Minimum Cost Method Research Articles

A Minimum Cost and Maximum Flow Model of Hazardous Materials Transportation

A vehicle routing problem of hazardous materials transportation is studied in this paper. Firstly, the maximum permissible flow of the road section is calculated according to the social risk and accident rate values. Secondly, based on the structural measures and basic transportation network, the maximum permissible flow of the road section is taken as road section capacity, the minimum risk is taken as general minimum cost , and a minimum cost and maximum flow method is introduced to solve the flow distribution problem of hazardous materials transportation. Finally, the effectiveness of the method is illustrated with a simple numerical example.

멀티캐스트 서비스를 위한 센터기반 공유형 경로 지정 방법

최근 원격교육, 디지털 콘텐츠 및 IPTV를 포함한 멀티미디어 데이터를 다수의 수신자들에게 멀티캐스트 전송기술을 이용하여 전송하는 방법들에 대한 논의가 활발히 이루어지고 있다. 이를 위하여 크게 소스기반의 트리 구성과 센터 기반의 트리 구성 방식의 프로토콜이 제안되고 있으며, 특히 센터 기반의 트리 구성시 RP (Rendezvous Point) 라우터를 선정하기 위한 여러가지 알고리즘들이 제안되었다. 주요 알고리즘들에서는 RP와 멤버들사이의 최대거리, 평균거리 및 예측거리 등의 measure를 이용하여 이의 값들을 최소화하는 라우터를RP로 지정한다. 본 논문에서는 메시 네트워크 하에서 소스 및 멤버들이 랜덤하게 지정되는 상황을 가정하는 경우 효율적인 RP 선정 알고리즘을 제안한다. Recently, with the IPTV services, e-learning, real-time broadcasting and e-contents, many application services need the multicasting routing protocol. In this paper, the performance of the algorithm to assign the rendezvous router (RP: rendezvous point) in the center-based multicasting mesh network is analyzed. The estimated distance to select RP in the candidate nodes is calculated, and the node minimizing the distance is selected as the optimal RP. We estimate the distance by using the maximum distance, average distance, and mean of the maximum and average distance between the RP and members. The performance of the algorithm is compared with the optimal algorithm of all enumeration. With the assumptions of mesh network and randomly positioned for sources and members, the simulations for different parameters are studied. From the simulation results, the performance deviation between the algorithm with minimum cost and optimal method is evaluated as 6.2% average.

Energy management method for auxiliary energy saving in a passive-solar-heated residence using low-cost off-peak electricity

This paper describes the optimal control of operation of the auxiliary heating system used as backup in a passive-solar-heated system. The mathematical model of the general solution is given and the minimum cost method is applied. Furthermore, a specific application is studied for a residence heated using a combined sunspace-Trombe Wall passive solar system and an electrically heated thermal storage floor to satisfy the heating requirements of the house.

Minimum cost traffic shaping

We propose a minimum-cost method for traffic shaping in the context of quality of service (QoS)-based networks. Given the user's desired QoS and the network's resource availability, our method determines the least cost parameters for a shaper while guaranteeing access to the network and satisfying the QoS requirements.

A goal-programming method of stochastic allocative data envelopment analysis

Allocative Data Envelopment Analysis (ADEA) is a version of Data Envelopment Analysis (DEA) which measures relative efficiency for a group of similar operating units with known input prices. By using the actual input values, ADEA provides information to managers on the minimum cost method of operation for each unit. A major criticism of DEA methods is that they are deterministic and have no means of allowing for uncertainty. This paper applies the goal-programming approach, introduced by Banker (1991), to allocative efficiency and develops the Stochastic ADEA model. A two-stage solution method is introduced, which is needed because of the existence of alternate optimal solutions regarding which units are found to be significantly inefficient. We propose that identifying the significantly inefficient units is most useful to managers because it best facilitates improved efficiency. The concept of a minimum frontier is introduced and used to define the significantly inefficient units. We also show how bounds can be imposed which allow the ambiguity of the noise/inefficiency trade-off to be eliminated from the objective function. The use of bounds also allows the identification of the significantly inefficient unit based on the amount of uncertainty present for each operating unit. As a result of optimizing cost, this method has the important advantage of being ideally suited for multiple outputs.

Role of thermal insulation in passive designs of buildings

The utilization of solar energy for active and passive applications in buildings requires the careful selection of materials that have, (a) high thermal storage capacity, (b) thermal insulation efficiency, and (c) a balance of the two. The choice is not easy to make notably when the building falls in a temperate climatic zone. This paper tackles the selection of materials for usage in buildings from different angles to ensure the ease of selection. First, the optimal economic thickness of an insulation material is determined very accurately using the minimum life-cycle cost method and the annual heating degree days (AHDDs) for different sites. From this, the optimal thermal transmittance (U-value) for the building structure in a given climatic zone is determined. Second, a graphic relationship between U-value vs AHDDs is established. Third, annual energy savings in kWh ton−1 of insulation material is calculated. From this, the pay-back period of energy consumed in manufacturing the installed thermal insulation material is calculated. The higher the pay-back period the less desirable is the use of thermal insulation materials and more favourable is the use of high mass materials. This approach is well reasoned to allow its extensibility to all buildings in all climatic zones. It should certainly give insight to the possible contribution of solar energy in meeting demand for active and passive heating/cooling of buildings. Results are summarized in Figs 3 and 4.

The economics of simulation

This paper presents a framework for decision making about simulations before committing resources. The viewpoint is that of a manager who must decide whether to solve a problem by simulation or by some other means. A decision-analysis type model is introduced to allow the manager to determine the minimum cost method of simulation within the operating and policy constraints of his organization. Two case examples using the metholology are presented.

