Multi-choice Goal Programming Research Articles

A hybrid decision support framework for planning a risk-based audit engagement

Internal audit engagement planning is a fundamental component of internal audit activities, and a poor plan can impair the audit quality. However, studies on internal audit engagement planning have limited exposure in the literature. To the best of our knowledge, a comprehensive and robust model has not been established to address the decision-making problems associated with the audit engagement planning process. To fill these gaps, an integrated multi-stage framework is presented for developing a risk-based audit engagement plan. The designed framework includes three stages to carry out the decision procedure. First, a combined method of best worst method and weighted probability-impact score is used to evaluate the risk exposure. Second, risk assessment results are integrated into the weighted multi-choice goal programming (WMCGP) model for selecting the audit scope and allocating budgeted audit hours. Third, WMCGP model assigns audit staff to the selected audit scope to satisfy auditor preference and fitness. Two real-life case studies illustrate that the proposed framework is a useful tool for selecting the critical audit scope and deploying appropriate resources. The research results outperform the manual procedures, enabling the audit team to use constrained resources more effectively and efficiently.

A multi-objective model for selecting response strategies of primary and secondary project risks under interval-valued fuzzy uncertainty

Risks are uncertain events that can affect criteria such as project cost, quality, and completion time and ultimately lead to project failure. That is why the project risk management process, which is one of the most fundamental parts of project management, must be done properly. In this paper, a new multi-objective fuzzy model is proposed to respond appropriately to the primary and secondary project risks. The contributions, such as risk interdependency, considering the project with multi-mode activities, resource considerations, as well as attention to interval-valued fuzzy uncertainties are regarded simultaneously for the first time. The purpose of the proposed multi-objective mathematical model is to minimize cost, quality reduction, and project completion time. Then, a new two-stage uncertain solution approach is introduced in this paper. In this solution approach, using an equivalent method in the first step and then using an extended multi-choice goal programming method in the second step, for both lower and upper limits of the membership functions for interval-valued fuzzy numbers, the computational processes are performed. To show the application and effectiveness of the proposed model, a case study adapted from the literature is given. Finally, sensitivity analysis is conducted to analyze the behavior of the developed mixed-integer programming model. The analysis of obtained results indicates that the utilization of the proposed model leads to better and more appropriate solutions.

Pavement Condition Management Coupled with Skid Management at the Network Level: A Multi-Choice Goal Programming Approach

Highways promote economic growth and social development. Traditionally, pavement management considers pavement conditions for maintenance and rehabilitation (M&R) decision-making along with system constraints. One of the major tasks of highway agencies is to promote safety. Highway safety involves a complicated interaction of multiple factors. However, it is well established that there is a link between pavement surface friction and safety. Pavement surface characteristics affect the safety and comfort of passengers. Since the pavement management process considers only pavement physical conditions in M&R decision-making, there is a need to include safety indicators in the process to promote both riding comfort and safety. This study aims to integrate the pavement surface friction, that is, the skid resistance, into traditional pavement management. An optimization model has been proposed based on multi-choice goal programming by systematically integrating skid resistance into pavement M&R decision-making along with pavement conditions. Multi-choice goal programming allows setting interval aspirations rather than a fixed target. A case study has been conducted to demonstrate the usefulness of the proposed model in managing pavement conditions and pavement skids together. The case study considered five goals related to pavement conditions, skid states, and M&R costs. Interaction among the goals is discussed in different scenarios. The case study also uses friction treatment with traditional pavement management treatments. The model can capture the interaction between pavement conditions and pavement skids and determine the program that best achieves the goals. The proposed method provides a simple approach to selecting a network-level M&R program and obtaining insights into the management of conditions and skid together.

Sharing decision-making in knee osteoarthritis using the AHP-FMCGP method

This study presents a clinical treatment decision support system (CTDSS) through which clinicians can simplify the shared-decision making (SDM) process for treating knee osteoarthritis (OA). This system enables patients to participate in selecting the optimum treatment. There has been broad international acceptance of SDM, but its practical implementation in clinical setting has posed challenges. The proposed method incorporates analytic hierarchy process and fuzzy multi-choice goal programming (AHP-FMCGP) into CTDSS to determine treatment standards, beginning with a literature review and expert surveys. The method provides patients with flexibility to modify treatment goals and constraints during the decision-making (DM) process to achieve their preferred treatment and simplifying the traditional SDM. A total of 240 knee OA patients participated in this pilot study, and the results indicated a significant improvement in patient satisfaction (F = 4.104; P < 0.05) with the implementation of CTDSS. This study demonstrates the effectiveness of CTDSS by resolving several challenges in clinical implementation: (1) Improved efficiency of implementing SDM, promoting better clinician-patient communication and higher healthcare quality. (2) Eliminating the need for extensive health literacy and disease knowledge in patients during the DM process. (3) Saving time for physicians, by allowing them to better understand the patients’ preferences and select the most appropriate treatment option. (4) This method can be extended to primary clinics to benefit other patients.

