Showing papers on "Goal programming published in 2021"
TL;DR: In this paper, a multi-objective mathematical model is proposed to design a sustainable-resilient supply chain based on strategic and tactical decision levels to satisfy customer demands when the firm is highly vulnerable to operational and disruption risks.
66 citations
TL;DR: The developed unified IFMOLP model and method can not only effectively solve multi-objective decision problems with nonsatisfaction and hesitation degrees but also remarkably reduce the complexity of the nondeterministic polynomial-hard problems.
Abstract: Portfolio selection can be regarded as a type of multi-objective decision problem. However, traditional solution methods rarely discussed the decision maker’s nonsatisfaction and hesitation degrees...
53 citations
TL;DR: This work considers a fixed-charge solid transportation problem in multi-objective environment where all the data are intuitionistic fuzzy numbers with membership and non-membership function and reduces into a deterministic problem using accuracy function.
Abstract: During past few decades, fuzzy decision is an important attention in the areas of science, engineering, economic system, business, etc. To solve day-to-day problem, researchers use fuzzy data in transportation problem for presenting the uncontrollable factors; and most of multi-objective transportation problems are solved using goal programming. However, when the problem contains interval-valued data, then the obtained solution was provided by goal programming may not satisfy by all decision-makers. In such condition, we consider a fixed-charge solid transportation problem in multi-objective environment where all the data are intuitionistic fuzzy numbers with membership and non-membership function. The intuitionistic fuzzy transportation problem transforms into interval-valued problem using $$(\alpha ,\beta )$$
-cut, and thereafter, it reduces into a deterministic problem using accuracy function. Also the optimum value of alternative corresponds to the optimum value of accuracy function. A numerical example is included to illustrate the usefulness of our proposed model. Finally, conclusions and future works with the study are described.
50 citations
TL;DR: This research mainly focuses on presenting an innovative study of a multi-stage multi-objective fixed-charge solid transportation problem with a green supply chain network system under an intuitionistic fuzzy environment and incorporates an application example connected with a real-life industrial problem to display the feasibility and potentiality of the proposed model.
Abstract: This research mainly focuses on presenting an innovative study of a multi-stage multi-objective fixed-charge solid transportation problem (MMFSTP) with a green supply chain network system under an intuitionistic fuzzy environment. The most controversial issue in recent years is that greenhouse gas emissions such as carbon dioxide, methane, etc. induce air pollution and global warming, thus motivating us to formulate the proposed research. In real-world situations the parameters of MMFSTP via a green supply chain network system usually have unknown quantities, and thus we assume trapezoidal intuitionistic fuzzy numbers to accommodate them and then employ the expected value operator to convert intuitionistic fuzzy MMFSTP into deterministic MMFSTP. Next, the methodologies are constructed to solve the deterministic MMFSTP by weighted Tchebycheff metrics programming and min-max goal programming, which provide Pareto-optimal solutions. A comparison is then drawn between the Pareto-optimal solutions that are extracted from the programming, and thereafter a procedure is performed to analyze the sensitivity analysis of the target values in the min–max goal programming. Finally, we incorporate an application example connected with a real-life industrial problem to display the feasibility and potentiality of the proposed model. Conclusions about the findings and future study directions are also offered.
50 citations
TL;DR: The study found that a feasible portfolio, including consideration of citizens, business, and the environment, enables the public perceptions of government performance within the resource constraints of the organization.
47 citations
07 Jun 2021
TL;DR: A multi-criteria decision-making technique based on analytic hierarchy process (AHP) and fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) is used to evaluate and rate the suppliers and a robust goal programming approach based on Shannon entropy is applied.
