Showing papers on "Goal programming published in 2020"

Resilient supplier selection in logistics 4.0 with heterogeneous information

Abstract: Supplier selection problem has gained extensive attention in the prior studies. However, research based on Fuzzy Multi-Attribute Decision Making (F-MADM) approach in ranking resilient suppliers in logistic 4.0 is still in its infancy. Traditional MADM approach fails to address the resilient supplier selection problem in logistic 4.0 primarily because of the large amount of data concerning some attributes that are quantitative, yet difficult to process while making decisions. Besides, some qualitative attributes prevalent in logistic 4.0 entail imprecise perceptual or judgmental decision relevant information, and are substantially different than those considered in traditional suppler selection problems. This study develops a Decision Support System (DSS) that will help the decision maker to incorporate and process such imprecise heterogeneous data in a unified framework to rank a set of resilient suppliers in the logistic 4.0 environment. The proposed framework induces a triangular fuzzy number from large-scale temporal data using probability-possibility consistency principle. Large number of non-temporal data presented graphically are computed by extracting granular information that are imprecise in nature. Fuzzy linguistic variables are used to map the qualitative attributes. Finally, fuzzy based TOPSIS method is adopted to generate the ranking score of alternative suppliers. These ranking scores are used as input in a Multi-Choice Goal Programming (MCGP) model to determine optimal order allocation for respective suppliers. Finally, a sensitivity analysis assesses how the Supplier's Cost versus Resilience Index (SCRI) changes when differential priorities are set for respective cost and resilience attributes.

A chance constrained fuzzy goal programming approach for perishable pharmaceutical supply chain network design

Abstract: In this paper, a bi-objective mixed-integer linear programming model is formulated for designing a perishable pharmaceutical supply chain network under demand uncertainty The objectives of the proposed model are to simultaneously minimize the total cost of the network and lost demand amount The proposed model is multi-product and multi-period and includes simultaneous facilities location, vehicle routing, and inventory management; hence, it is considered an operational-strategic model Procurement discounts, the lifetime of products, storing products for future periods, lost demand, and soft and hard time windows are the main assumptions of the proposed model A novel hybrid approach, based on fuzzy theory, chance constrained programming, and goal programming approach, is developed for solving the proposed bi-objective model The validity of the proposed model and developed solution approach is evaluated using data from Avonex, a prefilled syringe distribution chain serving 11 health centers in Tehran The proposed model indicates that some lost sales exist, and to overcome the lost sales, the case company needs to invest a little more in addition to the initial investment of around 2 billion tomans The results obtained from implementing the model and the sensitivity analysis, using real-world data, confirm the efficiency and validity of the proposed mathematical model and solution approach

Emergency logistics planning under supply risk and demand uncertainty

Abstract: One of the most important issues in the crisis management is to supply and deliver the correct type and quantity of relief items in a dynamic environment of crisis situations. Current research presented a novel bi-objective bi-level optimization model in order to design an integrated framework for relief logistics operations. The Upper level objectives are to minimize total operational cost and total unsatisfied demand considering the effect of distribution locations of relief supplies. The lower level in the hierarchical decision process, proposes suppliers with lower supply risk. The proposed nonlinear model is reformulated as a single-level linear problem, and for the upper-level decision, the goal programming (GP) approach is employed for the exact solution of the model to minimize deviations from the goals of the bi-objective problem. Finally, a case study of emergency planning for earthquake disaster verifies the performance of the presented model.

