Formulation Of Optimization Model Research Articles

Optimization of process parameters of mechanical type advanced machining processes using genetic algorithms

Generally, unconventional or advanced machining processes (AMPs) are used only when no other traditional machining process can meet the necessary requirements efficiently and economically because use of most of AMPs incurs relatively higher initial investment, maintenance, operating, and tooling costs. Therefore, optimum choice of the process parameters is essential for the economic, efficient, and effective utilization of these processes. Process parameters of AMPs are generally selected either based on the experience, and expertise of the operator or from the propriety machining handbooks. In most of the cases, selected parameters are conservative and far from the optimum. This hinders optimum utilization of the process capabilities. Selecting optimum values of process parameters without optimization requires elaborate experimentation which is costly, time consuming, and tedious. Process parameters optimization of AMPs is essential for exploiting their potentials and capabilities to the fullest extent economically. This paper describes optimization of process parameters of four mechanical type AMPs namely ultrasonic machining (USM), abrasive jet machining (AJM), water jet machining (WJM), and abrasive-water jet machining (AWJM) processes using genetic algorithms giving the details of formulation of optimization models, solution methodology used, and optimization results.

Combining cost-based and rule-based knowledge in complex resource allocation problems

A major challenge in the formulation of optimization models for large-scale, complex operational problems is that some data are impossible or uneconomical to collect, producing a cost model that suffers from incomplete information. As a result, even an optimal solution may be “wrong” in the sense that it is solving the wrong problem. In many operational settings, knowledgeable experts will already know, at least approximately, how a model should behave, and can express this knowledge in the form of low dimensional patterns: “high powered locomotives should pull intermodal trains” (because they need to move quickly) or “loaded C-141s should not be flown into Saudi Arabia” (for maintenance reasons). Unlike the literature on inverse optimization which uses observed actions to train the parameters of a cost model, we used exogenous patterns to guide the behavior of a model using a proximal point term that penalizes deviations from these patterns. Under the assumption that the patterns are derived from rational behaviors, we establish the conditions under which incorporating patterns will reduce actual costs rather than just the engineered costs. The effectiveness of the approach is demonstrated in a controlled, laboratory setting using data from a major railroad.

Combined Ring–Mesh Optical Transport Networks

To date in Sonet and DWDM transport environments, we see two dominant principles for network survivability: ring and mesh. Although both are used, they are virtually always deployed and operated on geographically separate networks or regions, or applied on a ring-access and mesh-core principle. We describe a new approach for optimized design of ring and mesh network components in a single hybrid transport network. A key concept in design of these hybrids is the “clipping” of forcer spans in the mesh network design by strategically added rings. We present a formal optimization model and a heuristic to form sych hybrid designs with lower total cost than a pure-mesh design. These ideas on optical network architecture are especially relevant to industry practice with the advent of OXC systems with integrated add–drop multiplexer functionality and by the development of ultra long reach (ULR) optics. This integration of OXC-OADM functionality effectively removes any extra costs associated with transitions from a ring to mesh environment and ULR optics permit all-optical bypass of OXC nodes which enhances the cost-effectiveness of the hybrid architectures.

Computer-based interface for an integrated solid waste management optimization model

Planning a regional waste management strategy is a critical step that, if not properly addressed, will lead to an inefficient integrated solid waste management (ISWM) system. Regional planning affects the design, implementation, and efficiency of the overall ISWM scheme. Consequently, decision-makers must look for optimized regional waste management planning to achieve a successful strategy. The optimization of an ISWM strategy for an area requires the knowledge of available solid waste management alternatives and technologies, economic and environmental costs associated with these alternatives, and their applicability to the specific area. Decision-makers often have to rely on optimization models to examine the impacts of mass balance, capacity limitations, operation, and site availability as well as to analyze different alternative options in the selection of a cost effective, environmentally sound waste management alternative. In this context, the complexity associated with the formulation of optimization models may hinder its use, and consequently, user friendliness is a major concern. This paper presents an interface that was developed to address this concern, that is to formulate the matrices associated with an integrated waste management optimization model.