Abstract: Along with the increased competition in production and service areas, many organizations attempt to provide their products at a lower price and higher quality. On the other hand, consideration of environmental criteria in the conventional supplier selection methodologies is required for companies trying to promote green supply chain management (GSCM). In this regard, a multi-criteria decision-making (MCDM) technique based on analytic hierarchy process (AHP) and fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) is used to evaluate and rate the suppliers. Then, considering the resource constraint, weight of criteria and a rank of suppliers are taken into account in a multi-objective mixed-integer linear programming (MOMILP) to determine the optimum order quantity of each supplier under uncertain conditions. To deal with the uncertain multi-objectiveness of the proposed model, a robust goal programming (RGP) approach based on Shannon entropy is applied. The offered methodology is applied to a real case study from a green service food manufacturing company in Iran in order to verify its applicability with a sensitivity analysis performed on different uncertainty levels. Furthermore, the threshold of robustness worthiness (TRW) is studied by applying different budgets of uncertainty for the green service food manufacturing company. Finally, a discussion and conclusion on the applicability of the methodology is provided, and an outlook to future research projects is given.
43 citations
TL;DR: Findings show that Germany and Estonia are the highest and the lowest-ranked countries in terms of eco-innovation, respectively, and this approach can provide a full ranking of decision-making units (DMUs).
37 citations
TL;DR: In this article, a mathematical model is formulated in light of fuzzy and weighted goal programming using membership function to quantify the achievement level towards vision 2030; hence aims to bridge the existing literature gap.
31 citations
27 citations
TL;DR: A stochastic integrated multi-objective mixed integer nonlinear programming model is developed in this paper, in which sustainability outcomes as well as efficiency of facility resource utilization are considered in the design of a sustainable supply chain network.
26 citations
TL;DR: A scenario-based multi-objective programming model is proposed to examine the resilient-sustainable routing-allocation problem considering the concept of fairness and a hybrid approach based on the multi-choice goal programming method and a heuristic algorithm is developed to solve the problem in a reasonable time.
Abstract: Natural or man-made disasters impose destructive effects like human injuries and urban infrastructure damages, which lead to disruptions that affect the entire distribution system. This research addresses the routing-allocation problem in the response phase of disaster management. The related literature shows that the researchers had less attention to some features like sustainability and resiliency in the mentioned problem. Hence, to cover these gaps, this study proposes a scenario-based multi-objective programming model to examine the resilient-sustainable routing-allocation problem considering the concept of fairness. The proposed model aims at minimizing total traveling time, total environmental impacts and total demand loss. The fuzzy robust stochastic optimization approach is utilized to cope with uncertain data arisen in disaster conditions. Then, due to the complexity of the research problem, a hybrid approach based on the multi-choice goal programming method and a heuristic algorithm is developed to solve the problem in a reasonable time. A case study is then selected to demonstrate the efficiency of the proposed model and the developed method. Finally, sensitivity analyses have been conducted in some parameters of the model and also the robustness of the solution has been investigated.
TL;DR: A goal programming model is developed to find suitable solutions to manage the cost, time and quality of the project and a case study has been accomplished in the project implemented by Arak Machine Sazi Company.
Abstract: Time, cost and quality are important objectives of each project, which project managers are always looking for completion of them in the shortest possible time, with the lowest cost and highest qua...
TL;DR: In this paper, a strategic decision-making model for the sustainable development of marine renewable energy is proposed, and a specific application to the United Kingdom (UK) is demonstrated, where the potential renewable energy projects are clustered in order to aid the decision making process and preferential weight sensitivity methods are employed.
TL;DR: F fuzzy membership function tactic based on goal programming to obtain the desired compromise solution of a multi-objective transportation problem (MOTP) in uncertain environment is proposed where the DM can choose a confidence level for different parameters.
Abstract: In a managerial position, the ultimate objective is to take the right decision for the decision maker (DM) when transportation parameters are uncertain due to the globalization and other uncontrollable influences. In this paper, fuzzy membership function tactic based on goal programming to obtain the desired compromise solution of a multi-objective transportation problem (MOTP) in uncertain environment is proposed where the DM can choose a confidence level for different parameters. On the basis of DM’s choice on a particular confidence level, a compromise solution is obtain indicating the satisfaction level of the DM if the problem is feasible for this chosen confidence level. Uncertain normal distribution is used to convert the parameters from uncertain to a certain one. Simple linear programming problem (LPP) is designed using fuzzy linear membership function where the upper and lower values of the objectives are the desired goals of the DM. A numerical illustration is furnished to establish the effectiveness of the designed model whereas the single objective transportation problems are solved by TORA and LPPs are solved by using LINGO for operations research.