A hybrid approach to resolve ambiguity in new product development

Improving technological innovation (TI) capabilities is an integral component of government policies aimed at improving the competitiveness of small and medium enterprises (SMEs). This study aims to address implementation challenges arising from the use of Qualitative Forecasting Method (QFM) in new product development programs and proposes a novel method to aid decision makers (DMs) in their decision-making process. To tackle this issue, a hybrid method is proposed, incorporating Fuzzy Delphi method (FDM), Fuzzy Analytic Hierarchy Process (FAHP), Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), and Multi-Choice Goal Programming with utility function (MCGP-U), while introducing prospect theory as a novel approach. is proposed. The proposed method offers several advantages, including effective early planning, accurate identification of key success factors (KSFs), selection of the most suitable project leader, and estimation of the most reasonable resource investment, all of which are critical factors for success in TI for enterprises. The research results show that (1) the proposed method reduces project execution time by 20% compared to the original manual planning, (2) it facilitates the acquisition of KSFs using a rational approach to ensure project success, and (3) it increases the financial returns of the company by 17% compared to the company’s forecast. In summary, this paper makes a significant contribution to practical applications and additionally contributes to decision-making field by introducing prospect theory into the proposed hybrid method.

Sustainable Freight Transportation: a Bi-Objective Multi-Choice Goal Programming Framework

Sustainable Freight Transportation: a Bi-Objective Multi-Choice Goal Programming Framework

A decision support framework to evaluate the sustainability performance of urban road transportation.

This study proposes a decision support framework (DSF) based on two data envelopment analysis (DEA) models in order to evaluate the urban road transportation of countries for sustainable performance management during different years. The first model considers different years independently while the second model, which is a type of network model, takes into account all the years integrated. A multi-objective programming model under two types of uncertainties is then developed to solve the proposed DEA models based on a revised multi-choice goal programming (GP) approach. The efficiency scores are measured based on the data related to several major European countries and the factors including the level of freight and passenger transportation, level of greenhouse gas emissions, level of energy consumption, and road accidents which are addressed as the main evaluation factors. Eventually, the two proposed models are compared in terms of interpretation and final achievements. The results reveal that the efficiency scores of countries are different under deterministic/uncertain conditions and according to the structure of the evaluation model. Furthermore, efficiency changes are not necessarily the same as productivity changes. The high interpretability (up to 99.6%) of the models demonstrates the reliability of DSF for decision-making stakeholders in the transport sector. Furthermore, a set of managerial analyses is conducted based on different parameters of the performance evaluation measures for these countries including the productivity changes during the period under consideration, resilience of the countries, detection of the benchmark countries, ranking of different countries, and detection of the patterns for improving the transportation system.

A hybrid meta-heuristic approach to design a Bi-objective cosmetic tourism supply chain: A case study

In recent decades, medical tourism especially cosmetic tourism has increased in many countries. In this study, a bi-objective mathematical model is designed for the cosmetic tourism supply chain for the first time. In the first objective function, the profit margin in the supply chain is maximized. In contrast, the distance between the medical and accommodation centers is minimized in the second objective function. The general revised multi-choice goal programming (GRMCGP) method is an exact method for optimizing the problem. To solve the problem, model is run by Lingo software. Then, a new hybrid genetic algorithm named genetic algorithm-general revised multi-choice goal programming (GA-GRMCGP), to solve large-scale instances, is utilized. Moreover, a case study is considered to validate the proposed mathematical model. Furthermore, sensitivity analyses are carried out to evaluate the effectiveness of the research. Finally, the results of the exact and meta-heuristic algorithms are compared and discussed.

Designing a sustainable-resilient-responsive supply chain network considering uncertainty in the COVID-19 era.