A Lagrangean Approach to Network Design Problems

Network design is a very important issue in the area of telecommunications and computer networks, where there is a large need for construction of new networks. This is due to technological development (fiber optics for telecommunication) and new ways of usage (Internet for computer networks). Optimal design of such networks requires formulation and solution of new optimization models. In this paper, we formulate several fixed charge network design models, capacitated or uncapacitated, directed or undirected, possibly with staircase costs, and survivability requirements. We propose a common solution approach for all these problems, based on Lagrangean relaxation, subgradient optimization and primal heuristics, which together form a Lagrangean heuristic. The Lagrangean heuristic can be incorporated into a branch‐and‐bound framework, if the exact optimal solution must be found. The approach has been tested on problems of various structures and sizes, and computational results are presented.

A Lagrangean approach to network design problems

Network design is a very important issue in the area of telecommunications and computer networks, where there is a large need for construction of new networks. This is due to technological development (fiber optics for telecommunication) and new ways of usage (Internet for computer networks). Optimal design of such networks requires formulation and solution of new optimization models. In this paper, we formulate several fixed charge network design models, capacitated or uncapacitated, directed or undirected, possibly with staircase costs, and survivability requirements. We propose a common solution approach for all these problems, based on Lagrangean relaxation, subgradient optimization and primal heuristics, which together form a Lagrangean heuristic. The Lagrangean heuristic can be incorporated into a branch-and-bound framework, if the exact optimal solution must be found. The approach has been tested on problems of various structures and sizes, and computational results are presented.

Blocking Probability Approximations and Revenue Optimization in Multirate Loss Networks

For the end-to-end (Originator-Destination pair) call blocking probability computation in multirate loss networks the so-called reduced load approximation under link independence assumption is often used, because it allows the derivation of analytical and numerical results. Its accuracy and extendibility to multirouting or multicasting networks (like me B-ISDN), however, is seldom studied. This paper attempts to generalize this assumption and to assess the usefulness of this generalization by comparing simulation and approximation results on link, route, and end-to-end blocking probability evaluation for these kinds of networks. The accuracy of the approximation is examined by a simulation tool called Flexible Simulation Platform for ATM Networks. An important application example of this generalized link-, route- and Originator-Destination pair blocking measure is the formulation of an optimization model for multirate loss networks, which optimizes carried traffic and network revenue.

Optimization of flexure environment of concrete beams reinforced with fibre-reinforced plastic rebars

This study presents the optimum design and behavioural assessment of simply supported beams subjected to uniformly distributed loads and reinforced with fibre-reinforced plastic rebars. A formal optimization model is developed in which the cost per unit length of the beam represents the objective function to be minimized, and is expressed in terms of two design variables (the effective depth and the reinforcement ratio). The constraints on the optimization model are in accordance with the ACI code provisions. They include flexural demand due to the imposed loads and the serviceability requirements that specify an upper limit on the deflection of a beam under service loads. The optimization process is illustrated with the help of a beam example reinforced with glass fibre-reinforced plastic (GFRP) rebars. The results are compared with those of its counterpart reinforced with conventional steel bars. A parametric study to assess the influence of parameters such as the ultimate compressive strength of the concrete, span of the beam and cost of GFRP rebars is presented.

GENERATING ROBUST SOLUTIONS: A WATER QUALITY MANAGEMENT EXAMPLE

Abstract An approach is presented for.analyzing a water quality management problem that considefs multiple pollutant transport responses within an optimization model formulation. The multiple pollutant transport responses represent different possible scenarios that could occur in the water body. Solutions to the optimization model are obtained that are robust with respect to the actual pollutant transport response. The desirable characteristics of the solutions obtained to this waste load allocation problem are demonstrated through the application of the approach to a case study based on the Willamette River in Oregon.

