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Smart device networks form the backbone of many Internet of Things (IoT) applications. The performance of these systems depended on many factors. This includes their ability to connect to each other and network latency. In this paper, two algorithms are discussed that build a Peer-to-peer (P2P) overlay network. The focus is on real-time end-to-end remote instrumentation for IoT applications. P2P overlay networks are used for various purposes from content distribution through to media sharing. In contrast to traditional P2P systems, IoT systems can be highly sensitive to network latency and require low bandwidth. The main contributions of this paper are two algorithms to create overlay networks that assign peer nodes to super-peer nodes. The network contains a large number of smart devices and the key aim is to minimize system wide network latency. Other constraints regarding the nodes ability include the ability to become a super-peer node such as connectivity and limited processing capacities of the smart devices are also considered in context of a P2P remote instrumentation system. Both algorithms create a 3-hop network where the latency is lowered as much as possible with the given set of nodes. The algorithms have been validated through simulation. [ABSTRACT FROM AUTHOR]

In intelligent warehouse, the problem of transporting goods in intelligent warehouse is becoming increasingly complex, and the traditional way of automatically guiding vehicles (AGVs) is inefficient, so automated robot systems are introduced into intelligent warehouses. In this paper, a task assignment model for robots is presented with the transportation problem of robots in intelligent warehouse as the research background. To solve the robot task assignment problem in intelligent warehouse, a novel Pareto-based multiobjective optimization algorithm (MOEA) is proposed, and the aggregation function is invoked to replace the crowding distance; the brain storm operator is used for crossover and mutation. Finally, the ability of the algorithm to solve the benchmark test problem suite and real-world problems is experimentally confirmed. [ABSTRACT FROM AUTHOR]

• Proposed model with harbor constraints can reduce the conflicts in harbor apron. • The model optimizes the efficiency of gate utilization while ensuring safety. • A better solution is obtained by an improved branch-and-price method. • Experiments are designed to verify the value of harbor safety constraints. The airport gate assignment problem is the main problem in airport operation management. Most existing studies on the airport gate assignment problem focus more on the improvement of gate utilization efficiency and ignore apron operation safety. Aiming at addressing this problem, an airport gate assignment problem with harbor safety constraints is proposed in this paper. A two-phase mathematical optimization model is constructed to optimize the efficiency of gate utilization considering the safety constraints in harbors. To the best of our knowledge, this is the first study to explicitly focus on harbor apron security in the gate assignment problem. Then, the exact branch-and-price method is improved by incorporating a label-based pricing algorithm and two acceleration strategies. These strategies include an upper bound prediction strategy for handling the large number of subproblems and a lower parameter symmetry elimination strategy to overcome column generation degradation. The results show that the proposed gate assignment model and improved branch-and-price method optimize the efficiency of gate utilization under the condition of avoiding security conflicts in harbor aprons. The improved branch-and-price method has advantages in terms of both accuracy and efficiency compared with those of other solvers. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a model predictive control (MPC) approach to the periodic implementation of the optimal solutions of a class of resource allocation problems in which the allocation requirements and conditions repeat periodically over time. This special class of resource allocation problems includes many practical energy optimization problems such as load scheduling and generation dispatch. The convergence and robustness of the MPC algorithm is proved by invoking results from convex optimization. To illustrate the practical applications of the MPC algorithm, the energy optimization of a water pumping system is studied. [ABSTRACT FROM AUTHOR]

Abstract: In the modern battlefields smart weapons inherently rely on the sensors. The benefit of assigning a given weapon to a target often depends on the pre-assigned sensor. In this paper we present an efficient algorithm to optimally assign sensors and weapons to targets. This algorithm is derived from the well-known auction algorithm, and it is named as Swt-opt. We prove that Swt-opt converges to an optimal solution. [Copyright &y& Elsevier]

Abstract: This paper develops an optimal solution procedure for the multi-period online fulfillment assignment problem to determine how many and which of a retailer/e-tailer’s capacitated regional warehouse locations should be set up to handle online sales over a finite planning horizon. To reduce the number of candidate solutions in each period, dominance rules from the facility location literature are extended to handle the nonlinear holding and backorder cost implications of our problem. Computational results indicate that multi-period considerations can play a major role in determining the optimal set of online fulfillment locations. In 92% of our test problems, the multi-period solution incorporated fewer openings and closings than myopic single period solutions. To illustrate the use of the model under changing demands, the multi-period solution yielded different supply chain configurations than the myopic single period solution in over 37% of the periods. [Copyright &y& Elsevier]