Environmental sustainability and multifaceted development: multi-criteria decision models with applications

Abstract: In recent years, decision makers, policy analysts, and other actors, have become increasingly aware of sustainability, and begun to combine economic, social and environmental criteria in their efforts to maintain competitiveness, long-term growth and development. In multiple stakeholder settings, the presence of diverse objectives and conflicting criteria often leads to a complex multi-criteria decision problem. The multi-criteria decision analysis (MCDA) offers an integrated framework to model and study sustainability criteria and related inter-criteria relationships. In this paper, we review some of the most significant literature on environmental sustainability, and categorise it to show how and why MCDA models are widely used and becoming increasingly popular. Our systematic analysis suggests that, there has been significant growth in environmental applications of MCDA in diverse areas, ranging from energy management and policy to land use, recycling management and sustainable tourism. Among the various MCDA methods and techniques, analytical hierarchy process, TOPSIS, and goal programming are the most frequently used approaches. Many authors use a combination of different MCDA techniques to balance various factors important to achieve sustainability related goals. We expect sustainability related criteria to be an essential consideration in most future multi-criteria models.

A solving approach for fuzzy multi-objective linear fractional programming and application to an agricultural planting structure optimization problem

Abstract: In this paper, an algorithm is presented to solve fuzzy multi-objective linear fractional programming (FMOLFP) problems through an approach based on superiority and inferiority measures method (SIMM). In the model for the proposed approach, each of fuzzy goals defined for the fractional objectives and some of constraints have fuzzy numbers. To achieve the highest membership value, SIMM is adopted to deal with fuzzy number in constraints, then a linear goal programming methodology is introduced to solve the problem in which the fractional objectives is fuzzy goals. A case of agricultural planting structures optimization problem is solved to illustrate the application of the algorithm. The results show that winter wheat and summer corn acreage should be 38,386.4 ha, and cotton acreage should be 20,669.6 ha. Because of high risk in cotton cultivation at present, the ratio of grain planted area to cotton planted area is unreasonable. An improved support in policy is necessary for the government to enhance the enthusiasm of farmers to plant cotton and sustain the development of cotton market in the long term.

Neutrosophic goal programming strategy for multi-level multi-objective linear programming problem

Abstract: Neutrosophic set theory plays an important role in dealing with the impreciseness and inconsistency in data encountered in solving real life problems. This article aims to present a novel goal programming based strategy which will be helpful to solve Multi-Level Multi-Objective Linear Programming Problem (MLMOLPP) with parameters as neutrosophic numbers (NNs). Difficulty in decision making arises due to the presence of multiple decision makers (DMs) and impreciseness in information. Here each level DM has multiple linear objective functions with parameters considered as NNs which are represented in the form $$c + dI$$ , where c and d are considered real numbers and the symbol I denotes indeterminacy. The constraints are also linear with the parameters as NNs. Firstly the NNs are changed into intervals and the problem turns into a multi-level multi-objective linear programming problem considering interval parameters. Then interval programming technique is employed to obtain the target interval of each objective function. In order to avoid decision deadlock which may arise in hierarchical (multi-level) problem, a possible relaxation is imposed by each level DM on the decision variables under his/her control. Finally a goal programming strategy is presented to solve the MLMOLPP with interval parameters. The method presented in this paper facilitates to solve MLMOLPP with multiple conflicting objectives in an uncertain environment represented through NNs of the form $$c + dI$$ , where indeterminacy I plays a pivotal role. Lastly, a mathematical example is solved to show the novelty and applicability of the developed strategy.

Evaluating service innovation and business performance in tourism: a multicriteria decision analysis approach

Abstract: The purpose of this study is to evaluate the effectiveness of the new service development (NSD) process in tourism. For this reason, factors influencing the process of service innovation in the hospitality sector were explored and correlated with business performance in the hospitality industry through a multicriteria decision analysis approach.,An original multicriteria decision analysis approach is applied in order to estimate the efficiency of the NSD process. The approach follows the principles of ordinal regression analysis, using goal programming techniques. Collected data are based on in-depth structured and questionnaire-based interviews of 77 hotel managers in 147 new services in a representative sample of 99 hotels in Greece. Several financial ratios, covering different aspects of business performance, are used in order to evaluate the NSD process for three years after the services innovation had been launched.,These findings reveal the importance of financial liquidity and business efficiency for the hotel industry (i.e. the ability of a firm to use available resources in order to achieve specific sales goals). The aforementioned variables can determine how quickly and effectively assets are converted to cash. In general, the findings show the emphasis that should be given to customer needs, as well as to the effective management of a NSD project.,Findings of this study may support hotel managers to make complex strategic decisions for future development. These findings have suggested that service innovation should be included as a strategic tool to assess differentiation effort in the hotel industry.