Effective supply chain management is crucial for economic growth, and sustainability is becoming a key consideration for large companies. COVID-19 has presented significant challenges to supply chains, making PCR testing a vital product during the pandemic. It detects the presence of the virus if you are infected at the time and detects fragments of the virus even after you are no longer infected. This paper proposes a multi-objective mathematical linear model to optimize a sustainable, resilient, and responsive supply chain for PCR diagnostic tests. The model aims to minimize costs, negative societal impact caused by shortages, and environmental impact, using a scenario-based approach with stochastic programming. The model is validated by investigating a real-life case study in one of Iran's high-risk supply chain areas. The proposed model is solved using the revised multi-choice goal programming method. Lastly, sensitivity analyses based on effective parameters are conducted to analyze the behavior of the developed Mixed-Integer Linear Programming. According to the results, not only is the model capable of balancing three objective functions, but it is also capable of providing resilient and responsive networks. To enhance the design of the supply chain network, this paper has considered various COVID-19 variants and their infectious rates, in contrast to prior studies that did not consider the variations in demand and societal impact exhibited by different virus variants.

A data-driven model for sustainable and resilient supplier selection and order allocation problem in a responsive supply chain: A case study of healthcare system

This research attempts to study the Supplier Selection and Order Allocation Problem (SSOAP) considering three crucial concepts, namely responsiveness, sustainability, and resilience. To do so, the current research develops a Multi-Stage Decision-Making Framework (MSDMF) to select potential suppliers and determine the quantity of orders. The first stage aims at computing the scores of the suppliers based on several indicators. To do this, a novel decision-making approach named the Stochastic Fuzzy Best–Worst Method (SFBWM) is developed. Then, in the second stage, a Multi-Objective Model (MOM) is suggested to deal with supplier selection and order allocation decisions. In the next step, a data-driven Fuzzy Robust Stochastic (FRS) optimization approach, based on the fuzzy robust stochastic method and the Seasonal Autoregressive Integrated Moving Average (SARIMA) methods, is employed to efficiently treat the hybrid uncertainty of the problem. Afterwards, a novel solution method named the developed Chebyshev Multi-Choice Goal Programming with Utility Function (CMCGP-UF) is developed to obtain the optimal solution. Moreover, given the crucial role of the Medical Equipment (ME) industry in society’s health, especially during the recent Coronavirus disease, this important industry is taken into account. The outcomes of the first stage demonstrate that agility, cost, GHG emission, quality, robustness, and Waste Management (WM), respectively, are the most important criteria. The outcomes of the second stage determine the selected suppliers, utilized transportation systems, and established sites. It is also revealed that demand directly affects all the objective functions while increasing the rate of disruptions has a negative effect on the sustainability measures.

Designing blood supply chain networks with disruption considerations by a new interval-valued fuzzy mathematical model: M/M/C queueing approach

The healthcare system of countries encounters several challenges during their performance, but the most significant one is to provide sufficient and safe required blood products for the individuals in need in the areas affected by disasters and to manage all of the aspects in those critical conditions. This paper presents a new multi-objective mathematical model to design an efficient blood supply chain in disasters considering temporary facilities along with fixed ones when it would be necessary and investigate potential disruptions both in blood facilities and links. Given the uncertainties caused by the arrival flows and to minimize the waiting time in blood banks (BBs), employing the queue theory leads the model to be efficient. Likewise, as the operations related to disaster responses deal with uncertainties, this paper presents a new extended interval-valued fuzzy (IVF) solution method based on weighted multi-choice goal programming (weighted MCGP) approach capable of helping decision-makers to set various weights for every goal and to find appropriate solutions. The optimization model is then solved by two metaheuristics, including genetic algorithm (GA) and red deer algorithm (RDA). Finally, the application of the proposed model is examined through a case study and numerical examples, and some managerial insights are provided based on the analysis of results.

