PRAM processor allocation: a hidden bottleneck in sublogarithmic algorithms

In response to furious competition from emerging online retailers, traditional supermarkets begin leveraging their brick-and-mortar store chains by setting up small in-store front warehouses to facilitate order picking processes for online orders. In this article, we investigate how to select products and allocate space for the in-store front warehouse under dynamic and static strategies to minimize the total order-picking workload. For the dynamic allocation problem, we formulate it as a mixed-integer programming (MIP) model, which can be decomposed into a master multichoice knapsack problem (MCKP) and an order picking problem (OPP) as subproblems. An exact algorithm and a heuristic that is 1/2-bounded under certain routing policies are proposed. For the static allocation problem, we construct a stochastic gradient estimator of the expected travel distance by infinitesimal perturbation analysis (IPA) and introduce a discrete Frank-Wolfe algorithm to solve it. Numerical results reveal that the logic of dynamic allocation for the in-store front warehouse is different from that for regular warehouses, and the storage location assignment for the shelf area in supermarkets greatly impact the performance of these allocation strategies. The experimental results using data from a real retailer validate the effectiveness of proposed algorithms.

Worldwide, Internet of Things (IoT) devices will surpass a range of five billion by 2025 and developed countries will extend to advance by supplying almost two-thirds of such connections. With existing infrastructure, allocating bandwidth to billions of IoT devices is going to be cumbersome. This paper addresses the problem of Dynamic bandwidth allocation in IoT devices. We enhanced the dynamic bandwidth allocation algorithms to support QoS in different bandwidth ranges. Our Proposed innovative Machine learning-based Intelligent Dynamic Bandwidth Allocation (IDBA) algorithm allocates the bandwidth effectively between IoT devices based on utilization patterns observed through machine learning methods. Moreover, we showed that an IDBA algorithm results in supporting quality of service in terms of ensuring uninterrupted bandwidth to critical IoT application where bandwidth tolerance is zero percent, along with that IDBA increasing the network throughput correlated to other dynamic bandwidth allocation algorithms. We demonstrate simulations in different applications. The results show that IDBA achieves better throughput even in low bandwidth range.

This research deals with the dynamic and continuous berth allocation problem (DCBAP) in which both arrived and incoming ships are considered and a quay is used as a continuous line to accommodate as many ships as possible at one time. The DCBAP is solved by a two-stage procedure. In the first stage a heuristic/metaheuristic is used to generate alternative ship placement sequences while in the second stage a specific heuristic is employed to place ships and resolve overlaps of ships for the development of a feasible solution. Different methods, including FCFS (First Come First Served), SFLA (Shuffled Frog-Leaping Algorithm), and ISFLA (Improved Shuffled Frog-Leaping Algorithm), were employed in the first stage for comparison. The experimental results show that the ISFLA outperforms the others in terms of solution quality, implying that the ISFLA has the potential to deal with the DCBAP in a container terminal.

A discrete time optimal control model with uncertainty for dynamic machine allocation problem and its application to manufacturing and construction industries

WDM Network Issues: QOT Impairments, Dynamic Bandwidth Allocation, Modeling and Monitoring Schemes based on ML

Honey bee social foraging algorithms for resource allocation: Theory and application

Distributed processing is an effective way to improve performance of database systems. Therefore, fragmentation and proper allocation of fragments across various sites of the network is considered as a key research area in distributed database environment. However, the issue of allocating fragments to the most appropriate sites is not an easy task to perform. In this paper, a synchronized horizontal fragmentation, replication and allocation model in the context of relational database is proposed. A heuristic technique to satisfy horizontal fragmentation and allocation using a cost model to minimize the total cost of distribution is developed. Experimental results are consistent with the hypothesis confirming that the proposed model can efficiently solve dynamic fragmentation and allocation problem in a distributed relational database systems.

This paper discusses the problem of dynamic internet protocol address allocation with new virtual server configuration method. With the help of virtual machine system, dynamic domain name system resolution software (such as PeanutHull) and internet information service system components, a virtual server which embodies the function of allocating dynamic internet protocol address is built via a series of software configuration. The practice shows that active server pages could be connected to local and external network in conducting experiments with the well-designed virtual server, which has realized the goal of dynamic allocation and expansion of internet protocol address as well as solving the potential problem of internet protocol address in short supply around the corner.

