Approximate Dynamic Programming for Stochastic Resource Allocation Problems

A stochastic resource allocation model, based on the principles of Markov decision processes(MDPs), is proposed in this paper. In particular, a general-purpose framework is developed, which takes into account resource requests for both instant and future needs. The considered framework can handle two types of reservations(i.e., specified and unspecified time interval reservation requests), and implement an overbooking business strategy to further increase business revenues. The resulting dynamic pricing problems can be regarded as sequential decision-making problems under uncertainty, which is solved by means of stochastic dynamic programming(DP) based algorithms. In this regard, Bellman’s backward principle of optimality is exploited in order to provide all the implementation mechanisms for the proposed reservation pricing algorithm. The curse of dimensionality, as the inevitable issue of the DP both for instant resource requests and future resource reservations,occurs. In particular, an approximate dynamic programming(ADP) technique based on linear function approximations is applied to solve such scalability issues. Several examples are provided to show the effectiveness of the proposed approach.

Resource Planning and Allocation Problem Under Uncertain Environment

This paper generalizes the classic resource allocation problem to the resource planning and allocation problem, in which the resource itself is a decision variable and the cost of each activity is uncertain when the resource is determined. The authors formulate this problem as a two-stage stochastic programming. The authors first propose an efficient algorithm for the case with finite states. Then, a sudgradient method is proposed for the general case and it is shown that the simple algorithm for the unique state case can be used to compute the subgradient of the objective function. Numerical experiments are conducted to show the effectiveness of the model.

An Intelligent System for a Resource Allocation Problem Based on Fuzzy Reasoning

The development of the assistive abilities regarding the decision-making process o fan Intelligent Control System （ICS） like a fuzzy expert system implies the development of its functionality and its ability of specification. Fuzzy expert systems can model fuzzy controllers, i.e., the knowledge representation and the abilities of making decisions corresponding to fuzzy expert systems are much more complicated that in the case of standard fuzzy controllers. The expert system acts also as a supervisor, creating meta-level reasoning on a set of fuzzy controllers, in order to choose the best one for the management of the process. Knowledge Management Systems （KMSs） is a new development paradigm of Intelligent Systems which has resulted from a synergy between fuzzy sets, artificial neural networks, evolutionary computation, machine learning, etc., broadening computer science, physics, economics, engineering, mathematics. This paper presents, after a synergic new paradigm of intelligent systems, as a practical case study the fuzzy and temporal properties of knowledge formalism embedded in an ICS. We are not dealing high with level reasoning methods, because we think that real-time problems can only be solved by rather low-level reasoning. Solving the match-time predictability problem would allow us to build much more powerful reasoning techniques.

A COMPREHENSIVE DEA APPROACH FOR THE RESOURCE ALLOCATION PROBLEM BASED ON SCALE ECONOMIES CLASSIFICATION

This paper is concerned with the resource allocation problem based on data envelopment analysis （DEA） which is generally found in practice such as in public services and in production process. In management context, the resource allocation has to achieve the effective-efficient-equality aim and tries to balance the different desires of two management layers： central manager and each sector. In mathematical programming context, to solve the resource allocation asks for introducing many optimization techniques such as multiple-objective programming and goal programming. We construct an algorithm framework by using comprehensive DEA tools including CCR, BCC models, inverse DEA model, the most compromising common weights analysis model, and extra resource allocation algorithm. Returns to scale characteristic is put major place for analyzing DMUs＇ scale economies and used to select DMU candidates before resource allocation. By combining extra resource allocation algorithm with scale economies target, we propose a resource allocation solution, which can achieve the effective-efficient-equality target and also provide information for future resource allocation. Many numerical examples are discussed in this paper, which also verify our work.

