Showing papers on "Goal programming published in 2016"

Deriving the priority weights from incomplete hesitant fuzzy preference relations in group decision making

Abstract: The concept of hesitant fuzzy preference relation (HFPR) has been recently introduced to allow the decision makers (DMs) to provide several possible preference values over two alternatives. This paper introduces a new type of fuzzy preference structure, called incomplete HFPRs, to describe hesitant and incomplete evaluation information in the group decision making (GDM) process. Furthermore, we define the concept of multiplicative consistency incomplete HFPR and additive consistency incomplete HFPR, and then propose two goal programming models to derive the priority weights from an incomplete HFPR based on multiplicative consistency and additive consistency respectively. These two goal programming models are also extended to obtain the collective priority vector of several incomplete HFPRs. Finally, a numerical example and a practical application in strategy initiatives are provided to illustrate the validity and applicability of the proposed models.

An intuitionistic fuzzy goal programming approach for finding pareto-optimal solutions to multi-objective programming problems

Abstract: We propose a new intuitionistic fuzzy approach for solving multi-objective problems.The approach is an interactive procedure.The approach considers the degrees of satisfaction and dissatisfaction of objectives.An illustrative example is presented to discuss the properties of the approach. Multi-objective optimization in the intuitionistic fuzzy environment is the process of finding a Pareto-optimal solution that simultaneously maximizes the degree of satisfaction and minimizes the degree of dissatisfaction of an intuitionistic fuzzy decision. In this paper, a new method for solving multi-objective programming problems is developed that unlike other methods in the literature, provides compromise solutions satisfying both the conditions of intuitionistic fuzzy efficiency and Pareto-optimality. This method combines the advantages of the intuitionistic fuzzy sets concept, goal programming, and interactive procedures, and supports the decision maker in the process of solving programming problems with crisp, fuzzy, or intuitionistic fuzzy objectives and constraints. A characteristic of the proposed method is that it provides a well-structured approach for determining satisfaction and the dissatisfaction degrees that efficiently uses the concepts of violation for both objective functions and constraints. Another feature of the proposed method comes from its continuous interaction with the decision maker. In this situation, through adjusting the problem's parameters, the decision maker would have the ability of revisiting the membership and non-membership functions. Therefore, despite the lack of information at the beginning of the solving process, a compromise solution that satisfies the decision maker's preferences can be obtained. A further feature of the proposed method is the introduction of a new two-step goal programming approach for determining the compromise solutions to multi-objective problems. This approach ensures that the compromise solution obtained during each iterative step satisfies both the conditions of intuitionistic fuzzy efficiency and Pareto-optimality. The application of the proposed model is also discussed in this paper.

Dynamic rescheduling in FMS that is simultaneously considering energy consumption and schedule efficiency

Abstract: The dynamic rescheduling problem in flexible manufacturing systems (FMS) has historically emphasized the schedule efficiency. However, energy consumption is a basic need for different purposes in manufacturing systems around the world. This paper proposes an innovative approach to study the dynamic scheduling problem in FMS, taking the objectives of minimum or maximum energy consumption into account. A new goal programming mathematical model, which considers the energy consumption and the schedule efficiency simultaneously, is presented for solving this problem. A rescheduling method based on the genetic algorithm is introduced to address the dynamic rescheduling problem in FMS. A period policy is selected to deal with the dynamic feature of the problem. Numerical experiments have been designed to test and evaluate the performance of the proposed model. The experimental results show that the minimum energy consumption model can save the energy consumption and enhance the schedule efficiency.

Designing healthy, climate friendly and affordable school lunches

Abstract: This study aims to develop a model with which to build diets taking into account nutritional, climate change and economic aspects. A case study is used to test the proposed model, consisting of finding the optimal menus for school children in Spain from combinations of 20 starters, 20 main dishes and 7 desserts for a 20-day planning period. An optimizing technique, specifically integer goal programming, is used as a means of designing diets which take into account the aforementioned aspects. Goal programming (GP) is used to design those menus that meet, or nearly meet, all the requirements with respect to caloric content, caloric share among macronutrients, nutrients to encourage and nutrients to limit, while reducing the carbon footprint (CFP) and the lunch budget. In order to have real, acceptable dishes, a school catering company provided information about the typical dishes they serve. The CFP of each dish was assessed, based on literature about life cycle assessment and CFP studies on food products. The nutritional value of each dish was obtained from databases, whereas prices were gathered from a wholesaler. After solving the goal programming model for several CFP and budget goals, the results show reductions with respect to the average CFP of between −13 and −24 %, and reductions with respect to the average budget between −10 and −15 % while maintaining the nutritional aspects similar to the average of the proposed menus. The results show that a wide range of budget is available, maintaining an almost constant CFP and meeting nutritional requirements to a similar degree; therefore, it is possible to avoid trade-offs between the CFP and the budget. The analysis of the dishes selected shows how the optimization model, in general, avoids the dishes which have a high CFP and high price and which are low in iron content, but high in protein and cholesterol. Goal programming constitutes a suitable tool for designing diets which are economically, environmentally and nutritionally sustainable. Its flexibility enables specific issues to be studied, such as the existence of possible trade-offs between budget and CFP, attained by changing the budget and the CFP goals. By means of an iterative process, new dishes could be introduced or the existing ones could be improved, thus providing catering companies with useful information.