This article introduces a novel heuristic algorithm based on Non-Linear Threshold Accepting Function to solve the challenging non-convex economic dispatch problem. Economic dispatch is a power system management tool; it is used to allocate the total power generation to the generating units to meet the active load demand. The power systems are highly nonlinear due to the physical and operational constraints. The complexity of the resulting non-convex objective cost function led to inabilities to solve the problem by using analytical approaches, especially in the case of large-scale problems. Optimization techniques based on heuristics are used to overcome these difficulties. The Non-Linear Threshold Accepting Algorithm has demonstrated efficiency in solving various instances of static and dynamic allocation and scheduling problems but has never been applied to solve the economic dispatch problem. Existing benchmark systems are used to evaluate the performance of the proposed heuristic. Additional random instances with different sizes are generated to compare the adopted heuristic to the Harmony Search and the Whale Optimization Algorithms. The obtained results showed the superiority of the proposed algorithm in finding, for all considered instances, a high-quality solution in minimum computational time.

This paper presents a distributed reinforcement learning solution to the problem of dynamic channel allocation for cellular telecommunication networks in the presence of mobile call handoffs. The performance of various dynamic channel allocation schemes are compared via extensive computer simulations, and it is shown that a reduced-state SARSA reinforcement learning algorithm can achieve superior new call and handoff blocking probabilities. A new distributed reduced state SARSA algorithm is also developed which uses only local environment information readily available to the learning agent. By way of computer simulations, the distributed SARSA algorithm is shown to be capable of producing call blocking probabilities that are comparable to those obtained by the centralised learning agent.

Dynamic logistics control of a perishable commodity is especially crucial due to the difficulties in preservation of freshness, disposal of deteriorated commodities, and uncertainty of supply caused by seasonal fluctuation or abrupt variation of the weather. First, this paper formally presents the Dynamic Allocation Problem with Uncertain Supply (DAP/US) for the perishable commodity supply chain (PC-SC). The objectives of the DAP/US problem are to maximize the total net profit of the strategic alliance of the PC-SC and to determine the optimal orders placed to suppliers and the resultant amount of perishable commodities allocated to retailers. Secondly, a two-stage extended-Genetic Algorithm (eGA) is developed to control the dynamic orders and allocation quantities to prioritized suppliers and retailers, respectively. Thirdly, simulation experiments are conducted and it is shown that eGA demonstrates promising performance under various sizes of problem domains and different statuses of supply uncertainty. Lastly, analytical simulations are also conducted to compare eGA with the traditional approach that ignores the differences among suppliers' supply uncertainties. The simulation results show that eGA achieves great improvement in both the net profit and shortage rates for all sizes of the PC-SCs, with half of their suppliers and retailers having high supply capabilities and fast turnover demands, respectively.

We construct and analyze some stochastic models for optimal allocation of memory in fragmented arenas. The arenas, as well as the item types they carry, are of various shapes: linear, cubic, pentagonal pie, S2 (surface of sphere). Formulated as a Markov dynamic programming problem, the choice of optimal memory allocation closely resembles the choice of an optimal route for a telephone call. Much can be guessed about optimal operation from scrutiny of the partial ordering of the possible states reduced under symmetries; especially, in many specific examples, a natural relation B (read “better than ”) of preference can be defined among alternative allocations, and a topological fixed-point argument based on B given, to solve the allocation problem without resort to numerical solution of the Bellman equation.

In many allocation problems, understanding individual agents' needs, wants, and tradeoffs is crucial for providing fair and efficient solutions. This paper begins with motivating applications and critical definitions. We review existing results, such as advising agents on relaxing restrictions for improved resource allocation, optimizing task allocation in online settings without rejection of a task, and more. We conclude by outlining three potential directions for future research.

The design of logistic distribution systems is one of the most critical and strategic issues in industrial facility management. The aim of this study is to develop and apply innovative mixed integer programming optimization models to design and manage dynamic (i.e. multi-period) multi-stage and multi-commodity location allocation problems (LAP). LAP belong to the NP-hard complexity class of decision problems, and the generic occurrence requires simultaneous determination of the number of logistic facilities (e.g. production plants, warehousing systems, distribution centres), their locations, and assignment of customer demand to them. The proposed models use a mixed integer linear programming solver to find solutions in complex industrial applications even when several entities are involved (production plants, distribution centres, customers, etc.). Lastly, the application of the proposed models to a significant case study is presented and discussed.

Dynamic storage allocation with limited compaction - complexity and some practical implications