Showing papers on "Goal programming published in 2018"

Designing a Sustainable Development Goal Index through a Goal Programming Model: The Case of EU-28 Countries

Abstract: The 17 Sustainable Development Goals (SDGs) adopted by the United Nations are at the center of the global political agenda to eradicate extreme poverty, achieve universal education, promote gender equality and ensure environmental sustainability between others These goals are organised in 169 indicators, which give an accurate perspective on the main dimensions related with country sustainable development To gain insight into the relative position of involved countries, it is necessary to develop a composite index that summarises the global progress in the achievement of these goals, but considering possible conflicts and trade-offs between individual SDGs The objective of this paper is to introduce a Goal Programming model to calculate a composite SDG index, capable of overcoming some of the limitations of celebrated approaches such as arithmetic and geometric averages The proposed model balances between two extreme solutions: one which calculates a consensus index that reflects the majority trend of the SDGs, and another one which biases the estimated index towards those SDGs that show the most discrepancy with the rest The model is applied on the EU-28 countries, and shows that the best performing countries regarding the sustainable development are Austria and Luxembourg, while Greece and Romania remain as the worst performers

New approach for solving intuitionistic fuzzy multi-objective transportation problem

Abstract: Multi-objective transportation problem (MOTP) under intuitionistic fuzzy (IF) environment is analysed in this paper. Due to the fluctuation of market scenario, we assume that the transportation cost, the supply and the demand parameters are not always precise. Hence, the parameters are imprecise, i.e., they are IF numbers. Considering the specific cut interval, the IF transportation cost matrix is converted to interval cost matrix in our proposed problem. Again, using the same concept, the IF supply and the IF demand of the MOTP are reduced to the interval form. Then the proposed MOTP is changed into the deterministic MOTP, which includes interval form of the objective functions. Two approaches, namely intuitionistic fuzzy programming and goal programming, are used to derive the optimal solutions of our proposed problem, and then the optimal solutions are compared. A numerical example is included to illustrate the feasibility and the applicability of the proposed problem. Finally, we present the conclusions with the future scopes of our study.

Effect of inspection performance in smart manufacturing system based on human quality control system

Abstract: Quality control at every stage of manufacturing is a key aspect of the quality management system of any organization. Inspection at different stages of manufacturing is essential to achieve required quality of the product. This knowledge area has been studied extensively in the past with respect to inspection strategies, inspection location, and inspection intervals to minimize inspection cost. However, there is a lack of literature that examines the relationship between inspection performance and factors related to human labor and inspection time of different products. Here, offline inspection is investigated to achieve the process target values by determining the optimal number of inspectors for different products. Three skill levels for inspectors are selected on the basis of their inspection errors, inspection quantities, and inspection cost. The purpose of this study is to achieve the optimum results of objective functions that consist of inspection cost, outgoing quality, and inspection quantity by determining the optimal value of decision variables, i.e., the number of inspectors with respect to their skill. A multi-objective optimization model is developed using a stochastic approach to determine the optimal results of the objective functions and decision variables. Firstly, goal programming is employed to verify the optimization model by using numerical examples. Secondly, sensitivity analysis is considered to illustrate the effect of incoming quantity on inspection performance and optimal combination of decision variables.

Goal Programming Approaches to Managing Consistency and Consensus for Intuitionistic Multiplicative Preference Relations in Group Decision Making

Abstract: Intuitionistic multiplicative preference relations (IMPRs), as an extension of multiplicative preference relations (MPRs), are suitable to capture hesitation and indeterminacy of the experts’ judgments. This paper aims to build several goal programming models to manage consistency and consensus of IMPRs and develop a consistency and consensus-based approach for dealing with group decision making (GDM) with IMPRs. First, the study offers a consistency index to quantify the consistency level for IMPRs as well as to define acceptably consistent IMPRs. For an IMPR, which is unacceptably consistent, several consistency-based programming models are developed to deal with the inconsistency and to establish an acceptable consistent IMPR. A consistency-based method to decision making with an IMPR is presented. Subsequently, considering the consensus in GDM, a consensus index is proposed for gauging the agreement degree among individual IMPRs. As to the individual IMPRs, which do not exhibit acceptable consistency or acceptable consensus, several goal programming models to derive new IMPRs with acceptable consistency and consensus are provided. Afterward, individual IMPRs are fused into a group IMPR by an aggregation operator that can guarantee the consistency of the obtained group IMPR. A consistency and consensus-based GDM method with a group of IMPRs is developed. Finally, two practical numerical examples are offered and a comparative analysis is presented.

