Multi-segment Goal Programming Research Articles

Assessment of Sustainable Reverse Logistic Provider Using the Fuzzy TOPSIS and MSGP Framework in Food Industry

As consumers become ever more conscious of environmental issues, socially responsible corporate practices, and government regulations, companies are increasingly motivated to incorporate reverse logistics (RLs) into their operations, thus raising the question of provider selection. In previous studies, the food industry generally lacked a systematic reference method for RLs provider selection, especially during the post-COVID-19 pandemic. This study aims to develop a comprehensive approach that combines a technique for order preference by similarity to ideal solution (TOPSIS) and multi-segment goal programming (MSGP) models to select optimal RLs providers. Furthermore, this method will enable decision makers (DMs) to evaluate and select the best RLs provider considering the limited resources of the business. This approach allows DMs to consider both qualitative and quantitative criteria, set multiple target segmentation expectations, and achieve optimal RLs provider selection. This study also provides case studies of applications by food manufacturers. The main finding is that considering multiple criteria in making a decision produces better results than using a single criterion.

Integrated Fuzzy-MSGP Methods for Clothing and Textiles Supplier Evaluation and Selection in the COVID-19 Era

Supplier selection is an important issue in supply chain management (SCM) which, as with most dimensions of business, has been strongly impacted by the COVID-19 pandemic. Previous research on the clothing and textiles (C&T) industry has overlooked efforts to provide a regular reference method for addressing the problem of supplier selection. This study discusses the COVID-19 pandemic’s impact on, and the relative importance of, supplier selection implementation. This study applied fuzzy techniques for order preference by similarity to ideal solution (TOPSIS) and multisegment goal programming (MSGP) method to the problem of supplier selection and combined these two methods to propose a novel multicriteria decision-making (MCDM) method to be used by C&T companies. It proposes a simple method to provide decision-makers (DMs) with guidelines for supplier selection considering existing constraints on business resources. The advantage of this method is the incorporation of both qualitative and quantitative criteria (e.g., both tangible and intangible resources), which allows DMs to set multisegment aspiration levels (MSALs) for supplier selection. A case study of a C&T manufacturer’s application of the model is also presented.

On the personal diet considering qualitative and quantitative issues

This study integrates the analytic hierarchy process method, multi-choice goal programming (MCGP), and multi-segment goal programming (MSGP) as a new model to resolve the problem of customized suggestions on exercise plans and diet meals. For personal diet decision support (PDDS), a model-based system is implemented to solve both the exercise selection and diet meals problems. Moreover, the novel pseudo-integer (PI) technique is proposed to solve the large size of PDDS problem efficiently. Also, the MCGP-U technique is adopted to consider users’ risk attitude in the decision-making of diet and exercise.It is intended to help people design their daily personal diet menus and exercise suggestions in achieving their weight loss goals and satisfying the requirements of a balanced diet. The proposed model has the following contributions: (1) it provides a customized exercise plan according to the user’s personal needs of exercise preference and risk attitudes, (2) it allows users to design their daily diet menu according to their individual needs for calories and nutrients, (3) it offers a novel PI method that can easily solve large-sized PDDS problem efficiently, (4) it adopts the MCGP-U technique to consider PDDS’s qualitative issue, and (5) it provides the total solution of exercise and meal suggestion for users to achieve the goal of weight loss. These advantages are of great help to both diabetic patients and healthy people. This improves the healthcare services in developed and developing countries considering multiple qualitative and quantitative criteria.

Selection of In-Flight Duty-Free Product Suppliers Using a Combination Fuzzy AHP, Fuzzy ARAS, and MSGP Methods

Over a billion people travel by air all over the world every year, and there are many in-flight retailing opportunities for the airline industry. This paper proposes a novel integration fuzzy analytical hierarchy process (FAHP), fuzzy additive ratio assessment (FARAS), and multisegment goal programming (MSGP) methods to select the best supplier for in-flight duty-free product in airline industry. The advantage of this proposed method is that it allows decision makers (DMs) to set multisegment aspiration vague levels considering both the qualitative and quantitative criteria for supplier selection simultaneously. To the best of our knowledge, a simultaneous consideration of qualitative and quantitative criteria for supplier selection of in-flight duty-free product has never been applied under the airline retail industry context. This research will fill into the gaps of supplier selection in in-fight duty-free product for airline industry. The integrated model is illustrated by an example in an airline company in Taiwan.

