Meta-Goal Programming Research Articles

Incorporation of poverty principles into goal programming

This paper presents a methodology for the incorporation of poverty related concepts into the goal programming paradigm. The concepts of poverty line, absolute poverty and relative poverty are discussed in the context of their potential usage in the field of Operations Research. The synergy between goal programming and poverty lines is examined. Two additional meta-goals are proposed for inclusion in a meta-goal programming framework based around absolute and relative poverty respectively. The resulting poverty incorporating meta-goal programming model is compared against other goal programming variants over a test set of school budget allocation models. The results demonstrate how the incorporation of poverty principles allows for greater modelling flexibility in the goal programming framework. This in turn allows decision makers to avoid solutions that are below threshold levels for a subset of stakeholders in either a relative or absolute sense. Appropriate conclusions and suggestions for future research are given.

Designing a virtual cellular manufacturing system with route selection and workers’ considerations: A multi-objective robust possibilistic model

In today’s economy, the necessity for responsiveness to variations in customers’ permanent requirements is increasing significantly. Achieving immediate response or success in the competitive market requires boosting flexibility, agility, and efficiency in utilizing available production equipment and workforce. Implementing a Virtual Cellular Manufacturing System (VCSM) prevents the reconfiguration costs and is suitable for many systems where the machines are not removable. Owing to the significant role of human resources in VCMS, this paper assigns the workers based on their skills, interest in cooperation, and workload balancing among cells. The considerable novelty is balancing physical and time workloads simultaneously. Another strength of this research is the consideration of various routes to process each part. A comprehensive linear multi-objective mathematical model is developed for a virtual cellular manufacturing system. This paper develops the model by stating social and technical dimensions, which minimize exceptional elements and voids, maximize the workers' interest in cooperation, and minimize workload deviation. Stemming from uncertain parameters, a robust possibilistic approach is addressed. Due to the multi-objective model, the multi-choice meta-goal programming with the utility function is applied. Afterward, a real-case study is presented to validate the model. Eventually, the outcomes and sensitivity analysis are brought up.

A new interval meta-goal programming for sustainable planning of agricultural water-land use nexus

Meta-Goal Programming (MGP) is a simultaneous cognitive evaluation of the degree of achievements for original decision goals considered in a GP model. However, in most real-world situations, environmental coefficients and related parameters are not easily available. In such a situation, the decision-maker must consider various conflicting targets in a framework of uncertain aspiration levels at the same time. On the other side, Interval Programming (IP) is a method used to increase the range of available decision-maker preference structures in GP. In the perspective of solving the conflicts between agriculture and water use towards sustainability, this paper proposes an Interval Meta-Goal Programming Model (IMGPM) dealing with imprecision in data that covers interval coefficients, target intervals, and interval bounds of meta-goals. This novel methodology has been tested in a study area in Iran to validate its added value in solving conflicting uses of natural resources by economic sectors. This integration together with its application for sustainable optimal cropping patterns (agroecosystem planning) represents a novelty in the field of ecological modeling. The management solutions of our method in terms of land allocation are different from those in Sen and Pal (2013) model. In the case of Iran, many socio-ecological-economic strategies and policies should be necessary for improving the agricultural sector. More specifically, on the basis of rainfall amounts and spatial patterns, this approach can represent a decision-support system able to define strategies for additional water storage useful to support crop production. Furthermore, the availability of water together with the sustainable use of fertilizers can mitigate the risk of land degradation, guaranteeing people employment, food security, and economic profits. Although the present methodology seems to solve the problem of multi-goals decision-making, the inclusion of spatial relationships is able to introduce dependencies between the management of land use in adjacent areas, making the present approach nearer to real-world functioning.