Multi-objective Transportation Problem with Cost Reliability Under Uncertain Environment

Abstract: This paper analyzes the study of a Multi-Objective Transportation Problem (MOTP) under uncertain environment. Assuming the uncertainty in real-life decision making problems, the concept of reliability is incorporated in the transportation cost and the effectiveness is justified through the proposed MOTP. Again, considering the real phenomenon in the MOTP, we consider the transportation parameters, like as supply and demand as uncertain variables. Also, we consider the fuzzy multi-choice goals to the objective functions of the MOTP; and Fuzzy Multi-Choice Goal Programming (FMCGP) is used to select the proper goals to the objective functions of the proposed MOTP. Here, the proposed study is not only confined to obtain the compromise solution but also to fix up the proper goals to the objective functions of the MOTP. A numerical example is presented to illustrate and justify the proposed study. Finally, the paper ends with the conclusion and future study.

Multi-criteria decision making for choosing socially responsible investment within a behavioral portfolio theory framework: a new way of investing into a crisis environment

Abstract: The current economic crisis fuels the financial social responsibility after an epoch of many excesses with damaging effects. This work tackles two emerging streams in the financial literature: the behavioral portfolio theory with mental accounting and the socially responsible investment (SRI). Promoting SRI is regarded by a lot of financial experts, policymakers and researchers from the field of economic and social sciences, as one of the potential solutions in order to avoid future crises. Therefore, new models for this investment approach are necessary. We try to support the class of investors that select their investments under a mental accounting framework and also they want to achieve a certain level of SR quality in their portfolios. In order to reconcile the two choice frames, avoiding unnecessary sacrifices in financial performance, we have designed a model based on goal programming that integrates the two cornerstones of the investor. Furthermore, we propose a fuzzy inference system to determine the amount of money allocated to each mental account as well as the confidence level assigned to each mental account. This tool is based on expert knowledge modeled by fuzzy if–then rules.

Integrating sustainability into strategic supplier portfolio selection

Abstract: Purpose – The purpose of this paper is to propose a comprehensive methodology and a problem-specific model for the configuration of the optimal strategic supplier portfolio in terms of traditional, performance-related objectives and sustainability targets. Design/methodology/approach – To bridge the research gap, i.e., to align strategic supplier portfolio selection with corporate sustainability targets, a hybrid model of the analytic network process (ANP) and goal programming (GP) is developed. To validate the model, a case example is presented and managerial feedback is collected. Findings – By enabling the integration of sustainability targets into strategic supplier portfolio configuration, the hybrid ANP-GP model contributes to research in the area of sustainable supply chain management. Results indicate that simplifying the model by omitting one or more details may lead to unfortunate actions. Research limitations/implications – The model has been applied using a case example in the automotive indus...

Multiobjective Stochastic Fractional Goal Programming Model for Water Resources Optimal Allocation among Industries

Abstract: This paper developed a multiobjective stochastic fractional goal programming (MOSFGP) model for optimal water resources allocation among industries based on analyses of water resources quantity, quality, and uncertainty. The model integrated chance constrained programming, fractional programming, and multiobjective goal programming. The developed model was then applied in a real-world case study. The developed MOSFGP has the following advantages: (1) the model deals with economic and social objectives simultaneously, thus reflecting the actual situation more realistically; (2) the model in fractional form can determine water resources use efficiency directly; (3) water quantity and water quality are coupled in the model; and (4) the model can generate different optimal schemes under different risk probabilities. Based on the results of the MOSFGP model, decision makers could use water resources efficiently and obtain higher economic and social benefits. The model is also of great significance to i...

