Showing papers on "Goal programming published in 2011"

An integrated fuzzy TOPSIS and MCGP approach to supplier selection in supply chain management

Abstract: Supplier selection is an important issue in supply chain management. In recent years, determining the best supplier in the supply chain has become a key strategic consideration. However, these decisions usually involve several objectives or criteria, and it is often necessary to compromise among possibly conflicting factors. Thus, the multiple criteria decision making (MCDM) becomes a useful approach to solve this kind of problem. Considering both tangible and intangible criteria, this study proposes integrated fuzzy techniques for order preference by similarity to ideal solution (TOPSIS) and multi-choice goal programming (MCGP) approach to solve the supplier selection problem. The advantage of this method is that it allows decision makers to set multiple aspiration levels for supplier selection problems. The integrated model is illustrated by an example in a watch firm.

Goal programming : methodology and applications

Abstract: List of Figures. List of Tables. Preface. 1. Introduction to Goal Programming. 2. Goal Programming Model Formulation Strategies. 3. Goal Programming Solution Methodology. 4. Goal Programming Applications. 5. Future Trends in Goal Programming. Appendix A: Textbooks, Readings Books and Monographs on Goal Programming. Appendix B: Journal Research Publications on Goal Programming. Index.

Designing a sustainable supply chain using an integrated analytic network process and goal programming approach in quality function deployment

Abstract: Sustainable supply chain management (SSCM) provides economic, social end environmental requirements in material and service flows occurring between suppliers, manufacturers and customers. SSCM structure is considered as a prerequisite for a sustainable success. Thus designing an effective SCM structure provides competitive advantages for the companies. In order to achieve an effective design of this structure, it is possible to apply quality function deployment (QFD) approach which is successfully applied as an effective product and system development tool. This study presents a decision framework where analytic network process (ANP) integrated QFD and zero-one goal programming (ZOGP) models are used in order to determine the design requirements which are more effective in achieving a sustainable supply chain (SSC). The first phase of the QFD is the house of quality (HOQ) which transforms customer requirements into product design requirements. In this study, after determining the sustainability requirements named customer requirements (CRs) and design requirements (DRs) of a SSC, ANP is employed to determine the importance levels in the HOQ considering the interrelationships among the DRs and CRs. Furthermore ZOGP approach is used to take into account different objectives of the problem. The proposed method is applied through a case study and obtained results are discussed.

The optimal priority models of the intuitionistic fuzzy preference relation and their application in selecting industries with higher meteorological sensitivity

Abstract: This paper proposes a least squares method and a goal programming method to get the priority of the intuitionistic fuzzy preference relation (IFPR). The relation between the IFPR and the interval fuzzy number preference relation (IFNPR) is established by splitting the IFPR into the membership degree interval judgment matrix and the non-membership degree interval judgment matrix. Considering the fact that the priority method must be based on the consistent condition, we propose the additive consistent conditions of the IFPR according to that of the IFNPR. However, in real-life decision situations, such consistent conditions are hard to be satisfied. For deriving the priority vector of the IFPR, the least squares model and the goal programming model are put forward. It is also shown that the same methods apply to the circumstance of group decision making. The numerical examples are provided to show that the methods proposed are valid, and the case study of selecting industries with higher meteorological sensitivity by using the intuitionistic fuzzy hierarchic structure model is given to show that the methods proposed are practical.

A combined approach for equipment selection: F-PROMETHEE method and zero-one goal programming

Abstract: With the labor and material, equipments will be used in production processes, are one of the essential components of the production systems. Equipments used in production process are an important subject, effecting the system efficiency, the labor effectiveness and the product quality, and using inappropriate equipment effects all of this issues negatively. Equipment selection is a very important point for an efficient production system and is a complex and exhaustive problem necessitating the most proper selection among the various types of equipments seeming almost identical, generally. Therefore, an equipment selection problem is a multi-criteria decision making problem entailing taking into account of several criteria and generally involving linguistic datas. In this study, a multi-criteria decision making problem compromising a ranking due to criterias expressed in a linguistic way, concerning a welding machine selection problem of a company is handled. The vagueness of the linguistic terms in the evaluation process required employment of fuzzy numbers and accordingly the fuzzy version of PROMETHEE method, which is a multi-criteria ranking technique, is applied to the selection problem. The information obtained from F-PROMETHEE results is then used as a constraint in formulating a zero-one goal programming model. We demonstrated how a combined F-PROMETHEE and ZOGP model can be used for a real world application problem as an aid for equipment selection.

A multiobjective chance constrained programming model for supplier selection under uncertainty

Abstract: Risk management is an inherent part of supplier selection. While companies are enjoying the benefits of outsourcing, risks brought by this practice should be taken into account in the process of decision making. This paper presents a multiobjective stochastic sequential supplier allocation model to help in supplier selection under uncertainty. Demand for products, capacities at suppliers as well as transportation and other variable costs are the main sources of uncertainty and are modeled using probability distributions. Disruptions are exogenous events and the model provides proactive mitigation strategies against disruptions by assigning backup suppliers who can be used in case of a default at a primary supplier. When there is no disruption, the model’s solution is an optimal supplier order assignment, considering operational risks.

