Capacity Planning Method Research Articles

Novel procedure to formulate load profiles for off-grid rural areas

In this paper, we describe the development, implementation and application of a novel mathematical procedure devoted to formulating the daily load profiles of off-grid consumers in rural areas. The procedure aims at providing such profiles as input data for the design process of off-grid systems for rural electrification. Indeed, daily load profiles represent an essential input for off-grid systems capacity planning methods based on steady-state energy simulation and lifetime techno-economic analyses, and for the analysis of the logics to control the energy fluxes among the different system components. Nevertheless, no particular attention has been devoted so far in the scientific literature as regards specific approaches for daily load profiles estimates for rural consumers. In order to contribute to covering this gap, we developed a new mathematical procedure taking into consideration the specific features of rural areas. The procedure is based on a set of data that can be surveyed and/or assumed in rural areas, and it relies on a stochastic bottom-up approach with correlations between the different load profile parameters (i.e. load factor, coincidence factor and number of consumers) in order to build up the coincidence behavior of the electrical appliances. We have implemented the procedure in a software tool (LoadProGen) which can eventually support the off-grid systems design process for rural electrification. Finally, we have applied the procedure to a case study in order to clarify the proposed approach.

Optimal capacity and type planning of generating units in a bundled wind–thermal generation system

Integration of large-scale wind power creates challenges for power system operations. One of the effective ways of dealing with these challenges is to build thermal power plants to form bundled wind–thermal generation system (BWTGS), i.e., using thermal power to alleviate the fluctuation of wind power. This paper presents a method for optimal capacity and type planning of BWTGS with the given wind farms. Branch-descending technique (BDT) is used to generate candidate schemes of thermal generating units by analyzing the rules of total cost changing with the reduction of the number of thermal generating units. The optimal scheme of BWTGS can be obtained by simulating a long-term operation process of BWTGS and comparing the total costs of all schemes. Techniques to accelerate computation, such as combining redundant states in dynamic programming (DP) algorithm and the saving-branch-cost technique in BDT, are developed to reduce the computational complexity. The major advantage of the proposed method is that it can be used to obtain not only the optimal capacity of thermal generating units, but also the optimal type and number of thermal generating units. Case studies are conducted to demonstrate the effectiveness of this proposed method.

Workforce management: Risk-based financial planning and capacity provisioning

We propose a principled risk-based framework that enables organizations or complex systems to jointly perform financial planning and human-resource management functions in an environment characterized by uncertainty in demand and supply and by other exogenous factors. This framework consists of a set of risk-based capacity planning models and methods that, first, allow for a rigorous quantification of how the financial metrics of an institution depend on its resource provisioning decisions. Second, they provide the capability to make the best decisions to achieve the financial objectives of the institution. Consequently, these models and methods enable interactions between financial and operational planning processes. Decisions including, but not limited to, investments, capacity adjustments, and demand prioritization as well as product or service pricing can all be evaluated quantitatively within this framework, and an overall optimal strategy and plan can be produced with user-specified objectives. These models and methods are based on analysis and optimization algorithms developed for large-scale stochastic loss networks.

A review on strategic capacity planning for the semiconductor manufacturing industry

Due to the high capital investment cost in the semiconductor manufacturing industry, strategic capacity planning plays an important role in improving business performance. The reasons that make these problems especially difficult are: the complex fabrication process, rapid changes in technology and products, the long lead time and the high cost of capacity increment, and the high uncertain demand and capacity. Hence, a review of the existing solutions to capacity planning to cope with these difficulties is important for developing new methodologies and practices. In this paper, we provide a review of the current research and fundamental methods in strategic capacity planning, identify emerging methods for capacity planning, and take an in-depth look at future research interests.

Admission Control Threshold in Cellular Relay Networks with Power Adjustment

In the cellular network with relays, the mobile station can benefit from both coverage extension and capacity enhancement. However, the operation complexity increases as the number of relays grows up. Furthermore, in the cellular network with cooperative relays, it is even more complex because of an increased dimension of signal-to-noise ratios (SNRs) formed in the cooperative wireless transmission links. In this paper, we propose a new method for admission capacity planning in a cellular network using a cooperative relaying mechanism called decode-and-forward. We mathematically formulate the dropping ratio using the randomness of "channel gain." With this, we formulate an admission threshold planning problem as a simple optimization problem, where we maximize the accommodation capacity (in number of connections) subject to two types of constraints. (1) A constraint that the sum of the transmit powers of the source node and relay node is upper-bounded where both nodes can jointly adjust the transmit power. (2) A constraint that the dropping ratio is upper-bounded by a certain threshold value. The simplicity of the problem formulation facilitates its solution in real-time. We believe that the proposed planning method can provide an attractive guideline for dimensioning a cellular relay network with cooperative relays.

