series of Virtual Network Functions (VNFs), and then arrive at the target application in the MEC for processing. The high mobility of users and the real-time variability of network traffic in IoT-MEC networks lead to constant changes in the network state, which results in a mismatch between the performance requirements of the currently deployed SFCs and the allocated resources. Meanwhile, there are usually multiple instances of the same VNF in the network, and proactively reconfiguring the deployed SFCs based on the network state changes to ensure high quality of service in the network is a great challenge. In this paper, we study the SFC Reconfiguration Strategy (SFC-RS) based on user mobility and resource demand prediction in IoT MEC networks, aiming to minimize the end-to-end delay and reconfiguration cost of SFCs. First, we model SFC-RS as Integer Linear Programming (ILP). Then, a user trajectory prediction model based on codec movement with attention mechanism and a VNF resource demand prediction model based on the Long Short-Term Memory (LSTM) network are designed to accurately predict user trajectories and node computational and storage resources, respectively. Based on the prediction results, a Prediction-based SFV Active Reconfiguration (PSAR) algorithm is proposed to achieve seamless SFC migration and routing update before the user experience quality degrades, ensuring network consistency and high quality service. Simulation results show that PSAR provides 51.28%, 28.60%, 21.75%, and 16.80% performance improvement over the existing TSRFCM, DDQ, OSA, and DPSM algorithms in terms of end-to-end delay reduction, and 33.32%, 18.94%, 67.42%, and 60.61% performance optimization in terms of reconfiguration cost reduction.

A Digital Twin-Based Approach for the Optimization of Floor-Ball Manufacturing

The increasing complexity of products and manufacturing processes, combined with the constantly advancing technological integration of the manufacturing sector, raised new challenges for world-class industries to optimize time-to-market, resources, and cost. Simulation, as an essential Industry 4.0 enabling technology, allows one to emulate the steps of a manufacturing process, thereby achieving significant improvements in all the product and process development phases. A simulation process can be implemented and improved by creating the Digital Twin of the manufacturing system, which can be realized on a single-line scale or extended to the whole factory. The Digital Twin merges physics-based system modeling and real-time process data to generate a virtual copy of an observable object to reduce and optimize the extensive time and cost of physical design, prototyping, commissioning, reconfiguration, and maintenance. This study aims to investigate how the implementation of digital twin technology can help optimize the balance between power consumption and productivity, taking into account existing barriers and limitations. By following this outline, this study shows the design and development of a digital twin for a floor-ball manufacturing line present in the Smart Factory of Ostschweizer Fachhochschule (Switzerland). The entire production process is reproduced with Siemens Technomatix Plant Simulation software 2201, and data connection and processing are handled by a tailored toolchain consisting of an agent, a database, Python packages, and the COM interface from Tecnomatix. This toolchain feeds the digital twin with data from the physical operating environment. In particular, this study compares direct power measurements with the ones expected by the digital twin to assess digital model accuracy.

Designing a virtual cellular manufacturing system with route selection and workers’ considerations: A multi-objective robust possibilistic model

In today’s economy, the necessity for responsiveness to variations in customers’ permanent requirements is increasing significantly. Achieving immediate response or success in the competitive market requires boosting flexibility, agility, and efficiency in utilizing available production equipment and workforce. Implementing a Virtual Cellular Manufacturing System (VCSM) prevents the reconfiguration costs and is suitable for many systems where the machines are not removable. Owing to the significant role of human resources in VCMS, this paper assigns the workers based on their skills, interest in cooperation, and workload balancing among cells. The considerable novelty is balancing physical and time workloads simultaneously. Another strength of this research is the consideration of various routes to process each part. A comprehensive linear multi-objective mathematical model is developed for a virtual cellular manufacturing system. This paper develops the model by stating social and technical dimensions, which minimize exceptional elements and voids, maximize the workers' interest in cooperation, and minimize workload deviation. Stemming from uncertain parameters, a robust possibilistic approach is addressed. Due to the multi-objective model, the multi-choice meta-goal programming with the utility function is applied. Afterward, a real-case study is presented to validate the model. Eventually, the outcomes and sensitivity analysis are brought up.

