Showing papers on "Goal programming published in 2014"

Gas Network Optimization: A comparison of Piecewise Linear Models

Abstract: Gas network optimization manages the gas transport by minimizing operating costs and fulfilling contracts between consumers and suppliers. This is an NPhard problem governed by non-convex and nonlinear gas transport functions that can be modeled by mixed integer linear programming (MILP) techniques. Under these methods, piecewise linear functions describe nonlinearities and binary variables avoid local optima due to non-convexities. This paper compares theoretically and computationally basic and advanced MILP formulations for the gas network optimization in dynamic or in steady-state conditions. Case studies are carried out to compare the performance of each MILP formulation for different network configurations, sizes and levels of complexity. In addition, since the accuracy of linear approximations significantly depends on the number and location of linear segments, this paper also proposes a goal programming method to construct a-priori the piecewise linear functions. This method is based on the minimization of the mean squared error of each approximation subject to predefined error goals.

How to use multi-criteria decision analysis methods for reimbursement decision-making in healthcare: a step-by-step guide

Abstract: In recent years, the quest for more comprehensiveness, structure and transparency in reimbursement decision-making in healthcare has prompted the research into alternative decision-making frameworks. In this environment, multi-criteria decision analysis (MCDA) is arising as a valuable tool to support healthcare decision-making. In this paper, we present the main MCDA decision support methods (elementary methods, value-based measurement models, goal programming models and outranking models) using a case study approach. For each family of methods, an example of how an MCDA model would operate in a real decision-making context is presented from a critical perspective, highlighting the parameters setting, the selection of the appropriate evaluation model as well as the role of sensitivity and robustness analyses. This study aims to provide a step-by-step guide on how to use MCDA methods for reimbursement decision-making in healthcare.

On solving multi-level multi objective linear programming problems through fuzzy goal programming approach

Abstract: In this paper, we propose an alternate technique based on fuzzy goal programming approach for solving multi-level multi objective linear programming problem (ML-MOLPP) which is simpler and requires less computational works than that of proposed algorithm by Baky, I. A. (Applied Mathematical Modelling, 34(2010), 2377–2387). In formulation of FGP model each objective functions at each level are transformed into fuzzy goals. Suitable membership function for every fuzzily described transformed objective functions at each level as well as the control vectors of each level decision makers are defined by determining individual optimal solution of each objective function at each of the decision making level. Then FGP approach is used for achieving highest degree of each of these membership goals by minimizing the sum of negative deviational variables. To avoid decision deadlock, solution preferences by the decision makers at each level are not taken into account of proposed FGP technique. The aim of this paper is to present simple technique to obtain compromise optimal solution of ML-MOLP problems. A comparative analysis based on numerical examples is also carried out to show similarity between two solution methodologies.

Solving multi-choice multi-objective transportation problem: a utility function approach

Abstract: This paper explores the study of multi-choice multi-objective transportation problem (MCMTP) under the environment of utility function approach. MCMTP is converted to multi-objective transportation problems (MOTP) by transforming the multi-choice parameters like cost, demand, and supply to real-valued parameters. A general transformation procedure using binary variables is illustrated to reduce MCMTP into MOTP. Most of the MOTP are solved by goal programming (GP) approach. Using GP, the solution of MOTP may not be satisfied all the time by the decision maker (DM) when the proposed problem contains interval-valued aspiration level. To overcome this difficulty, here we propose the approaches of revised multi-choice goal programming (RMCGP) and utility function into the MOTP and then compared the solution between them. Finally, numerical examples are presented to show the feasibility and usefulness of our paper.

Participatory Goal Programming in Forest Management: An Application Integrating Several Ecosystem Services

Abstract: In this study, we propose a procedure for integrating several ecosystem services into forest management by using the well-known multi-criteria approach called goal programming It shows how interactions with various stakeholders are essential in order to choose the goal programming model applied, as well as some of its basic components (variant, targets, preferential weights, etc) This methodology has been applied to a real forest management case where five criteria have been selected: timber production, wild edible mushroom production, carbon sequestration, net present value of the underlying investment, and a criterion associated with the sustainability of forest management defined by the idea of a normal forest Given the characteristics of some of these criteria, such as mushroom production, the model has been developed in two scenarios: one deterministic and another with a Monte Carlo analysis The results show a considerable degree of conflict between the proposed criteria By applying several goal programming models, different Paretian efficient solutions were obtained In addition, some results in Monte Carlo analysis for several criteria show notable variations This fact is especially notable for the mushroom production criterion Finally, the proposed approach seems attractive and can be directly applied to other forest management situations

Multiobjective Optimization Using Goal Programming for Industrial Water Network Design

Abstract: The multiobjective optimization (MOO) of industrial water networks through goal programming is studied using a mixed-integer linear programming (MILP) formulation. First, the efficiency of goal programming for solving MOO problems is demonstrated with an introductive mathematical example and then with industrial water and energy networks design problems, formerly tackled in literature with other MOO methods. The first industrial water network case study is composed of 10 processes, 1 contaminant, and 1 water regeneration unit. The second, a more complex real industrial case study, is made of 12 processes, 1 contaminant, 4 water regeneration units, and the addition of temperature requirements for each process, which implies the introduction of energy networks alongside water networks. For MOO purposes, several antagonist objective functions are considered according to the case, such as total freshwater flow rate, number of connections, and total energy consumption. The MOO methodology proposed is demonstrated to be very reliable as an a priori method, by providing Pareto-optimal compromise solutions in significant less time compared to other traditional methods for MOO.