Investigating the green inventory control problem considering liquidity risk: Application in the dairy industry

In recent years, increasing concern regarding environmental issues has attracted the attention of researchers and managers to incorporate the greenness concept into business activities. Furthermore, one of the critical problems that plays an important role in the productivity of each company is the inventory management problem considering financial issues. Liquidity risk and inventory value must be closely monitored. Hence, this research studies the green inventory control problem considering liquidity to help managers to make best decisions in competitive environment. To achieve this objective, a multi-objective mathematical model is proposed that maximizes profit, minimizes risk, and maximizes greenness. Also, the proposed model is solved by a recent efficient method named multi-choice meta-goal programming by considering a real-world case study in the dairy industry. In addition, the impact of sales in different sales channels with different accounts receivable days and material substitution are examined. The obtained results show that increasing the raw milk allocated totally increases the quick ratio and contribution margin. Moreover, the usage of environmentally friendly materials have an extreme effect on customers to be loyal customers. Thus, the company's efficiency and profitability have increased due to considering hidden expenses. Finally, managerial insights and theoretical implications are presented.

Robust design of a green-responsive closed-loop supply chain network for the ventilator device.

This study aims to investigate the closed-loop supply chain network design problem considering the environmental and responsiveness features. For this purpose, a multi-objective mathematical model is suggested that minimizes the carbon emissions and the total costs and maximizes the responsiveness of the system. Due to the dynamic space of the business environment, uncertainty is an integral part of the supply chain problem. Therefore, this research applies the robust possibilistic programming method to cope with uncertainty. Afterwards, since the research problem has a high level of the complexity, a hybrid solution approach based on a heuristic method and the meta-goal programming method is developed to solve the research problem in a reasonable time. Then, due to the importance of the ventilator device during the recent pandemic (COVID-19), this study considers this product as a case study. The main contribution of the current study is to design a green-responsive closed-loop supply chain network under uncertainty using a multi-objective robust possibilistic programming model, for the first time in the literature, especially in the medical devices industry. On the other side, the other contribution of this study is to develop an efficient hybrid solution method. The achieved results demonstrate the efficiency of the offered model and the developed hybrid method. Eventually, by carrying out sensitivity analysis, the impact of some of the critical parameters on the model is investigated. Based on the obtained results, an increase in the demand sizes leads to increasing the environmental damages and the total costs while reducing the responsiveness level. On the other side, an increase in the rate of return leads to an increase in all of the objective functions. Also, the achieved results show that when the capacity parameter is increased, the total costs are decreased, but the responsiveness and environmental impacts are increased.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11356-022-19105-1.

Application of goal programming in the textile apparel industry to resolve production planning problems a meta-goal programming technique using weights

In the present business environment, rapidly developing technology and the competitive world market pose challenges to the available assets of industries. Hence, industries need to allocate and use available assets at the optimum level. Thus, industrialists must create a good decision plan to guide their performance in the production sector. As a result, the present study applies the Meta-Goal Programming technique to attain several objectives simultaneously in the textile production sector. The importance of this study lies in pursuing different objectives simultaneously, which has been almost ignored till now. The production scheduling problem in a textile firm is used to illustrate the practicability and mathematical validity of the suggested approach. Analysis of the results obtained demonstrates that the solution met all three meta-goals with some original goals being met partially. An analysis of the sensitivity of the approach to the weights of the preferences was conducted.

A multi-objective fuzzy robust stochastic model for designing a sustainable-resilient-responsive supply chain network

This study proposes a multi-objective mixed-integer programming model to configure a sustainable supply chain network while considering resilience and responsiveness measures. The model aims at minimizing the total costs and environmental damages while maximizing the social impacts, as well as the responsiveness and resilience levels of the supply chain network. An improved version of the fuzzy robust stochastic optimization approach is proposed to tackle the uncertain data arising in the dynamic business environment. Furthermore, a new version of meta-goal programming named the multi-choice meta-goal programming associated with a utility function is developed to solve the resulting multi-objective model. A case study in the water heater industry is investigated to illustrate the application of the proposed model and its solution approach. The numerical results validate the proposed model and the developed solution method. Finally, interactions between the sustainability, responsiveness, and resilience dimensions are investigated and several sensitivity analyses are performed on critical parameters by which useful managerial insights are provided.

