Multi-choice Goal Programming Model Research Articles

Designing an incentive scheme for producer responsibility organization of waste tires: A MCGP cooperative game approach

Extended producer responsibility (EPR) of waste tires is usually implemented by a producer responsibility organizations (PRO) composed of a group of tire producers. As a collective EPR system, the main goals of PRO are to achieve recycling cost efficiency as well as offer incentives for all member producers to design their products for recyclability (or called eco-design). Nevertheless, current PROs for tire recycling are far from their goals and do not provide sufficient incentives because they ignore the differentiated recyclability of products from different producers and charge producers a uniform unit fee. To resolve these problems, we first create a mixed recovery decision model for tire recycling of PROs based on multi-choice goal programming (MCGP) approach towards system optimality and obtain the optimal total recovery cost. In particular, the proposed MCGP model allows for integrating a variety of different regulations under a carbon-policy regime into a single programming model through the formulated utility functions and penalty functions. This makes it easy to adjust the regulatory intensity to compromise the conflicting objectives regarding cost efficiency and carbon emission reduction. Further, recognizing the optimal total recovery cost as a baseline, a cost-sharing scheme is proposed to maintain the voluntary participation of producers for PRO as well as offer eco-design incentives for producers. This study proves that the solutions of the proposed hybrid method is more suitable for the research problem than the eminent solutions such as the Shapley value. The feasibility of the proposed hybrid model is examined via a numerical study with calibrated real-world datasets.

An integrated approach to site selection for a big data center using PROMETHEE-MCGP methodology

In today’s economy, information technology (IT) is vitally important, and the increasing use of the Internet, telecommunications services, and internal IT networks in organizations have led to rapid growth in the demands on big data processing. In general, site selection is a fundamental part of the design of a big data center (BDC), and a poor site decision can affect the sustainability of the facility. To construct a comprehensive assessment framework for a BDC, the following three categories of indicators are determined based on the “Specification for Design of Data Center” in GB50174-2017 of China: economic factors, natural climate environment factors, and energy resources factors. After explaining the rationality of choosing these indicators in detail, an integrated method that combines the multi-criteria decision-making (MCDM) method and the multi-choice goal programming (MCGP) model is proposed. The proposed approach uses two phases to conduct the decision procedure. First, the preference ranking organization method for enrichment evaluation (PROMETHEE) method is applied to evaluate the economic factors. Then, the evaluation results are added to the MCGP model as one of the goals of multi-objective programming. Second, the remaining five sub-indicators and the evaluation results generated from the first phase are formulated as a complete MCGP model. Finally, an empirical study on the site selection for the BDC is implemented based on the proposed method. The result shows that Guiyang is the most suitable place for locating a BDC in China.

Supplier Selection Analysis Using Minmax Multi Choice Goal Programming Model

Production control, inventory and distribution is an important factor in trading activities. These three factors are discussed in a system called Supply Chain Management (SCM). Procurement of goods from a company or trading business related to suppliers. In some cases, there are several supplier that can be assessed by considering certain factors. In certain cases, the data from several factors that are considered are uncertainty, so the fuzzy approach can be used. The MINMAX Multi Choice Goal Programming model can be used to solve fuzzy supplier selection problems with linear membership function. It can be applied to selecting supplier of Brastagi Oranges. There are four suppliers, namely Jaya, Mako, Baros. Gina. There are three factor to consider, cost, quality and delivery. The decision maker select the best supplier for ordering 17000 kg Brastagi oranges. The results, the best supplier is Gina with an order quantity of 10000 kg and Mako with a total order of 7000 kg

Growth Scale Optimization of Discrete Innovation Population Systems with Multichoice Goal Programming