A decision-tree framework for estimating the costs of simulation

This paper presents a framework for analyzing the anticipated costs of simulation before committing resources. The viewpoint taken is that of a manager who must decide whether to solve a problem by simu lation or some other means. The decision-analysis model that is formulated allows the manager to deter mine the minimum-cost method of simulation within the operating and policy constraints of his firm. Two examples are discussed.

Time-Capacity Expansion of Waste Treatment Systems

A minimum cost method is developed for quantifying the opposing cost factors of economies-of-scale and the time cost of money as they jointly affect optimal capital investment decisions for the expansion of wastewater treatment systems. For a linear demand growth rate, optimal facility expansion, with regard to both timing and capacity requirements, is determined using a mathematical model. The economic implications of different expansion policies are graphed for various interest rates and scalar cost effects, using cost data mathematically formulated as a function of system capacity. The model is used to demonstrate that the optimal timing of facility expansion is a function of the time cost of money and the economy-of-scale characteristics of that facility, and is independent of the absolute capacity of the facility. Further, additional economies inherent in individually expanding separable components of a wastewater treatment system are quantified by the model.

Time‐capacity expansion of urban water systems

This paper presents a minimum cost method to time and size water system component expansion to meet an exogenously determined growth rate. The method analyzes the economic impact both of the economies of scale in construction and of the real cost of capital. The result is a capacity expansion model which determines when and how much excess capacity should be installed to meet increasing demands. The economic effects of timing and sizing decisions on total costs are plotted for various interest rates and levels of economies of scale. The paper demonstrates that the costs of expansion are sensitive to the increase in demand specific to the facility being planned and that the design capacity must be set individually for each separable water system component according to its particular cost function. These findings suggest that the standard practice of designing excess capacity in a water system to meet a fixed future demand (i.e., 25‐year system) should be revised.

Gas Storage Field Development Optimization

Abstract Optimization of gas storage field development requires that many combinations of flow system variables, together with their related costs, be examined in a systematic fashion until the minimum development cost method is obtained for any potential field deliverability requirement. Graphical methods of optimizing gas storage field development are presented. It is also shown graphically that, for any given peak-day rate design, there exists a definite upper feasible limit to the number of wells. Introduction The development of a field for gas storage usage involves considerations of multiple factors, many of which are mutually dependent. Some of the main variables which must be considered in storage field development arepotential cyclic capacity and peak-day rate market requirements,number of wells,compressor-inlet line pressure (and related compression),overpressuring,average well open flow (and back-pressure slope),casing sizes andgathering system design. Each of these variables affects the field deliverability performance and each also affects the total project development cost. The term "storage field design requirement" is defined (for the purposes of this article) as a given combination of peak-day rate and cyclic capacity. The peak-day rate equals the daily gas rate which will be required to be produced from this field under the most austere condition of system demand (usually maximum required rate imposed late in the withdrawal season). The cyclic capacity equals the amount of gas that can be sold at which time the peak-day rate can just be met. The storage field design requirement is, thus, the maximum output requirement to be placed upon the field. The term "field design" will be used here. Optimization of storage field design involves obtaining the optimum method of total field development and determining the economic limits of field development. The optimum method of field development is defined here as the method (or methods) which results in the maximum desired field usage (i.e., the maximum desired peak-day rate and maximum desired cyclic capacity) obtained with the minimum total cost of development. The economic limits of field development are defined by the potential range of field operations (i.e., peak-day rate and/or cyclic capacity) which clearly are not economically feasible due to high cost of development, and/or those ranges of operation which are not attainable, regardless of cost. Optimization of storage field design requires that many combinations of the previously mentioned flow system variables, together with their related costs, be studied and examined in a systematic fashion until the minimum development cost method is obtained for any potential field design condition. Field Design Optimization Graphs As an aid in determining the optimum storage field design, a new graphical technique has been devised. This analytical tool has been called a field design optimization graph. The optimization graphs enable one to rapidly scan the entire spectrum of potential field operations and read total development costs (wells, compression and gathering system, if included) directly for any point in the design field (i.e., corresponding to any peak-day rate and cyclic capacity combination). The optimization graphs enable one to study storage field design analytically and on a more quantitative basis. They help to provide an insight into the effect of key variables in field design (such as the number of wells, line pressure, open flow, overpressuring and casing sizes) on the ultimate field performance and on the corresponding total development costs. The main type of field design optimization graph used to optimize field development consists of a graph showing the peak-day rate effect on field design and costs (Fig. 1). The graph basically consists of a plot on the vertical axis of the number of wells required to meet a given total field peak-day rate vs the gas inventory level (with respect to original) plotted on the horizontal axis, at which point the peak-day rate can just be met by that number of wells. Each curve on the graph corresponds to a specific peak-day rate, and rates from q = 400 to q = 2,000 MMscf/D are covered by the family of curves on this graph. JPT P. 323ˆ

Interpretation of Costs

The use of electronic data processing now permits more alternatives to be analyzed for a given engineering project and has focused attention on the suitability of the various economic analysis techniques used throughout the profession. A critique of methods of economic analysis is presented. The implications of input decisions are examined, including the compatibility of alternative plans, the handling of generated traffic, and the conversion of trips from an alternate mode. A theoretical investigation of the most commonly used methods of analysis is presented. It is shown that the normal benefit-cost ratio method may not produce rational solutions or solutions which are compatible with other methods of analysis. The minimum cost method is shown to be consistent with the incremental cost methods. The adoption of this method is proposed because of its simplicity and its ability to retain indentification of the various component costs.