Placement Problem Research Articles

Optimal placement of multiple finite-size rectangular facilities in an existing layout

ABSTRACT In this paper, we investigate a new problem of optimal placement of multiple finite-size rectangular facilities with known dimensions in the presence of existing rectangular facilities. This problem has applications in facility layout (re)design in manufacturing, distribution systems, services, and electronic circuit design. Three types of facility interactions are considered: between new facilities and existing facilities; between pairs of existing facilities; and between pairs of new facilities. All interactions are serviced through a finite number of input/output points located on the facility boundaries. Travel is assumed to occur according to the rectilinear (or Manhattan) metric and travel through facilities is prohibited. The objective is to find the simultaneous and non-overlapping placement of new facilities, which minimises the total weighted distance (Minisum objective) between the interacting facilities. To arrive at a solution, we divide the feasible region into sub-regions and prove that the candidates for optimal placement of the new facilities can be drawn from the sub-region boundaries. Being a continuous generalisation of the quadratic assignment problem, the solution complexity of this procedure is exponential in the number of new facilities. Our main contribution is the rigorous treatment of an important problem that unifies facility location and layout theories with minisum objective and rectilinear metrics.

Optimal and heuristic solutions for placing multiple finite-size rectangular facilities in an existing layout

In a companion paper (Date, K., and R. Nagi. [(2023) Optimal Placement of Multiple Finite-size Rectangular Facilities in an Existing Layout. International Journal of Production Research, THIS ISSUE]) we investigated a new problem of optimal placement of multiple finite-size rectangular facilities with known dimensions in the presence of existing rectangular facilities. We introduced the requisite theory to arrive at a solution by dividing the feasible region into sub-regions whose boundaries provide the candidates for the optimal placement. In this paper, we focus on developing optimal and heuristic solution methods for solving problem instances with various facility counts. To solve small instances with fewer facilities, we develop two optimal methods: (1) efficient explicit enumeration based on a tree state-space representation and (2) an implicit enumeration scheme that uses a flow-decomposition-based lower bound to reduce the search space and time. To solve industrial-sized instances, we have to resort to a family of construction and improvement heuristics given that the problem is NP-hard. The heuristic procedures perform well for non-pathological cases with an acceptable optimality gap. The main contribution of this paper is a range of optimal to heuristic methods that can be applied by the practitioner depending on their problem characteristics and desired efficiency.

Subswarm-guided ant colony optimization with enhanced pheromone update mechanism and beam search for VNF placement and routing

Network Function Virtualization (NFV) is a novel technology that enables flexible and cost-effective networks by replacing the hardware-based middleboxes with software run on virtual machines called virtual network function (VNF). In NFV, each Service Function Chain (SFC) is required to be a set of ordered VNF from that must be optimally located across servers/distributed data centers. This paper studies the multi-objective virtual network function placement and routing problem (MO-VNFPR), which involves placing VNFs optimally in servers nodes and assigning server resources efficiently to these VNFs to satisfy user’s requests in the networks. Most existing methods for this problem only handled one requests or considered VNF placement and SFC routing separately. In this paper, we consider MO-VNFPR with two objectives: (i) minimize the overall cost by deploying servers and installing VNFs; (ii) reduce the network delay. A metaheuristic called subswarm-guided ant colony optimization (named SgACO) is introduced to efficiently solve MO-VNFPR. In SgACO, a subswarm-guided mechanism is adopted to satisfy requests at once by reorganizing the ant colony into subswarms. Moreover, an enhanced pheromone update mechanism and beam search are developed to investigate the search space efficiently. Experimental simulations on COGENT, CONUS, and NSF network topologies demonstrate that our algorithm achieves competitive results and performs better against other existing algorithms.