IPSO-based hybrid approaches for reliability-redundancy allocation problems

The problem of maximizing system reliability through component reliability choices and component redundancy is called tell-ability-redundancy allocation problem （RAP）, and it is a difficult but realistic nonlinear mixed-integer optimization prob- lem. For the RAP. we pay attention to an improved particle swarm optimization （IPSO）, and introduce four hybrid approaches for combining the IPSO with other conventional search techniques, such as harmony search （HS） and LXPM （a real coded GA）. The basic structure of the hybrid approaches includes two phases. After devising an initial solution by the HS or LXPM technique in the first phase, the IPSO performs an optimal search in the next phase. In addition, a new procedure by using golden search, named GS, is developed for further improving the solutions obtained by IPSO. Consequently, four ISPO-based hybrid approaches are proposed including HS-IPSO, LXPM-IPSO, HS-IPSO-GS, and LXPM-IPSO-GS. In order to validate the per-formance of proposed approaches, five nonlinear mixed-integer RAPs are investigated where both the number of re- dundancy components and the corresponding component reliability in each subsystem are to be decided simultaneously. As shown, the proposed approaches are all superior in terms of both optimal solutions and robustness to those by IPSO. Especially the pro-posed LXPM-IPSO-GS has shown more excellent performance than other typical approaches in the literature.

Distributed stochastic mirror descent algorithm for resource allocation problem

In this paper,we consider a distributed resource allocation problem of minimizing a global convex function formed by a sum of local convex functions with coupling constraints.Based on neighbor communication and stochastic gradient,a distributed stochastic mirror descent algorithm is designed for the distributed resource allocation problem.Sublinear convergence to an optimal solution of the proposed algorithm is given when the second moments of the gradient noises are summable.A numerical example is also given to illustrate the effectiveness of the proposed algorithm.

Uncertain multiobjective redundancy allocation problem of repairable systems based on artificial bee colony algorithm

Based on the uncertainty theory, this paper is devoted to the redundancy allocation problem in repairable parallel-series systems with uncertain factors, where the failure rate, repair rate and other relative coefficients involved are considered as uncertain variables. The availability of the system and the corresponding designing cost are considered as two optimization objectives. A crisp multiobjective optimization formulation is presented on the basis of uncertainty theory to solve this resultant problem. For solving this problem efficiently, a new multiobjective artificial bee colony algorithm is proposed to search the Pareto efficient set, which introduces rank value and crowding distance in the greedy selection strategy, applies fast non-dominated sort procedure in the exploitation search and inserts tournament selection in the onlooker bee phase. It shows that the proposed algorithm outperforms NSGA-II greatly and can solve multiobjective redundancy allocation problem efficiently. Finally, a numerical example is provided to illustrate this approach.

A Two-Player Coalition Cooperative Scheme for the Bodyguard Allocation Problem

We address the bodyguard allocation problem （BAP）, an optimization problem that illustrates the conflict of interest between two classes of processes with contradictory preferences within a distributed system. While a class of processes prefers to minimize its distance to a particular process called the root, the other class prefers to maximize it; at the same time, all the processes seek to build a communication spanning tree with the maximum social welfare. The two state-of-the-art algorithms for this problem always guarantee the generation of a spanning tree that satisfies a condition of Nash equilibrium in the system; however, such a tree does not necessarily produce the maximum social welfare. In this paper, we propose a two-player coalition cooperative scheme for BAP, which allows some processes to perturb or break a Nash equilibrium to find another one with a better social welfare. By using this cooperative scheme, we propose a new algorithm called FFC-BAPs for BAP. We present both theoretical and empirical analyses which show that this algorithm produces better quality approximate solutions than former algorithms for BAP.

Distributed Communication-Sliding Mirror-Descent Algorithm for Nonsmooth Resource Allocation Problem

This paper considers a distributed nonsmooth resource allocation problem of minimizing a global convex function formed by a sum of local nonsmooth convex functions with coupled constraints.A distributed communication-efficient mirror-descent algorithm,which can reduce communication rounds between agents over the network,is designed for the distributed resource allocation problem.By employing communication-sliding methods,agents can find aε-solution in O(1/ε)communication rounds while maintaining O(1/ε^(2))subgradient evaluations for nonsmooth convex functions.A numerical example is also given to illustrate the effectiveness of the proposed algorithm.