How are limited resources efficiently allocated among different innovation populations? The performances of different innovation populations are quite different with either synergy or competition between them. If the innovation population is kept under an appropriate scale, full use can be made of the allocated resources. The maximization of the development and performance for a certain scale of innovation population is a typical multichoice development problem. Therefore, the scale optimization of the innovation population should be analyzed. According to the population dynamics, a resource constraint model for the growth of innovation population is developed, and the growth of innovation population under resource constraints is in equilibrium accordingly. With the help of a multichoice goal programming model, the scale optimization of innovation population performance can be obtained. The results of the resource constraint model and multichoice goal programming model are used to determine the optimal scale of the innovation population. From the panel data of the innovation population in Jiangsu Province from 2000 to 2017, we have found that R&D investment was the main innovation resource variable and that patent number was the main innovation output variable. Based on these data, the scale optimization of the innovation population under resource constraints can be calculated. The results of the study show that, in the observation period, the enterprise innovation population is often in the appropriate scale state. The scale development of enterprise innovation population is often more suitable for innovation ecosystem than that of scientific research institutions. According to these results, the government can provide appropriate guiding policies and incentives for different innovation populations. The innovative population can adjust its own development strategy and plan in time accordingly.

Study on the coordinated development of urbanization and water resources utilization efficiency in China

Abstract This paper uses the Lotka-Volterra model to illustrate the synergistic relationship between urbanization and water resources utilization efficiency. Combined with a multi-choice goal programming model, the ideal cooperation coefficient between urbanization and water resources utilization efficiency in each provincial region was calculated under the condition of a coordination equilibrium. The results show that the urbanization level of China's provincial regions is uneven. The urbanization level of the eastern coastal developed areas is the highest, followed by the central area, and the western area is the lowest. Guangdong, Jiangsu, Zhejiang, Shanghai, Beijing and Shandong are at a high level of urbanization. The total factor productivity (tfp) of two-thirds of the provincial regions changed to be >1. During the observation period, water use efficiency of most provinces in China improved. The distribution characteristics of urbanization level and water resources utilization efficiency are not consistent. There is no mutually beneficial relationship between urbanization and water resources utilization in most provincial regions. In a few areas, there is a partial benefit cooperation relationship between them. There is a mutually beneficial relationship in a few regions but the level of coordination in the provincial Chinese mainland is relatively low, and needs to be improved.

Multiobjective Evaluation of Coevolution among Innovation Populations Based on Lotka–Volterra Equilibrium

The collaborative evaluation of enterprise innovation populations is a hot issue. The Lotka–Volterra model is a mature method used to evaluate the interaction mechanism of populations and is widely used in innovation ecology research studies. The Lotka–Volterra model mainly focuses on the quantitative characteristics of the interactive populations. The growth mechanisms cannot explain all the synergy mechanisms of the innovative populations. The collaborative evaluation between enterprise innovation populations is a typical multiobjective evaluation problem. The multichoice goal programming model is a mature method to solve multiobjective optimization problems. This paper combines the Lotka–Volterra model and multichoice goal programming method to construct a three-stage multiobjective collaboration evaluation method based on Lotka–Volterra equilibrium. An evaluation example is used to illustrate the application process of this method. The method proposed in this paper has excellent performance in computing, parameter sensitivity analysis, and model stability analysis.

Five-Echelon Multiobjective Health Services Supply Chain Modeling under Disruption

Medicine unreliability problem is taken into consideration as one of the most important issues in health supply chain management. This research is associated with the development of a multiobjective optimization problem for the selection of suppliers and distributors. To achieve the purposes, the optimal quota allocation is determined with respect to disruption of suppliers in a five-echelon supply chain network and consideration of the distributor centers as a hub location-allocation mode. The objective of the optimization model is involved in simultaneous minimization of transactions costs dealing with suppliers, expected purchasing costs from suppliers, expected percentages of delayed and returned products in each distributor, as well as transportation cost in each echelon and fixed cost for distributor centers, and finally maximization of the expected scores for suppliers and high priority of product customers. The optimization problem is formulated as a mixed-integer nonlinear programming model. The proposed optimization model is utilized to investigate a numerical case study for asthma-specific medicines. The analyzing procedure is conducted based on the collected real data from Cobel Darou pharmaceutical company in 2019. Furthermore, a fuzzy multichoice goal programming model is considered to solve the proposed optimization model by R optimization solver. The numerical results confirmed the authenticity of the model.