INTEGRATED FAHP, ARAS-F AND MSGP METHODS FOR GREEN SUPPLIER EVALUATION AND SELECTION

In recent years, determining the best supplier in the green supply chain has become a key strategic task for a firm. Since the decision usually involves several objectives or criteria, the green supplier selection process is a fuzzy multiple criteria decision-making (FMCDM) problem. Considering both qualitative and quantitative criteria, this study proposes new integrated fuzzy techniques for fuzzy analytic hierarchy process (FAHP), fuzzy additive ratio assessment (ARASF) and multi-segment goal programming (MSGP) approach to solve the green supplier selection problems. The advantage of this method is that it allows decision makers to set multiple segment aspiration levels for green supplier selection problems. The integrated model is illustrated by an example in a watch firm.

Multi-coefficient goal programming in thinning schedules to increase carbon sequestration and improve forest structure

Current decision analysis techniques are ineffective for planning thinning operation to improve the forest structure. The purpose of this study is to use multi-coefficient goal programming (GP) to plan a thinning schedule that allows more carbon sequestration and diverse forest structure. A multi-coefficient GP is applied to plan a thinning schedule for a 2,633-ha plantation forest. This technique efficiently fine-tunes the thinning schedule to obtain 420,500 tons of carbon sequestration which was a little higher than the result by a multi-segment goal programming (MSGP). Moreover, a fixed-ratio multi-coefficient GP is applied to efficiently generate mosaic of thinned areas with various thinning intensities. Although the captured carbon by a fixed-ratio multi-coefficient GP is lower, the thinned areas can provide various habitats for forest life with multiform contrasting edges. The use of a multi-coefficient GP allows practicable planning of better thinning alternatives to increase carbon sequestration and forest structure.

An evaluation approach to logistics service using fuzzy theory, quality function development and goal programming

Logistics customer service is an important factor in the success of supply chain management. The aim of this study is to propose a novel approach for customer service management. For the improvement of logistics service operations, the proposed method integrates quality function development (QFD), fuzzy extended analytic hierarchy process (FEAHP), and multi-segment goal programming (MSGP). The advantage of the method includes the consideration of various logistics goals and the flexibility of setting multi-aspiration levels of evaluation criteria.

Supplier selection using revised multi-segment goal programming model

Selecting the best supplier has been turned into a strategic subject in the competitive market, in the recent years. Supplier selection is one of the significant multiple criteria decision-making problems, which is studied in this paper. This research applies the multi-segment goal programming formulation, which considers different positions in the goal programming. This new kind of goal programming is revised in this investigation. The advantage of the proposed method is that it allows decision makers to set multiple aspiration levels for the coefficient of variables. Hence, the decision makers obtain more revenue and match with the reality of their problems. Unlike the multi-segment goal programming proposed in the literature, the revised formulation is linearized in the current study. The supplier selection problem is solved by this new formulation in a special circumstance. Finally, some concluding notes are remarked.

Integrated multi-choice goal programming and multi-segment goal programming for supplier selection considering imperfect-quality and price-quantity discounts in a multiple sourcing environment

Supplier selection (SS) is a multi-criteria and multi-objective problem, in which multi-segment (e.g. imperfect-quality discount (IQD) and price-quantity discount (PQD)) and multi-aspiration level problems may be significantly important; however, little attention had been given to dealing with both of them simultaneously in the past. This study proposes a model for integrating multi-choice goal programming and multi-segment goal programming to solve the above-mentioned problems by providing the following main contributions: (1) it allows decision-makers to set multiple aspiration levels on the right-hand side of each goal to suit real-world situations, (2) the PQD and IQD conditions are considered in the proposed model simultaneously and (3) the proposed model can solve a SS problem with n suppliers where each supplier offers m IQD with r PQD intervals, where only extra binary variables are required. The usefulness of the proposed model is explained using a real case. The results indicate that the proposed model not only can deal with a SS problem with multi-segment and multi-aspiration levels, but also can help the decision-maker to find the appropriate order quantities for each supplier by considering cost, quality and delivery.