A resilient global supplier selection strategy—a case study of an automotive company

Abstract: Disaster studies have recently received considerable attention. Determining how to build a resilient supply chain to mitigate uncertainty is the priority for automotive companies. Supplier selection strategies have been identified as vital for mitigating this uncertainty. This study sought to verify the criteria for selecting suppliers by using global performance measurements to identify optimal supply resources and locations in an uncertain disaster environment. The study focuses on an automotive company case study and evaluates the results through weighted goal programming (WGP) and preemptive goal programming (PGP) methods. The findings are as follows: (1) The investigated automotive company must arrange additional supply resources in the involved region by using a global supplier selection strategy, (2) an appropriate location in the involved region is identified through a global performance measurement and evaluation, and (3) disaster damage mitigation is determined to be a priority in long-term business strategies. This case study improves the preparedness of the automotive industry to manage unpredictable disasters in the supply chain through the effective use of a global selection strategy.

Multiobjective differential evolution algorithm based on decomposition for a type of multiobjective bilevel programming problems

Abstract: This paper considers the multiobjective bilevel programming problem (MOBLPP) with multiple objective functions at the upper level and a single objective function at the lower level. By adopting the Karush-Kuhn-Tucker (KKT) optimality conditions to the lower level optimization, the original multiobjective bilevel problem can be transformed into a multiobjective single-level optimization problem involving the complementarity constraints. In order to handle the complementarity constraints, an existing smoothing technique for mathematical programs with equilibrium constraints is applied. Thus, a multiobjective single-level nonlinear programming problem is formalized. For solving this multiobjective single-level optimization problem, the scalarization approaches based on weighted sum approach and Tchebycheff approach are used respectively, and a constrained multiobjective differential evolution algorithm based on decomposition is presented. Some illustrative numerical examples including linear and nonlinear versions of MOBLPPs with multiple objectives at the upper level are tested to show the effectiveness of the proposed approach. Besides, NSGA-II is utilized to solve this multiobjective single-level optimization model. The comparative results among weighted sum approach, Tchebycheff approach, and NSGA-II are provided.

A Review of Goal Programming

Abstract: The field of goal programming is continuing to develop at a rapid pace. New variants of the goal programming model are being introduced into the literature and existing variants combined together to form a more comprehensive and flexible modelling structure. Goal programming is also being applied to wide a range of modern applications and is increasingly being used in combination with other techniques from operations research and artificial intelligence to enhance its modelling flexibility. This paper presents a review of the field of goal programming focussing on recent developments. The current range of goal programming variants is described. The range of techniques that goal programming has been combined or integrated with is discussed. A range of modern applications of goal programming are given and conclusions are drawn.

Truck-shovel allocation optimisation: a goal programming approach

Abstract: Decision-making in mining is a challenging task Optimal decisions regarding shovel and truck allocations, in consideration to the short-term production schedule, are very important to keep the operations inline with the planned objectives of the company in long term This paper presents a mixed integer linear goal programming (MILGP) model to optimise the operations based on four desired goals of the company: (a) maximise production, (b) minimise deviations in head grade, (c) minimise deviations in tonnage feed to the processing plants from the desired feed, and (d) minimise operating cost The model provides shovel assignments and the target productions; as an input to the dispatching system while meeting the desired goals and constraints of the mining operation The model implementation with an iron ore mine case study provided average plant utilisation above 99%, average truck utilisation above 92% and average shovel utilisation above 95%

An efficient resource allocation in strategic management using a novel hybrid method

Abstract: – The purpose of this paper is to suggest a novel hybrid method by integrating a decision sciences approach with balanced scorecard (BSC) in order to scientifically enable the efficient strategic management of an organization under limited resources. The proposed research model endeavors to improve critical basis deficiencies of the original BSC as well as formerly improved forms of BSC by appropriately integrating three disparate methods: BSC, analytic network process (ANP), and zero-one goal programming (ZOGP). , – The designed approach is separated into three major parts. At first, the traditional BSC, concentrating on both financial and intellectual capital, was adopted as the strategic management framework, and then priorities as well as the importance of tactical drivers derived from BSC application were consecutively identified by the application of ANP. Finally, the study further applied the obtained results of integrated BSC and ANP to ZOGP in order to scientifically identify the optimal strategic investment under simulated constraints of the considered organization. , – An application of BSC, ANP, and ZOGP with a case study of an academic institution provided an improved strategic management approach for optimally and scientifically utilizing the limited resources of the organization. The suggested results indicated that only 11 of the 23 strategic projects should be executed. Moreover, the selected tactical tasks would efficiently use less than 36 percent of the strategic expenses of the traditional management approach. , – Based on the intensive literature reviews, the proposed method could be determined as a novel hybrid approach. It newly conveyed the practical management approach by innovatively including the proper decision sciences method to BSC. This improvement scientifically considered on the resource allocation process that has never been studied before in formerly improved BSC.

