Multi-choice Goal Programming Research Articles

Multi-Criteria Decision-Making Techniques for Solving the Airport Ground Handling Service Equipment Vendor Selection Problem

Appropriate airport ground handling service (AGHS) equipment vendor selection (AGHSEVS) can prevent aircraft damage and delays in airlines schedules, and ensure reliable and high-quality ground handling service. Previous research has seldom integrated multi-criteria decision-making techniques with goal programming to solve the AGHSEVS problem. This paper describes a new system evaluation model for AGHSEVS by considering both qualitative and quantitative methods. We compare the fuzzy TOPSIS method based on fuzzy weighted average left and right score methods with multi-choice and multi-aspiration goal programming approach of an AGHS company in Taiwan. These study results can help airport ground handling service company managers make optimal decisions for AGHSEVS problems. We hope the practicability of the comparable model with slight modifications of real situation data can be used in other AGHS companies.

The supplier selection problem of a manufacturing company using the weighted multi-choice goal programming and MINMAX multi-choice goal programming

• This paper investigates the weighted multi-choice goal programming and MINMAX multi-choice goal programming. • The weighted multi-choice goal programming provides the minimum aggregate deviation for all multiple goals. • The MINMAX multi-choice goal programming advocates the most balanced solution between all multiple goals. This paper presents two methods of decision making, Weighted multi-choice goal programming (MCGP) and MINMAX MCGP. With the proposed Weighted MCGP method, decision makers can set different weights w i for each goal with linguistic terms , such as high, average and low, which can be transformed into trapezoidal fuzzy numbers. Meanwhile, with the proposed MINMAX MCGP method, this study also let decision makers set the satisfaction membership function for each goal according to their preference in order to eliminate the effect of different scales in each goal. This paper also investigates the relationship between Weighted multi-choice goal programming and MINMAX multi-choice goal programming. According to the sensitivity analysis, decision makers can get the solution with the minimum aggregate deviation for all multiple goals from the Weighted multi-choice goal programming. Meanwhile, decision makers can get the solution with the most balanced solution between all multiple goals from the MINMAX multi-choice goal programming method. The weight variable is introduced to the above two methods to provide decision-makers with a mechanism to evaluate the discrepancy between the maximum aggregate achievement and the most balanced solution, enabling decision-makers to reach the preferable decision for their situation. A real-world problem of supplier selection by the purchasing and sales managers of a manufacturing company is used to illustrate the differing solutions given by the two models.

Multiple-criteria fuzzy group decision-making with multi-choice goal programming for supplier selection: A case study

In today’s highly turbulent and competitive environment, the success of the organization depends on the performance of its suppliers. However, supplier selection problems are complex as they involve a large number of criteria and, frequently, some of the criteria cannot be evaluated precisely. Moreover, fluctuations of supplier performances and unknown information always exist in real-world decision-making. It is a complex multiple-criteria decision-making (MCDM) problem as it involves a trade-off among various criteria with vagueness and imprecision and also involves a group of experts with diverse opinion. Therefore, to make more practical decisions, this paper is intended to propose an integrated technique for order preference by similarity to ideal solution (TOPSIS) in fuzzy environment with multi-choice goal programming (MCGP) to handle the supplier assessment and order allocation for a battery manufacturing organization. Using linguistic variables, the decision-makers assess the rating of suppliers as well as the importance of various factors. Linguistic variables are expressed in trapezoidal fuzzy numbers (TrFN). Fuzzy-TOPSIS method is proposed to obtain the rank of suppliers and MCGP method is used to allocate suitable orders to the selected suppliers. A case study is implemented to find the applicability and validity of the proposed model. Finally, sensitivity analysis is performed to observe the effect of weights of criteria on supplier evaluation problem.

An integrated approach to catering supplier selection using AHP-ARAS-MCGP methodology

Catering supplier selection is multi-criteria decision-making (MCDM) problem faced by airline industry. The evaluation process primarily involves the assessment of several complex factors for selecting a catering supplier that will best meet the airline requirements. There are a few applications of the catering supplier selection model. The approach of this paper is towards the solution for catering supplier selection. This study integrates the analytical hierarchy process (AHP) with a type of performance rating involving the determination a degree of utility by additive ratio assessment (ARAS) and multi-choice goal programming (MCGP). A real-life case study in selecting a catering supplier is also presented.

A robust bi-objective programming approach to environmental closed-loop supply chain network design under uncertainty

Imposition of strict environmental protection acts and the imperative need of the best possible allocation of resources have given birth to the concept of low carbon logistics. Environmental laws force the manufacturers to extend their existing supply chains to form a closed-loop supply chain (CLSC) through the setup of an efficient recovery system. In this paper, a multi-objective robust optimisation model is proposed for the design of CLSC network under uncertainty. First, a deterministic bi-objective mixed integer linear programming (BOMILP) model is developed for designing a CLSC network. Then, the robust counterpart of the proposed BOMILP is presented to cope with the real-world uncertainty. The first objective aims to maximise the total profit generated in the CLSC network and the second objective minimises the environmental pollution of the CLSC network. The proposed bi-objective model is solved using a multi-choice goal programming (MCGP) approach.