Three-Stage Rapid Physical Design Algorithm for Continuous-Flow Microfluidic Biochips Considering Actual Fluid Manipulations

With the continuous development of microfluidic technology, continuous-flow microfluidic biochips (CFMBs) are being increasingly used in the Internet of Things. The automation design of CFMBs has also received widespread attention. The architecture design of CFMBs is divided into a high-level synthesis stage and a physical design stage. Among them, the problem of the physical design stage is very complex. At this stage, the chip architecture is generated based on the device library and a set of flow paths, taking into account the actual fluid manipulations, while minimizing the cost of the chip, such as the number of ports, total length of flow channels, number of flow channel intersections. As fabrication technology advances, the number of devices integrated into CFMBs is increasing. The existing physical design algorithms can no longer meet the design requirements of CFMBs in terms of time. Therefore, we propose a three-stage rapid physical design algorithm for CFMBs considering the actual fluid manipulations. The proposed algorithm includes a port-driven preprocessing stage, a force-directed quadratic placement stage, and a negotiation-based routing stage. In the port-driven preprocessing stage, a port-driven preprocessing algorithm is proposed to generate connection matrices between ports and devices to reduce the number of ports introduced. In the force-directed quadratic placement stage, we model the placement problem as an extremum problem of a quadratic cost function, which mathematically reduces the search space significantly and shortens the running time of the algorithm significantly. In the negotiation-based routing stage, a heuristic negotiation-based routing algorithm and a flow channel strategy that prioritizes the construction of parallel execution are proposed to reduce the running time of the algorithm while ensuring that the number of crossings in the routing solution is close to the optimal solution. Experimental results confirm that our proposed method is able to generate the high-quality solutions quickly. Under general scale problems, compared to the existing method based on ILP, our proposed method achieves a speedup ratio of 23,171 in terms of CPU time and optimizations in terms of number of ports and port reuse of 3.18% and 6.52%, respectively. These optimizations come at the cost of only a slight increase in the number of intersections, the flow length, and the number of flow valves. In addition, our proposed method can effectively solve large-scale problems that cannot be solved by existing method based on ILP.

Multi-criteria Optimization of Real-time DAGs on Heterogeneous Platforms under P-EDF

This article tackles the problem of optimal placement of complex real-time embedded applications on heterogeneous platforms. Applications are composed of directed acyclic graphs of tasks, with each directed-acyclic-graph (DAG) having a minimum inter-arrival period for its activation requests and an end-to-end deadline within which all of the computations need to terminate since each activation. The platforms of interest are heterogeneous power-aware multi-core platforms with Dynamic Voltage and Frequency Scaling (DVFS) capabilities, including big.LITTLE Arm architectures and platforms with GPU or FPGA hardware accelerators with Dynamic Partial Reconfiguration capabilities. Tasks can be deployed on CPUs using partitioned EDF-based scheduling. Additionally, some of the tasks may have an alternate implementation available for one of the accelerators on the target platform, which are assumed to serve requests in non-preemptive FIFO order. The system can be optimized by minimizing power consumption, respecting precise timing constraints, maximizing the applications’ slack, respecting given power consumption constraints, or even a combination of these, in a multi-objective formulation. We propose an off-line optimization of the mentioned problem based on mixed-integer quadratic constraint programming (MIQCP). The optimization provides the DVFS configuration of all the CPUs (or accelerators) capable of frequency switching and the placement to be followed by each task in the DAGs, including the software-vs.-hardware implementation choice for tasks that can be hardware accelerated. For relatively big problems, we developed heuristic solvers capable of providing suboptimal solutions in a significantly reduced time compared to the MIQCP strategy, thus widening the applicability of the proposed framework. We validate the approach by running a set of randomly generated DAGs on Linux under SCHED_DEADLINE, deployed onto two real boards, one with Arm big.LITTLE architecture, the other with FPGA acceleration, verifying that the experimental runs meet the theoretical expectations in terms of timing and power optimization goals.