Addressing conflicting stakeholders’ priorities in surgical scheduling by goal programming

Abstract: In this study, we propose a mixed integer multi-objective model which allows determining the number and typology of surgeries to be scheduled in each OR, day and session of a multiple day planning period, in context where each OR session of the planning horizon has already been assigned to a specialty, with the (multiple) objective of obtaining a desired: (i) patient due date fulfilment rate, (ii) OR utilisation, (iii) bed utilisation, (iv) number of scheduled surgeries. These objectives, reflect heterogeneous priorities of different stakeholders of the surgical scheduling process. To address the described multi-objective problem, we use a goal programming approach and we show how the exploration of the weight space, typical of such approach, can be more efficient if informed by a correlation analysis. The results presented in this study are based on real data from the Meyer University Children’s Hospital in Florence.

Evaluation and selection of sustainable suppliers in supply chain using new GP-DEA model with imprecise data

Abstract: Nowadays, with respect to knowledge growth about enterprise sustainability, sustainable supplier selection is considered a vital factor in sustainable supply chain management. On the other hand, usually in real problems, the data are imprecise. One method that is helpful for the evaluation and selection of the sustainable supplier and has the ability to use a variety of data types is data envelopment analysis (DEA). In the present article, first, the supplier efficiency is measured with respect to all economic, social and environmental dimensions using DEA and applying imprecise data. Then, to have a general evaluation of the suppliers, the DEA model is developed using imprecise data based on goal programming (GP). Integrating the set of criteria changes the new model into a coherent framework for sustainable supplier selection. Moreover, employing this model in a multilateral sustainable supplier selection can be an incentive for the suppliers to move towards environmental, social and economic activities. Improving environmental, economic and social performance will mean improving the supply chain performance. Finally, the application of the proposed approach is presented with a real dataset.

On multi-criteria chance-constrained capacitated single-source discrete facility location problems

Abstract: This work aims at investigating multi-criteria modeling frameworks for discrete stochastic facility location problems with single sourcing. We assume that demand is stochastic and also that a service level is imposed. This situation is modeled using a set of probabilistic constraints. We also consider a minimum throughput at the facilities to justify opening them. We investigate two paradigms in terms of multi-criteria optimization: vectorial optimization and goal programming. Additionally, we discuss the joint use of objective functions that are relevant in the context of some humanitarian logistics problems. We apply the general modeling frameworks proposed to the so-called stochastic shelter site location problem. This is a problem emerging in the context of preventive disaster management. We test the models proposed using two real benchmark data sets. The results show that considering uncertainty and multiple objectives in the type of facility location problems investigated leads to solutions that may better support decision making.

Tire forward and reverse supply chain design considering customer relationship management

Abstract: During the last decade, reverse logistics networks have grown dramatically within many supply chains in different industries. Several evolving factors including economic climate, green image, environment protection laws and social respolities force companies to revise their strategies. In this paper, a tire forward and reverse supply chain is designed, and a multi-objective, multi-period, multi-product mixed integer linear programming model considering uncertainty is developed. Moreover, a novel idea of integrating customer relationship management concept and supply chain management is proposed and incorporated into the mathematical modeling framework. The proposed scenario-based multi-objective model is then solved following robust optimization and revised multi-choice goal programming approaches. In order to discuss the managerial implications of the model and its results, the realization rates of the objectives, considering their importance to the supply chain, are illustrated. The model is implemented in LINGO 9 software package and solved utilizing the branch-and-bound method. The results demonstrate the applicability of the model in real world situations.

Neutrosophic Goal Programming

Abstract: In this chapter, the goal programming in neutrosophic environment is introduced. The degree of acceptance, indeterminacy and rejection of objectives is considered simultaneous. In the two proposed models to solve Neutrosophic Goal Programming Problem (NGPP), our goal is to minimize the sum of the deviation in the model (I), while in the model (II), the neutrosophic goal programming problem NGPP is transformed into the crisp programming model using truth membership, indeterminacy membership, and falsity membership functions. Finally, the industrial design problem is given to illustrate the efficiency of the proposed models. The obtained results of Model (I) and Model (II) are compared with other methods.

Single-objective linear goal programming problem with neutrosophic numbers

Abstract: This paper deals with single-objective linear goal programming problem with neutrosophic numbers. The coefficients of objective function and the constraints are considered as neutrosophic numbers of the form (p +qI), where p, q are real numbers and I denotes indeterminacy. In the solution process, the neutrosophic numbers are transformed into interval numbers.

A new trapezoidal fuzzy linear programming method considering the acceptance degree of fuzzy constraints violated

Abstract: The fuzzy linear program has various applications in knapsack problem, investment problem, capital budgeting problem, and transportation problem, etc. Considering the acceptance degree of decision maker that the fuzzy constraints may be violated, this paper develops a new method for the fuzzy linear program in which all the objective coefficients, technological coefficients and resources are trapezoidal fuzzy numbers (TrFNs). The order relationship for TrFNs is firstly given by using the interval expectation of TrFNs. According to the order relationship of TrFNs, the trapezoidal fuzzy linear program is transformed into the interval objective program. Combined with the ranking order relation between intervals and the acceptance degree of fuzzy constraints violated, the interval objective program is further transformed into the bi-objective linear program which is solved by the proposed goal programming approach. The proposed method of this paper is not only mathematically rigorous, but also can sufficiently consider the acceptance degree of the fuzzy constraints violated. The effectiveness and superiority of the proposed method are verified with a fuzzy knapsack problem and an investment problem. Finally, a decision support system is developed for the proposed method.

