Complex Resource Allocation Research Articles

Application Analysis of BIM Technology in Metro Rail Transit

With the rapid development of urban roads, especially the construction of subway rail transit, it is an effective way to alleviate urban traffic congestion. There are limited site space, complex resource allocation, tight schedule, underground pipeline complex engineering problems. BIM technology, three-dimensional visualization, parameterization, virtual simulation and many other advantages can effectively solve these technical problems. Based on the project of Shenzhen Metro Line 9, BIM technology is innovatively researched throughout the lifecycle of BIM technology in the context of the metro rail transit project rarely used at this stage. The model information file is imported into Navisworks for four-dimensional animation simulation to determine the optimum construction scheme of the shield machine. Subway construction management application platform based on BIM and private cloud technology, the use of cameras and sensors to achieve electronic integration, dynamic monitoring of the operation and maintenance of underground facilities. Make full use of the many advantages of BIM technology to improve the engineering quality and construction efficiency of the subway rail transit project and to complete the operation and maintenance.

Toward Dynamic Resources Management for IoT-Based Manufacturing

The cyber-physical production system (CPPS), which combines information communication technology, cyberspace virtual technology, and intelligent equipment technology, is accelerating the path of Industry 4.0 to transform manufacturing from traditional to intelligent. The Industrial Internet of Things integrates the key technologies of industrial communication, computing, and control, and is providing a new way for a wide range of manufacturing resources to optimize management and dynamic scheduling. In this article, OLE for process control technology, software defined industrial network, and device-to-device communication technology are proposed to achieve efficient dynamic resource interaction. Additionally, the integration of ontology modeling with multiagent technology is introduced to achieve dynamic management of resources. We propose a load balancing mechanism based on Jena reasoning and Contract-Net Protocol technology that focuses on intelligent equipment in the smart factory. Dynamic resources management for IoT-based manufacturing provides a solution for complex resource allocation problems in current manufacturing scenarios, and provides a technical reference point for the implementation of intelligent manufacturing in Industry 4.0.

Logical Control of Complex Resource Allocation Systems

This document epitomizes the main points of a plenary presentation under the same title that was given by the author at the 14th Workshop on Discrete Event Systems (WODES 2018).

Twenty Questions for Every M&A: Improving Postmerger Integration Performance

This note is used in Darden's Postmerger Integration elective. The authors present a guide to help managers prepare for integration challenges and allocate integration resources more efficiently. Although simple at first glance, addressing 20 yes-or-no questions can lead to fruitful in-depth conversations that provide a sound foundation for successful PMI planning and execution. By answering these questions in open dialogue, PMI managers and senior executives will expose the potential weaknesses in an upcoming merger and highlight the arenas in which detailed planning is not only warranted but fundamental. Excerpt UVA-S-0226 Sept. 5, 2013 Twenty questions for every m&A: IMPROVING POSTMERGER INTEGRATION PERFORMANCE It is clear from the numbers that mergers and acquisitions (M&A) are difficult. By one estimate, about one-half fail to create shareholder value and around one-quarter actually destroy it. Yet despite this dismal track record, a 2011 McKinsey survey of senior executives noted that a majority of respondents still believe M&A remains necessary for growth. Much of the difficulty around acquisitions surely arises from the fact that every M&A is different, and most require a tailored, rigorous integration process to be successful. To price a deal right and successfully integrate the acquired business requires understanding what factors will make a given acquisition difficult and what resources the acquirer will need to tackle them. As the results show, many companies fail to do this successfully. Unfortunately, many of the firms that are considering M&A appear unprepared or inexperienced. In fact, a majority of the survey respondents noted that they do less than one acquisition a year, while just about one-half do not believe they have the required people or organizational resources to meet their M&A aspirations and only about one-third have dedicated integration teams. Thus it is not surprising that, since these integrations can require complex resource allocation decisions around how many employees, which specific employees, and what resources should be dedicated to the integration process, many fail to create value. . . .

Mitigating Herding in Hierarchical Crowdsourcing Networks.

