Showing papers on "Goal programming published in 2006"

Interactive fuzzy goal programming for multi-objective transportation problems ☆

Abstract: This paper presents an interactive fuzzy goal programming approach to determine the preferred compromise solution for the multi-objective transportation problem. The proposed approach considers the imprecise nature of the input data by implementing the minimum operator and also assumes that each objective function has a fuzzy goal. The approach focuses on minimizing the worst upper bound to obtain an efficient solution which is close to the best lower bound of each objective function. The solution procedure controls the search direction via updating both the membership values and the aspiration levels. An important characteristic of the approach is that the decision maker's role is concentrated only in evaluating the efficient solution to limit the influences of his/her incomplete knowledge about the problem domain. In addition, the proposed approach can be applied to solve other multi-objective decision making problems. The performance of this solution approach is evaluated by comparing its results with that of the two existing methods in the literature.

Incorporating multiple criteria into the design of conservation area networks : a minireview with recommendations

Abstract: We provide a review of multicriteria decision-making (MCDM) methods that may potentially be used during systematic conservation planning for the design of conservation area networks (CANs). We review 26 methods and present the core ideas of 19 of them. We suggest that the computation of the non-dominated set (NDS) be the first stage of any such analysis. This process requires only that alternatives be qualitatively ordered by each criterion. If the criteria can also be similarly ordered, at the next stage, Regime is the most appropriate method to refine the NDS. If the alternatives can also be given quantitative values by the criteria, Goal Programming will prove useful in many contexts. If both the alternatives and the criteria can be quantitatively evaluated, and the criteria are independent of each other but may be compounded, then multi-attribute value theory (MAVT) should be used (with preferences conveniently elicited by a modified Analytic Hierarchy Process (mAHP) provided that the number of criteria is not large).

Design of optimal food-based complementary feeding recommendations and identification of key "problem nutrients" using goal programming.

Abstract: The WHO is urging countries to promote improved complementary feeding practices to ensure optimal health, growth, and development of young children. To help achieve this, a rigorous 4-phase approach for designing optimal population- specific food-based complementary feeding recommendations (CFRs) was developed and is illustrated here. In phase I, an optimized diet is selected, using goal programming (Model #1), which aims to provide a desired nutrient content with respect to habitual diet patterns and cost. Based on its food patterns, a set of draft CFRs is designed. In phase II, their success for ensuring a nutritionally adequate diet is assessed via linear programming (Model type #2) by sequentially minimizing and maximizing the level of each nutrient (i.e., worst and best-case scenarios) while respecting the CFRs. For nutrients that are <70% of desired levels, the best food sources are identified via linear programming in phase III (Model #3). Different combinations of these foods are incorporated into the original draft of the CFRs to produce alternative CFRs, which are then compared on the basis of their cost, flexibility, and "worst-case scenario" nutrient levels (Model type #2) to select, in phase IV, a final set of CFRs. A hypothetical example is used to illustrate this approach. Outcomes include a set of optimal, population-specific CFRs and practical information regarding key "problem nutrients" in the local diet. Such information is valuable for nutrition promotion, as well as nutrition program planning and advocacy, to help achieve global initiatives for improving the complementary feeding practices of young children living in disadvantaged environments.

An application of the integrated AHP‐PGP model in supplier selection

Abstract: Purpose – The supplier selection problem has gained great attention in business management literature. The first objective of this study is to determine the required variables in selecting the best suppliers and to develop a supplier selection model based on these selected variables. The second objective is to explain how an integrated AHP‐PGP model can be used in supplier selection decisions while minimizing suppliers' defects rate, rate of late order delivery, purchasing costs, and maximizing suppliers' scores and after‐sales service levels.Design/methodology/approach – An integration of an analytic hierarchy process (AHP) and multi‐objective pre‐emptive goal programming (PGP) is used to consider both quantitative and qualitative factors in selecting the best suppliers and allocating the optimum order quantities among them.Findings – The integrated model is presented with a real‐world application using source data provided by the manufacturing firm operating in an automotive industry in Turkey. Findings...