Capacity Planning for Group-Mobility Users in OFDMA Wireless Networks

Because of the random nature of user mobility, the channel gain of each user in a cellular network changes over time causing the signal-to-interference ratio (SNR) of the user to fluctuate continuously. Ongoing connections may experience outage events during periods of low SNR. As the outage ratio depends on the SNR statistics and the number of connections admitted in the system, admission capacity planning needs to take into account the SNR fluctuations. In this paper, we propose new methods for admission capacity planning in orthogonal frequency-division multiple-access (OFMDA) cellular networks which consider the randomness of the channel gain in formulating the outage ratio and the excess capacity ratio. Admission capacity planning is solved by three optimization problems that maximize the reduction of the outage ratio, the excess capacity ratio, and the convex combination of them. The simplicity of the problem formulations facilitates their solutions in real time. The proposed planning method provides an attractive means for dimensioning OFDMA cellular networks in which a large fraction of users experience group-mobility.

Capacity Planning for and Performance Analysis of Large Distributed Transaction Systems

Every transaction system supporting business processes must satisfy some computational performance requirements. If it does not, the system may have a significantly reduced value or become useless. The number of factors that influence performance of distributed transaction systems is so large, that the traditional capacity planning methods appropriate for centralized mainframe systems are not applicable. The most cost-effective method has proven to be predictive modeling. A combination of analytic and simulation modeling is most useful for analyzing and tuning performance of systems that have been designed or are in production. The task of designing a system ab initio given expected workloads and required performance/cost constraints is much harder. In this special issue, the paper by Marc Brittan and Janusz Kowalik describes a method for designing initial systems configurations and capacities. This paper focuses on predictive simulation modeling that is used for detailed performance analysis of existing systems. Such analyses may be motivated by what-if studies or by a desire to improve system's unsatisfactory performance.

Extending the effective bandwidth concept to networks with priority classes

ATM switches are now being designed to allow connections to be partitioned into priority classes, with packets being emitted for higher priority classes before packets are emitted for lower priority classes. Accordingly, allocation of network resources based on different priority levels is becoming a realistic possibility. Thus, we need new methods for connection admission control and capacity planning that take account of the priority structure. We show that the notion of effective bandwidths can be used for these purposes when appropriately extended. The key is to have admissibility of a set of connections determined by a linear constraint for each priority level, involving a performance criterion for each priority level. For this purpose, connections are assigned more than one effective bandwidth, one for its own priority level and one for each lower priority level. Candidate effective bandwidths for each priority level can be determined by using previous methods associated with the first-in first-out discipline. The proposed effective bandwidth structure makes it possible to apply product-form stochastic loss network models to perform dimensioning.

Integrating MRP and finite capacity planning

Traditional production control systems based on the manufacturing resource planning concept do not sufficiently support the planner in solving capacity problems, ignore capacity constraints and assume that lead times are fixed. This leads to problems on the shop floor, that cannot be resolved in the short term. This paper focuses on solving these capacity problems by improving capacity planning at the material requirements planning MRP level through integration of MRP and finite capacity planning. This results in a planning method for simultaneous capacity and material planning. The planning method is based on a new and more accurate primary process model, giving the planning algorithm more flexibility in solving capacity problems. The algorithm is based on advanced scheduling techniques and uses aggregated information, thus combining speed and accuracy. The algorithm is designed to use the available flexibility: alternative routeings, safety stock, and replanning of production orders and requirements. This paper also discusses such related issues as robustness, memory and the role of the human planner.

Nonlinear programming for multiperiod capacity planning in a manufacturing system

In this paper, we present nonlinear programming methods for capacity planning in a manufacturing system that consists of a set of machines or work stations producing multiple products. We model the facility as an open network of queues where capacity at each work station in the system may be changed in each of a finite number of time periods. To determine the timing and size of capacity changes, we present two nonlinear programming models and methods for solving the resulting problems. One model involves minimizing total capacity costs such that plant congestion is controlled via upper limits on work-in-process. The other model involves minimizing a weighted sum of product lead times subject to budget constraints on capacity costs. We present solution methods for continuous and discrete capacity options and convex and nonconvex (e.g., economies of scale) capacity cost functions. We use branch and bound and outer approximation techniques to determine globally optimal solutions to the nonconvex problems. Computational testing of the algorithms is reported.

A product load profile approach to MRP capacity planning

This paper discusses a feasibility study of applying the product load profile (PLP) approach to capacity planning in MRP systems. The advantage of the PLP approach is that it greatly simplifies the computation of capacity requirements. But the accuracy of the resulting capacity plans has to be compromised because of certain simplifying assumptions made in constructing individual product load profiles. Simulation experiments of a real MRP system are performed to test the effectiveness of the PLP approach under conditions of uncertainty in operation work contents and demand fluctuation. The results reveal that under a wide range of test conditions the PLP approach is able to generate accurate capacity plans, demonstrating the potential of PLP as an efficient and effective method for capacity planning in MRP.