Knowledge-assisted optimization model formulation: UNIK-OPT

In this paper, we describe a knowledge-assisted optimization model formulation system UNIK-OPT (UNIfied Knowledge-OPTimization). To envision the desirable features of UNIK-OPT, we first establish the design criteria of knowledge-assisted modeling systems. The most distinctive criterion pursued in this research is the independent management of knowledge base from multiple optimization models. To achieve these criteria, we articulate four levels of modeling views: semantic view, modeling language view, mathematical notational view and tabular view. In semantic view, the associations between attributes, blocks of terms and constraints are represented in a constraint network, and a block of terms is represented as a pair of coefficient and variable. Thus, the formulation reasoning is esteemed as a process of helping the construction of a specific semantic model by adding user's problem definition to the extracted relevant semantic level modeling knowledge. Then the specific semantic model can be transformed into other views of model automatically. The prototype UNIK-OPT is developed to realize this idea, and is illustrated with a refinery plant.

Deterministic Mathematical Modelling for the Spatial Allocation of Multi-categorical Resources: with an Application to Real Health Data

This paper presents some mathematical formulations of deterministic non-linear optimization models for planning the spatial distribution of public service facilities and their utilization. The modelling is performed as a function of multi-categorical resource types and the consumer's zone of residence over a large geographical domain. The mathematical solution to the deterministic model, its parameter estimation by log-linear regression, and some preliminary results of simulation for a Massachusetts hospital database are presented.

Deterministic Mathematical Modelling for the Spatial Allocation of Multi-Categorical Resources: with an Application to Real Health Data

This paper presents some mathematical formulations of deterministic non-linear optimization models for planning the spatial distribution of public service facilities and their utilization. The modelling is performed as a function of multi-categorical resource types and the consumer's zone of residence over a large geographical domain. The mathematical solution to the deterministic model, its parameter estimation by log-linear regression, and some preliminary results of simulation for a Massachusetts hospital database are presented.

Highest and Best Use: The Evolving Paradigm

Highest and Best Use is often identified as the key concept supporting real estate use and value decisions. However, at best the concept has received ambiguous if not conflicting consideration as to its relevance in the literature of economics, finance, real estate, appraisal and other areas of study concerned with land use decisions and valuation. This paper addresses this ambiguity and identifies the theoretical premises of Highest and Best Use as employed in the various land use disciplines. The theoretical foundations as they are synthesized form the basis of a formal constrained optimization model for land use decisions. The model's logic identifies the need to include the cost of capital and location along with the physical, legal, infrastructure and market parameters discussed in the bulk of the economic and appraisal literature (including courses and professional practice). The financial and locational variables are needed in order to advance the use paradigm to fit the current context of problems facing real estate decisionmakers.

Some deterministic and stochastic nonlinear optimization modelling for the spatial allocation of multicategorical resources: with an application to real health data

This paper is a sequel to two previous articles, which presented deterministic mathematical modelling for the prediction of single-category patient/client/customer “flows” as an aggregate within a large geographical domain and formulations of several deterministic nonlinear optimization models based on objectives of equity, efficiency, and accessibility for single-categorical resources. This paper presents some formulations of both deterministic and stochastic nonlinear optimization models for planning the spatial distribution of public service facilities and their utilization as a function of multicategorical resource types and the consumer's zone of residenceover a large geographical domain. The stochastic model is useful for large-scale systems planning when parameter values are subject to change over the planning horizon rather than being held fixed, as in the deterministic model. The mathematical solution to the deterministic model, its parameter estimation by log-linear regression, and some preliminary results for a Massachusetts hospital data base are presented.

Simultaneous optimization of horizontal and vertical alignments for highways

This paper presents the formulation of a three-dimensional highway alignment optimization model. The coordinates of the highway is parametrized by using a three-dimensional cubic spline polynomial interpolation. The solution is obtained by locating the optimal horizontal and vertical alignments simultaneously while subject to a wide range of possible constraints. These constraints may be bounds on the slope or curvature, as well as avoidance of inaccessible region. It is an extension from a previous research work where optimal vertical highway alignment was determined for a preselected horizontal alignment. Two numerical examples based upon the actual ground conditions in Singapore are presented to illustrate the applicability of the proposed model. Some computational details such as scaling and selection of initial variables which affect the rate of convergence are also briefly discussed.

