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Fulltext Hospital efficiency: concept, measurement techniques and review of hospital efficiency studies

Hossein Moshiri, Syed Mohammed Aljunid, Rahmah Mohd Amin

Malaysian Journal of Public Health Medicine, 2010;10(2):35-43.
MyJurnal

In a time of rising demands on hospital reimbursement levels, focus on efficient operations is becoming more imperative. In health care systems, the measurement of efficiency is usually the first step in auditing individual performance of production units, e.g. hospitals, health centers, etc. It constitutes the rational framework for the distribution of human and other resources between and within health care facilities. The term efficiency is broadly used in economics and refers to the best utilization of resources in production. Typical example of efficiency is technical efficiency, referring to the effective use of resources in producing outputs. In the Farrell framework, a hospital is judged to be technically efficient if it is operating on the best practice production frontier in its hospital industry. In general, there are two main frontier methods in measuring efficiency. The first is Data Envelopment Analysis (DEA), a linear programming method which enables the measurement of efficiency consistent with the theoretically based concept of production efficiency. DEA typically examines the relationship between inputs to a production process and the outputs of that process. The second technique for assessing efficiency that is employed is Stochastic Frontier Analysis (SFA). This is an econometric technique to estimate a conventional function; with the difference being that efficiency is measured using the residuals from the estimated equation. The error term is therefore divided into a stochastic error term and a systematic inefficiency term.

Matched MeSH terms: Programming, Linear
A Stackelberg solution to a two-level linear fractional programming problem with interval coefficients in the objective functions

Borza M, Rambely A, Saraj M

Sains Malaysiana, 2012;41:1651-1656.

In this paper, two approaches were introduced to obtain Stackelberg solutions for two-level linear fractional programming problems with interval coefficients in the objective functions. The approaches were based on the Kth best method and the method for solving linear fractional programming problems with interval coefficients in the objective function. In the first approach, linear fractional programming with interval coefficients in the objective function and linear programming were utilized to obtain Stackelberg solution, but in the second approach only linear programming is used. Since a linear fractional programming with interval coefficients can be equivalently transformed into a linear programming, therefore both of approaches have same results. Numerical examples demonstrate the feasibility and effectiveness of the methods.

Matched MeSH terms: Programming, Linear
Solving linear multi-objective geometric programming problems via reference point approach

Bazikar F, Saraj M

Sains Malaysiana, 2014;43:1271-1274.

In the last few years we have seen a very rapid development on solving generalized geometric programming (GGP) problems, but so far less works has been devoted to MOGP due to the inherent difficulty which may arise in solving such problems. Our aim in this paper was to consider the problem of multi-objective geometric programming (MOGP) and solve the problem via two-level relaxed linear programming problem Yuelin et al. (2005) and that is due to simplicity which occurs through linearization i.e. transforming a GP to LP. In this approach each of the objective functions in multi-objective geometric programming is individually linearized using two-level linear relaxed bound method, which provides a lower bound for the optimal values. Finally our MOGP is transformed to a multi-objective linear programming problem (moLP) which is solved by reference point approach. In the end, a numerical example is given to investigate the feasibility and effectiveness of the proposed approach.

Matched MeSH terms: Programming, Linear
Fulltext An Efficacious Multi-Objective Fuzzy Linear Programming Approach for Optimal Power Flow Considering Distributed Generation

Warid W, Hizam H, Mariun N, Abdul-Wahab NI

PLoS One, 2016;11(3):e0149589.
PMID: 26954783 DOI: 10.1371/journal.pone.0149589

This paper proposes a new formulation for the multi-objective optimal power flow (MOOPF) problem for meshed power networks considering distributed generation. An efficacious multi-objective fuzzy linear programming optimization (MFLP) algorithm is proposed to solve the aforementioned problem with and without considering the distributed generation (DG) effect. A variant combination of objectives is considered for simultaneous optimization, including power loss, voltage stability, and shunt capacitors MVAR reserve. Fuzzy membership functions for these objectives are designed with extreme targets, whereas the inequality constraints are treated as hard constraints. The multi-objective fuzzy optimal power flow (OPF) formulation was converted into a crisp OPF in a successive linear programming (SLP) framework and solved using an efficient interior point method (IPM). To test the efficacy of the proposed approach, simulations are performed on the IEEE 30-busand IEEE 118-bus test systems. The MFLP optimization is solved for several optimization cases. The obtained results are compared with those presented in the literature. A unique solution with a high satisfaction for the assigned targets is gained. Results demonstrate the effectiveness of the proposed MFLP technique in terms of solution optimality and rapid convergence. Moreover, the results indicate that using the optimal DG location with the MFLP algorithm provides the solution with the highest quality.