An Integrated MCDM Model for Sustainable Course Planning: An Empirical Case Study in Accounting Education

As an essential element of higher education, course planning at the program level is a complicated multi-criteria decision making (MCDM) problem. In addition, a course planning process tailored to sustainable development is exceptionally important to sustaining the quality of academic programs. However, there is a scarcity of research on the program course planning problem at the operational level due to a diverse set of stakeholder requirements in practice. Motivated by the challenge, this study proposes an innovative MCDM model for sustainable course planning based on He-Xie management theory. In the introduced framework, the best worst method (BWM) can obtain the optimal weights of sustainability competencies, which are then embedded into the fuzzy filter ranking (FFR) method to generate the ranking of candidate courses by each course module, considering the connectivity between courses and the development of sustainability competencies. Finally, multi-choice goal programming (MCGP) is adopted to allocate each selected course to a semester, aiming to balance total credits and average difficulty level among semesters as much as possible. The practicability and reliability of the proposed course planning model is validated through a case study of an undergraduate accounting program. Results show that the proposed framework is a feasible tool for course planning. This research extends the existing literature on course planning by explicitly capturing the fuzzy nature of human decision making and avoids underestimation of the decision. The implications of the paper are not restricted to developing a sustainable course plan for an accounting program.

An Integrated Quality Function Deployment and Multichoice Goal Programming Approach for Sustainable Transportation: The Case of Eskişehir

Limiting individual transportation and increasing the share of public transportation has an important place in planning a sustainable transportation system in the urban environments of today. The most important goals for city governments include the ability to attract automobile users to public transportation. At this stage, there is a need for a public transportation system that aims to optimize the criteria of cost, safety, organization, and comfort. It is highly important to measure, assess, and improve the satisfaction of those who use public transportation for sustainable transportation. This study used an integrated quality function deployment (QFD) and multichoice goal programming approach to assess the satisfaction of people who use public transportation and to improve the transportation systems quality. The proposed approach was implemented for the tram system that is currently operational in Eskişehir in Turkey. According to the results that were obtained, with a low cost, it was seen that customer priorities could be met to a great extent as 93.1%. The results of the study are helpful for the decision makers to develop incentive policies regarding public transportation systems.

A fuzzy programming model for optimizing the inventory management problem considering financial issues: A case study of the dairy industry

In today's competitive environment as well as the dynamic nature of the business environment, managing inventory and financial flows has become more challenging. As inventory and financial flows are interdependent, setting an optimal plan for managing them simultaneously can have a dramatic impact on the company's efficiency The literature indicates that previous studies have rarely considered financial considerations in the inventory management problem. Thus, this study proposes a multi-objective programming model to optimize the inventory management problem for dairy products. In the model, material substitution is considered in order to assist in managing the inventory value. A fuzzy model is developed to deal with uncertain parameters such as inventory costs, raw milk volume, and the cost of purchasing raw materials. Then, a real-world case study is conducted in the Iranian dairy industry. Additionally, the proposed model is solved using a multi-choice goal programming approach. Further, sensitivity analyses are conducted in order to investigate the impact of critical parameters on the research question. Finally, managerial insights and theoretical implications are discussed. Based on the results, raw milk volume decreases inventory costs, and material substitution decreases inventory value as obsolete materials are used in products.

A multi-objective optimization approach for integrated risk-based internal audit planning.

Annual audit planning is a multi-criteria decision-making problem faced by internal audit departments of all organizations. Due to the constrained audit resources, the planning process primarily involves the analysis and evaluation of complex factors for selecting auditable units that maximize the full potential of internal audit. Previous research on internal audit planning only focused on the goal of risk minimization and applied ranking methods to prioritize alternatives. In order to enable internal audit activities to add more value to the organization, the integrated risk-based internal audit planning is proposed to assist audit department in achieving multiple objectives in addition to risk management. Meanwhile, a multi-stage framework is proposed to support the development of such value-added internal audit plan. The new framework integrates the risk assessment of auditable units with the selection of audit activities and resource allocation through a combined analytic hierarchy process (AHP), fuzzy comprehensive evaluation (FCE) and weighted multi-choice goal programming (WMCGP) approach. The model considers both qualitative and quantitative decision criteria. A real-life case study of the development of an integrated risk-based annual audit plan is presented, and sensitivity analysis is performed to illustrate the validity of the proposed approach. The results indicate that the proposed framework is a useful tool for internal audit planning and the implications of the study can be extended to various selection and allocation problems.