TL;DR: A two-stage coordinated scheduling method is proposed for the user-side integrated energy system that considers energy storage multiple services to minimize long-term operation costs and to ensure the system’s economy and the most possibility of the events of power balance in an uncertain environment.
TL;DR: In this article, a multi-objective mathematical model is presented to minimize total costs, environmental impacts, and transportation risks while maximizing social impacts and resilience of the logistics system, and a real-world case study is studied in Golestan province, Iran.
TL;DR: A multi-objective simulated annealing (MOSA) algorithm to allocate tasks securely on the fog and cloud nodes based on deadline constraints is proposed and has obtained acceptable results close to the average of other algorithms.
Abstract: Fog Computing continues to extend its usage by solving cloud computing challenges about Internet of Things (IoT). Fog nodes as a processing resource, can perform tasks generated by IoT devices. IoT as a client are concerned with the timely execution of their tasks and also lower cost services, and on the other hand, they are looking for a secured task execution. In this paper, we propose a multi-objective simulated annealing (MOSA) algorithm to allocate tasks securely on the fog and cloud nodes based on deadline constraints. The Goal Programming Approach (GPA) is applied to find a compromised solution which will satisfy multiple goals. Also, regarding the distribution of IoT tasks between fog and cloud nodes, a new goal is created called access level and scheduling based on client demand. Simulation results in four low, normal, medium, and high load scenarios showing that the proposed algorithm is on average 9.5% more efficient in terms of service delay time, 87% in terms of access level control and 49.8% in terms of deadline compared to multi-objective Particle Swarm Optimization (MOPSO), multi-objective Tabu Search (MOTS), and multi-objective Moth-Flame optimization (MOMF). Also, in terms of service cost, it has obtained acceptable results close to the average of other algorithms.
TL;DR: A multi-objective optimization model for determining shelter location-allocation in response to humanitarian relief logistics is proposed to improve both efficiency and effectiveness and could provide an advantage to decision-makers considering appropriate strategies for disaster response.
Abstract: Decision-making for shelter location-allocation influences the success of disaster response and affects the security of victims. This paper proposes a multi-objective optimization model for determining shelter location-allocation in response to humanitarian relief logistics. Three objective functions are formulated to improve both efficiency and effectiveness. The first objective is to minimize total costs, including fixed costs for opening the shelters, transportation costs, and service costs. The second objective is to minimize the total time for evacuating victims from all affected areas to allocated shelters. The third objective is to minimize the number of shelters required to provide thorough service to victims. The Epsilon Constraint method (EC) and Goal Programming (GP) are employed for solving the proposed model. The applicability of the proposed model is validated through a case study of flooding in Surat Thani, Thailand. The Pareto efficiency obtained from solving the proposed model is compared with current shelter location-allocation plans determined by the government sector. The comparisons reveal that the results obtained from solving the proposed model outperform current shelter location-allocation plans. Furthermore, the results of this study could provide an advantage to decision-makers considering appropriate strategies for disaster response.
TL;DR: This paper proposed a multi-objective optimization model integrating economic growth, electricity consumption, greenhouse gas emission and the number of employees across the primary, secondary and tertiary sectors of Indian economy using the concept of GP with a satisfaction function.