Association Between Flexible Work Arrangement and Sleep Problems Among Paid Workers: Using 6th Korean Working Conditions Survey

BackgroundAs social distancing persists and interest in work-life balance grows, more companies are adopting flexible work policies. While there have been studies on sleep disorders associated with different types of work, such as shift work, research exploring the relationship between flexible work schedules and sleep disorders is still limited, particularly among Korean workers. MethodsWe performed a secondary analysis of the 6th Korean Working Conditions Survey, focusing on 31,243 paid workers out of a total of 50,538 participants. We defined flexible workers as those who set their own working hours. Sleep disorders were divided into three categories: ‘difficulty falling asleep,’ ‘frequent waking during sleep,’ and ‘waking up feeling exhausted and fatigued.’ Using scores derived from three specific symptoms, the Minimal Insomnia Symptoms Scale (MISS) was calculated to assess the prevalence of insomnia. We used chi-square tests to analyze demographic and job-related differences. A multivariate logistic regression analysis was employed to identify any relationship between flexible work schedules and sleep disorders. ResultsSignificant differences were found between flexible and non-flexible workers regarding age, income level, education level, and job type. Flexible workers reported sleep-related symptoms significantly more often. The odds ratio for insomnia was 1.40 (95% CI 1.21–1.61). For males, the odds ratio was 1.68 (1.36–2.08). ConclusionThis study establishes a correlation between flexible work schedules and sleep disorders among Korean salaried workers. Potential causes could include changes in circadian rhythm, increased work demands, and extended working hours. To precisely determine causality and associated diseases, further research is required.

Flow-based Service Time optimization in software-defined networks using Deep Reinforcement Learning

A Ternary Content-Addressable Memory (TCAM) constitutes a memory variant employed within Software Defined Networking (SDN) node flow tables. These TCAMs deliver swift processing, enabling rapid parallel lookups. Nonetheless, due to their high energy consumption and cost, TCAMs have limited dimensions. This size constraint influences rule capacity, and suboptimal rule management can degrade network quality of service. Although different techniques for flow table management have been proposed during recent years, such as eviction, idle and hard timeout mechanisms, this paper proposes a Deep-Reinforcement Learning (DRL) solution, namely DRL-Idle, that is able to maximize the service time of the flows in an SDN network without considering any assumption about their status throughout time. By means of a continuous learning, DRL-Idle is able to also minimize the number of rule installations that are required to serve the target flows. Based on the idea of dynamically modifying the idle timeout value of the flows according to their needs, DRL-Idle outperforms existing approaches aiming at solving the Rule Placement Problem, averaging a 30% increase in performance.

Genetic Multi-Objective Optimization of Sensor Placement for SHM of Composite Structures

Increasingly often, due to the high sensitivity level of diagnostic systems, they are also sensitive to the occurrence of a significant number of false alarms. In particular, in structural health monitoring (SHM), the problem of optimal sensor placement (OSP) is appearing due to the need to reach a balance between performance and cost of the diagnostic system. The applied approach of considering nondominated solutions allows for adaption of the system parameters to the user’s expectations, treating this optimization problem as multi-objective. For this purpose, the NSGA-II algorithm was selected for the determination of an optimal set of parameters in the OSP problem for the detection of delamination in composite structures. The objectives comprise minimization of type-I and type-II errors, and number of sensors to be placed. The advantage of the proposed approach is that it is based on experimental data from the healthy structure, whereas all cases with a presence of delamination were acquired from numerical experiments. This makes it possible to develop a customized SHM system for the arbitrary location of damage.

Pole placement with minimum gain and sparse static feedback

The work addresses the issue of developing polar positioning algorithms for linear control systems with despotic thinning constraints on a static feedback matrix. The design of sparse-limiting feedback control systems is considered to be one of the more complex issues, but the issue of defining the sparse feedback matrix, which allows the selection of eigenvalues or, in other words, the placement of poles, has not been fully studied. Algorithms for determining the eigenvalues, i.e., solving the problem of pole placement and finding the optimal solution of the technological process control system are considered in the work. The results of numerical analysis confirmed the effectiveness of these algorithms, which allows them to be used in solving practical problems of synthesizing adaptive control systems for technological objects.