Dynamic virtual cell formation considering new product development

Nowadays, factories should be coordinated with changes in the dynamic environment due to the intense competition in the businesses. Different strategies and systems are existing to help factories in a dynamic situation. In this article, a new multi-objective mathematical model is presented by the implementation of dynamic virtual cellular manufacturing and also considering new product development which enables factories to be successful in their business. This paper contains three objectives including maximizing the total profits of the factory in all the periods, the grouping efficacy and also the number of the new product. After linearization of the proposed model, multi-choice goal programming with utility function is used to solve the model. In addition, a case study has been conducted in the real world to show the effectiveness of the proposed model and finally, the results show that the integration of virtual cellular manufacturing with new product development can be helpful for managers and companies and leads to more efficiency.

Robust stochastic multi-choice goal programming for blood collection and distribution problem with real application

Robust stochastic multi-choice goal programming for blood collection and distribution problem with real application

Tire forward and reverse supply chain design considering customer relationship management

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.

On fuzzy multiple objective linear programming problems

In recent years, multiple objective decision making (MODM) has become more and more important, and multiple objective linear programming (MOLP) approaches have been widely used for solving MODM problems. Three popular fuzzy mathematical programming approaches, including maxmin, arithmetical average, and two-phase methods, are often employed to solve MOLP problems. However, maxmin and two-phase methods cannot obtain an efficient solution. Ideal and anti-ideal solutions are required in all traditional methods. Such solutions cannot easily be established as a model for a knowledge-based system or expert system. In order to enrich the body of knowledge related to the field of MOLP, a new multi-choice goal programming (MCGP) model is proposed to solve fuzzy MOLP (FMOLP) problems in which the ideal and anti-ideal solutions are no longer required. This reduces the complexity of the solution process for solving MODM. The original feasible region can be expended to a potential feasible region to determine the appropriate aspiration level for decision makers. The proposed methods can systematically solve MODM problem to obtain satisfactory solutions in one step. Thus, a model-based can easily be established for expert systems, knowledge-based system, and artificial intelligence systems. In addition, a revised utility function (UF) is derived to solve qualitative and quantitative MODM problems. Based on illustrative examples, the five methods (maxmin, arithmetical average, two-phase, goal programming (GP), and proposed models) are compared to reveal managerial implications. On the basis of these comparisons, DMs can easily determine the best suited solution for their specific MODM problems. Finally, a realistic example is provided to demonstrate the usefulness of the proposed methods.

An integrated Taguchi loss function–fuzzy cognitive map–MCGP with utility function approach for supplier selection problem

Due to the effects of supplier evaluation and selection problem on the quality of products and companies’ business activities, supplier selection is considered as a strategic issue in organizations’ development plans. The purpose of this study is to provide an integrated framework for supplier selection problem regarding to the loss of criteria deviation from specification limits, causal relationships between criteria and the preferences of decision makers (DMs) in the supplier selection problem. Thus, in the first step, the loss of each criterion is calculated using Taguchi loss function (TLF), then fuzzy cognitive map (FCM) and hybrid learning algorithm are applied to determine criteria weights. Finally, considering outputs of TLF and FCM methods, multi-choice goal programming with utility function (MCGP-U) is used to select an optimal supplier and to increase the DMs’ expected utility values, simultaneously. The results of implementation of proposed framework based on the extended MCGP-U model on an active company in paint and coating industry show that delivery time criterion has the most effect and priority on suppliers’ evaluations. Also among six qualified suppliers, a supplier with the least total loss value and the most utility values is selected as the optimal supplier for the under consideration company.

Multi-choice goal programming approach to solve multi-objective probabilistic programming problem

Stochastic Programming is an art of modeling optimization problems in an environment, where randomness occurs. In this manuscript, we present a multi-objective probabilistic programming problem, where the random parameter follow logistic distribution. We transform the probabilistic programming model to an equivalent deterministic mathematical model by using chance constrained technique. Multiple number of aspiration levels are allocated to the objective function by Decision maker, the main aim is to obtain such a decision. After allocating several aspiration levels to the objective function, which will provide minimum deviation from objective function and aspiration level. Such minimization of deviation is possible by using multi-choice goal programming technique. Multi-choice parameters are handled by three different techniques viz; binary variable approach, Vandermonde’s interpolating polynomial approach and linear least square approximation approach. To illustrate the methodology, a numerical example is presented.

A bi-objective sustainable supplier selection and order allocation considering quantity discounts under disruption risks: A case study in plastic industry

In recent years, the supplier selection problem as one of the important components in sustainable development is considered which this is due to the importance of quality and quantity of the product and sustainability issues. Besides, in this paper, a decision-making tool is provided to solve the sustainable supplier selection and order allocation problem in a multi-period, multi-item, and multi-supplier environment considering quantity discounts and disruption risks. At first, one of the efficient Multiple Criteria Decision Making (MCDM) approaches called Best Worst Method (BWM) is employed to find a global importance weight of each determined criterion and the preference weights of each potential supplier according to these criteria. Then the proposed MILP model attempt to minimize the total costs and maximize the total score of all suppliers in respect to three sustainability aspects. Also, in order to solve the proposed bi-objective mathematical model, a Revised Multi-Choice Goal Programming (RMCGP) method is applied. Additionally, a consistency test is applied to check the quality of expert comments. To investigate the robustness of the approach, the sensitivity analysis is utilized. Also, a real-world case study in the north of Iran is presented to highlight the performance of the proposed model. Finally, the selected suppliers, amount of inventory and shortage, ordered quantity along with other variables are calculated. Also, based on sensitivity analysis, the effect of quantity discounts and disruption risks are revealed. The results approve the efficiency of the proposed approach.