Radio Network Planning and Optimization for 5G Telecommunication System Based on Physical Constraints

The paper mainly focuses on the network planning and optimization problem in the 5G telecommunication system based on the numerical investigation.There have been two portions of this work,such as network planning for efficient network models and optimization of power allocation in the 5G network.The radio network planning process has been completed based on a specific area.The data rate requirement can be solved by allowing the densification of the system by deploying small cells.The radio network planning scheme is the indispensable platform in arranging a wireless network that encounters convinced coverage method,capacity,and Quality of Service necessities.In this study,the eighty micro base stations and two-hundred mobile stations are deployed in the-15km×15km wide selected area in the Yangon downtown area.The optimization processes were also analyzed based on the source and destination nodes in the 5G network.The base stations’location is minimized and optimized in a selected geographical area with the linear programming technique and analyzed in this study.

Research on the Prediction of Quay Crane Resource Hour based on Ensemble Learning

In Container terminals,a quay crane’s resource hour is affected by various complex nonlinear factors,and it is not easy to make a forecast quickly and accurately.Most ports adopt the empirical estimation method at present,and most of the studies assumed that accurate quay crane’s resource hour could be obtained in advance.Through the ensemble learning(EL)method,the influence factors and correlation of quay crane’s resources hour were analyzed based on a large amount of historical data.A multi-factor ensemble learning estimation model based quay crane’s resource hour was established.Through a numerical example,it is finally found that Adaboost algorithm has the best effect of prediction,with an error of 1.5%.Through the example analysis,it comes to a conclusion:the error is 131.86%estimated by the experience method.It will lead that subsequent shipping cannot be serviced as scheduled,increasing the equipment wait time and preparation time,and generating additional cost and energy consumption.In contrast,the error based Adaboost learning estimation method is 12.72%.So Adaboost has better performance.

Efficient Multi-Tenant Virtual Machine Allocation in Cloud Data Centers

Virtual Machine（VM） allocation for multiple tenants is an important and challenging problem to provide efficient infrastructure services in cloud data centers. Tenants run applications on their allocated VMs, and the network distance between a tenant＇s VMs may considerably impact the tenant＇s Quality of Service（Qo S）. In this study, we define and formulate the multi-tenant VM allocation problem in cloud data centers, considering the VM requirements of different tenants, and introducing the allocation goal of minimizing the sum of the VMs＇ network diameters of all tenants. Then, we propose a Layered Progressive resource allocation algorithm for multi-tenant cloud data centers based on the Multiple Knapsack Problem（LP-MKP）. The LP-MKP algorithm uses a multi-stage layered progressive method for multi-tenant VM allocation and efficiently handles unprocessed tenants at each stage. This reduces resource fragmentation in cloud data centers, decreases the differences in the Qo S among tenants, and improves tenants＇ overall Qo S in cloud data centers. We perform experiments to evaluate the LP-MKP algorithm and demonstrate that it can provide significant gains over other allocation algorithms.

Evolutionary approach for spatial architecture layout design enhanced by an agent-based topology finding system

This paper presents a method for the automatic generation of a spatial architectural layout from a user-specified architectural program. The proposed approach binds a multi-agent topology finding system and an evolutionary optimization process. The former generates topology satisfied layouts for further optimization, while the latter focuses on refining the layouts to achieve predefined architectural criteria. The topology finding process narrows the search space and increases the performance in subsequent optimization. Results imply that the spatial layout modeling and the muLti-floor topology are handled.

IMPROVING SOLVER PERFORMANCE THROUGH REDUNDANCY

It is well known that hierarchies of mathematical programming formulatlons with different numbers of variables and constraints have a considerable impact regarding the quality of solutions obtained once these formulations are fed to a commercial solver. In addition, even if dimensions are kept the same, changes in formulations may largely influence solvability and quality of results. This becomes evident especially if redundant constraints are used. We propose a related framework for information collection based on these constraints. We exemplify by means of a well-known combinatorial optimization problem from the knapsack problem family, i.e., the multidimensional multiple-choice knapsack problem （MMKP）. This incorporates a relationship of the MMKP to some generalized set partitioning problems. Moreover, we investigate an application in maritime shipping and logistics by means of the dynamic berth allocation problem （DBAP）, where optimal solutions are reached from the root node within the solver.