Quantum‐Based Combinatorial Optimization for Optimal Sensor Placement in Civil Structures

Over the last decade, concepts such as industry 4.0 and the Internet of Things (IoT) have contributed to the increase in the availability and affordability of sensing technology. In this context, structural health monitoring (SHM) arises as an especially interesting field to integrate and develop these new sensing capabilities, given the criticality of structural application for human life and the elevated costs of manual monitoring. Due to the scale of structural systems, one of the main challenges when designing a modern monitoring system is the optimal sensor placement (OSP) problem. The OSP problem is combinatorial in nature, making its exact solution infeasible in most practical cases, usually requiring the use of metaheuristic optimization techniques. While approaches such as genetic algorithms (GAs) have been able to produce significant results in many practical case studies, their ability to scale up to more complex structures is still an area of open research. This study proposes a novel quantum‐based combinatorial optimization approach to solve the OSP problem approximately, within the context of SHM. For this purpose, a quadratic unconstrained binary optimization (QUBO) model formulation is developed, taking as a starting point of the modal strain energy (MSE) of the structure. The framework is tested using numerical simulations of Warren truss bridges of varying scales. The results obtained using the proposed framework are compared against exhaustive search approaches to verify their performance. More importantly, a detailed discussion of the current limitations of the technology and the future paths of research in the area is presented to the reader.

Energy-Efficient Relay Node Placement in Wireless Heterogeneous Networks with Capacity Constraints

In wireless heterogeneous networks (HetNets), rational relay node (RN) placement can not only expand network coverage and capacity, but also significantly reduce energy consumption of the network. However, existing RN placement schemes mainly focus on improving network performance, and do not take the network energy consumption and capacity constraints of RNs into consideration, which makes their solutions impractical. Therefore, in this paper, we investigate energy-efficient RN placement (EERPL) problem, and try to reduce energy consumption of a wireless HetNet within the range of RN’s capacity constraints. More specifically, we first present a fine-grained energy consumption model and formulate the EERPL problem, which can be translated to the degree constrained minimum Steiner tree problem. Then, we propose a heuristic RN-placement algorithm HBAPS, which involves two steps, i.e., generating an initial minimum-energy RN placement solution and adjusting the solution to be with in the capacity constraints. Finally, extensive simulations are conducted in both randomly generated scenarios and real trace-based scenarios. Experimental results demonstrate that HBAPS achieves nearly optimal results when the scenario is small, and even in a large scenario, the proposed algorithm still obtains proper solutions within the acceptable time, and outperforms the other two existing heuristic algorithms.

The satellite network cache placement strategy based on content popularity and node collaboration.

Proposed is a Satellite network cache placement strategy (PNCCP) based on popularity and node cooperation to address the issue of significant delays in end-to-end connectivity due to instability among satellites. Initially, the strategy employs spectral clustering algorithm to partition the satellite network's topology, limiting the retrieval scope of content and reducing unnecessary propagation delays. Within each partition, a cache collaboration open mechanism among satellites is devised to share cache resources, utilizing the proximity of neighboring nodes to share popular content and cache space. Furthermore, the data naming network (NDN) cache model is enhanced and integrated with the open mechanism, with an update mechanism designed to address the invalidation caused by the dynamic nature of satellite networks. Finally, aiming to minimize users' average retrieval delay, the artificial bee colony algorithm is employed to solve the optimal cache placement problem. Simulation results demonstrate that compared to three contrasting cache strategies, the proposed strategy reduces user content retrieval delays, improves cache hit rates, and holds an advantage in reducing request hop counts.

A power generation accumulation-based adaptive chaotic differential evolution algorithm for wind turbine placement problems

A power generation accumulation-based adaptive chaotic differential evolution algorithm for wind turbine placement problems

Algorithms for Cameras View-Frame Placement Problems in the Presence of an Adversary and Distributional Ambiguity

Algorithms for Cameras View-Frame Placement Problems in the Presence of an Adversary and Distributional Ambiguity

SOLUTION OF THE EXPONENTIAL DIOPHANTINE EQUATION 10^X + 400^Y=Z^2

Diophantine equations are so important in solving important real-world problems like network flow problems, pole placement problems, business investment problems, and data privacy problems that researchers are becoming more interested in developing new techniques for analyzing the nature and solutions of the various Diophantine equations. In the present study, authors examined the Diophantine equation〖10〗^X+〖400〗^Y=Z^2, where X,Y,Zare non-negative integers, for the non-negative integer solution of this equation. A result of the present study shows that it has no non-negative integer solution.