Hierarchical crowdsourcing networks (HCNs) provide a useful mechanism for social mobilization. However, spontaneous evolution of the complex resource allocation dynamics can lead to undesirable herding behaviours in which a small group of reputable workers are overloaded while leaving other workers idle. Existing herding control mechanisms designed for typical crowdsourcing systems are not effective in HCNs. In order to bridge this gap, we investigate the herding dynamics in HCNs and propose a Lyapunov optimization based decision support approach - the Reputation-aware Task Sub-delegation approach with dynamic worker effort Pricing (RTS-P) - with objective functions aiming to achieve superlinear time-averaged collective productivity in an HCN. By considering the workers’ current reputation, workload, eagerness to work, and trust relationships, RTS-P provides a systematic approach to mitigate herding by helping workers make joint decisions on task sub-delegation, task acceptance, and effort pricing in a distributed manner. It is an individual-level decision support approach which results in the emergence of productive and robust collective patterns in HCNs. High resolution simulations demonstrate that RTS-P mitigates herding more effectively than state-of-the-art approaches.

Energy-Efficient Frequency and Power Allocation for Cognitive Radios in Television Systems

Energy-efficient resource allocation for cognitive radios operating in television systems (TV white spaces) presents a unique challenge compared with other cognitive radios because the interference constraint is for the whole frequency band rather than per carrier. This paper presents a subchannel and power allocation protocol that maximizes the energy efficiency (EE) of transmissions from a cognitive base station operating in the TV white spaces. The system model conforms to the IEEE 802.22 standard, and the proposed two-step solution to the EE maximization problem satisfies users' minimum rate requirements and keeps the interference to the primary users in the neighboring areas below a specified threshold. The first step of the protocol is a near-optimal but low-complexity subchannel assignment. This is followed by an optimal power allocation procedure that is obtained by analyzing the Karush–Kuhn–Tucker conditions. The computational complexity of the resulting resource allocation protocol is the same as that of the least complex resource allocation protocol for orthogonal frequency division multiple access (OFDMA) downlinks in the literature. Simulation results show that our protocol achieves higher EE compared with a modified and improved version of the OFDMA protocol from the literature.

Knowledge-Based Resource Allocation for Collaborative Simulation Development in a Multi-Tenant Cloud Computing Environment

Cloud computing technologies have enabled a new paradigm for advanced product development powered by the provision and subscription of computational services in a multi-tenant distributed simulation environment. The description of computational resources and their optimal allocation among tenants with different requirements holds the key to implementing effective software systems for such a paradigm. To address this issue, a systematic framework for monitoring, analyzing and improving system performance is proposed in this research. Specifically, a radial basis function neural network is established to transform simulation tasks with abstract descriptions into specific resource requirements in terms of their quantities and qualities. Additionally, a novel mathematical model is constructed to represent the complex resource allocation process in a multi-tenant computing environment by considering priority-based tenant satisfaction, total computational cost and multi-level load balance. To achieve optimal resource allocation, an improved multi-objective genetic alqorithm is proposed based on the elitist archive and the K -means approaches. As demonstrated in a case study, the proposed framework and methods can effectively support the cloud simulation paradigm and efficiently meet tenants’ computational requirements in a distributed environment.

Real-time management of complex resource allocation systems: Necessity, achievements and further challenges

Many contemporary applications, ranging from flexibly automated production systems, to automated material handling and intelligent transportation systems, to internet-based workflow management systems, and more recently, to the massively parallelized software systems that emerge in the context of the novel multi-core computing architectures, can be perceived as a set of finite resources that support a number of concurrently running processes. These processes execute in a staged manner and, at each stage, they vie for the allocation of various subsets of the system resources. To effectively support and manage the extensive levels of concurrency and operational flexibility that are contemplated for these environments, and the ensuing complexity, there is a substantial need for formal models and tools that will enable the modeling, analysis and eventually the control of the aforementioned resource allocation function so that the resulting dynamics are, both, behaviorally correct and operationally efficient. This article overviews a research program that seeks to address the aforementioned need by using the unifying abstraction of the resource allocation system (RAS) and supporting modeling frameworks, like automata, Petri nets, and Markov reward and decision processes, borrowed from the area of Discrete Event Systems (DES) theory. The presented results take advantage of the special structure that exists in the considered RAS classes, and they are characterized by their analytical rigor and computational tractability. The article also highlights the further challenges that must be addressed for the successful completion and promotion of the pursued framework.

