scholarly journals Optimization using Adjusted Program Budget Marginal-Analysis (PBMA) for Decision Making Process and Budget Planning Purposes

2021 ◽  
Vol 5 (2) ◽  
pp. 60-68
Author(s):  
Bilkisu Maijamaa
Keyword(s):  
Programming Model ◽  
Health Sector ◽  
Budget Allocation ◽  
Decision Makers ◽  
Mathematical Programming Model ◽  
Marginal Analysis ◽  
Budget Planning ◽  
Program Budget ◽  
Allocation Process ◽  
Health Organization

Program budget marginal-analysis is a framework used by decision makers to allocate and reallocate resources with maximized benefit or specified goals. Evidently most application of PBMA as employed in most studies have focused mainly within the health organization. To implement the PBMA for optimizing budget allocation problems it involves seven stages. This research will look at applying the PBMA in other organizations that are strategically based for budget allocations. To implement on other organizations, some adjustment on the existing PBMA need to be made. This was achieved through introduction of suitable quantitative approach instead of using the traditional qualitative approach to calculate the marginal cost for the activities/strategies. By introducing and implementation of a suitable mathematical programming model for the final budget allocation process. The adjusted PBMA has proven to be a flexible and workable framework that can be used in other organization not just the health sector where it originated. Hence it is recommended to be used by other organizations for optimal budget allocations

scholarly journals Adjusted-Program-Budget Marginal-Analysis for Student Development Activity Planning and Budget Allocation

2018 ◽  
Vol 7 (3.20) ◽  
pp. 55
Author(s):  
Bilkisu Maijama’a ◽  
Engku M. Nazri
Keyword(s):  
Student Development ◽  
Planning Process ◽  
Performance Indicator ◽  
Programming Model ◽  
Strategic Plan ◽  
Budget Allocation ◽  
Marginal Analysis ◽  
Development Activity ◽  
Program Budget ◽  
The University

Setting priorities and making decisions on allocation and reallocation of university resources based on the direction of the university as translated in its strategic plan must be executed with transparency and accountability and will be of great importance. It is becoming even more crucial, particularly for universities in Malaysia with the recent budget cut imposed by the Malaysian government. In this paper, we proposed an implementation of Program-Budget Marginal-Analysis (PBMA) which is currently being employed for strategic budget planning in the health industry to be applied for the university strategic budget plan as part of the overall strategic planning process. Firstly, the similarities between the steps in PBMA with the steps involved in planning and executing the university strategic plan were studied. Next, the existing PBMA was adjusted and modeled to suit the needs of the steps involved in selecting and allocating budget for the students of U-ABC’s 2017 development activities. The outcome of this implementation using 0-1 integer programming model showed that the targeted achievements could be realized within the allocated budget that was provided by the university. This adjusted-PBMA will be useful and suitable to be implemented by organizations with key performance indicator-oriented programs and having limited budget allocation issues. 

scholarly journals A Mathematical Programming Model to Determine Objective Weights for the Interval Extension of TOPSIS

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Hai Shen ◽  
Lingyu Hu ◽  
Kin Keung Lai
Keyword(s):  
Mathematical Programming ◽  
Input Data ◽  
Programming Model ◽  
Multiple Criteria Decision Making ◽  
Decision Makers ◽  
Mathematical Programming Model ◽  
Topsis Method ◽  
Data Set ◽  
Interval Extension ◽  
Interval Valued

Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method has been extended in previous literature to consider the situation with interval input data. However, the weights associated with criteria are still subjectively assigned by decision makers. This paper develops a mathematical programming model to determine objective weights for the implementation of interval extension of TOPSIS. Our method not only takes into account the optimization of interval-valued Multiple Criteria Decision Making (MCDM) problems, but also determines the weights only based upon the data set itself. An illustrative example is performed to compare our results with that of existing literature.

scholarly journals A MODEL-DRIVEN DECISION SUPPORT SYSTEM FOR REALLOCATION OF SUPPLY TO ORDERS UNDER UNCERTAINTY IN CERAMIC COMPANIES

2015 ◽  
Vol 21 (4) ◽  
pp. 596-625 ◽  
Author(s):  
M. M. E. ALEMANY ◽  
A. A. ◽  
Andrés BOZA ◽  
Vicente S. FUERTES-MIQUEL
Keyword(s):  
Decision Support ◽  
Mathematical Programming ◽  
Decision Support System ◽  
Decision Maker ◽  
Support System ◽  
Programming Model ◽  
Decision Makers ◽  
Mathematical Programming Model ◽  
Finished Good ◽  
Model Driven