A robust-heuristic optimization approach to a green supply chain design with consideration of assorted vehicle types and carbon policies under uncertainty

Adoption of carbon regulation mechanisms facilitates an evolution toward green and sustainable supply chains followed by an increased complexity. Through the development and usage of a multi-choice goal programming model solved by an improved algorithm, this article investigates sustainability strategies for carbon regulations mechanisms. We first propose a sustainable logistics model that considers assorted vehicle types and gas emissions involved with product transportation. We then construct a bi-objective model that minimizes total cost as the first objective function and follows environmental considerations in the second one. With our novel robust-heuristic optimization approach, we seek to support the decision-makers in comparison and selection of carbon emission policies in supply chains in complex settings with assorted vehicle types, demand and economic uncertainty. We deploy our model in a case-study to evaluate and analyse two carbon reduction policies, i.e., carbon-tax and cap-and-trade policies. The results demonstrate that our robust-heuristic methodology can efficiently deal with demand and economic uncertainty, especially in large-scale problems. Our findings suggest that governmental incentives for a cap-and-trade policy would be more effective for supply chains in lowering pollution by investing in cleaner technologies and adopting greener practices.

Collaborative Product Portfolio Design Based on the Approach of Multichoice Goal Programming

Product portfolio optimization is a typical multiobjective problem. The multichoice goal programming method becomes a popular means of resolving multiobjective decision problems. However, the classic multichoice goal programming method treats the product portfolio optimization in isolation and does not consider the mutual influence between portfolio products. Researchers should consider the interaction between products in portfolio optimization so that they can be adjusted to “real world” problems. The interaction between products can be explained by population dynamics. Logistic model is a classical method to analyze the population interaction. The equilibrium point of logistic model can show the ideal state of product population coordinated development. The combination of logistic and multichoice goal programming method is an effective approach to analyze the interaction of product portfolio. This paper therefore proposes a new alternative method to formulate the multiobjective problem and also uses an illustrative example to demonstrate the usefulness of the proposed method. The comparative analysis of model optimization results shows that logistic multichoice goal programming model can take into account resource constraints, product collaboration, and output maximization. Logistic multichoice goal programming model shows good performance in the aspects of operation complexity, operation time, sensitivity analysis, and collaborative entropy evaluation.

Sustainable feedstocks selection and renewable products allocation: A new hybrid adaptive utility-based consensus model

Nowadays, preferred compromise response of renewable energies' demands regarding the candidate sustainable feedstocks is a crucial issue for market change management. Thus, selecting the most suitable sustainable feedstock is a key factor for optimum renewable products allocation problem. To address the issue, this study proposes a hybrid adaptive framework based on consensus evaluation approach, weighting and ranking procedure, and preferred demand assignment under dynamic hesitant fuzzy sets. In this respect, the consensus evaluation approach is tailored regarding the direct and indirect feedback mechanisms to enhance the quality evaluation of candidate sustainable feedstocks under assessment criteria. Thereby, the weight of each criterion is determined based on the developed dynamic hesitant fuzzy entropy method and the candidate sustainable feedstocks are ranked with respect to developed dynamic hesitant fuzzy positive and negative ideal solutions. Then, a revised multi-choice goal programming model is extended regarding the dynamic hesitant fuzzy closeness indexes to attend to preferred compromise response of demand centers by optimum renewable products allocation. Meanwhile, the presented hybrid adaptive framework is implemented to a real case study to represent the applicability and efficiency of the proposed approach. Furthermore, a comparative analysis is provided by defining eight comparison indexes to compare the obtained results with two recent studies in relevant literature for representing the validation and verification of the proposed approach. The comparative analysis shows that the proposed approach versus the two other approaches has merits such as modeling of uncertainty, experts' weights, adaptive structure, unanimous agreement-based approach, and last aggregation framework. Finally, a sensitivity analysis is represented to show the sensitiveness and robustness of the obtained results from changing the criteria weights, goals values, and consensus elimination. Thereby, the sensitivity analysis indicates that the obtained ranking results are sensitive to sustainability criteria unlike the technical criterion.