A FUZZY APPROACH TO BUSINESS TRAVEL AIRLINE SELECTION USING AN INTEGRATED AHP-TOPSIS-MSGP METHODOLOGY

Airline selection is a multi-criteria decision-making (MCDM) problem faced in business travel. The evaluation process primarily involves the evaluation of several complex factors for selecting an airline that will best meet business travel needs. Previous studies have proposed several different methods that a company or organization can use to evaluate or select an airline. In its proposed approach to solving airline selection, this study integrates the analytical hierarchy process (AHP) with a type of preference ordering involving the determination a solution's similarity to an ideal solution (TOPSIS) and multi-segment goal programming (MSGP). A real-life case study on selecting an airline is also presented.

Revised multi-segment goal programming: Percentage goal programming

The multi-segment goal programming (MSGP) model is an extension model of GP wherein the core thinking is inherited from the multi-choice goal programming (MCGP) model. In this paper, we recommend certain points of the MSGP model and offer a Revised MSGP Model as an aid to burdened decision makers who cannot expect an either-or selection of coefficients in practice. The proposed model takes into account a scenario in which the selection of all possible coefficients pertaining to each decision variable in the MSGP model can be an in-between selection instead of an exclusive-or selection. We hope this study can fill in a possible gap that might exist when applying the MSGP model, and can offer an extension model for practitioners when they use this model to solve related decision problems.

Efficiently mapping an appropriate thinning schedule for optimum carbon sequestration: An application of multi-segment goal programming

Thinning is an important strategy for carbon sequestration in forest management. Linear programming (LP) and goal programming (GP) can only set fixed parameters for the left hand side constraints, which are incapable of simulating different thinning intensities at thinned stands to map an appropriate thinning schedule for optimum carbon sequestration efficiently. However, multi-segment goal programming (MSGP) with the flexibility to set multi-level parameters can be applied by forest managers to quickly choose an appropriate level from different thinning intensities. The purpose of this study was to combine MSGP with LP to efficiently adopt thinned area and thinning intensity together as decision variables. In a demonstrated case, an appropriate thinning schedule for three age-classes was chosen from 768 combinations of thinning intensity in just one step. Each age-class was allocated well such as practicing medium thinning intensity on young age-class and strong thinning intensity on the old age-class. Totally 1379643.03 tons of carbon sequestration was obtained after two planning horizons, which was 34.75% higher than no thinning. Besides, a stable supply of wood form thinning is made for carbon sequestration in each period and the stocking of each age-class is also improved.

Fuzzy analytical hierarchy process and multi-segment goal programming applied to new product segmented under price strategy

New product development (NPD) is becoming an important competitive advantage in the marketing strategies of current businesses. Developing a new product will incur fixed and variable costs, which then determine the product prices. Although this is a fundamental issue of marketing theory and practice, only a few papers on marketing models deal with price levels. The objective of this paper is proposing a model based on fuzzy analytical hierarchy process (AHP) and multi-segment goal programming (MSGP) to help decision makers to select the best pricing strategy for NPD. A case study of NPD under market selection strategy in multiple segment pricing levels for a Taiwan-based Watch Company is presented to illustrate the proposed methodology. The proposed method will guide the product development team to select the best market strategy by taking into account the price level and product/market segmentation.

Formulating the multi-segment goal programming

Goal programming (GP) is an important analytical approach devised to solve many real-word problems. However, the condition of multi-segment aspiration levels (MSAL) may exist in many marketing or decision management problems. The problem cannot be solved by current GP techniques. In order to improve the effective of GP and solve the multi-segment goal programming (MSGP) problem, this paper provides a new idea for programming the MSAL problem from multi-aspiration contribution levels viewpoint. This significantly improved the utility of GP in real application; in addition, two illustrative examples are included to demonstrate the solution procedure of the proposed model.