An extended goal programming model for site selection in the offshore wind farm sector

Abstract: This paper presents an application of extended goal programming in the field of offshore wind farm site selection. The strategic importance of offshore shore wind farms is outlined, drawing on the case of the United Kingdom proposed round three sites as an example. The use of multi-objective modelling methodologies for the offshore wind farm sector is reviewed. The technique of extended goal programming is outlined and its flexibility in combining different decision maker philosophies described. An extended goal programming model for site selection based on the United Kingdom future sites is then developed and a parametric analysis undertaken at the meta-objective level. The results are discussed and conclusions are drawn.

Fuzzy model on selecting processes in Business Process Reengineering

Abstract: The purpose of this paper is to present a methodology to assist enterprise decision makers (DMs) to select from a number of processes during Business Process Reengineering (BPR) according to organizational objectives. Indeed, after the identification and classification of process and illustration of the organizational objectives and criteria, the effect of each process on each objective and criterion is calculated to select the most appropriate processes for reengineering purposes.,The proposed methodology uses fuzzy quality function deployment (QFD) technique to convert the qualitative data (DM’s opinion) to quantitative ones and then calculates the effects of each process on the organizational objectives and criteria. Then, by using the result of fuzzy QFD, the amount of satisfaction of each process according to each criterion is calculated. By combining this data with other effective variables in BPR projects such as “cost” and “time,” a multi-objective goal programming (GP) model is formulated and solved to identify the most appropriate business processes.,In fact, a quantitative model is presented in which fuzzy QFD and GP methods are combined to help DMs to adopt an appropriate strategy for implementing BPR projects successfully by selecting proper processes for reengineering purposes. In addition, the presented model uses both qualitative and quantitative data and turns them into quantitative ones. An example is also provided to show how the model works.,Following this investigation, other researchers could able to complete the model with more dynamic and local variables to enhance the accuracy of the model.,The introduced model will support organizations and managers to select appropriate processes for BPR; so in practice, the mentioned projects will be more efficient and successful.,The paper study is essential for organizations, because the presented decision-making model is based on fuzzy QFD and GP methods that enable the enterprises to select the business processes during the BPR projects easily. In this paper, a GP model is presented to create a balance between organizational satisfaction level and cost and time of implementing BPR projects considering organizational constraints. The proposed model was applied to a real case and the authors showed that it is an easy-to-use, valid, and powerful tool for implementing BPR projects.

Using Goal-Programming to Model the Effect of Stakeholder Determined Policy and Industry Changes on the Future Management of and Ecosystem Services Provision by Ireland’s Western Peatland Forests

Abstract: Recent studies have highlighted land-use conflicts between stakeholder groups in Ireland. Some of these conflicts can be attributed to European directives, designed with sustainable forest management principles in mind, but imposing incoherencies for land-owners and stakeholders at the local level. This study, using Ireland’s Western Peatland forests as a case study area, focused on the development and implementation of a goal programming model capable of analysing the long term impact of policy and industry changes at the landscape level. The model captures the essential aspects of the changes identified by local level stakeholders as influencing forest management in Ireland and determines the future impact of these changes on ecosystem services provisions. Initially, a business as usual potential future is generated. This is used as a baseline against which to compare the impact of industry and policy changes. The model output indicated that the current forest composition is only really suited to satisfy a single, financial objective for forest management. The goal programming model analysed multiple objectives simultaneously and the results indicated that the stakeholders’ desired ecosystem service provisions in the future will be more closely met by diversifying the forest estate and/or by changing to an alternative, non-forest land-use on less productive areas.

A group decision making model based on goal programming with fuzzy hierarchy: an application to regional forest planning

Abstract: In this paper a multi-criteria group decision making model is presented in which there is a heterogeneity among the decision makers due to their different expertise and/or their different level of political control. The relative importance of the decision makers in the group is handled in a soft manner using fuzzy relations. We suppose that each decision maker has his/her preferred solution, obtained by applying any of the techniques of distance-based multi-objective programming [compromise, goal programming (GP), goal programming with fuzzy hierarchy, etc.]. These solutions are used as aspiration levels in a group GP model in which the differences between the unwanted deviations are interpreted in terms of the degree of achievement of the relative importance amongst the group members. In this way, a group GP model with fuzzy hierarchy (Group-GPFH) is constructed. The solution for this model is proposed as a collective decision. To show the applicability of our proposal, a regional forest planning problem is addressed. The objective is to determine tree species composition in order to improve the values achieved by Pan-European indicators for sustainable forest management. This problem involves stakeholders with competing interests and different preference schemes for the aforementioned indicators. The application of our proposal to this problem allows us to be able to comfortably address all these issues. The results obtained are consistent with the preferences of each stakeholder and their hierarchy within the group.