A New Decision Model Approach for Health Technology Assessment and a Case Study for Dialysis Alternatives in Turkey

Abstract: Background: This paper presents a generic Multi-Criteria Decision Analysis (MCDA) model for Health Technology Assessment (HTA) decision-making, which can be applied to a wide range of HTA studies, regardless of the healthcare technology type under consideration. Methods: The HTA Core Model® of EUnetHTA was chosen as a basis for the development of the MCDA model because of its common acceptance among European Union countries. Validation of MCDA4HTA was carried out by an application with the HTA study group of the Turkish Ministry of Health. The commitment of the decision-making group is completed via an online application of 10 different questionnaires. The Analytic Hierarchy Process (AHP) is used to determine the weights. Scores of the criteria in MCDA4HTA are gathered directly from the HTA report. The performance matrix in this application is run with fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), fuzzy Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR), and goal programming MCDA techniques. Results: Results for fuzzy VIKOR, fuzzy TOPSIS, and goal programming are 0.018, 0.309, and 0.191 for peritoneal dialysis and 0.978, 0.677, and 0.327 for hemodialysis, respectively. Conclusions: Peritoneal dialysis is found to be the best choice under the given circumstances, despite its higher costs to society. As an integrated decision-making model for HTA, MCDA4HTA supports both evidence-based decision policy and the transparent commitment of multi-disciplinary stakeholders.

A Multi-Objective Credit Evaluation Model Using MOORA Method and Goal Programming

Abstract: The selection process of a suitable credit applicant firm becomes a more complex decision making process as the decision makers in the banking industries have to assess a wide range of firms based on a set of conflicting financial ratios. This study investigates the new model of the multi-objective optimization on the basis of ratio analysis and the goal programming method together to solve credit lending decision making problems for firms as frequently encountered in real-time commercial banking environments. A multi-objective credit evaluation model is developed to use in all the stages of the credit evaluation process.

Managing sustainable development through goal programming model and satisfaction functions

Abstract: Managing the sustainable development path of a nation requires the aggregation of incommensurable and conflicting objectives related to economic, environmental and social dimensions. Their aggregation requires some tradeoffs from stakeholders with different priorities and preferences. The aim of this paper is to develop a multiple objectives decision aid model where stakeholders’ preferences are explicitly integrated within a group decision-making process based on consensus and tradeoffs. This model is based on the concept of the satisfaction function, where stakeholders’ preferences are explicitly taken into consideration. The developed model is illustrated through an example related to the Canadian 2030 agenda for sustainable development.

Robust goal programming for multi-objective optimization of data-driven problems: A use case for the United States transportation command's liner rate setting problem