On the feasibility of using an autonomic control loop and automated planning for tenant-oriented reconfiguration of network slices

In 5G, 6G and beyond networks, tenants need capabilities to reconfigure network slices since they must react quickly to changing network conditions and end-users’ demands to meet Service Level Agreements. Offering reconfiguration operations to tenants is challenging since those functions must be autonomous and governed by high-level network management policies. Existing approaches face the network slice reconfiguration problem from the provider perspective, disregarding the importance of providing slice reconfiguration capabilities to tenants. This paper presents an approach called ATRAP to demonstrate the feasibility of using an autonomic control loop and automated planning for reconfiguring network slices from the tenant perspective. ATRAP introduces a tenant-oriented architecture based on the monitor-analyze-plan-execute-knowledge method and automated planning for computing reconfiguration plans autonomously. An ATRAP’s plan aims to turn a network from a source configuration where one or more intents representing high-level network management policies are unmet into a target configuration in which the network meets the intents again. As ATRAP represents a network slice as a Service Function Chain comprising Virtual Network Functions (VNFs) and virtual links connecting them, a reconfiguration plan involves tasks to migrate VNFs and virtual links into the substrate network in a particular order. Results revealed as ATRAP computes plans with few migrations of VNFs and their adjacent links, leading to low reconfiguration costs related to short service disruptions. Also, results showed as ATRAP’s planning time depends on the size of the substrate network and the tenant’s slices. The results evidence that ATRAP is an attractive and feasible solution for the in-tenant reconfiguration of network slices.

Configuration design of reconfigurable single-product robotic assembly line for capacity scalability

Rapid product changes and fierce global competition force companies to react quickly to market changes associated with dynamic capacity. This paper investigates how to design and reconfigure configurations of the single-product scalable reconfigurable robotic assembly line (RAL) over multiple demand periods (DPs) with fluctuating demands. A two-phase approach is proposed, in phase 1, the configuration of RAL is designed to minimize the capital cost following the initial demand. In phase 2, the reconfiguration for each DP is performed according to demand fluctuations to minimize the sum of capital costs and reconfiguration costs between consecutive DPs. Mathematical models for the two phases are established. Then a hybrid particle swarm algorithm combined with simulated annealing (PSOSA) is proposed for practical-sized problems, and PSO and SA are chosen for comparison. Two other different approaches are selected for comparison. In approach 1 (robust strategy), each DP of the product lifecycle is calculated separately, and then a fixed configuration that can satisfy the maximal capacity of all DPs is obtained. In approach 2 (changeable strategy), the minimal-cost configuration for each DP is calculated separately first, and then the reconfiguration cost between adjacent DPs is counted. Furthermore, a set of instances is designed and the computational results of three approaches accompanied by PSOSA, PSO and SA versus the devised instances demonstrate the superiority and robustness of the proposed approach and PSOSA.

Dynamically Scalable NoC Architecture for Implementing Run-Time Reconfigurable Applications.

The paper proposes two architectures for a dynamically scalable network-on-chip (NoC) for dynamically reconfigurable intellectual properties (IPs) to save power. The first architecture is a run-time scalable column-based NoC, where the columns of the NoC are scaled up and down at run-time depending on the demands to connect reconfigurable IPs. The second architecture is an extension of the first, where both the rows and columns of the NoC are dynamically scaled up and down on demand. A robust control manager is developed to control the IP and sub-NoC reconfigurations by optimizing the reconfiguration costs. The proposed architectures have been implemented and tested in actual prototypes on a Virtex 6 FPGA mounted on the ML605 board. The results show that dynamically scalable architectures are capable of significant power reduction as compared to traditional static architectures for the same size of the NoC. It is anticipated that the scalable NoC can be very useful for sharing the FPGA resources among IPs at runtime.

Deep reinforcement learning for proactive spectrum defragmentation in elastic optical networks

The immense growth of Internet traffic calls for advanced techniques to enable the dynamic operation of optical networks, efficient use of spectral resources, and automation. In this paper, we investigate the proactive spectrum defragmentation (SD) problem in elastic optical networks and propose a novel deep reinforcement learning-based framework DeepDefrag to increase spectral usage efficiency. Unlike the conventional, often threshold-based heuristic algorithms that address a subset of the defragmentation-related tasks and have limited automation capabilities, DeepDefrag jointly addresses the three main aspects of the SD process: determining when to perform defragmentation, which connections to reconfigure, and which part of the spectrum to reallocate them to. By considering service attributes, the spectrum occupancy state expressed by several different fragmentation metrics, and the reconfiguration cost, DeepDefrag is able to consistently select appropriate reconfiguration actions over the network lifetime and adapt to changing conditions. Extensive simulation results reveal superior performance of the proposed scheme over a scenario with exhaustive defragmentation and a well-known benchmark heuristic from the literature, achieving lower blocking probability at a smaller defragmentation overhead.