Wheat sustainable supply chain network design with forecasted demand by simulation

One of the most important challenges in any society is the issue of food and water supply. Undoubtedly, agriculture has a major role in water consumption, and grains have the largest use in the food of people. Bread is also an essential commodity in households and constitutes the staple food of many people. Proper investigation and management of its various stages, from production to distribution, can solve many problems. This research optimizes the sustainable supply chain network of wheat and its products. A multi-objective mathematical model is proposed to minimize network costs and water consumption and maximize job opportunities. Then, the multi-objective problem is transformed into a single-objective problem using the meta-goal programming method. Also, the uncertainty of the demand for wheat flour is addressed through simulation. A case study is considered to evaluate the proposed model, and the results, sensitivity analyses, managerial insights, and suggestions for future research are reported.

A Meta-Goal Programming approach to cardinal preferences aggregation in multicriteria problems

Solving multicriteria decision making problems often requires the assessment of certain preferential information. In some occasions, this information must be given by several individuals or social groups, and these individual assessments need to be aggregated into single global preferences. This cardinal preferences aggregation problem has been tackled using different techniques, including multicriteria decision making ones. In this paper, a Meta-Goal Programming approach is proposed, where different target values can be set on several achievement functions that measure the goodness of the global assessments. This methodology presents strong advantages due to its modeling flexibility and its ability to find balanced solutions. The proposed approach is demonstrated with an illustrative example and a series of computational experiments, and it is shown that the Meta-Goal Programming method produces results with better values of the achievement functions than other classical and multicriteria approaches.

Optimization of Portfolio Stock Selection with Meta Goal Programming

This article discusses the optimization of portfolio stock selection using the Meta Goal Programming (MGP) model. The optimization problem of stock portfolio selection with the MGP model is solved by combining the weight of trust in each type of MGP and comparing it with the Goal Programming (GP) portfolio. The final result is in the form of the selection of five stocks which are designated as optimal portfolios. This new MGP portfolio produces a higher return value and a lower standard MGP portfolio deviation compared to the GP portfolio.

Allocation of Stratified Random Sample Using Meta Goal Programming

In this paper, a meta goal programming model(MGP) is introduced to determine the optimumallocation of stratified random sample. The mainobjectives in this research are using the suggested modelwhich is flexible enough to interactive with the decisionmaker and find practical solutions, in addition tominimize the estimated variance in the sampling, besideminimize the variance of estimated variance, and alsominimize the cost and time of users in the study.

Bayesian Estimation For Topp-Leone Weibull Distribution Based on Dual Generalized Order Statistics

In this paper, a meta goal programming model(MGP) is introduced to determine the optimumallocation of stratified random sample. The mainobjectives in this research are using the suggested modelwhich is flexible enough to interactive with the decisionmaker and find practical solutions, in addition tominimize the estimated variance in the sampling, besideminimize the variance of estimated variance, and alsominimize the cost and time of users in the study.

Stochastic Multi-Objectives Supply Chain Optimization with Forecasting Partial Backordering Rate: A Novel Hybrid Method of Meta Goal Programming and Evolutionary Algorithms

This study proposes a model for a multi-objective, multi-buyer, multi-vendor, multi-product and multi-constraint supply chain. The buyers’ demand rates are stochastic variables with known probability distribution functions. The classical ([Formula: see text]) inventory control system is used to manage the inventories of all buyers where the lead times are production rate dependent. Shortage is permitted and is partially backordered where the partial back ordering rate is forecasted. The model is considered as a multi-objective integer nonlinear programming problem including cost, service level and lead time objectives and using a novel hybrid method, a hybrid of Meta Goal Programming (MGP) and Firefly Algorithm (FA) are solved. Numerical examples are given to illustrate the proposed method in the study. The results of the study are compared to other hybrid methods of Meta Goal Programming with other evolutionary algorithms such as Bees Algorithms (BA), Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Simulated Annealing (SA).

