Goal Programming Method Research Articles

Nonpreemptive Goal Programing Method in Optimization Nurse Scheduling by Considering Education Level

Nurse scheduling is one of the problems that often arise in hospital management systems. Head of ICU room and nurse to cooperate in making good nurse scheduling for the creation of optimal service. In this paper, we study a hospital nurse schedule design by considering the level of nurse education and the provision of holidays. Nurses with undergraduate education (S1) Nurses become leaders on every shift and are accompanied by nurses with diploma education (D3). The scheduling model in this study using the nonpreemptive goal programming method and LINGO 11.0 software. The preparation of the schedule of nurses assigned to this method can optimize the need for efficient nurses per shift based on education level. The data in the research was obtained by collecting administrative data at Aloei Saboe Gorontalo hospital. The data used are the published schedule by the head of the ICU room. In making a nurse schedule, there are limitations to consider such ashospital regulation. The results of the study obtained an optimal solution in the form of meeting all the desired obstacles. Computational results shows that nurse scheduling using the nonpreemptive goal programming method and LINGO 11.0 software better than the schedule created manually. Every shift is a maximum of one leader with an undergraduate education (S1) background and accompanied by a nurse with a diploma education (D3) background. Keywords: scheduling, goal programming, nonpreemptive goal programming.

A bi-objective robust optimization approach for the management of infectious wastes with demand uncertainty during a pandemic

The current global COVID-19 pandemic attracts public attention to the management of waste generated by health-care activities. Due to the hazardous nature, infectious waste requires the design of a multi-tiered system to provide cost-efficient and eco-friendly services of waste collection, transportation, treatment, and final disposal. However, the impact of uncertainties has not been well studied in the existing literature. Considering the presence of random waste generation during a pandemic, we aim to answer the following questions: 1) where to locate temporary transfer stations and temporary treatment centers; 2) how to plan collection tours among the small generation nodes and temporary transfer stations; 3) how to plan the direct transportation from large generation nodes to treatment centers; 4) how to transport waste from temporary transfer stations to treatment centers, and 5) how to transport wastes from treatment centers to disposal facilities. The relevant cost and associated risk are respectively formulated and assessed using a scenario-based bi-objective robust approach. The complexity of the resulting mathematical model motivated the adaption and comparison of three multi-objective optimization approaches, including the goal programming method, a lexicographic weighted Tchebycheff approach, and an augmented ϵ-constraint solution technique. A case study based on the real situation in Wuhan, China, during the COVID-19 outbreak is conducted to demonstrate the workability of the proposed model and provide managerial insights for infectious waste management. The computational results show that our proposed model can more than double the demand fulfillment rate at an approximately 40% lower cost when facing a distinctively high increment in the amount of infectious waste.

Multiobjective Evaluation of Coevolution among Innovation Populations Based on Lotka–Volterra Equilibrium

The collaborative evaluation of enterprise innovation populations is a hot issue. The Lotka–Volterra model is a mature method used to evaluate the interaction mechanism of populations and is widely used in innovation ecology research studies. The Lotka–Volterra model mainly focuses on the quantitative characteristics of the interactive populations. The growth mechanisms cannot explain all the synergy mechanisms of the innovative populations. The collaborative evaluation between enterprise innovation populations is a typical multiobjective evaluation problem. The multichoice goal programming model is a mature method to solve multiobjective optimization problems. This paper combines the Lotka–Volterra model and multichoice goal programming method to construct a three-stage multiobjective collaboration evaluation method based on Lotka–Volterra equilibrium. An evaluation example is used to illustrate the application process of this method. The method proposed in this paper has excellent performance in computing, parameter sensitivity analysis, and model stability analysis.

Optimization of Stock Portfolios Using Goal Programming Based on the Kalman-Filter Method

Long-term stock investment development is carried out by means of portfolio optimization. Selection of stocks for portfolios is not only based on high-value stock prices but also takes into account their fluctuations. Estimation of future stock price fluctuations has an indirect impact on future portfolio formation. This research has implemented the Kalman filter method to obtain the best estimation results from various stock prices with a high degree of accuracy. The results are then used to form a stock portfolio on the basis of Goal Programming. This study has compared the optimization results with the real value of stock prices. The results obtained, Kalman filter-based Goal Programming is more effective for predicting future portfolios compared to the Goal Programming method with a return difference of Rp. 178,039,848. This suggests that optimization with the Kalman Filter-based Objective Programming can be used as a tool to determine future stock portfolios.