Salience-based VNF placement method for evolving multiple network slices

Network Function Virtualization (NFV) technology enables the deployment of a distinct network slice composed of various virtualized network functions (VNFs) required for offering a communication service. As the user population or network traffic volume fluctuates, the capacity of the network slices must be dynamically adjusted. It is necessary to decide whether to place VNFs in appropriate locations such that the quality of services (QoS) is guaranteed while optimally utilizing the allocated resources. The above challenge can be addressed by solving an optimization problem, such as integer linear programming. However, as solving the optimization problem requires a considerable amount of time, it is not applicable in agile network control mechanisms. In this paper, we propose a network function placement algorithm based on salience, which is one of the characteristic metrics of links in a graph. Our proposal is aimed to provide the adequate placement in short time to achieve the following objectives of keeping the slice reconstruction cost low and avoiding the cases of QoS degradation. The simulation results show that our scheme performs equally well for different sized slices (i.e., the user population). We found that the network reconfiguration costs are low and almost the same for various sized slices when they are reconstructed by the proposed method in a substrate network of 200 nodes. Furthermore, we confirmed that the proposed method keeps the slice reconstruction cost low and avoids blocking of slice reconstruction even in the situations when a resource competition occurs among multiple slices.

Randomized distributed self-adjusting tree networks

In this paper we investigate the benefits of randomization in the design of self-adjusting network topologies: networks that dynamically adapt themselves toward the demand they currently serve, in an online manner. We present a randomized self-adjusting tree network, which leverages randomization to reduce the expected network reconfiguration cost by a constant factor, compared to existing deterministic solutions. The new solution is simple, easy-to-implement, fully distributed and concurrent. We prove algorithm correctness, provide expected amortized cost limits, and present simulation results on workloads with variable spatial and temporal complexity.

A multi-objective joint optimisation method for simultaneous part family formation and configuration design in delayed reconfigurable manufacturing system (D-RMS)

ABSTRACT In the era of Industry 4.0, the demand fluctuation has become fiercer due to the characteristics of diversification, customisation, and uncertainty. Reconfigurability of manufacturing systems has been proven to be a useful and necessary feature when it comes to handling demand uncertainty. This feature can be achieved through the implementation of reconfigurable manufacturing system (RMS) and delayed reconfigurable manufacturing system (D-RMS). D-RMS is a subclass of RMS that focuses primarily on improving the convertibility of the manufacturing system. The two main phases involved in implementing D-RMS are part family formation and configuration design. Therefore, we proposed a multi-objective joint optimisation method of part family formation and configuration design according to the philosophy of D-RMS. Firstly, we develop a multi-objective joint optimisation model that takes into account investment cost, reconfiguration cost, similarity coefficient, and delayed reconfiguration to optimise the part family and configuration of D-RMS simultaneously. Three types of machine tools namely dedicated machine tools, flexible machine tools, and reconfigurable machine tools are considered in the optimisation model. Secondly, the non-dominated sorting genetic algorithm-III (NSGA-III) is adopted to solve the proposed multi-objective integer programming problem. Finally, numerical experiments are presented to demonstrate the effectiveness of the proposed multi-objective joint optimisation method.

Deep Reinforcement Learning Based Link Adaptation Technique for LTE/NR Systems

Outdated channel quality indicator (CQI) feedback causes severe performance degradation of traditional link adaptation (LA) techniques in long term evolution (LTE) and new radio (NR) systems. This paper puts forth a deep reinforcement learning (DRL) based link adaptation (LA) technique, referred to as deep reinforcement learning link adaptation (DRLLA), to select efficient modulation and coding scheme (MCS) in the presence of the outdated CQI feedback. The goal of DRLLA is to maximize the link throughput while achieving a low block error rate (BLER). We first give explicit definitions of state, action, and reward in DRL paradigms, thereby realizing DRLLA. Then, to trade off the throughput against the BLER, we further develop a new experience replay mechanism called classified experience replay (CER) as the underpinning technique in DRLLA. In CER, experiences are separated into two buckets, one for successful experiences and the other for failed experiences, and then a fixed proportion from each is sampled to replay. The essence of CER is to obtain different trade-offs via adjusting the proportion among different training experiences. Furthermore, to reduce the signaling overhead and the system reconfiguration cost caused by frequent MCS switching, we propose a new action selection strategy termed as switching-controlled <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -greedy (SC- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -greedy) for DRLLA. Simulation results demonstrate that compared with the state-of-the-art OLLA, LTSLA, and DRLLA with other experience replay mechanisms, DRLLA with CER can achieve higher throughput and lower BLER in various time-varying scenarios, and be more robust to different CQI feedback delays and CQI reporting periods. Furthermore, with the SC- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -greedy policy, DRLLA can capture better trade-offs between the link transmission quality and the MCS switching overhead compared with other baselines.