Abstract: In this paper we study optimization problems with verifiable one-parameter selfish agents introduced by Auletta et al. [V. Auletta, R. De Prisco, P. Penna, P. Persiano, The power of verification for one-parameter agents, in: Proceedings of the 31st International Colloquium on Automata, Languages and Programming, ICALP, in: LNCS, vol. 3142, 2004, pp. 171–182]. Our goal is to allocate load among the agents, provided that the secret data of each agent is a single positive real number: the cost they incur per unit load. In such a setting the payment is given after the load completion, therefore if a positive load is assigned to an agent, we are able to verify if the agent declared to be faster than she actually is. We design truthful mechanisms when the agents’ type sets are upper-bounded by a finite value. We provide a truthful mechanism that is -approximate if the underlying algorithm is -approximate and weakly-monotone. Moreover, if type sets are also discrete, we provide a truthful mechanism preserving the approximation ratio of its algorithmic part. Our results improve the existing ones which provide truthful mechanisms dealing only with finite type sets and do not preserve the approximation ratio of the underlying algorithm. Finally, we give applications for our payment schemes. Firstly, we give a full characterization of the problem by using our techniques. Even if our payment schemes need upper-bounded type sets, every instance of can be “mapped” into an instance with upper-bounded type sets preserving the approximation ratio. In conclusion, we turn our attention to binary demand games. In particular, we show that the Minimum Radius Spanning Tree admits an exact truthful mechanism with verification achieving time (and space) complexity of the fastest centralized algorithm for it. This contrasts with a recent truthful mechanism for the same problem [G. Proietti, P. Widmayer, A truthful mechanism for the non-utilitarian minimum radius spanning tree problem, in: Proceedings of the 17th ACM Symposium on Parallelism in Algorithms and Architectures, SPAA, ACM Press, 2005, pp. 195–202] which pays a linear factor with respect to the complexity of the fastest centralized algorithm. Such a result is extended to several binary demand games studied in literature. [Copyright &y& Elsevier]

Abstract: The optimization of parallel applications is difficult to achieve by classical optimization techniques because of their diversity and the variety of actual parallel and distributed platforms and/or environments. Adaptive algorithmic schemes, capable of dynamically changing the allocation of jobs during the execution to optimize global system behavior, are the best alternatives for solving this problem. In this paper, we focus on non-clairvoyant scheduling of parallel jobs with known resource requirements but unknown running times, with emphasis on the regulation of idle periods in the context of general list policies. We consider a new family of scheduling strategies based on two phases which successively combine sequential and parallel execution of jobs. We generalize known worst-case performance bounds by considering two extra parameters, in addition to the number of processors and maximum processor requirements considered in the literature, namely, job parallelization penalty and idle regulation factor. Furthermore, we prove that under certain conditions of idle regulation, the performance guarantee of parallel job scheduling in space-sharing mode can be improved. [Copyright &y& Elsevier]

Application-layer peer-to-peer (P2P) networks are considered to be the most important development for next generation Internet infra-structure. For these systems to be effective, load balancing among the peers is critical. Most structured P2P systems rely on ID-space partitioning schemes to solve the load imbalance problem, and has been known to result in an imbalance factor of Θ(log N) in the zone sizes. This paper makes two contributions. First, we propose to address the virtual server-based load balancing problem systematically using an optimization based approach, and derived an effective algorithm to re-arrange loads among the peers. We demonstrate the superior performance of our proposal in general, and its advantages over previous strategies in particular. We also explore other important issues vital to the performance in the virtual server framework, such as the effect of the number of directories employed in the system, and the performance ramification of user registration strategies. Secondly, and perhaps more significantly, we characterize systematically the effect of heterogeneity on load balancing algorithm performance, and the conditions in which heterogeneity may be easy or hard to deal with based on extensive study of a wide spectrum of load and capacity scenarios. [ABSTRACT FROM AUTHOR]

This paper studies some possible combinations of the best features of the quasi-Newton symmetric rank-one (SR1), BFGS and extra updating BFGS algorithms for solving nonlinear unconstrained optimization problems. These combinations depend on switching between the BFGS and SR1 updates so that certain desirable properties are imposed. The presented numerical results show that the proposed switching algorithm outperforms the robust BFGS method. [ABSTRACT FROM AUTHOR]

This paper deals with the location of service facilities on a transportation network where the closest (in distance, time or cost) facility is known not to service some significant portion of demand. The concept of vector assignment of demand nodes to facilities is introduced to account for non closest facility service. A generalized formulation of the p-median problem incorporating vector assignment is presented. It is shown that an optimal solution to this vector assignment p-median problem exists which consists entirely of nodes of the graph; however, there may be more than one facility per node. Three different solution procedures are discussed for the vector assignment p-median problem. Computational experiments for three different moderately sized data sets give encouraging results. A locational example is included which gives a comparison of an optimal p-median solution and an optimal p-median solution with vector assignment. [ABSTRACT FROM AUTHOR]

The combined distribution and assignment problem has been studied by many authors. The problems examined have been of different types. Some authors focus their attention on the user optimized combined distribution and assignment problem whereas others study a system optimized version. Apart from this difference there is also a difference in the way the distribution part and the assignment part are incorporated into the models. In this paper we study a combined distribution and assignment problem with entropy objective and cost constraints and give an algorithm based on Benders decomposition for its solution. The resulting solution method will be a "dual" method. [ABSTRACT FROM AUTHOR]

The article presents O(N log N) algorithm for the rectilinear round-trip location problem. It refers to papers by authors A.W. Chan and D.W. Hearn, where the problem definition is given and by authors T. Ichimori and T. Nishida, where it is shown that this problem can be solved in O(N log N) time.