Designing parsimonious scheduling policies for complex resource allocation systems through concurrency theory

In a recent work we have proposed a theoretical framework for developing optimized scheduling policies for complex resource allocation systems (RAS). This framework relies heavily on the expression of the RAS dynamics in the modeling framework of the Generalized Stochastic Petri Nets (GSPNs), and the employment of this GSPN-based representation towards the establishment of a systematic trade-off between the representational economy of the target scheduling policies and their operational efficiency. In this paper, we enhance the representational economy of the target policies in the aforementioned framework by taking advantage of some notions of "(non-)conflict" in the transitional dynamics of the underlying RAS-modeling GSPNs. A series of numerical experiments demonstrate that the representational gains resulting from the presented methodology can be very substantial.

A BDD-Based Approach for Designing Maximally Permissive Deadlock Avoidance Policies for Complex Resource Allocation Systems

In order to develop a computationally efficient implementation of the maximally permissive deadlock avoidance policy (DAP) for complex resource allocation systems (RAS), a recent approach focuses on the identification of a set of critical states of the underlying RAS state-space, referred to as minimal boundary unsafe states. The availability of this information enables an expedient one-step-lookahead scheme that prevents the RAS from reaching outside its safe region. The work presented in this paper seeks to develop a symbolic approach, based on binary decision diagrams (BDDs), for efficiently retrieving the (minimal) boundary unsafe states from the underlying RAS state-space. The presented results clearly demonstrate that symbolic computation enables the deployment of the maximally permissive DAP for complex RAS with very large structure and state-spaces with limited time and memory requirements. Furthermore, the involved computational costs are substantially reduced through the pertinent exploitation of the special structure that exists in the considered problem.

Modulation-Mode and Power Assignment in SVD-Assisted Broadband MIMO Systems using Polynomial Matrix Factorization

Singular-value decomposition (SVD)-based multiple-input multiple-output (MIMO) systems have attracted a lot of attention in the wireless community. However, applying SVD to frequency-selective MIMO channels results in unequally weighted single-input single-output (SISO) channels requiring complex resource allocation techniques for optimizing the channel performance. Therefore, a different approach utilizing polynomial matrix factorization for removing the MIMO interference is analyzed, outperforming conventional SVD-based MIMO systems in the analyzed channel scenario.

Real-Time Management of Complex Resource Allocation Systems: Necessity, Achievements and Further Challenges

Abstract: Many contemporary applications, ranging from flexibly automated production systems, to automated material handling and intelligent transportation systems, to internetbased workflow management systems, and more recently, to the massively parallelized software systems that emerge in the context of the novel multi-core computing architectures, can be perceived as a set of finite resources that support a number of concurrently running processes; these processes execute in a staged manner and vie for the allocation of various subsets of the system resources. To effectively support and manage the extensive levels of concurrency and operational flexibility that are contemplated for these environments, and the ensuing complexity, there is a substantial need for formal models and tools that will enable the modeling, analysis and eventually the control of the aforementioned resource allocation function so that the resulting dynamics are, both, behaviorally correct and operationally efficient. This write-up overviews a research program that seeks to address the aforementioned need by using the unifying abstraction of the resource allocation system (RAS) and supporting modeling frameworks, like automata, Petri nets, and Markov reward and decision processes, borrowed from the area of Discrete Event Systems (DES) theory. The presented results take advantage of the special structure that exists in the considered RAS classes, and are further characterized by, both, their analytical rigor and computational tractability. The write-up also highlights the further challenges that must be addressed for the successful completion and promotion of the pursued framework.