Disassembly to order system under uncertainty

Abstract: This paper presents a multi-criteria optimization model of a disassembly-to-order (DTO) system under uncertainty. The goal of the proposed model is to determine the best combination of the number of each product type to be taken back from the last user and/or collectors. The EOL products are then disassembled for the retrieval of reusable components and materials and resold in order to meet a certain level of demand under a variety of physical, financial and environmental constraints. The surplus components are recycled, stored for usage in subsequent periods or properly disposed. The problem is modeled as a multi-criteria decision-making problem under uncertainty, where the aspiration levels for various goals are more likely to be in the “approximately more (less) than” and/or “more (less) is better” form. We employ fuzzy goal programming technique to solve the problem. When solved, the model provides the number of EOL products to be taken back as well as the number of items reused, recycled, stored and disposed. The values of a host of other performance measures are also obtained, including total profit, materials and items sales revenues, take back cost, transportation costs as well as costs of preparation of EOL products, destructive disassembly, non-destructive disassembly, recycling, storage and disposal. A case example is presented to illustrate the model's implementation.

Incorporating Quality Considerations Into Project Time/Cost Tradeoff Analysis and Decision Making

Abstract: Existing models and methods of project scheduling implicitly assume uniform quality when evaluating time/cost tradeoffs, but do not model quality explicitly. For a project manager such as a general contractor who subcontracts most tasks of a project, or other project managers who face decisions concerning the level of quality to perform for each task, this is not a realistic assumption. In this paper, we extend the standard discrete time/cost tradeoff problem by assuming that each option for each task is evaluated for its duration, cost, and also its quality. We then provide a mixed integer linear programming model for solving one version of this problem, and illustrate with an example. We next formulate a goal programming mixed integer linear program for a very general version of the problem. This formulation allows for many different definitions of quality, and many combinations of time, cost, and quality goals with preemptive and/or weighted priorities, and is also illustrated with an example. We show how these models can be used to generate quality level curves to illustrate the tradeoffs among time, cost, and quality. These level curves can then be used by project managers to make project scheduling decisions that explicitly model and consider quality as well as time and cost, so that better and more appropriate decisions can be made for a particular situation

An Integrated Approach for Supplier Selection

Abstract: Purchasing decisions are no longer made based on best price but companies are increasingly considering both tangible and intangible benefits that suppliers bring to the long term growth of the company. This paper addresses the multi-objective criteria pertaining to supplier selection process by a combination of Quality Function Deployment (QFD), Analytical Hierarchy Process (AHP) and Preemptive Goal Programming (PGP) techniques. QFD facilitates in blending the requirement for suppliers and supplier evaluating criteria. AHP then helps in systematically prioritizing the relative importance of the requirements enumerated as part of the QFD. Finally, PGP aids in the formulation to maximize the value proposition and to minimize the cost involved by exploiting volume discounts.

Hybrid enhanced genetic algorithm to select optimal machining parameters in turning operation

Abstract: The current paper presents a hybrid enhanced genetic algorithm that is developed for solving the optimization problems in design and manufacturing. The present approach is applied to optimize turning operation for the determination of cutting parameters considering minimum production cost under a set of machining constraints. A refined design space for population is introduced by integrating the robust parameter design concept into the genetic algorithm to solve multi-objective and single-objective optimization problems. First, the proposed approach is validated using test problems and metrics taken from literature. Finally, it is applied to the turning optimization problem. The computational experimental results show the effectiveness of the proposed approach in the turning optimization problem.

Multiple criteria decision-making techniques in higher education

Abstract: Purpose: The purpose of this paper is to review the literature which focuses on four major higher education decision problems. These are: resource allocation; performance measurement; budgeting; and scheduling. Design/methodology/approach: Related articles appearing in the international journals from 1996 to 2005 are gathered and analyzed so that the following three questions can be answered: "What kind of decision problems were paid most attention to?"; "Were the multiple criteria decision-making techniques prevalently adopted?"; and "What are the inadequacies of these approaches?" Findings: Based on the inadequacies, some improvements and possible future work are recommended, and a comprehensive resource allocation model is developed taking account of these factors. Finally, a new knowledge-based goal programming technique which integrates some operations of analytic hierarchy process is proposed to tackle the model intelligently. Originality/value: Higher education has faced the problem of budget cuts or constrained budgets for the past 30 years. Managing the process of the higher education system is, therefore, a crucial and urgent task for the decision makers of universities in order to improve their performance or competitiveness. © Emerald Group Publishing Limited.