Order allocation and procurement transport planning in apparel supply chain: A utility-based possibilistic-flexible programming approach

The purpose of this paper is to study the integrated decisions of the physical supply channel on the global scale including the supplier selection, order allocation, and transportation planning under uncertainty. In detail, this study addresses the supply decision-making problem in a multi-source, multi-product, and multi-mode transportation in accordance with the price discounts. To this end, a multi-objective mixed integer linear programming model is developed to jointly determine the best suppliers, and purchasing quantities along with the number and type of transportation modes. Three important objective functions, i.e., total logistics costs, total late deliveries and total value of purchasing are optimized in our proposed mathematical model. Then, a fuzzy multi-choice goal programming model with the utility function is presented to solve the multi-objective formulation. A novel flexible-possibilistic programming approach is proposed to cope with the epistemic uncertainty and the flexibility of constraints based on the expected value and chance constraint programming concept. Finally, a numerical study in the apparel supply chain is carried out to show the model usefulness and the solution algorithm effectiveness.

Addressing uncertainty in sugarcane harvest planning through a revised multi-choice goal programming model

In this paper a new revised multi-choice goal programming (RMCGP-LHS) model is proposed to deal with uncertainty in sugar cane harvest scheduling for sugar and ethanol milling companies. The RMCGP-LHS model uses a weekly decision-making horizon and takes into account the time and condition of land management, cane cutting decisions, and agricultural logistics. Its objective is to obtain information in order to harvest sugar cane plots in the period closest to the highest saccharose levels, while also minimizing agro-industrial costs. The RMCGP-LHS model was applied to a real case sugar and ethanol mill, and its optimization has provided harvesting policies that were validated by the company’s managers. Besides that the RMCGP-LHS model is a very practical tool for simulating in a fast way different scenarios involving uncertainties on model parameters and helping the managers in decision making process in real time.

An Integrated Maximizing Consistency and Multi-Choice Goal Programming Approach for Hybrid Multiple Criteria Group Decision Making Based on Interval-Valued Intuitionistic Fuzzy Number

Interval-valued intuitionistic fuzzy numbers (IVIFNs) characterized by a membership function and a non-membership function with values that are intervals, have strong ability to handle imprecise and ambiguous information in real-world applications. This paper proposes an integrated maximizing consistency and multi-choice goal programming (MCGP) approach to handle hybrid multi-criteria group decision making problems based on IVIFNs. Firstly, the hybrid decision infor- mation (including crisp numbers, intervals, intuitionistic fuzzy numbers and linguistic variables) are normalized into the IVIFNs. Then, an ordinal consistency index and a cardinal consistency in- dex are proposed to measure the consistency between the individual opinion and the group opinion, respectively. And an optimal model based on maximizing consistency is constructed to derive the weights of experts. Afterwards, the comprehensive ratings and the ranking values of alternatives are obtained by the hybrid weighted aggregation operator and the proposed ratio function of IVIFNs, respectively. Furthermore, a MCGP model based on the ranking values is constructed to identify the optimal alternatives and their optimum quantities. At length, an illustrative case is provided to verify the proposed approach.

Soft computing based on maximizing consensus and fuzzy TOPSIS approach to interval-valued intuitionistic fuzzy group decision making

Multi-attribute group decision making (MAGDM) is an important research topic in decision theory. In recent decades, many useful methods have been proposed to solve various MAGDM problems, but very few methods simultaneously take them into account from the perspectives of both the ranking and the magnitude of decision data, especially for the interval-valued intuitionistic fuzzy decision data. The purpose of this paper is to develop a soft computing technique based on maximizing consensus and fuzzy TOPSIS in order to solve interval-valued intuitionistic fuzzy MAGDM problems from such two aspects of decision data. To this end, we first define a consensus index from the perspective of the ranking of decision data, for measuring the degree of consensus between the individual and the group. Then, we establish an optimal model based on maximizing consensus to determine the weights of experts. Following the idea of TOPSIS, we calculate the closeness indices of the alternatives from the perspective of the magnitude of decision data. To identify the optimal alternatives and determine their optimum quantities, we further construct a multi-choice goal programming model based on the derived closeness indices. Finally, an example is given to verify the developed method and to make a comparative analysis.

Knowledge management system selection by multi-choice goal programming

Goal programming (GP) is an important technique for solving many practice applications. This paper adopts multi-choice goal programming model (MCGP) approach to solve a multi-objectives decision-making problem. The decision model is developed as a multi-choice aspiration levels goal programming model base on a case study. The overall objective is to select an effective resource distribution in a knowledge management system (KMS) projects selection process. The main contribution of this work is development a mathematical model for KMS project evaluate and selection. The advantage of this method is that it allows decision makers to set multi-objective aspiration levels for KMS selection problems. The MCGP model facilitates decision-making process in business resources planning.