Efficient Enumeration of Minimal Unsafe States in Complex Resource Allocation Systems

An earlier work of ours has proposed a novel approach for the deployment of the maximally permissive deadlock avoidance policy for complex resource allocation systems (RAS), that is based on the identification and the efficient storage of a critical subset of states of the underlying RAS state space; the availability of this information enables an expedient one-step-lookahead scheme for the identification and blockage of transitions that will take the system behavior outside its safe region. This paper complements the aforementioned results by introducing a novel algorithm that provides those critical states while avoiding the complete enumeration of the RAS state space.

A Symbolic Approach for Maximally Permissive Deadlock Avoidance in Complex Resource Allocation Systems

To develop an efficient implementation of the maximally permissive deadlock avoidance policy (DAP) for complex resource allocation systems (RAS), a recent approach focuses on the identification of a set of critical states of the underlying RAS state-space, referred to as minimal boundary unsafe states. The availability of this information enables an expedient one-step-lookahead scheme that prevents the RAS from reaching outside its safe region. This paper presents a symbolic approach that provides those critical states. Furthermore, by taking advantage of certain structural properties regarding RAS safety, the presented method avoids the complete exploration of the underlying RAS state-space. Numerical experimentation demonstrates the efficiency of the approach for developing the maximally permissive DAP for complex RAS with large structure and state-spaces, and its potential advantage over similar approaches that employ more conventional representational and computational methods

Designing Optimal Deadlock Avoidance Policies for Sequential Resource Allocation Systems Through Classification Theory: Existence Results and Customized Algorithms

A recent line of work has sought the implementation of the maximally permissive deadlock avoidance policy (DAP) for a broad class of complex resource allocation systems (RAS) as a classifier that gives effective and parsimonious representation to the dichotomy of the underlying behavioral space into the admissible and inadmissible subspaces defined by that policy. The work presented in this paper complements the past developments in this area by providing 1) succinct conditions regarding the possibility of expressing the aforementioned classifier as a set of linear inequalities in the RAS state variables, and 2) an efficient customized algorithm for the synthesis of pertinent nonlinear classifiers that implement the target DAP with minimum run-time computational overhead, in the case that a linear-classifier-based representation of this policy is not possible.

SGSA: A SDMA–OFDMA greedy scheduling algorithm for WiMAX networks

Future wireless networks need to address the predicted growth in mobile traffic volume, expected to have an explosive growth in the next 5years mainly driven by video and web applications. Transmission schemes based on Orthogonal Frequency Division Multiple Access (OFDMA) combined with Space Division Multiple Access (SDMA) techniques are key promising technologies to increase current spectral efficiencies. A Joint SDMA–OFDMA system has to allocate resources in time, frequency and space dimensions to different mobile stations, resulting in a highly complex resource allocation problem. In contrast to related work approaches, in this paper we take a comprehensive view at the complete SDMA–OFDMA scheduling challenge and propose a SDMA–OFDMA Greedy Scheduling Algorithm (sGSA) for WiMAX systems. The proposed solution considers feasibility constraints in order to allocate resources for multiple mobile stations on a per packet basis by using (i) a low complexity SINR prediction algorithm, (ii) a cluster-based SDMA grouping algorithm and (iii) a computationally efficient frame layout scheme which allocates multiple SDMA groups per frame according to their packet QoS utility. A performance evaluation of the proposed sGSA solution as compared to state of the art solutions is provided, based on a comprehensive WiMAX simulation tool.

A Practical Approach for Maximally Permissive Liveness-Enforcing Supervision of Complex Resource Allocation Systems

Past works towards the effective deployment of the maximally permissive liveness-enforcing supervisor (LES) for sequential resource allocation systems (RAS) have been stalled by: (i) the NP-Hardness of the computation of this policy for the majority of the considered RAS classes and (ii) the inability of the adopted more compact representations of the underlying RAS dynamics to provide an effective representation of the target policy for all RAS instantiations. This paper proposes a novel approach to the aforementioned problem, that can be perceived as a two-stage process: The first stage computes the maximally permissive LES by employing an automaton-based representation of the RAS behavior and techniques borrowed from the Ramadge & Wonham (R&W) Supervisory Control framework. The second stage seeks the development of a more compact representation for the dichotomy into admissible and inadmissible-or “safe” and “unsafe”-subspaces of the RAS state space, that is effected by the LES developed in the first stage. This compact representation is obtained by: (i) taking advantage of certain properties of the underlying subspaces and (ii) the employment of pertinent data structures. The resulting approach is also “complete,” i.e., it will return an effectively implementable LES for any given RAS instantiation. Numerical experimentation demonstrates the efficacy of the approach and establishes its ability to support the deployment of maximally permissive LES for RAS with very large structure and state spaces.