A flexible programming approach based on intuitionistic fuzzy optimization and geometric programming for solving multi-objective nonlinear programming problems

Abstract: In this paper, a novel method is proposed to support the process of solving multi-objective nonlinear programming problems subject to strict or flexible constraints. This method assumes that the practical problems are expressed in the form of geometric programming problems. Integrating the concept of intuitionistic fuzzy sets into the solving procedure, a rich structure is provided which can include the inevitable uncertainties into the model regarding different objectives and constraints. Another important feature of the proposed method is that it continuously interacts with the decision maker. Thus, the decision maker could learn about the problem, thereby a compromise solution satisfying his/hers preferences could be obtained. Further, a new two-step geometric programming approach is introduced to determine Pareto-optimal compromise solutions for the problems defined during different iterative steps. Employing the compensatory operator of “weighted geometric mean”, the first step concentrates on finding an intuitionistic fuzzy efficient compromise solution. In the cases where one or more intuitionistic fuzzy objectives are fully achieved, a second geometric programming model is developed to improve the resulting compromise solution. Otherwise, it is concluded that the resulting solution vectors simultaneously satisfy both of the conditions of intuitionistic fuzzy efficiency and Pareto-optimality. The models forming the proposed solving method are developed in a way such that, the posynomiality of the defined problem is not affected. This property is of great importance when solving nonlinear programming problems. A numerical example of multi-objective nonlinear programming problem is also used to provide a better understanding of the proposed solving method.

Uncertain random goal programming

Abstract: Goal programming provides an efficient technique to deal with decision making problems with multiple conflicting objectives. This paper joins the streams of research on goal programming by providing a so-called uncertain random goal programming to model the multi-objective optimization problem involving uncertain random variables. Several equivalent deterministic forms are derived on the condition that the set of parameters consists of uncertain variables and random variables. Finally, an example is given to illustrate the application of the approach.

Scheduling and Planning in Service Systems with Goal Programming: Literature Review

Abstract: Background: People want to be able to evaluate different kinds of information in a good way. There are various methods that they develop in such situations. Among the optimization methods, the goal programming method is often used when there are multiple objectives that decision makers want to accomplish. Because scheduling and planning problems have multiple objectives that are desired to be achieved, the goal programming method helps the researcher in contradictory situations between these goals. Methods: This study includes, examines, and analyzes recent research on service scheduling and planning. In the literature, service scheduling and planning studies have been examined using goal programming method from past to today. Results: The studies are detailed according to the type of goal programming, according to scheduling types, the purpose used in the studies, and the methods integrated with the goal programming. There are 142 studies in Emerald, Science Direct, Jstor, Springer, Taylor and Francis, Google Scholar, etc. databases that are examined in detail. For readers, diversification has been made to facilitate the identification of these studies and a detailed overview has been presented. Conclusion: As a result of the study, studies with the goal programming in the literature have been seen. The readers’ perspectives for planning and scheduling are discussed.

Solving a multi-objective location routing problem for infectious waste disposal using hybrid goal programming and hybrid genetic algorithm

Abstract: Article history: Received January 15 2017 Received in Revised Format April 1 2017 Accepted April 11 2017 Available online April 12 2017 Infectious waste disposal remains one of the most serious problems in the medical, social and environmental domains of almost every country. Selection of new suitable locations and finding the optimal set of transport routes for a fleet of vehicles to transport infectious waste material, location routing problem for infectious waste disposal, is one of the major problems in hazardous waste management. Determining locations for infectious waste disposal is a difficult and complex process, because it requires combining both intangible and tangible factors. Additionally, it depends on several criteria and various regulations. This facility location problem for infectious waste disposal is complicated, and it cannot be addressed using any standalone technique. Based on a case study, 107 hospitals and 6 candidate municipalities in UpperNortheastern Thailand, we considered criteria such as infrastructure, geology and social & environmental criteria, evaluating global priority weights using the fuzzy analytical hierarchy process (Fuzzy AHP). After that, a new multi-objective facility location problem model which hybridizes fuzzy AHP and goal programming (GP), namely the HGP model, was tested. Finally, the vehicle routing problem (VRP) for a case study was formulated, and it was tested using a hybrid genetic algorithm (HGA) which hybridizes the push forward insertion heuristic (PFIH), genetic algorithm (GA) and three local searches including 2-opt, insertion-move and interexchange-move. The results show that both the HGP and HGA can lead to select new suitable locations and to find the optimal set of transport routes for vehicles delivering infectious waste material. The novelty of the proposed methodologies, HGP, is the simultaneous combination of relevant factors that are difficult to interpret and cost factors in order to determine new suitable locations, and HGA can be applied to determine the transport routes which provide a minimum number of vehicles and minimum transportation cost under the actual situation efficiently in this case. © 2018 Growing Science Ltd. All rights reserved