Abstract: Modelling for long-term goals involving multiple factors and criteria often require incorporating decision-makers preferences to realize optimum satisfaction. Goal programming (GP) is an operational research technique that is relevant to analysing decision-making problems with multiple competing and conflicting objectives. Multi-objective goal programming approach takes advantage of striking the trade-off between the overachievement and underachievement of the decision-makers future aspirations. The concept of GP with a satisfaction function integrates the preference of the decision-makers explicitly. In this paper, we proposed a multi-objective optimization model integrating economic growth, electricity consumption, greenhouse gas emission and the number of employees across the primary, secondary and tertiary sectors of Indian economy using the concept of GP with a satisfaction function. The model validated with data from the three economic sectors, and the results provided a quantitative justification for achieving economic growth, electricity consumption, with optimal employment strength across the sectors, for the sustainable development goals of India vision 2030. Also, a strong suggestion for improvement and encouragement in the use of renewable energies such as wind and solar and reduction in fossil fuels utilization to arrest the high emission tendencies shortly was evidence by the solution.
TL;DR: In this paper, the authors tackle the main pain points in decision making during supplier selection and order allocation under uncertainties for a multi-item, multi-period setting, where each supplier has its own pricing policy.
TL;DR: The results of this research indicate that the proposed FAHP-MCGP approach is a useful tool for selecting potential projects and allocating staff time during risk-based internal audit panning and has substantial practical application.
TL;DR: A multi-objective selective maintenance allocation problem is formulated with fuzzy parameters under neutrosophic environment and a new defuzzification technique is introduced based on beta distribution to convert fuzzy parameters into crisp values.
Abstract: Selective maintenance problem plays an essential role in reliability optimization decision-making problems. Systems are a configuration of several components, and there are situations the system needs small intervals or break for maintenance actions, during the intervals expert carried out the maintenance actions to replace or repair the deteriorated components of the systems. Because of the uncertainty associated with the component’s operational time, failure, and next mission duration create a new challenge in determining optimal components allocation and evaluating future missions successfully. In this paper, a multi-objective selective maintenance allocation problem is formulated with fuzzy parameters under neutrosophic environment. A new defuzzification technique is introduced based on beta distribution to convert fuzzy parameters into crisp values. The neutrosophic goal programming technique is used to determine the compromise allocation of replaceable and repairable components based on the system reliability optimization. A numerical illustration is used to validate the model and ascertain its effectiveness. The result is compared with two other approaches and found to be better. The method is flexible and straightforward and can be solved using any available commercial packages. The extension of the concept can be useful to other complex system reliability optimization.
TL;DR: In this paper, an integrated location-allocation problem is proposed to plan the operations such as collection, recycling, disposal, and transportation under uncertainty, and a triangular fuzzy number approach is employed and finally, a fuzzy chance-constrained programming model is developed.
TL;DR: The results show that exerting environmental issues in humanitarian logistics does not necessarily increase the relief costs, but can be in contrast with the social aspect, and a minor increase in the budget of the preparedness phase drastically decreases the response costs.
Abstract: As the occurrence of disasters has increased frequently and has resulted in growing concern about their adverse effects on the environment, Sustainable Humanitarian Logistics (SHL) has received great attention recently. SHL aims to reduce disaster damages in an environmentally-friendly manner in the shortest possible time. The terms including ‘environmentally-friendly’ and ‘shortest possible time’ refer to the environmental and social aspects of sustainability. This research proposes a stochastic multi-objective mixed-integer programming model to configure an SHL network during the response phase. Having compared to the research literature, this is the first study that considers economic, social, and environmental aspects of sustainability by incorporating relief cost, deprivation cost, and carbon emissions, respectively. Then, the improved multi-choice goal programming approach is applied to solve the proposed multi-objective model. To indicate the validity of the proposed model, an earthquake that occurred in a region of Kermanshah, Iran, in 2017 is investigated as a real case study. Finally, sensitivity analysis is performed and several managerial and theoretical insights are provided. The results show that exerting environmental issues in humanitarian logistics does not necessarily increase the relief costs, but can be in contrast with the social aspect. Furthermore, a minor increase in the budget of the preparedness phase drastically decreases the response costs.
TL;DR: It is shown that the proposed model and solution approach can significantly improve the SAR performance and provide decision support for planners in developing effective and efficient resource location-allocation schemes.
TL;DR: The model determines the routes of the vehicles and the number of bins to be assigned to each potential location, while minimizing the collection costs and the environmental impact, and leads to lower environmental impact and an almost 38% reduction in the economic costs.