Method for in-orbit redesign for TianQin orbit configuration

The space-based gravitational wave observatory mission consists of three drag-free controlled spacecraft, which form an equilateral triangle configuration. Due to the injection error, the configuration will deviate from the originally designed one, and the injection configuration cannot keep stability for a long term, which will affect the performance of the gravitational wave detection. In this study, we propose a method for reconfiguration using multiple-revolution Lambert solutions. In addition, assuming that the real-time data of spacecraft’s motion states can be obtained with high accuracy and the propulsion system can ensure the magnitude and accuracy of maneuvers, and taking the cost of reconfiguration and configuration stability requirements into account, a novel method of redesigning configuration based on differential evolution algorithm is proposed. Finally, the numerical simulations show that the redesigned configuration not only can reduce the maneuver cost significantly, but also can meet the requirements of long-term stability requirements.

Self-adjusting grid networks

Emerging networked systems become increasingly flexible, reconfigurable, and “self-⁎”. This introduces an opportunity to adjust networked systems in a demand-aware manner, leveraging spatial and temporal locality in the workload for online optimizations. However, it also introduces a tradeoff: while more frequent adjustments can improve performance, they also entail higher reconfiguration costs. This paper studies self-adjusting grid networks in which frequently communicating nodes (e.g., virtual machines) are moved topologically closer in an online and demand-aware manner, striking a balance between the benefits and costs of reconfigurations. The paper presents a general Ω(log⁡n) lower bound for this problem, even in scenarios where the demand graph is constant once learned. To demonstrate the challenge of adapting a network to pair-wise communication requests, we also design an O(log⁡n)-competitive algorithm for 1-dimensional grids.

Joint Application Placement and Request Routing Optimization for Dynamic Edge Computing Service Management

As mobile edge computing (MEC) hosting applications at the network edge with limited capacities, service providers are facing the new challenge of how to make full use of the scarce edge resources to maximize the system performance. Accommodating this challenge requires careful application placement and request routing to coordinate diverse MEC nodes. However, frequent application re-placement would greatly increase the system reconfiguration cost, indicating a performance-cost trade-off. In response, in this paper, we study the problem of joint optimization on application placement and request routing to maximize the system performance, under a long-term budget of the application reconfiguration cost. Solving this problem is non-trivial since the long-term budget is coupled with the future system states (e.g., user request arrivals) that are typically unpredictable. To address this challenge, we first advocate an approximated dynamic optimization framework to decompose the long-term optimization problem into a series of one-shot problems which do not require the future system states. Moreover, since the decomposed problem is a mixed integer linear program (MILP) which is proven to be NP-hard, we then devise an efficient dependent rounding based approximation algorithm, which can achieve the near-optimal performance in a fast manner. Both rigorous theoretical analysis and extensive trace-driven evaluations demonstrate the proposed framework can achieve superior performance gain over existing schemes.

Robust Index Selection for Stochastic Dynamic Workloads

Fast query processing is a primary goal of modern database systems. The use of indexes is crucial to reduce the execution times of database queries. Hence, it is of great interest to determine an efficient selection of indexes for a database management system (DBMS). However, index selection problems are highly challenging as indexes cause additional memory consumption and the individual benefit of an index is influenced by the selection of others. In this paper, we consider index selection problems accounting for non-standard features, such as (i) multiple potential workloads, (ii) different risk-averse objectives, (iii) multi-index configurations, (iv) reconfiguration costs, and (v) anticipation of dynamic workload scenarios. For the different problem extensions, we propose specific model formulations, which can be solved efficiently using solver-based solution techniques. The applicability and performance of our concepts are demonstrated using reproducible synthetic workloads as well as standard TPC-H and TPC-DS-based benchmark workloads.

Hybrid swarm intelligent algorithm for multi-UAV formation reconfiguration

Formation flight of unmanned aerial vehicles (UAVs) utilizes reconfiguration procedures to handle a variety of emergencies, such as collision avoidance, malfunctions, fuel savings, and member replacement. As UAVs have limited computing power and energy resources, it is necessary to optimize the control inputs to reduce the distance travelled by UAVs while reducing the computing costs during formation reconfiguration. In this paper, the problem of multi-UAV reconfiguration is decoupled into two stages: task assignment and control input optimization of UAVs. For a solution to the above problem, we propose an adaptive hybrid particle swarm optimization and differential evolution algorithm (AHPSODE) to optimize minimize the distance of the total movement and reduce the computing cost of formation reconfiguration. Based on the idea of receding horizon control (RHC) and the nonlinear model of multi-UAV formation reconfiguration, an RHC controller using AHPSODE is designed to optimize the control input of the UAV group to obtain the shortest movement distance, and this method can reduce the computation time. We use the CEC 2017 test suit to test the performance of our proposed AHPSODE algorithm, and simulate the AHPSODE-based RHC controller to manage formation reconfiguration. The results show that our proposed AHPSODE performed well in convergence and accuracy and the RHC controller is effective.