In ceramic companies, uncertainty in the tone and gage obtained in first quality units of the same finished good (FG) entails frequent discrepancies between planned homogeneous quantities and real ones. This fact can lead to a shortage situation in which certain previously committed customer orders cannot be served because there are not enough homogeneous units of a specific FG (i.e., with the same tone and gage). In this paper, a Model-Driven Decision Support System (DSS) is proposed to reassign the actual homogeneous stock and the planned homogeneous sublots to already committed orders under uncertainty by means of a mathematical programming model (SP-Model). The DSS functionalities enable ceramic decision makers to generate different solutions by changing model options. Uncertainty in the planned homogeneous quantities, and any other type of uncertainty, is managed via scenarios. The robustness of each solution is tested in planned and real situations with another DSS functionality based on another mathematical programming model (ASP-Model). With these DSS features, the ceramic decision maker can choose in a friendly fashion the orders to be served with the current homogeneous stock and the future uncertainty homogeneous supply to better achieve a balance between the maximisation of multiple objectives and robustness.

scholarly journals DYNAMIC FUZZY MULTIPLE CRITERIA DECISION MAKING FOR PERFORMANCE EVALUATION

2015 ◽  
Vol 21 (5) ◽  
pp. 705-719 ◽  
Author(s):  
Guangxu LI ◽  
Gang Gang KOU ◽  
Yi PENG
Keyword(s):  
Decision Making ◽  
Distance Measure ◽  
Programming Model ◽  
Multiple Criteria Decision Making ◽  
Unit Interval ◽  
Multiple Criteria ◽  
Decision Makers ◽  
Basic Unit ◽  
Mathematical Programming Model ◽  
Order Preference

The paper proposes a dynamic fuzzy multiple criteria decision making (DFMCDM) method. The method considers the integrated weight of the decision makers with the subjective and objective preference and the effect of time weight. In the proposed method, a mathematical programming model is used to determine the integrated weight, and a basic unit-interval monotonic (BUM) function based approach is used to calculate the time weight. In addition, a distance measure of membership function is introduced to effectively measure the degree of difference between the alternatives in the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS). Finally, a numerical example is introduced to illustrate the proposed method.

scholarly journals Cooperative Strategies for Maximum-Flow Problem in Uncertain Decentralized Systems Using Reliability Analysis

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Hadi Heidari Gharehbolagh ◽  
Ashkan Hafezalkotob ◽  
Ahmad Makui ◽  
Sadigh Raissi
Keyword(s):  
Cost Estimation ◽  
Flow Problem ◽  
Programming Model ◽  
Maximum Flow ◽  
Reliability Function ◽  
Decision Makers ◽  
Mathematical Programming Model ◽  
Real Cost ◽  
Cooperative Strategies ◽  
Proper Estimation

This study investigates a multiowner maximum-flow network problem, which suffers from risky events. Uncertain conditions effect on proper estimation and ignoring them may mislead decision makers by overestimation. A key question is how self-governing owners in the network can cooperate with each other to maintain a reliable flow. Hence, the question is answered by providing a mathematical programming model based on applying the triangular reliability function in the decentralized networks. The proposed method concentrates on multiowner networks which suffer from risky time, cost, and capacity parameters for each network’s arcs. Some cooperative game methods such asτ-value, Shapley, and core center are presented to fairly distribute extra profit of cooperation. A numerical example including sensitivity analysis and the results of comparisons are presented. Indeed, the proposed method provides more reality in decision-making for risky systems, hence leading to significant profits in terms of real cost estimation when compared with unforeseen effects.

Setting Priorities and Allocating Resources in Regional Health Authorities: A Report from Two Pilot Exercises Using Program Budgeting and Marginal Analysis

2002 ◽  
Vol 15 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Craig Mitton ◽  
Cam Donaldson ◽  
Lisa Halma ◽  
Nadine Gall
Keyword(s):  
Priority Setting ◽  
Health Sector ◽  
Decision Makers ◽  
Regional Health ◽  
Marginal Analysis ◽  
Health Authorities ◽  
Program Budgeting ◽  
A Priority ◽  
Regional Health Authorities

A significant mandate of Canadian regional health authorities is to set priorities and allocate resources within a limited funding envelope. Program budgeting and marginal analysis is a priority-setting framework used in the health sector. This article discusses the application of the framework in two regional health authorities in Alberta. The framework was demonstrated to be effective in aiding decision makers to set priorities, and wider application of the framework in these health authorities is planned.

