Fairness by design in shared-energy allocation problems

In this paper, we address the dependent joint bit allocation problem in H.264/AVC statistical multiplexing. In most existing methods, to improve the overall visual quality, the bit allocation is based upon the instantaneous relative frame complexity of different video programs. However, due to the temporal prediction employed in H.264, the influence of a frame on the rate-distortion characteristics of the future frames should be taken into account as well. In this paper, we use our previously proposed simple, but accurate, inter-frame dependency model (IFDM) to quantitatively measure the coding dependency between the current frame and its reference frame. Based on the IFDM, we formulate the dependent joint bit allocation problem, considering both the inter-program relative frame complexity and intra-program coding dependency. We prove that the dependent joint bit allocation (DeJoBA) problem can actually be formulated and relaxed into a convex optimization problem, which can be optimally and efficiently solved. Experimental results suggest that the proposed DeJoBA method can achieve 36.81% and 13.11% bitrate reduction, on average, compared with the equal bit allocation and optimal independent joint bit allocation methods, respectively.

To exploit large deviation approximations for allocation and occupancy problems one must solve a deterministic optimal control problem (or equivalently, a calculus of variations problem). As this paper demonstrates, and in sharp contrast to the great majority of large deviation problems for processes with state dependence, for allocation problems one can construct more or less explicit solutions. Two classes of allocation problems are studied. The first class considers objects of a single type with a parameterized family of placement probabilities. The second class considers only equally likely placement probabilities but allows for more than one type of object. In both cases, we identify the Hamilton–Jacobi–Bellman equation, whose solution characterizes the minimal cost, explicitly construct solutions, and identify the minimizing trajectories. The explicit construction is possible because of the very tractable properties of the relative entropy function with respect to optimization.

Integrated Laycan and Berth Allocation and time-invariant Quay Crane Assignment Problem in tidal ports with multiple quays

Aim: The transtheoretical model is often used in substance abuse treatment planning. For polydrug abusing patients, operationalizing the stages of change is more difficult, as their readiness to change may differ depending on the substances. It was the aim of this study to investigate if the same structure of the Dutch Readiness to Change Questionnaire (RCQ-D) in a group of alcohol abusers could be found in an inpatient group of polydrug abusers for all substances.Design: For each substance, the structure of RCQ-D was tested using factor-analysis. Internal consistency was evaluated with Cronbach's alpha. Mean scores were calculated to evaluate conformity with stage assignment. Around 305 polydrug abusers completed 1248 RCQ-D during their first week of stay.Findings: The RCQ-D had a different, but interpretable structure from the expected one. A two-factor structure was found for alcohol, nicotine and opiates, a three-factor structure for benzodiazepines, cannabis, methadone and cocaine. The underlying construct, based on the transtheoretical model, seemed to fit for alcohol and nicotine. There were some problems of stage allocation for benzodiazepines, cannabis and methadone. But, people in action could be distinguished from those not in action. More serious problems of allocation were found for opiates and cocaine.Conclusions: RCQ-D can be used to measure polydrug abusers’ thinking and use-behaviour separately by substance. The validity of RCQ-D to assign stages in case of opiates and cocaine is unclear.

In recent years, object oriented (OO) models have emerged as preferred data models for a wide range of application domains. This is primarily due to the capability of the OO-models to manage and represent information of any arbitrary complexity. Distributed Objectbase Systems (DOBS) combines the benefit of distributed processing and the power of manipulation and abstraction of complex information to provide a powerful environment for distributed computing that is not available in conventional distributed database systems. Application performance and processing cost in a DOBS are greatly influenced by communication overhead involved in accessing nonlocal data and making remote method invocations. Efficient distribution design is mandatory to ensure optimal performance of the distributed system at minimum cost. Distribution design in DOBS involves fragmentation of the objectbase and allocation of the resulting class fragments between the nodes of the network.We adopt a top-down approach for the distribution design for a DOBS and assume that the global conceptual schema is partitioned into a set of class fragments. We focus our attention to optimal allocation of the fragments between the sites of the network subject to a set of constraints. Our entity of allocation is a class fragment. The allocation scheme defines the local conceptual schema at every site.The problem of allocation has been addressed for distributed file systems (DFS) and distributed (relational) database systems (DDBS). The additional complexity introduced by the object model has only recently been investigated with no result appearing that address the specific problem of allocation. This paper addresses the problem of object allocation in DOBS. A general allocation taxonomy is defined based on data model, degree of redundancy and design objective, and different allocation models are classified based on this taxonomy. An allocation model for DOBS is formulated and an algorithm is presented for static nonredundant allocation in DOBS.KeywordsAllocation SchemeAllocation ProblemCommunication CostAllocation ModelInitial AllocationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

There are several placement machines connected by a conveyor in a printed circuit board assembly line. The objective of the line balancing problem is to minimize the cycle time of the assembly line, which is the maximum production time of the placement machines. In this paper, the nozzle factor, which is often ignored, is considered in estimating the production time of the placement machine, and the nozzle change is also allowed. The production time of a machine is a linear function of the number of components, the number of turns and the number of nozzle changes performed by the machine, which are determined by the component allocation problem, the nozzle set allocation problem and the head allocation problem. These three allocation problems compose the line balancing problem and are solved iteratively. First, the component allocation problem is solved by proposed genetic algorithms (GAs), which generate feasible allocation solutions directly. To search efficiently, non-selective and selective allocation strategies are proposed to solve the component allocation problem. A greedy heuristic (GH) is proposed to solve the nozzle set allocation problem and the head allocation problem simultaneously. Then, the GAs for the component allocation and the GH for the nozzle set and head allocation are integrated according to their interactive relations. Finally, the efficiency of the composite algorithm is illustrated by numerical analysis.