Prioritizing quality characteristics in dynamic quality function deployment

Abstract: Due to the combination of rapid influx of new technology, high pressure on time-to-market and increasing globalization, the number of products that have highly uncertain and dynamic specifications or customer requirements might significantly increase. In order to deal with these inherently volatile products or services, we need to adopt a more pro-active approach in order not to produce an unwanted product or service. Thus, based on the idea of the quality loss function and the zero-one goal programming, an intuitively simple mathematical model is developed to prioritize the quality characteristics (QCs) in the dynamic quality function deployment (QFD). It incorporates a pro-active approach towards providing products and services that meet the future voice of the customer (FVOC). The aim is to determine and prioritize only the ‘important’ QCs with a greater confidence in meeting the FVOC. It is particularly useful when the number of the potentially dominant QCs is very large so that, by using the prioriti...

Multi-Objective Single Product Robust Optimization: An Integrated Design and Marketing Approach

Abstract: We present an integrated design and marketing approach to facilitate the generation of an optimal robust set of product design alternatives to carry forward to the prototyping stage. The approach considers variability in both (i) engineering design domain, and (ii) customer preferences in marketing domain. In the design domain, the approach evaluates performance and robustness of a design alternative due to variations in its uncontrollable parameters. In the marketing domain, in addition to considering competitive product offerings, the approach considers designs that are robust in customer preferences with respect to: (1) the variations in the design domain, and (2) the inherent variations in the estimates of preferences given the fit of the preference model to the sampled data. Our overall goal is to obtain design alternatives that are multi-objectively robust and optimal, i.e., (1) are optimal for nominal values of parameters, and (2) are within a known acceptable range in their multi-objective performance, and (3) maintain feasibility even when they are subject to applications and environments that are different from nominal or standard laboratory conditions. We illustrate the highlights of our approach with the design of a corded power tool example.

A fuzzy goal-programming model of machine-tool selection and operation allocation problem in FMS: a quick converging simulated annealing-based approach

Abstract: Fuzzy set theory has been widely accepted in modelling of some of the vague phenomena and relationships that are non-stochastic in nature. The problem of machine-tool selection and operation allocations in a flexible manufacturing system usually involves parameters that are non-deterministic and imprecise in nature. This paper adopts a fuzzy goal-programming model having multiple conflicting objectives and constraints pertaining to the machine-tool selection and operation allocation problem, and a new random search optimization methodology termed Quick Converging Simulated Annealing (QCSA) is being used to resolve the underlying issues. The main feature of the proposed QCSA algorithm is that it outperforms genetic algorithm and simulated annealing approaches as far as convergence to the near optimal solution is concerned. Moreover, it is also capable of eluding local optima. Extensive experiments are performed on a problem involving real-life complexities, and some of the computational results are reporte...

Programming declarative goals using plan patterns

Abstract: AgentSpeak is a well-known language for programming intelligent agents which captures the key features of reactive planning systems in a simple framework with an elegant formal semantics. However, the original language is too abstract to be used as a programming language for developing multi-agent system. In this paper, we address one of the features that are essential for a pragmatical agent programming language. We show how certain patterns of AgentSpeak plans can be used to define various types of declarative goals. In order to do so, we first define informally how plan failure is handled in the extended version of AgentSpeak available in Jason, a Java-based interpreter; we also define special (internal) actions used for dropping intentions. We then present a number of plan patterns which correspond to elaborate forms of declarative goals. Finally, we give examples of the use of such types of declarative goals and describe how they are implemented in Jason.

Goal programming models for multiple attribute decision making under linguistic setting

Abstract: In this paper,we study the multiple attribute decision making problems,in which the information about attribute weights is partly known and the attribute values take the form of linguistic variables or uncertain linguistic variables,and the decision maker has preferences on alternatives.We introduce the operation laws of linguistic variables and uncertain linguistic variables and a formula of possibility degree for the comparison between uncertain linguistic variables,and then define the concept of deviation degree between linguistic variables.We establish two goal programming models based on the concept of deviation degree under the situations where the attribute values are linguistic variables and uncertain linguistic variables respectively.By solving these two models, the attribute weights can be obtained.After that,when the attribute values are linguistic variables,we utilize the linguistic weighted averaging(LWA) operator to aggregate the given linguistic decision information,and then rank the alternatives and select the most desirable one(s);when the attribute values are uncertain linguistic variables,we utilize the uncertain linguistic weighted averaging(ULWA) operator to aggregate the given uncertain linguistic decision information and utilize the formula of possibility degree to construct a possibility degree matrix(or called complementary judgement matrix),and then utilize the priority formula of complementary judgement matrix to rank the alternatives and to select the most desirable one(s).Finally,an illustrative example is also given.