Designing Compact and Maximally Permissive Deadlock Avoidance Policies for Complex Resource Allocation Systems Through Classification Theory: The Linear Case

Most of the past research on the problem of deadlock avoidance for complex resource allocation systems (RAS) has acknowledged the fact that the computation of the maximally permissive deadlock avoidance policy (DAP) possesses super-polynomial complexity for most RAS classes, and therefore, it has resorted to solutions that trade off maximal permissiveness for computational tractability. In this paper, we distinguish between the off-line and the on-line computation that is required for the effective implementation of the maximally permissive DAP, and we seek to develop representations of this policy that will require minimal on-line computation. The particular representation that we adopt is that of a compact classifier that will effect the underlying dichotomy of the reachable state space into safe and unsafe subspaces. Furthermore, in this first study of the aforementioned problem, we restrict our attention to a particular RAS class that is motivated by an ongoing project of ours called Gadara, and accepts separation of the safe and unsafe subspaces of its instantiations through a set of linear inequalities. Through a series of reductions of the derived classification problem, we are also able to attain extensive reductions in the computational complexity of the off-line task of the construction of the sought classifier. We formally establish completeness and optimality properties for the proposed design procedures. We also offer heuristics that, if necessary, can alleviate the computational effort that is necessary for the construction of the sought classifier. Finally, we demonstrate the efficacy of the developed approaches through a series of computational experiments. To the best of our knowledge, these experiments also establish the ability of the proposed methodology to effectively compute tractable implementations of the maximally permissive DAP for problem instances significantly beyond the capacity of any other approach currently available in the literature.

Active Exploration in Robust Unmanned Vehicle Task Assignment

This paper presents new formulations for the robust allocation of unmanned vehicles (UVs) in the presence of parametric uncertainty. Standard robust optimization approaches for hedging against the worst-case plan performance can lead to overly conservative plans. Onewaytoreducethisconservatismistoemployactiveexplorationtoreducetheuncertainty in the model parameters, but the UV literature does not address the coupling between uncertainty reduction and improvement in worst-case mission performance.This paper presents a new algorithm that assigns active exploration tasks that provide the most benefit in reducing the conservatism of the robust plans. This algorithm is extended to a more complex, receding horizon task assignment framework which has been validated in previous UV hardware demonstrations. We then show that this novel formulation can be formulated as an integer program and improves overall mission score when compared to alternative decoupled formulations. Finally, this paper shows that this coupled formulation demonstrates more tightly coordinated behavior between heterogeneous UVs in complex resource allocation problems.

On bidding algorithms for a distributed combinatorial auction

Combinatorial auctions CAs are a great way to solve complex resource allocation and coordination problems. However, CAs require a central auctioneer who receives the bids and solves the winner determination problem, an NP-hard problem. Unfortunately, a centralized auction is not a good fit for real world situations where the participants have proprietary interests that they wish to remain private or when it is difficult to establish a trusted auctioneer. The work presented here is motivated by the vision of distributed CAs; incentive compatible peer-to-peer mechanisms to solve the allocation problem, where bidders carry out the needed computation. For such a system to exist, both a protocol that distributes the computational task amongst the bidders and strategies for bidding behavior are needed. PAUSE is combinatorial auction mechanism that naturally distributes the computational load amongst the bidders, establishing the protocol or rules the participants must follow. However, it does not provide bidders with bidding strategies. This article revisits and reevaluates a set of bidding algorithms that represent different bidding strategies that bidders can use to engage in a PAUSE auction, presenting a study that analyzes them with respect to the number of goods, bids, and bidders. Results show that PAUSE, along with the aforementioned heuristic bidding algorithms, is a viable method for solving combinatorial allocation problems without a centralized auctioneer.