A heterogeneous network (HetNet) can actively utilize the spectrum reuse with low power consumption, and thus is promising for the next-generation cellular networks. However, there are some technical challenges to be overcome in order for HetNets to be practical, and we address the following two in this paper. One is how to formulate the association and resource scheduling problem in a way that an optimal solution can be found in a reasonable amount of time, and the other is how to accommodate varying users’ demand. In order to minimize the power consumption and to satisfy varying users’ quality of service requirements, we propose a low-complex, distributed association and resource allocation scheme. By taking a cost-based approach, we first separate a nonconvex joint association and resource allocation problem into two subproblems. The channel allocation and base station assignment problem is then relaxed so that the problem becomes tractable. For the power allocation problem, we introduce a low-complex iterative algorithm by using the decomposition theory. The evaluation results show that the proposed solution can maintain the overall power consumption minimized while satisfying the QoS requirements.

Many resource allocation issues, such as land use- or irrigation planning, require input from extensive spatial databases and involve complex decision-making problems. Recent developments in this field focus on the design of allocation plans that utilize mathematical optimization techniques. These techniques, often referred to as multi criteria decision-making (MCDM) techniques, run into numerical problems when faced with the high dimensionality encountered in spatial applications. In this paper, it is demonstrated how both Simulated annealing, a heuristic algorithm, and Goal Programming techniques can be used to solve high-dimensional optimization problems for multi-site land use allocation (MLUA) problems. The optimization models both minimize development costs and maximize spatial compactness of the allocated land use. The method is applied to a case study in The Netherlands.

In this paper, we propose a computationally efficient method to solve the large dimension Adaptive Subcarrier Assignment and Bit Allocation (ASABA) problem of multiuser orthogonal frequency division multiplexing system. Our algorithm consists of three Ordinal Optimization (OO) stages to find a good enough solution to the considered problem. First of all, we reformulate the considered problem to separate it into subcarrier assignment and bit allocation problem such that the objective function of a feasible subcarrier assignment pattern is the corresponding optimal bit allocation for minimizing the total consumed power. Then in the first stage, we develop an approximate objective function to evaluate the performance of a subcarrier assignment pattern and use a genetic algorithm to search through the huge solution space and select s best subcarrier assignment patterns based on the approximate objective values. In the second stage, we employ an off-line trained artificial neural network to estimate the objective values of the s subcarrier assignment patterns obtained in stage 1 and select the l best patterns. In the third stage, we use the exact objective function to evaluate the l subcarrier assignment patterns obtained in stage 2, and the best one associated with the corresponding optimal bit allocation is the good enough solution that we seek. We apply our algorithm to numerous cases of large-dimension ASABA problems and compare the results with those obtained by four existing algorithms. The test results show that our algorithm is the best in both aspects of solution quality and computational efficiency.

The goal of delivering high-quality service has spurred research of 6G satellite communication networks. The limited resource-allocation problem has been addressed by next-generation satellite communication networks, especially multilayer networks with multiple low-Earth-orbit (LEO) and non-low-Earth-orbit (NLEO) satellites. In this study, the resource-allocation problem of a multilayer satellite network consisting of one NLEO and multiple LEO satellites is solved. The NLEO satellite is the authorized user of spectrum resources and the LEO satellites are unauthorized users. The resource allocation and dynamic pricing problems are combined, and a dynamic game-based resource pricing and allocation model is proposed to maximize the market advantage of LEO satellites and reduce interference between LEO and NLEO satellites. In the proposed model, the resource price is formulated as the dynamic state of the LEO satellites, using the resource allocation strategy as the control variable. Based on the proposed dynamic game model, an open-loop Nash equilibrium is analyzed, and an algorithm is proposed for the resource pricing and allocation problem. Numerical simulations validate the model and algorithm.

In this paper, an optimal allocation problem (APQ) with a quadratic objective function, a total resource constraint and an upper and lower bound constraint is considered. The APQ is a very basic and simple model but it can serve as a sub problem in the solution of the generalized allocation problem. Applying the Lagrange relaxation method, an explicit expression of the dual function associated with the APQ and an equation which the optimal dual variable must satisfy are derived first. Then, some properties of the equation are discussed. Finally, three algortihrns for solving the equation are proposed, and some computational results for the APQ are given. These results reveal the effectiveness of the algorithm. The APQ is a very basic and simple model but it can serve as a subproblem in the solution of the generalized allocation or transportation problems which have quadratic objective functions. From the standpoint of the mathematical programming theory. the APQ is a strictly convex separable programming problem and is a special class of quadratic programming problem: Therefore. its global optimality is guaranteed.