Abstract: This paper’s aim is to develop a model for the household waste collection and transportation problem in the city of Sousse, one of the largest cities in Tunisia. Several vehicles with a finite capacity are located at the depot. The vehicles must collect the waste accumulated in all bins. The waste is then delivered to a transfer center, before vehicles return to the depot. The proposed model determines the routes of the vehicles and the number of bins to be assigned to each potential location, while minimizing the collection costs and the environmental impact. The problem can be considered as a bi-objective optimization problem, as cost minimization will be ensured by the optimal assignment of the determined minimum number of bins. We also consider the stochastic aspect of population size, which is supposed to follow a normal distribution. Our model is then a stochastic bi-objective programming model. A solution is obtained with reasonable computational effort using a hierarchical approach consisting of two stages as “cluster-first route-second”. In the first stage, a set of n locations of bins is assigned into k disjoint clusters using the K-means clustering algorithm. In the second stage, a certainty equivalent program to the bi-objective stochastic program is proposed, based on a chance-constrained, recourse and a goal programming approach. The model is tested and implemented using real data from the municipality of Sousse. The study shows that our model leads to lower environmental impact and an almost 38% reduction in the economic costs.
TL;DR: It has been proven that plasma of the people who have fully recovered from COVID-19, can help other patients to recover from this insidious disease, and the proposed BSC network can supply the needs of this particular category of patients as well.
Abstract: The purpose of this paper is to design a green Blood Supply Chain (BSC) network regarding expiration date and backup facilities. The proposed model is a bi-objective Mixed Integer Programming (MIP) one. The two objective functions are to minimize the total cost and the detrimental environmental impacts of shipping between facilities and generated wastes in the network. A Goal Programming (GP) approach is used to convert the multi-objective model into a single one. Moreover, to meet the demand, blood groups and plasma expiration date are also investigated. Since it has been proven that plasma of the people who have fully recovered from COVID-19, can help other patients to recover from this insidious disease; therefore, the proposed BSC network can supply the needs of this particular category of patients as well. To examine the feasibility of the proposed model, some random examples with different dimensions are generated and solved using the CPLEX solver of GAMS software. Furthermore, a real-case problem in Esfahan (Iran) was investigated to illustrate the applicability of the proposed model, and the sensitivity analysis was performed as well. Results approved the applicability of the proposed model in a real situation.
TL;DR: A hybrid exploratory three-phased Multi-criteria Decision-making (MCDM) model that combines the Decision-Making Trial and Evaluation Laboratory Model (DEMATEL) approach, the Analytic Network Process (ANP), and Zero-One Goal Programming (ZOGP) for evaluating SHMS portfolios in a resource-limited environment is proposed.
Abstract: A Smart Healthcare Management System (SHMS) is an intelligent technology integration practice for public medical centers. During the implementation of the SHMS, information on strategy and decision-making problems play an important role in smart hospital development, which directly affects the effective workflow of the hospital operation and the service quality of patient care. Existing literature has explored the main factors for evaluation the SHMS, but a qualitative and quantitative analysis is rarely mentioned. To address the research gap, this study proposes a hybrid exploratory three-phased Multi-criteria Decision-making (MCDM) model that combines the Decision-Making Trial and Evaluation Laboratory Model (DEMATEL) approach, the Analytic Network Process (ANP), and Zero-One Goal Programming (ZOGP) for evaluating SHMS portfolios in a resource-limited environment. The results indicate that the Financial Subsidy Policy is the most important determinant for SHMS development, while the Medical Data Informational System (MDIS) and Medical Device and Drug Management System (MDMS) are the optimal SHMS portfolios that satisfy the goal of strategy weights and limited resources. The proposed hybrid decision model can improve the reliability of SHMS portfolios.
TL;DR: An innovative robust goal programming model with priority factors and optimize the distribution decisions under uncertain supply and capacity characterized by an uncertainty set can realize the trade-off between equity and effectiveness under uncertainty for humanitarian relief problem.