Production Planning Using Fuzzy Meta–Goal Programming Model

Production planning using fuzzy meta-goal programming model in a manufacturing plant is the focus of this paper. This technique is used for determining the number of units to be produced and hence sold within the given restrictions. The decision-maker is given flexibility by means of meta-goals in expressing their preferences. Here, three types of meta-goals are considered which are assumed to have fuzzy bounds to manipulate the degree of attainment for the prioritised goals. Further, LINDO 11.0 optimizer solver is used to draw results of the problem.

Using Meta-goal Programming for a New Human Development Indicator with Distinguishable Country Ranks

This paper builds on the extensive literature of the benefit-of-the-doubt (BoD) methodology to set weights for composite indicators (CIs). The proposed methodology, meta-goal programming benefit of the doubt (MGP-BoD), proved to overcome some of the BoD shortcomings and enhance its performance. MGP-BoD belongs to the family of common-weights BoD models. It comprises of two sets of goals and two meta-goals. Among other merits, results prove two additional benefits of the MGP-BoD over older BoD. First, it enhances BoD discriminating power by eliminating all ties in CI values and, hence, country ranks. This high discriminating power is achieved in only one totally endogenous step. Second, MGP-BoD weights add up to one. This makes weights more insightful, interpretable, comparable to weights from other weighting systems, and easier to interpret compared to BoD. These additional merits favor MGP-BoD over previous weighting methods. In the meantime, the proposed method preserves a significantly high correlation with previous methods results as shown a set of Pearson and Spearman correlation tests to compare it to various previous methodologies. To validate the proposed methodology, it has been thoroughly tested using sensitivity and classification analyses. All tests are found highly significant. Nevertheless, the method has its own limitations that suggest future research points. Finally, the paper offers a new human development indicator (HDI) using 2012 data using MGP-BoD. The proposed HDI offers an alternative to the currently used equal-weights HDI that offers distinguishable country ranks and more policy-guiding weights. The highest weights are assigned to education variables.

An interactive meta-goal programming-based decision analysis methodology to support collaborative manufacturing

Small and Medium Manufacturing Enterprises (SMMEs) show increasing interest in collaborative manufacturing. Since, collaborative manufacturing enables SMMEs to collaboratively, deliver total solutions to globally distributed customers in a highly competitive manner. Engaging in collaborative manufacturing however has been identified as a challenging task for most SMMEs. The complexity of the collaborative decision-making processes impels that challenge to the SMMEs. Thus the focus of this paper is to propose an Interactive Meta-Goal Programming (IMGP)-based decision analysis methodology to help SMMEs overcome this challenge. A major contribution is the improvement of the interaction interface between decision-makers and a Goal Programming decision model. Our methodology enables every functional entity involved in a collaborative manufacturing decision problem to individually analyse and propose its uniquely desired local goals for the concerned decision, and facilitates the application of different prioritised goal clustering schemes to all proposed goals in order to accurately convey the decision-makers’ preferences. Thirdly, meta-goals are used to manipulate the degree of attainment for the prioritised goals. A hypothetical example is provided to illustrate the application of this methodology.

Interactive meta-goal programming

The concept of meta-goal programming is developed and linked to an interactive framework. An algorithm is proposed, in which the decision maker can establish target values on several achievement functions and use an interactive procedure to update these values. This substantially alleviates the problems associated with assigning to each attribute a target value, in order to build the goals, as well as the selection of a suitable achievement function. The functioning of the proposed interactive approach is illustrated with the help of an example taken from the farm management literature.

Meta-goal programming

In most of the Goal Programming (GP) applications reported in the literature the election of the GP variant is made in a mechanistic way by the analyst. However, each variant implies a different preference structure and, thus, it should be chosen by the decision-maker. Moreover, the final solution of the problem depends critically on the variant chosen. In this paper, it is assumed that the use of a combination of GP variants may reflect in many cases more precisely the actual preferences of the decision-maker. In this way, a “sensitivity analysis” of the solution obtained as well as of the own model structure can be very helpful to let the decision-maker clarify his/her knowledge about the reality analysed. This type of problem requires a more general approach that leads to a GP formulation of the initial GP model. This type of Meta-GP approach is presented in this paper.