Multimarket competition effects on product line decisions – A multi-objective decision model in fast moving consumer goods industry

In a multimarket competition, firms experience increased contacts with competitors under various product-geographic markets, exhibiting mutual forbearance, which in turn deters firms to venture into product proliferation. However, firms do engage in the same through horizontal product differentiation through 'brand extensions.' Since marketers face imprecise goals, we propose a multi-objective product line decision model for pruning or continuing product lines. Specifically, this is done by using a fuzzy goal-programming method while validating it using a dataset (comprising of eighty thousand households) from the Indian tea industry. Our findings reveal that in the festive season, a firm should only focus on limited brands and prune the rest to maximize its revenue while increasing the spending on advertising and the sales and distribution overheads. However, during the end of the financial quarter, the focus should be on achieving targets by maintaining a broader product portfolio.

A heuristic-based multi-choice goal programming for the stochastic sustainable-resilient routing-allocation problem in relief logistics

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.

A Humanitarian Green Supply Chain Management Considering Minimum Cost and Time

Disaster is the sudden problem of the world. There is no time bound. By disaster, all the creatures of the earth are affected. Here, the authors have tried to show some issues which are related to the natural calamities and green transportation. The main investigation of the paper is to describe about humanitarian supply chain management with optimized transportation cost, time, and carbon emission. Here a real-life problem of flood affected area has been chosen. When such disasters happen, quick response can reduce the devastation and save lives, and thus, it requires fulfilling the basic humanitarian needs of the affected population. In such case, organizations should also maintain the emission of the vehicles in safe range to mitigate the further disaster by pollution. A multi-objective solid transportation problem considering cost, time, and emission has been presented here. To solve the problem, this paper has used goal programming method and pareto optimal solution method. A comparison of results is also shown later. Some managerial insights are drawn to describe the situation.

Designing a Quality System using the Goals Programming Method -An Applied Research

Due to the continuous development in society and the multiplicity of customers' desires and their keeping pace with this development and their search for the quality and durability of the commodity that provides them with the best performance and that meets their needs and desires, all this has led to the consideration of quality as one of the competitive advantages that many industrial companies compete for and which are of interest to customers and are looking for. The research problem showed that the Diyala State Company for Electrical Industries relies on some simple methods and personal experience to monitor the quality of products and does not adopt scientific methods and modern programs. The aim of this research is to design a system to monitor the production quality of the transformer (400/11 KVA) through the use of a method that helps to make decisions of a quantitative multi-objective nature, which is the method of lexicographic goal programming that the researcher used to integrate the preferences of the decision-maker in monitoring the quality of the production of the transformer (400/11 KVA). Using the two programs (Excel and Lingo) in constructing and solving the mathematical model that was applied in the sections (outer and inner iron core, winding and isolating, final assembly and quality control). The results obtained were efficient, as they achieved the least possible deviations from all the target values ​​for all goals

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.

Designing an integrated municipal solid waste management system using a fuzzy chance-constrained programming model considering economic and environmental aspects under uncertainty

In recent decades, municipal solid waste (MSW) management has become one of the major concerns of human societies. Therefore, an efficient MSW system of collecting, recycling, and disposing is required to be designed, maintained and improved continuously. However, designing a proper waste collection management system by controlling the spread of diseases and environmental pollutants is very costly. To provide an efficient MSW management system, this study proposes an integrated location-allocation problem to plan the operations such as collection, recycling, disposal, and transportation under uncertainty. To deal with the problem uncertainty, a triangular fuzzy number approach is employed and finally, a fuzzy chance-constrained programming model is developed. Moreover, the proposed bi-objective mathematical programming model aims to minimize the total costs and the total volume of pollutant emissions simultaneously. To solve and validate the model, a weighted goal programming (WGP) method is employed and solved by CPLEX solver of GAMS software. Finally, a real case study problem was implemented in Qazvin, Iran. The results showed that, in most cases, increasing the confidence level of the case study problem leads to the improvement of objective functions. Managers can take into account the output of this research provided along with a sensitivity analysis under different conditions.

An Optimization Model of Nurse Scheduling Using Goal Programming Method: A Case Study

Nurse scheduling problem involves assigning of each individual nurse in the hospital so that a series of tasks can be completed. To increase patient satisfaction, skilled and disciplined nurses are needed. In the field of health care, nurse scheduling is a challenging activity because of the different levels of nurses needed on different shifts and days. However, it often happens where a number of nurses assigned do not meet the specific needs of nurses so that the performance of nurses at each shift is not optimal. In this paper, an optimization model is proposed using goal programming method to determine the optimal schedule using Lingo 11.0 software. The model is implemented in the Yustisia Room, a special room for treating covid-19 inpatients in Universitas Sebelas Maret Hospital. The results of the optimization showed that all conditions that need to be achieved in scheduling which previously unfulfilled in manual schedule, such as the minimum requirement number of senior nurses and male nurses in each shift can be fulfilled. For additional target, which is total working days, the proposed model can produce a better schedule for regular nurses and better assignment patterns for all nurses, where all regular nurses have the same total number of working days, which is 17 working days.