A multiple objective methodology for sugarcane harvest management with varying maturation periods

Abstract: This paper addresses the management of a sugarcane harvest over a multi-year planning period. A methodology to assist the harvest planning of the sugarcane is proposed in order to improve the production of POL (a measure of the amount of sucrose contained in a sugar solution) and the quality of the raw material, considering the constraints imposed by the mill such as the demand per period. An extended goal programming model is proposed for optimizing the harvest plan of the sugarcane so the harvesting point is as close as possible to the ideal, considering the constrained nature of the problem. A genetic algorithm (GA) is developed to tackle the problem in order to solve realistically large problems within an appropriate computational time. A comparative analysis between the GA and an exact method for small instances is also given in order to validate the performance of the developed model and methods. Computational results for medium and large farm instances using GA are also presented in order to demonstrate the capability of the developed method. The computational results illustrate the trade-off between satisfying the conflicting goals of harvesting as closely as possible to the ideal and making optimum use of harvesting equipment with a minimum of movement between farms. They also demonstrate that, whilst harvesting plans for small scale farms can be generated by the exact method, a meta-heuristic GA method is currently required in order to devise plans for medium and large farms.

Common weights in dynamic network DEA with goal programming approach for performance assessment of insurance companies in Iran

Abstract: Purpose Conventional data envelopment analysis (DEA) models permit each decision-making unit (DMU) to assess its efficiency score with the most favorable weights. In other words, each DMU selects the best weighting schemes to obtain maximum efficiency for itself. Therefore, using different sets of weights leads to many different efficient DMUs, which makes comparing and ranking them on a similar basis impossible. Another issue is that often more than one DMU is evaluated as efficient because the selection of weights is flexible; therefore, all DMUs cannot be completely differentiated. The purpose of this paper is to development a common weight in dynamic network DEA with a goal programming approach. Design/methodology/approach In this paper, a goal programming approach has been proposed to generate common weights in dynamic network DEA. To validate the applicability of the proposed model, the data of 30 non-life insurance companies in Iran during 2013-2015 have been used for measuring their efficiency scores and ranking all of the companies. Findings Findings show that the proposed methodology is an effective and practical approach to measure the efficiency of DMUs with dynamic network structure. Originality/value The proposed model delivers more knowledge of the common weight approaches and improves the DEA theory and methodology. This model makes it possible to measure efficiency scores and compare all DMUs from multiple different standpoints. Further, this model allows one to not only calculate the overall efficiency of DMUs throughout the time period but also consider dynamic change of the time period efficiency and dynamic change of the divisional efficiency of DMUs.

Combination of linear and weighted goal programming with penalty function in optimisation of a daily dairy cow ration.

Abstract: The aim of the paper is to present a developed spreadsheet tool for the formulation of a daily dairy cow ration. it is constructed on the basis of two linked sub-models developed on the MS Excel platform. it merges the common linear programming model and the weighted-goal programming model with a penalty function. The first sub-model is included in the tool to make an estimate of the least-cost magnitude that might be expected. The obtained result is entered into the second sub-model as the goal that should be met as closely as possible. The tool was tested at two different values of preferential weights for dairy cows with a 25 kg daily milk yield. The results obtained confirm the benefits of the applied approach. in contrast to the common linear program tools, which terminate at formulation of the least-cost ration, our tool provides more efficient rations (in both economic and nutritive terms) by fine-tuning the nutritive goals and by allowing for harmless deviations from these goals by application of penalty functions.

Multi-criteria analysis of the GRI sustainability reports: an application to Socially Responsible Investment

Abstract: The aim of this paper is to construct a support decision-making system to evaluate the different items of corporate social responsibility. For this purpose, we propose a multi-criteria model that runs on two levels of decision-making in accordance with the hierarchical structure designed by the Global Reporting Initiative (GRI). Tools for modelling preferences and aggregating information are used in this framework. Arrays of normalized scores reflecting the company performance in the Aspects and Categories of GRI are then made available for the stakeholders. The design of investment portfolios uses the obtained measures of sustainability in an Extended Goal Programming model that combines financial and sustainability objectives. The proposal enables more informed decision-making for investors with social concerns that prefer direct investment and wish to make their own financial decisions. The developed methodology has been applied to 8 Spanish companies, which have been selected for their relevan...