Integrated Process for Developing and Selecting More or Less Better Products Based on QFD and MCGP

As consumer needs for product features change rapidly, companies must develop new products (NPD) that satisfy these needs in the shortest possible time. Consumer product selection behavior is generally overlooked when developing new products. This is a more complicated problem for consumers belonging to multiple types, such as hospitals and patients. Quality function deployment (QFD) is a good and commonly used method for product design and selection. Choosing the right product design for a company is a crucial strategic consideration to satisfy the needs of consumers and the company’s technical requirements. Designing a new product is multicriteria decision‐making (MCDM). In this study, the analytical hierarchy process (AHP), QFD, and multichoice goal programming (MCGP) were combined to address the design and selection of new products. This integrated method considers both tangible (qualitative) and intangible (quantitative) factors, as well as “the higher criterion is better” and “the lower criteria is better” when selecting new design projects. To prove the practicality and sustainable value of the model, we considered a Taiwanese company that developed medical aid products as an example. This paper suggests a good decision‐making tool for selecting a new product design based on “more is better/less is better” criteria.

An optimized model for neutrosophic multi-choice goal programming

An optimized model for neutrosophic multi-choice goal programming

Cost Sharing in Insurance Communities: A Hybrid Approach Based on Multiple-Choice Objective Programming and Cooperative Games

At present, utilizing the insurance community is a common method to deal with investment risks along the Belt and Road; however, there is no clear method or mechanism to deal with the decision-making optimization and cost allocation of the insurance community participants. We propose a hybrid approach to solve this problem. First, we construct an underwriting decision optimization model for the insurance community using the multi-choice goal programming method, which generates the cost characteristic function based on a cooperative alliance. Second, we use the cooperative game method combined with the modified Shapley value method to take risk factors into consideration, which allows us to optimize the cost allocation among members of the insurance community. Finally, our simulation analysis results show that the multi-choice goal programming method can optimize the insurance community’s underwriting decisions. Specifically, the total underwriting cost is lower than the sum of the underwriting costs under the insurance company’s single-action strategy, and the total underwriting scale is as large as possible. Compared with the classical Shapley value method, the modified Shapley value method can better reflect differences in the underwriting risks of different regions, encouraging governments to take measures to reduce underwriting risks. To conclude, we propose some suggestions based on our research findings. The possible contributions of this paper are as follows: our research provides a hybrid optimization method based on multiple-choice objective programming and cooperative games to solve the cost allocation problem facing the insurance community, and it has some reference value for improving the cost-sharing system of the insurance community.

Revised solution technique for a bi-level location-inventory-routing problem under uncertainty of demand and perishability of products

Bi-level programming is an efficient tool to tackle decentralized decision-making processes in supply chains with upper level (i.e., leader) and lower level (i.e., follower). The leader makes the first decision while the follower makes the second decision. In this research, a bi-level programming formulation for the problem of location-inventory-routing in a two-echelon supply chain, including a number of central warehouses in the first echelon and retailers in the second echelon with perishable products under uncertain demand, is proposed. The total operational costs at both levels are minimized considering capacity constraints. Due to the uncertain nature of the problem, a scenario-based programming is utilized. The economic condition or unforeseen events such as COVID-19 or Russia-Ukraine war can be good examples for uncertainty sources in today’s world. The model determines the optimal locations of warehouses, the routes between warehouses and retailers, number of received shipments and the amount of inventory held at each retailer. A revised solution method is designed by using multi-choice goal programming for solving the problem. The given revised method attempts to minimize the deviations of each decision maker’s solution from its ideal value assuming that the upper level is satisfied higher than the lower level. Base on some numerical analysis, the proposed solution technique is more sensitive to the upper bounds of the goals rather than the lower bounds.

Designing sustainable closed-loop supply chain network with considering spot-to-point inflation and carbon emission policies: A case study

This paper uses a fuzzy robust stochastic optimization (FRSO) approach and a new hybrid algorithm of genetic algorithm (GA) and multi-choice goal programming with Utility Function (MCGP-UF) to minimize environmental impacts (EI) while maximizing net present value (NPV) and social impacts (SI) in a sustainable closed-loop supply chain (SCLSC) in a case study has been proposed under hybrid uncertainty with considering spot-to-point inflation (STPI). The resulting mixed-integer linear programming (MILP) model has been applied to the electronic industry. The proposed model is evaluated in several experiments and discussed in different scenarios to prove the efficiency and performance comparison and validation of the proposed method. The results of this study double the share of the article (i) Effective improvement of the obtained solutions by reducing the solution time by 25% showed that the proposed method can be responsible for large-scale problems. (ii) Sensitivity analysis on parameters such as demand, inflation, costs, and capacity showed which scenario has the most impact on the strategic and tactical decisions of companies.