PurposeThe purpose of this paper is to design a parking space management platform to alleviate the parking problem and a two-stage solution for sharing and allocating parking spaces.Design/methodology/approachThe market design mechanism and auction mechanism are integrated to solve the problem of parking space sharing and allocation. In the first stage, the market design mechanism with two rules is applied for making the good use of idle parking spaces. In the second stage, two sequential auction mechanisms are designed by extending first/second-price sealed bid auction mechanism to allocate both private and public parking spaces, which are received in previous stage and owned by the platform. Two stages are connected through a forecasted price which is calculated through the exponential smoothing method.FindingsFirst, we prove three important properties of the proposed sequential auction mechanisms, namely, incentive compatibility, revenue equivalence and individual rationality. Second, a simulation study is used to verify the effectiveness of the mechanisms through numerical analysis. The impact of the system on three parts, namely, agents (private parking space suppliers), bidders (parking space customers) and the platform, is examined. Third, the results show that the sharing mechanism with monetrary incentive will attract a number of agents to join in the platform. The bidders are also able to obtain considerable utility, as compared with the (average) market parking fees. The platform can thus effectively allocate parking spaces with reasonable prices.Originality/valueThis paper combines the classical sequential auction mechanisms with the market design mechanism for the parking space sharing and allocation problem. The modeling and analysis method can also be used to address the similar allocation and pricing problems of other resources like bicycle sharing.

Elastic optical networking (EON) with space-division multiplexing (SDM) is the only evident long-term solution to the capacity needs of the future networks. The introduction of space via spatial fibers, such as multicore fibers (MCF) to EON provides an additional dimension as well as challenges to the network planning and resource optimization problem. There are various types of technologies for SDM transmission medium, switching, and amplification; each of them induces different capabilities and constraints on the network. For example, employing MCF as the transmission medium for SDM mitigates the spectrum continuity constraint of the routing and spectrum allocation problem for EON. In fact, cores can be switched freely on different links during routing of the network traffic. On the other hand, intercore crosstalk should be taken into account while solving the resource allocation problem. In the framework of switching for elastic SDM network, the programmable architecture on demand (AoD) node (optical white box) can provide a more scalable solution with respect to the hard-wired reconfigurable optical add/drop multiplexers (ROADMs) (optical black box). This study looks into the routing, modulation, spectrum, and core allocation (RMSCA) problem for weakly-coupled MCF-based elastic SDM networks implemented through AoDs and static ROADMs. The proposed RMSCA strategies integrate the spectrum resource allocation, switching resource deployment, and physical layer impairment in terms of intercore crosstalk through a multiobjective cost function. The presented strategies perform a cross-layer optimization between the network and physical layers to compute the actual intercore crosstalk for the candidate resource solutions and are specifically tailored to fit the type of optical node deployed in the network. The aim of all these strategies is to jointly optimize the switching and spectrum resource efficiency when provisioning demands with diverse capacity requirements. Extensive simulation results demonstrate that 1) by exploiting the dense intranodal connectivity of the ROADM-based SDM network, resource efficiency and provisioned traffic volume improve significantly related to the AoD-based solution, 2) the intercore crosstalk aware strategies improve substantially the provisioned traffic volume for the AoD-based SDM network, and 3) the switching modules grows very gently for the network designed with AoD nodes related to the one with ROADMs as the traffic increases, qualifying AoD as a scalable and cost-efficient choice for future SDM networks.

This paper presents a solution framework for the student project allocation (SPA) problem which is based on evolutionary algorithms (EAs), a biologically inspired stochastic, population-based search paradigm. Project-based assessment is a common component of engineering courses that are conducted in universities around the world. In their final year of study, a list of projects is made available by the academic staff and students are required to select a specific number of options from this list. The department then assigns a suitable project to each student such that preferred projects can be allocated to as many students as possible. While student interest is the primary criteria, several additional factors need to be considered such as project prerequisites, load balancing of staff commitments, and other specific university requirements. The allocation problem can therefore be seen as a complex multiobjective problem with multiple constraints. The EA-based project allocation system was recently developed and implemented in a large university department to automate this process, and to improve the matching of students to their desired projects. The solution which provides the highest level of satisfaction in meeting the varied objectives is then used to allocate projects to students. This new automated system is not only able to achieve a very high level of user satisfaction, but is also able to do so in a very short time, resulting in significant time savings.

In response to bandwidth-hungry applications, the field of Elastic Optical Networks (EON) has emerged. In this paper we investigate the provisioning and allocation problem in EONs, which presents a number of new and exciting challenges not otherwise present in optical networking solutions. We propose a novel technique for solving the Routing and Spectrum Allocation (RSA) problem which improves resource allocation efficiency for scheduled demands. This approach, called Delayed Spectrum Allocation (DSA) promises network resources to each demand upon request for scheduling, but delays the selection and allocation of those specific resources until immediately before transmission begins. Through simulation, we are able to conclude that DSA improves resource consumption efficiency and lowers call blocking.1