Scheduling a Bus Fleet for Evacuation Planning Using Stop-Skipping Method

2021 ◽  
pp. 036119812110200
Author(s):  
Hu Zhao ◽  
Shumin Feng ◽  
Yusheng Ci
Keyword(s):  
Waiting Time ◽  
Ant Colony Algorithm ◽  
Programming Model ◽  
Peer To Peer ◽  
Mathematical Programming Model ◽  
Fleet Size ◽  
Bus Stop ◽  
Passenger Demand ◽  
Service Strategies ◽  
Bus System

Sudden passenger demand at a bus stop can lead to numerous passengers gathering at the stop, which can affect bus system operation. Bus system operators often deal with this problem by adopting peer-to-peer service, where empty buses are added to the fleet and dispatched directly to the stop where passengers are gathered (PG-stop). However, with this strategy, passengers at the PG-stop have a long waiting time to board a bus. Thus, this paper proposes a novel mathematical programming model to reduce the passenger waiting time at a bus stop. A more complete stop-skipping model that including four cases for passengers’ waiting time at bus stops is proposed in this study. The stop-skipping decision and fleet size are modeled as a dynamic program to obtain the optimal strategy that minimizes the passenger waiting time, and the optimization model is solved with an improved ant colony algorithm. The proposed strategy was implemented on a bus line in Harbin, China. The results show that, during the evacuation, using the stop-skipping strategy not only reduced the total waiting time for passengers but also decreased the proportion of passengers with a long waiting time (>6 min) at the stops. Compared with the habitual and peer-to-peer service strategies, the total waiting time for passengers is reduced by 31% and 23%, respectively. Additionally, the proportion of passengers with longer waiting time dropped to 43.19% by adopting the stop-skipping strategy, compared with 72.68% with the habitual strategy and 47.5% with the peer-to-peer service strategy.

scholarly journals A Multicriteria Goal Programming Model for Ranking Universities

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 459
Author(s):  
Fernando García ◽  
Francisco Guijarro ◽  
Javier Oliver
Keyword(s):  
Goal Programming ◽  
Programming Model ◽  
Decision Makers ◽  
University Rankings ◽  
Central Tendency ◽  
Performance Variables ◽  
World University Rankings ◽  
Goal Programming Model ◽  
Objective Quantification

This paper proposes the use of a goal programming model for the objective ranking of universities. This methodology has been successfully used in other areas to analyze the performance of firms by focusing on two opposite approaches: (a) one favouring those performance variables that are aligned with the central tendency of the majority of the variables used in the measurement of the performance, and (b) an alternative one that favours those different, singular, or independent performance variables. Our results are compared with the ranking proposed by two popular World University Rankings, and some insightful differences are outlined. We show how some top-performing universities occupy the best positions regardless of the approach followed by the goal programming model, hence confirming their leadership. In addition, our proposal allows for an objective quantification of the importance of each variable in the performance of universities, which could be of great interest to decision-makers.

scholarly journals Approaches for multicriteria decision-making based on the hesitant fuzzy best–worst method

2021 ◽  
Author(s):  
Jian Li ◽  
Li-li Niu ◽  
Qiongxia Chen ◽  
Zhong-xing Wang
Keyword(s):  
Decision Making ◽  
Comparative Analysis ◽  
Programming Model ◽  
Multicriteria Decision Making ◽  
Decision Makers ◽  
Multicriteria Decision ◽  
Preference Relations ◽  
Fuzzy Preference Relations ◽  
Decision Making Models ◽  
Best Worst Method

AbstractHesitant fuzzy preference relations (HFPRs) have been widely applied in multicriteria decision-making (MCDM) for their ability to efficiently express hesitant information. To address the situation where HFPRs are necessary, this paper develops several decision-making models integrating HFPRs with the best worst method (BWM). First, consistency measures from the perspectives of additive/multiplicative consistent hesitant fuzzy best worst preference relations (HFBWPRs) are introduced. Second, several decision-making models are developed in view of the proposed additive/multiplicatively consistent HFBWPRs. The main characteristic of the constructed models is that they consider all the values included in the HFBWPRs and consider the same and different compromise limit constraints. Third, an absolute programming model is developed to obtain the decision-makers’ objective weights utilizing the information of optimal priority weight vectors and provides the calculation of decision-makers’ comprehensive weights. Finally, a framework of the MCDM procedure based on hesitant fuzzy BWM is introduced, and an illustrative example in conjunction with comparative analysis is provided to demonstrate the feasibility and efficiency of the proposed models.

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