A Cooperative Hybrid Quantum-Inspired Salp Swarm and Differential Evolution for Solving CEC 2022 Benchmark and Controller Placement Problems in Software Defined Networks

A Cooperative Hybrid Quantum-Inspired Salp Swarm and Differential Evolution for Solving CEC 2022 Benchmark and Controller Placement Problems in Software Defined Networks

ScalableTrack: Scalable One-Stream Tracking via Alternating Learning.

Transformer-based one-stream trackers are widely used to extract features and interact information for visual object tracking. However, the current one-stream tracker has fixed computational dimensions between different stages, which limits the network's ability to learn context clues and global representations, resulting in a decrease in the ability to distinguish between targets and backgrounds. To address this issue, a new scalable one-stream tracking framework, ScalableTrack, is proposed. It unifies feature extraction and information integration by intrastage mutual guidance, leveraging the scalability of target-oriented features to enhance object sensitivity and obtain discriminative global representations. In addition, we bridge interstage contextual cues by introducing an alternating learning strategy and solve the arrangement problem of the two modules. The alternating learning strategy uses alternate stacks of feature extraction and information interaction to focus on tracked objects and prevent catastrophic forgetting of target information between different stages. Experiments on eight challenging benchmarks (TrackingNet, GOT-10k, VOT2020, UAV123, LaSOT, LaSOT [Formula: see text] , OTB100, and TC128) show that ScalableTrack outperforms state-of-the-art (SOTA) methods with better generalization and global representation ability.

Genetic Algorithm-Based Well Placement Optimization: A Review of Studies

Well placement optimization is a crucial task in the oil and gas industry, as it can significantly impact the overall performance and economic viability of a reservoir. Genetic algorithms GAs are an effective optimization technique that can solve well placement problems. This review paper provides an overview of the literature on GA-based well placement optimization. The paper discusses the different types of GAs that have been used for well placement, as well as the different ways in which GAs have been modified to improve their performance. The paper also discusses the different applications of GAs to well placement, including the optimization of well number, location, and trajectory. The paper concludes by highlighting the advantages of using GAs for well placement optimization. GAs is able to handle complex problems with multiple objectives and constraints.

Intelligent pest trap monitoring under uncertainty in food industry

Pests bring significant losses in both quantity and quality of products in postharvest food facilities and their uncertain activities make imprecise obtained infestation information from trap monitoring, which causes unclear estimation on detriment costs, preventing effective pest management. Trap placement, which collects insect information through determining the exact location and number of traps, will enhance the precision for obtaining insect information. However, this method involves optimizing conflicting objectives. On one hand, smaller number of traps save the monitoring cost and enlarge the monitoring efficiency. On the other hand, number of traps may be insufficient and losses the monitoring accuracy. Existing studies to enhance the monitoring accuracy via both laboratory testing and high tech devices but they rarely considered economic efficacy in practice. In this paper, we characterize the trap placement problem as a Multi-Objective Optimization Problem under Uncertainty. Feedforward Neural Networks and Particle Swarm Optimization algorithms are used to design a tailored algorithm for modeling the conflicting objectives through process-based simulation. Then, robust multi-objective optimization evolutionary algorithm (RMOA) is developed to search for the Pareto optimal solution for the least number of traps settled at optimal positions. Results show the optimal and robust performance of exact trap locations to monitor insect infestation effectively under uncertain insect locations and dynamic insect growth. Compared to the studies that were mainly utilizing general algorisms for solving agricultural problems, the proposed model has implications for effective methods of capturing the characteristics of real-world insect monitoring applications in food industry.

Solution of the Diophantine equation 22x+40y=z2

Since Diophantine equations play an important role in solving important real-world problems such as business investment problems, network flow problems, pole placement problems, and data privacy problems, researchers are increasingly interested in developing new techniques for analyzing the nature and solutions of the various Dio phantine equations. In this study we investigate the Diophantine problem 22x+40y = z2, where x, y, z are non-negative integers, and discover that it does not have a non-negative integer solution.