A new multiobjective signal optimization for oversaturated networks

Abstract: This paper presents a new methodology for optimizing the signal timing controls of oversaturated networks based on the cell transmission model and a goal programming technique with multiple objectives. The proposed model accounts for intersection spillovers, equity in delays, and system throughputs. This new formulation is solved by genetic algorithms to obtain signal timing plans. A case study with a nine-intersection network and a comparison between the proposed model and the throughput-maximizing strategy are examined. It is found that the new method can efficiently minimize spillovers, balance delay equity, and provide reasonable system throughputs in their respective order for oversaturated networks. The result also indicates that the throughput-maximizing strategy does not always yield minimum spillovers for oversaturated networks and occasionally provides a larger difference in average link delay at a spillover intersection than the proposed model does.

A lexicographical goal programming based decision support system for logistics of Humanitarian Aid

Abstract: Each year people affected by disasters, either natural or human-made, can be counted by millions When a major disaster strikes a country, local and international communities usually respond with an outpouring of assistance, which has to be efficiently managed in order to arrive where it is needed as soon as possible and under adverse conditions Despite its importance, not until recently has Humanitarian Logistics received much attention as a specific field, and there is a lack of specific tools In this work, a lexicographical goal programming model for distribution of goods to the affected population of a disaster in a developing country is presented, which sustains a decision support system currently in development

Design and Implementation of Decision Support Systems in the Public Sector

Abstract: This article examines the implications of utilizing decision support systems (DSS) in the public sector based on a DSS developed and implemented for a community mental health system. The DSS includes a multiple objective (goal programming) allocation model and encompasses a multiple party decision process. The experiences and insights acquired during the development and implementation of this DSS are relevant to public sector decision support in general. The importance of a DSS as a process-support aid rather than a product-oriented aid (i.e., simply providing answers) and the interaction of system architecture and the chosen design strategy are key insights. In particular, the distinction between model-oriented and data-oriented DSS does not appear to be appropriate. The public sector decision maker's concern with issues of equity requires the ability to operate in a higher dimensional framework than the typical spreadsheet model and there is a critical need for communication support.

A New Multiobjective Signal Optimization for Oversaturated Networks

Abstract: This paper presents a new methodology for optimizing the signal timing controls of oversaturated networks based on the cell transmission model and a goal programming technique with multiple objectives. The proposed model accounts for intersection spillovers, equity in delays, and system throughputs. This new formulation is solved by genetic algorithms to obtain signal timing plans. A case study with a nine-intersection network and a comparison between the proposed model and the throughput-maximizing strategy are examined. It is found that the new method can efficiently minimize spillovers, balance delay equity, and provide reasonable system throughputs in their respective order for oversaturated networks. The result also indicates that the throughput-maximizing strategy does not always yield minimum spillovers for oversaturated networks and occasionally provides a larger difference in average link delay at a spillover intersection than the proposed model does.

Fuzzy Stochastic Multiobjective Programming

Abstract: Although studies on multiobjective mathematical programming under uncertainty have been accumulated and several books on multiobjective mathematical programming under uncertainty have been published (e.g., Stancu-Minasian (1984); Slowinski and Teghem (1990); Sakawa (1993); Lai and Hwang (1994); Sakawa (2000)), there seems to be no book which concerns both randomness of events related to environments and fuzziness of human judgments simultaneously in multiobjective decision making problems. In this book, the authors are concerned with introducing the latest advances in the field of multiobjective optimization under both fuzziness and randomness on the basis of the authors continuing research works. Special stress is placed on interactive decision making aspects of fuzzy stochastic multiobjective programming for human-centered systems under uncertainty in most realistic situations when dealing with both fuzziness and randomness. Organization of each chapter is briefly summarized as follows:Chapter 2 is devoted to mathematical preliminaries, which will be used throughout the remainderof the book. Starting with basic notions and methods of multiobjective programming, interactivefuzzy multiobjective programming as well as fuzzy multiobjective programming is outlined.In Chapter 3, by considering the imprecision of decision makers (DMs) judgment for stochasticobjective functions and/or constraints in multiobjective problems, fuzzy multiobjective stochasticprogramming is developed. In Chapter 4, through the consideration of not only the randomness of parameters involved inobjective functions and/or constraints but also the experts ambiguous understanding of the realized values of the random parameters, multiobjective programming problems with fuzzy random variables are formulated. In Chapter 5, for resolving conflict of decision making problems in hierarchical managerial orpublic organizations where there exist two DMs who have different priorities in making decisions, two-level programming problems are discussed. Finally, Chapter 6 outlines some future research directions.