A Selection Model to Logistic Centers Based on TOPSIS and MCGP Methods: The Case of Airline Industry

Abstract: The location selection of a logistics center is a crucial decision relating to cost and benefit analysis in airline industry. However, it is difficult to be solved because there are many conflicting and multiple objectives in location problems. To solve the problem, this paper integrates fuzzy technique for order preference by similarity to an ideal solution (TOPSIS) and multichoice goal programming (MCGP) to obtain an appropriate logistics center from many alternative locations for airline industry. The proposed method in this paper will offer the decision makers (DMs) to set multiple aspiration levels for the decision criteria. A numerical example of application is also presented.

Model Selection by Linear Programming

Abstract: Budget constraints arise in many computer vision problems. Computational costs limit many automated recognition systems while crowdsourced systems are hindered by monetary costs. We leverage wide variability in image complexity and learn adaptive model selection policies. Our learnt policy maximizes performance under average budget constraints by selecting “cheap” models for low complexity instances and utilizing descriptive models only for complex ones. During training, we assume access to a set of models that utilize features of different costs and types. We consider a binary tree architecture where each leaf corresponds to a different model. Internal decision nodes adaptively guide model-selection process along paths on a tree. The learning problem can be posed as an empirical risk minimization over training data with a non-convex objective function. Using hinge loss surrogates we show that adaptive model selection reduces to a linear program thus realizing substantial computational efficiencies and guaranteed convergence properties.

A QoS-oriented Web service composition approach based on multi-population genetic algorithm for Internet of things

Abstract: Internet of things (IoT) will create new opportunities to build applications that better integrate real-time state of the industry. With Web services accomplishing similar function proliferated, industrial enterprises have to choose appropriate Web services according Quality of Service (QoS) properties. It introduces the problem of QoS-oriented service composition (QSC). This study formulates the QSC problem as a multi-criteria goal programming (MCGP) model, and develops a multi-population genetic algorithm (MGA) to solve the model. MCGP not only automatically assigns high quality Web services to combine a composite service, but also finds non inferior composite services by relaxing QoS constraints to satisfy users' QoS requirements. Empirical comparisons demonstrate MGA outperforms to the GA. Moreover, the experiments indicate MGA is capable to solve the large-scale QSC problem in terms of efficiency and scalability.

Stochastic goal programming in forest planning

Abstract: Developing a forest management plan in a multicriteria perspective is traditionally accomplished utilizing simulation and optimization tools as a means to predict and optimize a variety of criteria...

Solving an outpatient nurse scheduling problem by binary goal programming

Abstract: The cyclic scheduling for staffs in hospitals has been widely studied but none in the literature is about the nurse scheduling problem in the outpatient departments. Considering fairness and relevant constraints in scheduling outpatient nurses, this study formulates the nurse assignment problem as a binary goal programming model. An optimized and “fair” weekly schedule satisfying the stated hospital’s policies and the outpatient nurses’ preferences is solved. A “fair” schedule is in the sense that the number of due shifts and the shift patterns are assigned as same as possible for all the full-time nurses during the weekdays. A sensitivity analysis on the number of full-time nurses is also performed to answer the question about the best utilization in scheduling nurses. This study replaces the manual-made schedule with a computerized one and provides hospital managers insights in identifying the best human-resource portfolio in outpatient nurse scheduling.

Evaluating Project Managers by an Interval Decision-Making Method Based on a New Project Manager Competency Model

Abstract: Selecting the competent project manager is one of the key factors for project success. This paper focuses on two main objectives: (1) presenting a project manager competency model in three dimensions of knowledge, performance and behavioral competency criteria and (2) proposing a proper interval decision-making method for evaluating project managers and selecting the competent project manager based on the presented competency model. The proposed evaluation method utilizes a goal programming technique to calculate interval weights of criteria and uses TOPSIS technique with interval weights and judgment data to rank candidate project managers and select the best of them.

Integrated multi-choice goal programming and multi-segment goal programming for supplier selection considering imperfect-quality and price-quantity discounts in a multiple sourcing environment

Abstract: Supplier selection SS is a multi-criteria and multi-objective problem, in which multi-segment e.g. imperfect-quality discount IQD and price-quantity discount PQD and multi-aspiration level problems may be significantly important; however, little attention had been given to dealing with both of them simultaneously in the past. This study proposes a model for integrating multi-choice goal programming and multi-segment goal programming to solve the above-mentioned problems by providing the following main contributions: 1 it allows decision-makers to set multiple aspiration levels on the right-hand side of each goal to suit real-world situations, 2 the PQD and IQD conditions are considered in the proposed model simultaneously and 3 the proposed model can solve a SS problem with n suppliers where each supplier offers m IQD with r PQD intervals, where only extra binary variables are required. The usefulness of the proposed model is explained using a real case. The results indicate that the proposed model not only can deal with a SS problem with multi-segment and multi-aspiration levels, but also can help the decision-maker to find the appropriate order quantities for each supplier by considering cost, quality and delivery.

Maintenance supplier selection considering life cycle costs and risks: a fuzzy goal programming approach

Abstract: In this study, we address a new variant of supplier selection problem named maintenance supplier selection problem faced by a manufacturer. The production system consists of different multi-component equipments whose maintenance activities require several components (parts) each of which could be provided by multiple suppliers. A multi-objective mathematical model is developed to decide about the supply base of each part as well as the purchasing quantity of each part from each selected supplier. The model accounts for the total life cycle costs of purchased parts and various risks threatening the candidate suppliers. A fuzzy/soft lexicographic goal programming approach with soft priorities between objectives is proposed to enable the decision-maker to make preferred trade-offs between objectives by which the effects of various risks in each phase of life cycle of procured parts are investigated. The capability and effectiveness of the proposed model is validated through a case study. Some sensitivity ana...