Showing papers on "Goal programming published in 2000"

Decision-Theoretic, High-Level Agent Programming in the Situation Calculus

Abstract: We propose a framework for robot programming which allows the seamless integration of explicit agent programming with decision-theoretic planning. Specifically, the DTGologmodel allows one to partially specify a control program in a highlevel, logical language, and provides an interpreter that, given a logical axiomatization of a domain, will determine the optimal completion of that program (viewed as a Markov decision process). We demonstrate the utility of this model with results obtained in an officedelivery robotics domain.

Exploration of the effectiveness of physical programming in robust design

Abstract: Computational optimization for design is effective only to the extent that the aggregate objective function adequately captures designer's preference. Physical programming is an optimization method that captures the designer's physical understanding of the desired design outcome in forming the aggregate objective function. Furthermore, to be useful, a resulting optimal design must be sufficiently robust/insensitive to known and unknown variations that to different degrees affect the design's performance. This paper explores the effectiveness of the physical programming approach in explicitly addressing the issue of design robustness. Specifically, we synergistically integrate methods that had previously and independently been developed by the authors, thereby leading to optimal-robust-designs. We show how the physical programming method can be used to effectively exploit designer preference in making tradeoffs between the mean and variation of performance, by solving a bi-objective robust design problem. The work documented in this paper establishes the general superiority of physical programming over other conventional methods (e.g., weighted sum) in solving multiobjective optimization problems. It also illustrates that the physical programming method is among the most effective multicriteria mathematical programming techniques for the generation of Pareto solutions that belong to both convex and non-convex efficient frontiers.

Building additive utilities for multi-group hierarchical discrimination: the M.H.Dis method *

Abstract: The discrimination problem is of major interest in fields such as environmental management, human resources management, production management, finance, marketing, medicine, etc.For decades this problem has been studied from a multivariate statistical point of view. Recently the possibilities of new approaches have been explored, based mainly on mathematical programming. This paper follows the methodological frame work of multicriteria decision aid (MCDA), to propose a new method for mutligroup discrimination based on a hierarchical procedure (Multi-Group Hierarchical Discrimination-M.H. DIS). The performance of the M.H.DIS method is evaluated along with eight real world case studies from the fields of finance and marketing. A Comparison is also performed with other MCDA methods.

MOCACEF 1.0: Multiple objective capability based approach to form part-machine groups for cellular manufacturing applications

Abstract: A pre-emptive goal programming formulation is developed for concurrently forming independent part/machine cells. Machine independent capability units, which are known as Resource Elements (RE), are used to define processing requirements of parts and processing capabilities of machine tools. Representation of unique and shared capability boundaries of machine tools is possible via RE, which increases the opportunity to form independent manufacturing cells and efficient utilization of them. RE-based operation sequences, processing times, capacities, demand, cell sizes, cell flexibility, load balance between cells, cell interaction, copies of each machine type in the job shop are all considered in the problem formulation. The model is solved by a specially developed tabu search algorithm.

Theory and Methodology Minimax reference point approach and its application for multiobjective optimisation

Abstract: In multiobjective optimisation, one of the most common ways of describing the decision maker’s preferences is to assign targeted values (goals) to conflicting objectives as well as relative weights and priority levels for attaining the goals. In linear and convex decision situations, traditional goal programming provides a pragmatic and flexible manner to cater for the above preferences. In certain real world decision situations, however, multiobjective optimisation problems are non-convex. In this paper, a minimax reference point approach is developed which is capable of handling the above preferences in non-convex cases. The approach is based on1-norm formulation and can accommodate both preemptive and non-preemptive goal programming. A strongly non-linear multiobjective ship design model is presented and fully examined using the new approach. This simulation study is aimed to illustrate the implementation procedures of the approach and to demonstrate its potential application to general multiobjective optimisation problems. ” 2000 Elsevier Science B.V. All rights reserved.

Decision making : recent developments and worldwide applications

Abstract: Editorial. 1: Management Information Systems. Empirical assessment of information technology chargeback systems decisions D.H. Drury. Lessons learnt from the successful adoption of an ERP: The central role of trust D. Gefen. Simultaneous analysis of heterogenous databases on the web: The ADDSIA project J.M. Lamb, C.R. Smart. 2: Education Innovations & Distance Learning. Decision support for the management of admissions to academic programs K.S. Dhir, et al. The use of tacit knowledge in selection decisions in universities M.A. Barrett, L.K. Hort. 3: International Business. Role of political violence in foreign direct investment decisions H. Singh. On the stability of countries' national technological systems W. Nasierowski, F.J. Arcelus. 4: Marketing. Marketing of differentiated fresh produce G. Baourakis, et al. A decision support system for the seller's return problem in the product line design G. Alexouda, K. Paparrizos. 5: Finance and Banking. Portfolio performance measures: A brief survey and hypothesis testing G.L. Ghai, et al. A system dynamics model of stock price movements P.L. Kunsch, et al. Information effects on the accuracy of neural network financial forecasting S. Walczak. Is the Taiwan stock market efficient? J.P. Gupta, et al. The dynamics of implied volatility surfaces G. Skiadopoulos, et al. Application of nonstationary Markovian models to risk management in automobile leasing D.L. Smith, et al. 6: Optimization & Decision Making. Decision making Under Various Types of Uncertainty R.R. Yager. Decision aid in the optimization of the interval objective function C.A. Antunes, J. Climaco. A fuzzy extension of a mixed integer MOLP model for solving the power generation expansion problem G. Mavrotas, D. Diakoulaki. Management science for marine petroleum logistics E.D. Chajakis. 7: Multi-Criteria Decision Analysis, Aid & Practice. Dealing with missing data in rough set analysis of multi-attribute and multi-criteria decision problems S. Greco, et al. Cardinal value measurement with MACBETH C.A. Bana e Costa, J.-C. Vansnick. Inferring a multicriteria preference model for rural development projects evaluation E. Krassadaki, Y. Siskos. An adaptable framework for educational software evaluation I. Stamelos, et al. Assessing country risk using a multi-group discrimination method: A comparative analysis M. Doumpos, et al. : Decision Support Systems and Information Technology. RODOS: Decision support for nuclear emergencies J. Bartzis, et al. DSS for the evaluation of national IT infrastructure investments: A study of cable television in Greece C.M. Giaglis, et al. Global IT outsourcing decisions: Contract structure, negotiations, and global deal teams S.T. Huhn, et al. Using Internet multimedia database information systems for decision support in conservation planning M. Angelides, M.C. Angelides. An interactive workload and risk balancing model and decision support system for probationer assignment J.R. Baker, et al. 9: Health Care Planning & Hospital Operations. A goal programming scheme to determine the budget assignment among the hospitals of a sanitary system J.J. Marti. A simulation model to evaluate the interaction between acute, rehabilitation, long stay care and the community E. El-Darzi, et al. Author Index.