A multi‐objective 3PL allocation problem for fish distribution

Abstract: Purpose – The primary objective of this paper is to show how mathematical modeling can be used for solving a third party logistics (3PL) allocation problem.Design/methodology/approach – The solution approach consists of finding a compromise solution for the six different strategies, defined in the paper by using lexicographic goal programming involving three objectives under some realistic constraints related to capacities of the markets.Findings – This study investigates the usefulness and efficacy of the proposed method for a 3PL allocation problem for a case example of a typical fish supply network. The decision‐makers can evaluate the alternative solutions with respect to a set of decision criteria. The result indicates substantial improvement by reducing the number of 3PL service providers and reallocating them to the case fish markets.Practical implications – The work provides a useful decision model for practicing managers, policy makers and researchers of this area.Originality/value – This model w...

A new hybrid fuzzy-goal programming scheme for multi-objective topological optimization of static and dynamic structures under multiple loading conditions

Abstract: This work presents a hybrid fuzzy-goal multi-objective programming scheme for topological optimization of continuum structures, in which both static and dynamic loadings are considered. The proposed methodology fortopological optimization first employs a fuzzy-goal programming scheme at the top level for multi-objective problems with static and dynamic objectives. For the static objective with multi-stiffness cases in the fuzzy-goal formulation, a hybrid approach, involving a hierarchical sequence approach or a hierarchical sequence approach coupled with a compromise programming method, is especially suggested for the statically loaded multi-stiffness structure at the sublevel. Concerning dynamic optimization problems of freevibration cases, nonstructural mass, oscillation of the objective function, and repeated eigenvalues are also discussed. Solid Isotropic Material with Penalization density–stiffness interpolation scheme is used to indicate the dependence ofmaterial modulus upon regularized element densities. The globally convergent version of the method of moving asymptotes and the sequential linear programming method areboth employed as optimizers. Several applications have been applied to demonstrate the validation of the presented methodologies.

ANP-GP approach for product variety design

Abstract: Developing product variety under a robust platform provides a company with an important competitive advantage. The competitive benefits include reducing engineering costs and time to market, extending product portfolios and expanding market share. This study illustrated an algorithm based design methodology for achieving optimal product architecture. The impact of customer requirements (CRs) and the interdependencies among the CRs and components should be considered when assessing the components that are the focus of redesign in market shifts. This paper employs the analytic network process (ANP) to fulfil this requirement. The goal programming (GP) approach that incorporates the results from the ANP is then is applied to select the variant components within budget limits. Therefore, this study investigates drivers of the component variations to ensure the redesigned parts meet the requirements of specialized niches in the segment markets. Finally, an example of coffee maker design is presented to illustrate the feasibility of the proposed method.

An enhanced goal programming method for generating priority vectors

Abstract: Generating priority vectors from pairwise comparison matrices is an essential part of the analytical hierarchy process. Besides the well-known right eigenvector method and the logarithmic least squares method, Bryson proposed a goal programming method (GPM) for achieving the above. Although the GPM always obtains an optimal solution, this study shows the existence of concealed alternative optimal solutions that might lead to different priority vectors. This study proposed a GPM for overcoming the problem of alternative optimal solution. In contrast with the GPM, the proposed method obtains a unique optimal solution and performs better in various aspects.

Cropping pattern planning under water supply from multiple sources

Abstract: Conventionally, irrigation development planning has been based on cropping pattern selection aiming at maximizing the revenue from irrigation activities. In the real world however, several complexities make the cropping pattern selection a more complicated mathematical problem. Of great interest is the case of water supply from multiple sources (e.g. surface and groundwater) in which a multi-criteria approach is most appropriate. Goal programming has been used in the past to solve cropping pattern selection problems, with criteria of a similar nature, the net benefit being included as a constraint. This paper presents a methodology, based on the fuzzy set theory, for enhancing the goal programming approach to solve similar problems under various sets of criteria of a different nature. In the proposed methodology the net benefit maximization is considered together with all other criteria. The methodology is illustrated using data from the Thessaly Plain in Greece.