Intelligent Stock Portfolio Management System

Abstract: For the selection of stock portfolios, the optimiastion models or expert systems are used separately. To take advantage of both approaches, the integration of optimisation model and expert system is attempted. The proposed architecture—ISPMS (Intelligent Stock Portfolio Management System)—accomplishes the integration by interpreting the knowledge as a part of the formulation of optimisation model. The other generic issues in ISPMS are the integration of personal preference with the expert knowledge and knowledge acquisition by machine learning. This paper thus describes the representation and the inference of an expert knowledge, the representation of personal preference and its integration with the expert knowledge, the interpretation of the knowledge and preference to associate with optimisation model, and the solution algorithm of the modified optimisation model accordingly.

Planning of National Agricultural Investment Policies with a Dynamic Spatial Optimization Model - Case, Model, Experiences

The paper discusses the formulation and use of large dynamic optimization models for agricultural sector planning. While most of the planning models in literature have been designed for a centralized or decentralized economy, our case deals with the agricultural sector in Yugoslavia, which is comprised of both, a social and a private subsector. This adds a further complexity to the formulation of an appropriate planning model. In the specific case, various internal recursive relationships have been introduced to improve the model's simulation capability with regard to the private farms ‘reaction to government incentives. Deficiencies in the information basis (incomplete and uncertain data) have been considered through an interactive communication procedure. The paper concludes with a general discussion of experiences with various model applications and its relevance for agricultural sector modelling and planning.

Assignment of emission abatement levels by stochastic programming

The paper deals with the problem of determining the optimal emission abatement policy in a region. The criterion used consists of looking for the policy which minimizes the overall abatement cost under the constraint of meeting a given ambient standard. This policy is determined by a formal optimization model (mathematical program). Pollutant dispersion is affected by uncertainty, related to the meteorological situation in the region, thus the satisfaction of the ambient standard constraint in the program can be regarded as a random event (‘stochastic programming approach’). Two solutions for the stochastic program are suggested: the distribution approach (which leads to the definition of Pareto alternatives between abatement cost and risk of violating the standard) and chance-constraints programming (the satisfaction of the standard is required with a given probability, at least).

Assignment of emission abatement levels by stochastic programming

The paper deals with the problem of determining the optimal emission abatement policy in a region. The criterion used consists of looking for the policy which minimizes the overall abatement cost under the constraint of meeting a given ambient standard. This policy is determined by a formal optimization model (mathematical program). Pollutant dispersion is affected by uncertainty, related to the meteorological situation in the region, thus the satisfaction of the ambient standard constraint in the program can be regarded as a random event (’stochastic programming approach‘). Two solutions for the stochastic program are suggested: the distribution approach (which leads to the definition of Pareto alternatives between abatement cost and risk of violating the standard) and chance-constraints programming (the satisfaction of the standard is required with a given probability, at least).

Strategies for Wheat Production in the Great Plains

of a single crop, which is wheat. Farmers in this region have only two economically relevant choices for land utilization in a given year-wheat and fallow. Range or other crops are not economically competitive with wheat for use of the land, and moisture limitations make periodic fallowing a common practice. An optimal frequency for fallowing in this region has been the subject of several studies [1, 2, 4, 7]; but that of Burt and Allison [1] was the only one which used a formal optimization model that took into consideration the entire planning horizon of the firm. It must be recognized that choice of a particular crop in a given year affects the optimal choice of crops in future years; in other words, we have a problem in economic dynamics, and a satisfactory solution cannot ignore interrelations in the time dimension. An additional complexity is the stochastic nature of climatic variables, which makes it necessary to view crop choice decisions in the context of a policy or strategy rather than to specify a sequence of crops for the entire planning horizon. Burt and Allison used a Markov chain model, with states determined by soil moisture at planting time, to derive conditional decision rules maximizing discounted expected net returns, where the choice for land utilization was fallow or wheat. A limitation of their work was the paucity of data for obtaining empirical estimates of parameters required for the model. Also, their analysis was limited to an expected-value criterion, with no consideration given to variability of income. The research presented here provides a model for which empirical data are more easily obtained and evaluates the problem by use of both expected value and variance of net returns. The general approach is to find a conditional decision rule or strategy that is optimal by some criterion, and the set of given information upon * Contribution from the Missouri Agricultural Experiment Station. Approved by the