Abstract: Robust goal programming (RGP) is a recently developed, powerful new optimization modeling technique that conjoins two widely accepted operations research disciplines: robust optimization (RO) and goal programming (GP). In lieu of applying a probability distribution over possible outcomes, an approach considered by stochastic programming, RO utilizes uncertainty sets to account for data uncertainty. This characteristic of RO is an important attribute because identifying such a probability distribution is challenging, at best. Given this RO context, RGP additionally incorporates GP, traditionally a deterministic procedure, to address optimization problems having multiple objectives. As such, RGP has potential to help address a wide array of data-driven applications, ranging from financial management to engineering design. As a motivating use case for the utility of an RGP approach, this paper demonstrates the applicability of RGP by way of the data-driven United States Transportation Command (USTRANSCOM) liner rate setting problem. USTRANSCOM is responsible for the technical direction and supervision of over $7 billion [1] of annual passenger, cargo, mobility, and personal property movements in support of the Department of Defense (DoD). Transporting people and material with both organic and contracted assets, USTRANSCOM supports DoD organizations and agencies on a reimbursable basis, annually setting and charging rates for air and liner (i.e., sea) transport for their customers and reimbursing the transportation providers accordingly. The Cost Recovery Branch within TCJ8, the Financial Management and Program Analysis staff organization for USTRANSCOM, annually sets liner shipping rates specific to each combination of origin, destination, commodity type, booking terms, and container size for the upcoming fiscal year (FY). As a government entity, USTRANSCOM seeks to neither make a profit nor operate at a loss in any given FY. The current rate setting methodology assumes existing data is deterministic, resulting in process inaccuracies that contribute to unexpected surpluses or deficits each FY. Moreover, the current method fails to consider an additional USTRANSCOM objective: meeting customer's expectations that liner rates will change annually in accordance with industry-specific inflation. Considering the different goals and inherent parametric variance, the use case herein incorporates a decision maker's risk preference regarding parametric variability via a priori analysis to inform RGP techniques and improve the USTRANSCOM liner rate setting process.

Multi-objective flexibility-complexity trade-off problem in batch production systems using fuzzy goal programming

Abstract: Several factors change the flexibility and the complexity of production systems. The flexibility of the production system means to meet the changing needs of the customers. As flexibility increases the complexity also increases. In this paper, a multi-objective linear programming is proposed to model the trade-off of the complexity and flexibility of a batch production system (BPS). Strategic, tactical and operational decision variables have been considered. Seven objective functions of the proposed model are assumed as flexibility and complexity. Sixteen tactical decision variables are defined to determine the level of the dimensions of flexibilities and complexities. Thirty-four operational decision variables are defined to tune the shop-floor operations. Several sets of constraints considering aspects of flexibility and complexity as well as the conditions of batch production systems have been considered. As the achievement to the objective functions is not possible simultaneously, and there is no unique and concise relation between these objective functions in a typical batch production system so, a fuzzy goal programming (FGP) approach is proposed to solve the model. Moreover, goal programming (GP), Fuzzy GP, multi-choice goal programming (MCGP) and fuzzy MCGP are proposed and used to compare the performance of solution procedures. The superior solution approach among GP, MCGP, FGP, and FMCGP is FMCGP which concurrently considers several aspiration levels for objective functions, maximization of the achievement level of objective functions, and satisfying uncertain preference of fuzzy objectives. An evolutionary algorithm, called non-dominated sorting genetic algorithm (NSGA-II) and a random weighted version of FMCGP are customized to regenerate several non-dominated designs for flexibility-complexity trade-off problem in BPS. The results are promising and the proposed model is capable to set the strategic, tactical and operational variables of a BPS.

Spatial multi-objective land use optimization toward livability based on boundary-based genetic algorithm: A case study in Singapore

Abstract: In this research, the concept of livability has been quantitatively and comprehensively reviewed and interpreted to contribute to spatial multi-objective land use optimization modelling. In addition, a multi-objective land use optimization model was constructed using goal programming and a weighted-sum approach, followed by a boundary-based genetic algorithm adapted to help address the spatial multi-objective land use optimization problem. Furthermore, the model is successfully and effectively applied to the case study in the Central Region of Queenstown Planning Area of Singapore towards livability. In the case study, the experiments based on equal weights and experiments based on different weights combination have been successfully conducted, which can demonstrate the effectiveness of the spatial multi-objective land use optimization model developed in this research as well as the robustness and reliability of computer-generated solutions. In addition, the comparison between the computer-generated solutions and the two real planned scenarios has also clearly demonstrated that our generated solutions are much better in terms of fitness values. Lastly, the limitation and future direction of this research have been discussed.