A fuzzy AHP-GP approach for integrated production-planning considering manufacturing partners

Abstract: Research highlights? We solve an integrated production-planning problem where manufacturing partners exist. ? To solve the problem, a fuzzy AHP-GP approach is proposed. ? The fuzzy AHP is utilized to determine relative weights of manufacturing partners. ? The GP is used to formulate the integrated production-planning problem. ? Our approach is illustrated by an example adopted from a TFT-LCD manufacturing firm. Most manufacturing companies cooperate with manufacturing partners in order to have flexible usable capacities to meet variable customer demand. In this environment, evaluations of manufacturing partners and integrated production-planning have become a critical issue. For integrated production-planning for all local manufacturing sites and manufacturing partners, the company should first evaluate the manufacturing partner, and then should decide how to allocate the production quota among internal facilities and manufacturing partners. In this paper, we propose a fuzzy analytic hierarchy process (AHP)-goal programming (GP) approach to solve this integrated production-planning problem. The fuzzy AHP is utilized to determine relative weights of manufacturing partners, while the GP is used to formulate the integrated production-planning problem. The proposed approach is illustrated by an example adopted from a TFT-LCD manufacturing firm.

Mixed valuation methods: a combined AHP-GP procedure for individual and group multicriteria agricultural valuation

Abstract: This paper introduces a new assessment method classification, in which a third procedure, mixed valuation, is jointly included with the traditional economic and non-economic methodologies. The paper considers a case of multiple actors (from a previous work by the same authors—Aznar et al. (Estudios de Economia Aplicada, 25(2):389–409, 2007), in which a new technique for multicriteria agriculture valuation (MAVAM) was proposed. The method is specifically designed for situations in which scarce information about the elements being compared (quantified or not) is available. It works in individual and group decision making contexts and attempts to both obtain and incorporate the objective information associated with the tangible aspects of the problem and the subjective knowledge associated with the human factor into the valuation process. It combines two of the most extended multicriteria decision making techniques: the Analytic Hierarchy Process (AHP) and Goal Programming (GP). The first of these enables tangible and intangible information stemming from known elements to be collected by using pairwise comparisons; the second allows the scarce information available and the personal approach to the valuation to be included in the valuation process. The proposed methodology is illustrated by means of its application to a case of individual and group valuation of an agricultural asset in the La Ribera district, Valencia (Spain).

Strategic Enterprise Resource Planning in a Health-Care System Using a Multicriteria Decision-Making Model

Abstract: This paper deals with strategic enterprise resource planning (ERP) in a health-care system using a multicriteria decision-making (MCDM) model. The model is developed and analyzed on the basis of the data obtained from a leading patient-oriented provider of health-care services in Korea. Goal criteria and priorities are identified and established via the analytic hierarchy process (AHP). Goal programming (GP) is utilized to derive satisfying solutions for designing, evaluating, and implementing an ERP. The model results are evaluated and sensitivity analyses are conducted in an effort to enhance the model applicability. The case study provides management with valuable insights for planning and controlling health-care activities and services.

A Multi-objective Stochastic Programming Model for Emergency Logistics Based on Goal Programming

Abstract: This paper addresses the problem of emergency location-allocation in multi-supplier, multi-affected area, multi-relief and multi-vehicle emergency logistics networks. A multi-objective stochastic programming model, which embeds the chance constraints and scenario planning, is proposed to handle the uncertainty of demand, supply and the availability of path. The optimization focuses on minimizing the expected travel time and the proportion of unmet demands, which represent efficiency and fairness respectively. There are also coverage limits for relief suppliers to cover affected areas. The goal programming approach is proposed to solve the multi-objective problem. In the end, computational results based on objective achievement scale indicate the validity of the model and solution.

A Distributed Algorithm for Resource Allocation in OFDM Cognitive Radio Systems

Abstract: We study the problem of allocating subchannels, bits, and powers in a cognitive radio system, in which available system resources are highly dynamic. The modulation scheme employed is orthogonal frequency-division multiplexing (OFDM). In a resource-limited situation under which the nominal-rate requirements of users cannot be satisfied, it is desirable to provide fair degradation among users. In a situation with abundant resources, we may choose to maximize system throughput while ensuring that user nominal-rate requirements are met. The problem is formulated as a single objective nonlinear optimization problem using techniques from goal programming. A distributed resource allocation algorithm is proposed and shown to provide good fairness. In resource-abundant situations, the proposed distributed algorithm yields significantly better system throughput compared with the proportional-rate algorithm.

Multi-Objective Decision-Making In Supplier Selection: An Application Of Visual Interactive Goal Programming

Abstract: Supplier selection by purchasing teams in a supply chain management environment is inherently a multi-objective problem. The authors discuss one of the multiple criteria decision support systems; Visual Interactive Goal Programming (VIG), to assist purchasing teams in their vendor selection decisions. VIG is based on a multi-criteria technique known as Pareto Race. Two examples illustrate the application of VIG in different multi-objective supplier selection environments. The first example demonstrates the allocation of a single product among multiple vendors, while the second example focuses on a multiple-replenishment purchasing problem in selecting supplier and allocating orders among them. The authors conclude with a discussion of VIGs benefits and limitations.