Multi-response simulation optimization using stochastic genetic search within a goal programming framework

Abstract: This study presents a new approach to solve multi-response simulation optimization problems. This approach integrates a simulation model with a genetic algorithm heuristic and a goal programming model. The genetic algorithm technique offers a very flexible and reliable tool able to search for a solution within a global context. This method was modified to perform the search considering the mean and the variance of the responses. In this way, the search is performed stochastically, and not deterministically like most of the approaches reported in the literature. The goal programming model integrated with the genetic algorithm and the stochastic search present a new approach able to lead a search towards a multi-objective solution.

Simulation optimization: multi-response simulation optimization using stochastic genetic search within a goal programming framework

Abstract: This study presents a new approach to solve multi-response simulation optimization problems. This approach integrates a simulation model with a genetic algorithm heuristic and a goal programming model. The genetic algorithm technique offers a very flexible and reliable tool able to search for a solution within a global context. This method was modified to perform the search considering the mean and the variance of the responses. In this way, the search is performed stochastically, and not deterministically like most of the approaches reported in the literature. The goal programming model integrated with the genetic algorithm and the stochastic search present a new approach able to lead a search towards a multi-objective solution.

Resource allocation in the North Sea demersal fisheries: A goal programming approach

Abstract: The management of a fishery is a complex task generally involving multiple, often conflicting, objectives. These objectives typically include economic, biological and social goals such as improving the income of fishers, reducing the catch of depleted species and maintaining employment.

An Algorithm to Solve Linear Fractional Bilevel Programming Problem Via Goal Programming

Abstract: In this paper an algorithm to solve a Bilevel Programming Problem in which the leader’s and the follower’s both objective functions are linear fractional is developed. The algorithm is based on Preemptive Goal Programming. The Bilevel Programming problem is solved by converting it into a goal programming problem. An example to illustrate it is also presented.

Goal programming applications in multidisciplinary design optimization

Abstract: In this paper, we begin to explore the ramifications of a goal programming approach for multidisciplinary design optimization based on the Collaborative Optimization framework. Working within an existing computing architecture for simulation-based design, we propose a bi-level goal programming formulation of Collaborative Optimization to automate the design synthesis of complex systems, with particular emphasis on undersea vehicles. A brief overview of the simulation-based design computing architecture that has been developed is given. To demonstrate the proposed bi-level goal programming approach, the multidisciplinary design and optimization of an undersea vehicle containing four subsystem level analyses and a system-level analysis is presented. A traditional single-level optimization solution is also presented to provide a benchmark for comparison. While the bi-level goal programming approach is computationally more expensive, it does improve overall system performance by automating subsystem synthesis and arbitration during optimization.

Applicability of existing continuous methods in determining the pareto set for nonlinear, mixed-integer multicriteria optimization problems

Abstract: This article investigates the applicability of existing techniques for generating the Pareto set in continuous problems to nonlinear multicriteria optimization problems where some of the optimization variables are discrete. Techniques such as the weighted sums approach, goal programming and Normal-Boundary Intersection are commonly used to generate points on the Pareto surface for continuous problems. These approaches are re-evaluated in the light of then- geometrical properies for mixed-integer nonlinear problems, which pose an additional challenge because the Pareto surface is not connected. It is demonstrated that the intuition we have gathered from continuous problems may not apply in the discrete setting.

On sensitivity in linear multiobjective programming

Abstract: In this paper, we prove that, if the data of a linear multiobjectiveprogramming problem are smooth functions of a parameter, then in theparameter space there is an open dense subset where the efficient solutionset of the problem can be locally represented as a union of some faces whosevertices and directions are smooth functions of the parameter.

Arithmetization of distributions and linear goal programming

Abstract: Linear goal programming can be used as a complementary technique when local moment matching method up to the second moment gives some negative mass. This could happen when manipulating a discrete or mixed type severity distribution. In that case we can avoid a simple retreat to the first moment and look for an arithmetic distribution with equal mean and the second moment closest to that of the original distribution.

Uncertain programming: optimization theory in uncertain environments

Abstract: By uncertain programming we mean the optimization theory in generally uncertain (random, fuzzy, fuzzy random, rough, etc.) environments. Stochastic programming, fuzzy programming, fuzzy random programming and rough programming are subtopics of uncertain programming. The paper provides a brief introduction to uncertain programming, including modeling ideas, hybrid intelligent algorithms, and applications in uncertain decision systems. Some further research problems appearing in this area are also posed.