Green closed-loop supply chain network design: a novel bi-objective chance-constraint approach

In this paper, a novel chance-constrained programming model has been proposed for handling uncertainties in green closed loop supply chain network design. In addition to locating the facilities and establishing a flow between them, the model also determines the transportation mode between facilities. The objective functions are applied to minimize the expected value and variance of the total cost CO2 released is also controlled by providing a novel chance-constraint including a stochastic upper bound of emission capacity. To solve the mathematical model using the General Algebraic Modeling System (GAMS) software, four multi-objective decision-making (MODM) methods were applied. The proposed methodology was subjected to various numerical experiments. The solutions provided by different methods were compared in terms of the expected value of cost, variance of cost, and CPU time using Pareto-based analysis and optimality-based analysis. In Pareto-based analysis, a set of preferable solutions were presented using the Pareto front; then optimality-based optimization was chosen as the best method by using a Simple Additive Weighting (SAW) method. Experimental experiments and sensitivity analysis demonstrated that the performance of the goal attainment method was 13% and 24% better that of global criteria and goal programming methods, respectively.

Supply chain design to tackle coronavirus pandemic crisis by tourism management

The rapid growth of the COVID-19 pandemic in the world and the importance of controlling it in all regions have made managing this crisis a great challenge for all countries. In addition to imposing various monetary costs on countries, this pandemic has left many serious damages and casualties. Proper control of this crisis will provide better medical services. Controlling travel and tourists in this crisis is also an effective factor. Hence, the proposed model wants to control the crisis by controlling the volume of incoming tourists to each city and region by closing the entry points of that region, which reduces the inpatients. The proposed multi-objective model is designed to aim at minimizing total costs, minimizing the tourist patients, and maximizing the number of city patients. The Improved Multi-choice Goal programming (IMCGP) method has been used to solve the multi-objective problem. The model examines the results by considering a case study. Sensitivity analyses and managerial insight are also provided. According to the results obtained from the model and case study, two medical centers with the capacity of 300 and 700 should be opened if the entry points are not closed.

OPTIMALISASI PENYUSUNAN MENU DIET PENDERITA STROKE DENGAN METODE GOAL PROGRAMMING

Stroke is a disorder of brain nerve function caused by blockage of blood circulation to the brain. In preparing the diet for stroke, it is possible to deviate from the recommended nutritional value and total energy. This problem could be overcome by compiling a stroke diet menu using the Goal programming method. The purpose of this study is to determine whether the results of the portion of food obtained using the Goal programming method can meet the recommended energy and nutritional content targets, as well as knowing the percentage of total energy content produced per day from the preparation of the diet menu to the recommended total energy content. The calculations in this study using the help of LINGO software with the results that the diet menu by Goal programming method has met the recommended nutrients and energy from the standard stroke diet at Klungkung Hospital, and the percentage of energy fulfilled from the stroke IIB diet for three days shows a 100% result.

Collaborative Product Portfolio Design Based on the Approach of Multichoice Goal Programming

Product portfolio optimization is a typical multiobjective problem. The multichoice goal programming method becomes a popular means of resolving multiobjective decision problems. However, the classic multichoice goal programming method treats the product portfolio optimization in isolation and does not consider the mutual influence between portfolio products. Researchers should consider the interaction between products in portfolio optimization so that they can be adjusted to “real world” problems. The interaction between products can be explained by population dynamics. Logistic model is a classical method to analyze the population interaction. The equilibrium point of logistic model can show the ideal state of product population coordinated development. The combination of logistic and multichoice goal programming method is an effective approach to analyze the interaction of product portfolio. This paper therefore proposes a new alternative method to formulate the multiobjective problem and also uses an illustrative example to demonstrate the usefulness of the proposed method. The comparative analysis of model optimization results shows that logistic multichoice goal programming model can take into account resource constraints, product collaboration, and output maximization. Logistic multichoice goal programming model shows good performance in the aspects of operation complexity, operation time, sensitivity analysis, and collaborative entropy evaluation.