Relief commodities distribution planning considering the features of demand areas: a robust multi-objective approach

Logistics planning plays an important role in providing services to the disaster -stricken areas. In this study, a scenario-based multi-objective model is presented to locate the distribution and evacuation centers and distribute the relief commodities with an appropriate allocation. It aims to serve the earthquake-stricken areas that are classified according to their construction qualities. The objective functions of cost, responsiveness and demand coverage are considered in the proposed optimization model. Moreover, due to the uncertain nature of a disaster and uncertainty in some model parameters, a robust optimization approach is utilized. A revised multi-choice goal programming method is applied to solve the multi-objective model. The proposed model is validated through a case study conducted in the city of Amol. The computational results show the efficiency of the proposed model in a real–world disaster situation.

Solving fuzzy transportation problem using multi-choice goal programming

This paper explores the study of fuzzy transportation problem (FTP) using multi-choice goal-programming approach. Generally, the decision variable in transportation problem (TP) is considered as real variable, but here the decision variable in each node is chosen from a set of multi-choice fuzzy numbers. Here, we formulate a mathematical model of FTP considering fuzzy goal to the objective function. Thereafter, the solution procedure of the proposed model is developed through multi-choice goal programming approach. The proposed approach is not only improved the applicability of goal programming in real world situations but also provided useful insight about the solution of a new class of TP. A real-life numerical experiment is incorporated to analyze the feasibility and usefulness of this paper. The conclusions about our proposed work including future studies are discussed last.

Multiobjective Transportation Problem Using Fuzzy Decision Variable Through Multi-Choice Programming

This paper analyzes the study of Multiobjective Transportation Problem (MOTP) under the consideration of fuzzy decision variable. Usually, the decision variable in a Transportation Problem is taken as real variable. But, in this paper, the decision variable in each node is selected from a set of multi-choice fuzzy numbers. Inclusion of multiple objectives into transportation problem with fuzzy decision variable makes it a Multiobjective Fuzzy Transportation Problem (MOFTP). In this paper, a new formulation of mathematical model of MOFTP with fuzzy goal of each objective function is enlisted. Thereafter the solution technique of the formulated model is described through multi-choice goal programming approach. Finally, a numerical example is presented to show the feasibility and usefulness of this article.

Combining 3-Level Multichoice Goal Programming with Multicoefficient Goal Programming to Evaluate Forest Ecosystem Service Potential Improved by Thinning

Combining 3-Level Multichoice Goal Programming with Multicoefficient Goal Programming to Evaluate Forest Ecosystem Service Potential Improved by Thinning

Sustainable supplier selection and order allocation through quantity discounts

Within activities related to the global supply chain, proper supplier selection regarding sustainability issues, especially economic, environmental and social, plays an important role in sustainable development. In this paper, a new multi-objective mathematical programming model is designed to consider sustainable supplier selection and order allocation simultaneously in a multi-period, multi-item and multi-supplier supply chain. In addition, quantity discounts are considered under two assumptions: all-unit and incremental. The proposed model is solved following a revised multi-choice goal programming approach in order to find a compromise solution. Furthermore, a real-life case is studied to justify the applicability of the proposed model.

Combining 3-Level Multichoice Goal Programming with Multicoefficient Goal Programming to Evaluate Forest Ecosystem Service Potential Improved by Thinning

Combining 3-Level Multichoice Goal Programming with Multicoefficient Goal Programming to Evaluate Forest Ecosystem Service Potential Improved by Thinning

Solving a bi-objective vehicle routing problem under uncertainty by a revised multi-choice goal programming approach

Solving a bi-objective vehicle routing problem under uncertainty by a revised multi-choice goal programming approach

Multi-objective two-stage grey transportation problem using utility function with goals

Multi-Objective Goal Programming is applied to solve problems in many application areas of real-life decision making problems. We formulate the mathematical model of Two-Stage Multi-Objective Transportation Problem (MOTP) where we design the feasibility space based on the selection of goal values. Considering the uncertainty in real-life situations, we incorporate grey parameters for supply and demands into the Two-Stage MOTP, and a procedure is applied to reduce the grey numbers into real numbers. Thereafter, we present a solution procedure to the proposed problem by introducing an algorithm and using the approach of Revised Multi-Choice Goal Programming. In the proposed algorithm, we introduce a utility function for selecting the goals of the objective functions. A numerical example is encountered to justify the reality and feasibility of our proposed study. Finally, the paper ends with a conclusion and an outlook to future investigations of the study.