Supplier Selection Using Integrated Fuzzy TOPSIS and MCGP: A Case Study

This study provides an overview of the fuzzy TOPSIS and Multi-Choice Goal Programming (MCGP) methods for Multi-Criteria Decision-Making (MCDM) problem under uncertain environments. Supplier selection is mostly a complex multi-criteria problem which consists of qualitative and quantitative criteria. Those criteria should be considered advertently as a final decision in comprehensive and objective supplier selection as well. This study deal with the optimum decision making for selecting supplier and allocating order by applying the proposed method of integrated fuzzy TOPSIS and MCGP (Multi-Choice Goal Programming). To deal with the uncertain and imprecise judgment of decision makers, a Fuzzy TOPSIS is utilized to express it by triangular fuzzy numbers. The final supplier selection and order allocation are obtained by integrating the closeness coefficients to the MCGP model. A numerical example is given to clarify the main results developed in this paper.

A Fuzzy Goal Programming Formulation with Multiple Target Levels

The essential activity of a manager is decision making, which is becoming more and more complex, mainly in the multi-criteria problems. Multi-choice goal programming (MCGP) is considered as a robust tool in operational research to solve this type of problem. However, in real world problems, determining precise targets for the goals is a difficult task. To deal with such situation, Tabrizi introduced and used in 2012 the concept of membership functions in the MCGP model in order to model the targets fuzziness of each goal. In their model, they considered just only one type of functions (triangular form), which does not reflect adequately the decision maker's preferences that are considered as an essential element for modelling the goal's fuzziness. Their model is called Fuzzy MCGP. In this paper, new ideas are presented to reformulate MCGP model to tackle all types of functions by introducing the (decision maker's) preferences. The concept of indifference thresholds is used in the new formulation for characterizing the imprecision and the preferences associated with all types of the goals. The proposed formulation provides useful insight about the solution of a new class of problems. A numerical example is given to demonstrate the validity and strength of the new formulation. Copyright © 2013 John Wiley & Sons, Ltd.

A multiobjective-optimization approach for a piloted quality-management system: A comparison of two approaches for a case study

In recent years, steering a quality-management system (QMS) has become a key strategic consideration in businesses. Indeed, companies constantly need to optimize their industrial tools to increase their productivity and to permanently improve the effectiveness and efficiency of their systems. To solve such problems, two approaches were developed: the Pareto Analytical-Hierarchy Process (PAHP) and the Multichoice Goal Programming (MCGP) methods. The first integrates the Pareto concept and Analytical-Hierarchy Process (AHP) methods and the second combines the MCGP model with AHP methods. The goal was to determine the best solution while simultaneously verifying multiobjective-optimization functions and satisfying different constraints for a real-world case study. The latter was chosen because it presents a major problem for controlling the quality levels of production lines. A comparative study between the two approaches provides a path for designing a tool for decision support to ensure the effectiveness of a corporate QMS.

An evaluation framework for product planning using FANP, QFD and multi-choice goal programming

Technological innovation and satisfaction of customer needs are the keys to survival and success for firms, especially in global competitive high-tech industries. Since new products are usually a source of new sales and profits, the success of new product development (NPD) is essential to maintain a competitive edge and to make a decent profit in a longer term. Therefore, how to develop products that deliver the quality and functionality customers demand while generating the desired profits becomes an important task for the manufacturers. In this paper, a framework with two phases is constructed for facilitating the selection of engineering characteristics (ECs) for product design. In the first phase, quality function deployment (QFD) is incorporated with the supermatrix approach of analytic network process (ANP) and the fuzzy set theory to calculate the priorities of ECs with the consideration of the interrelationship among factors and the impreciseness and vagueness in human judgments and information. In the second phase, multi-choice goal programming model is constructed by considering the outcome from the first phase and other additional goals, such as NPD cost and manufacturability, in the attempt to select the most suitable ECs. A case study of the product design process of backlight unit (BLU) in thin film transistor liquid crystal display (TFT-LCD) industry in Taiwan is carried out to verify the practicality of the proposed framework.