Goal programming associated with the non-archimedean infinitesimal: a case study applied in the agricultural sector

Abstract Paper aims This work presents a multi-objective method based on goal programming associated with non-Archimedean infinitesimal (NAI) (Improved Weighted Goal Programming method, input-oriented IWGP-MCDEA-BCC). Originality The MCDEA is applied for the first time in a large agricultural company (over 11 hectares). A new method is proposed which consists of an improvement on input-oriented WGP-MCDEA-BCC approaches. Research method The performance of the proposed method was compared to the classic Data Envelopment Analysis and the Weighted sum Goal Programming methods. The case study comprises an agricultural company located in the Sao Francisco Valley (Brazil). Main findings The proposed method can help decision makers to improve efficiency in the production of different types of fruits. Implications for theory and practice The proposed model is capable of overcoming the deficiencies associated with classical DEA and allows the company to identify effective ways to increase productivity by reducing input costs.

Estimasi Koefisien Regresi Linear Berganda dengan Metode Goal Programming

The alternative method that can be used to determine the value of a multiple linear regression variable coefficient is goal programming method. The kind of data used in this research is secondary data namely the production of freshwater fish pond system data in the Province of West Nusa Tenggara based on Fisheries Service of West Nusa Tenggara Province year 2018. Software used to assist in reckoning is POM for Windows.Settlement calculation procedure in this research was conducted in two stages, namely the calculation with variable ????????4 and the calculation without variables ????????4. The results obtained with the variable ????????3 namely ????????1 = ????????0 = 64,08 ; ????????2 = ????????1 = 2,67 ; ????????3 = ????????2 = 0,27 ; ????????4 = ????????3 = 0, and the results obtained without variables ????????4 namely ????????1 = ????????0 = 64,08 ; ????????2 = ????????1 = 2,67 ; ????????3 = ????????2 = 0,24. The production of fresh water fish pond system in the Province of West Nusa Tenggara were affected by extensive fish ponds and food availability, while the number of domestic aquaculture freshwater fish do not affect production.
 Keywords : Multiple Linear Regression, Goal Programming, Production of Fish.

The fuzzy goal programming approach to production planning of intermediate gear spare parts: a case study

Production planning has an important role in the company's business processes. A company engaged in the manufacture of intermediate gear parts has a problem in optimizing its production system. The production planning system that occurs is still based on predictions from decision-makers. This study aims to optimize the production planning system to maximize the 15T intermediate gear spare parts' production capacity and the 30T intermediate gear spare parts. Optimization of production planning uses the fuzzy goal programming method to optimize objectives based on existing constraints such as working hours, profit tolerance values, and demand tolerance values. The results showed that the use of fuzzy goal programming was able to increase the production level by 2.765, with an increase in profit of 2.57%. Fuzzy goal programming implementation provides an optimal solution in increasing profits in accordance with company goals based on the constraints that occur.

SUPPLIER SELECTION WITH AHP AND 0-1 GOAL PROGRAMMING: AN APPLICATION IN HEALTHCARE INDUSTRY

In order for health institutions to continue their activities, the goods and services they need must be supplied at the right time, in the right amount, at the right quality, at an affordable price and from the right source. This is possible with an effective supply chain management and selection of the right supplier. Supplier selection studies in the health sector are almost nonexistent, therefore, it was wanted to contribute to the literature by studying in this sector. In this study, it was aimed to work with the right suppliers to ensure that a dental health center provides critical medical supplies. First of all, the products of vital importance were determined by ABC (Always, Better Control)-VED (Vital, Essential, Desirable) matrix analysis and a supplier list was created. The best suppliers were selected with the Zero-One Goal Programming method based on AHP priorities, one of the multi-criteria decision making methods, by determining the criteria suitable for the sector.It is thought that this model will contribute significantly to the literature and will save time in supplier selection studies in the health sector.

Estimating priorities from relative deviations in pairwise comparison matrices

The problem of deriving the priority vector from a pairwise comparison matrix is at the heart of multiple-criteria decision-making problems. Existing prioritization methods mostly model the inconsistency in relative preference–the ratio of two preference weights–by allowing for a small deviation, either additively or multiplicatively. In this study, we alternatively allow for a deviation in each of the two preference weights, which we refer to as the relative deviation interconnection. Under this framework, we consider both relative additive and multiplicative deviation cases and define two types of norms capturing the magnitudes of the deviations, which gives rise to four conic programming models for minimizing the norms of the deviations. Through the model structures, we analyze the signs of the deviations. This further allows us to establish the expressiveness of our framework, which covers the logarithmic least-squares method and the goal-programming method. Using numerical examples, we show that our models perform comparably against existing prioritization methods, efficiently identify unusual and false observations, and provide further suggestions for reducing inconsistency.