Matched MeSH terms: Programming, Linear
Fulltext GIS routing and modelling of residential waste collection for operational management and cost optimization

Billa, L., Pradhan, B., Yakuup, A.

Pertanika Journal of Science & Technology, 2014;22(1):193-211.
MyJurnal

In this paper, optimum routing was developed based on the travel salesman method and integrated in ArcInfo GIS using linear programming. The results of the optimized travel distances and times for residential waste collection and routing to disposal site were used to calculate the number and type of required track collection, labour requirement, costing of waste collection and to determine the overall solid waste management efficiency through waste management operation research methods. The objective of the study was to optimize residential collection and hauling to disposal site through operation cost minimization for Petaling Jaya Municipality in the state of Selangor, Malaysia. The study determined that with optimized routes and recycling possibilities, the total cost of waste collections could be reduced from RM90,372 to RM20,967, with a reduction of 76.8%. It was also revealed that optimum routes might not necessarily be the shortest distance from point A to point B as travel time maybe high on short distances due to traffic congestion and the presence of many traffic lights. Techniques and methods developed using general GIS have proven effective in route optimization and allowed management of data to suit local conditions and limitations of waste management for the studied area. Thus, scenarios of travel distances, time and waste quantity value generated from the GIS enabled appropriate determination of the number of waste trucks and labour requirements for the operation and the overall calculation of costs of waste management based on the operation research methods used in the study.

Matched MeSH terms: Programming, Linear
Design, optimisation and reliability allocation for energy systems based on equipment function and operating capacity

Andiappan V, Benjamin MFD, Tan RR, Ng DKS

Heliyon, 2019 Oct;5(10):e02594.
PMID: 31720447 DOI: 10.1016/j.heliyon.2019.e02594

Designers of energy systems often face challenges in balancing the trade-off between cost and reliability. In literature, several papers have presented mathematical models for optimizing the reliability and cost of energy systems. However, the previous models only addressed reliability implicitly, i.e., based on availability and maintenance planning. Others focused on allocation of reliability based on individual equipment requirements via non-linear models that require high computational effort. This work proposes a novel mixed-integer linear programming (MILP) model that combines the use of both input-output (I-O) modelling and linearized parallel system reliability expressions. The proposed MILP model can optimize the design and reliability of energy systems based on equipment function and operating capacity. The model allocates equipment with sufficient reliability to meet system functional requirements and determines the required capacity. A simple pedagogical example is presented in this work to illustrate the features of proposed MILP model. The MILP model is then applied to a polygeneration case study consisting of two scenarios. In the first scenario, the polygeneration system was optimized based on specified reliability requirements. The technologies chosen for Scenario 1 were the CHP module, reverse osmosis unit and vapour compression chiller. The total annualized cost (TAC) for Scenario 1 was 53.3 US$ million/year. In the second scenario, the minimum reliability level for heat production was increased. The corresponding results indicated that an additional auxiliary boiler must be operated to meet the new requirements. The resulting TAC for the Scenario 2 was 5.3% higher than in the first scenario.

Matched MeSH terms: Programming, Linear
Integrated forward and reverse supply chain: A tire case study

Pedram A, Yusoff NB, Udoncy OE, Mahat AB, Pedram P, Babalola A

Waste Manag, 2017 Feb;60:460-470.
PMID: 27406308 DOI: 10.1016/j.wasman.2016.06.029

This paper attempts to integrate both a forward and reverse supply chain to design a closed-loop supply chain network (CLSC). The problem in the design of a CLSC network is uncertainty in demand, return products and the quality of return products. Scenario analyses are generated to overcome this uncertainty. In contrast to the existing supply chain network design models, a new application of a CLSC network was studied in this paper to reduce waste. A multi-product, multi-tier mixed integer linear model is developed for a CLSC network design. The main objective is to maximize profit and provide waste management decision support in order to minimize pollution. The result shows applicability of the model in the tire industry. The model determines the number and the locations of facilities and the material flows between these facilities.

Matched MeSH terms: Programming, Linear
Fulltext Utilization of a Diet Optimization Model in Ensuring Adequate Intake among Pregnant Women in Selangor, Malaysia

Sawal Hamid ZB, Rajikan R, Elias SM, Jamil NA

Int J Environ Res Public Health, 2019 11 27;16(23).
PMID: 31783474 DOI: 10.3390/ijerph16234720

Achieving nutritional requirements for pregnant women in rural or suburban households while maintaining the intake of local and culture-specific foods can be difficult. Usage of a linear programming approach can effectively generate diet optimization models that incorporate local and culturally acceptable menus. This study aimed to determine whether a realistic and affordable diet that achieves recommended nutrient intakes for pregnant women could be formulated from locally available foods in Malaysia. A cross-sectional study was conducted to assess the dietary intake of 78 pregnant women using a 24-h dietary recall and a 3-day food record. A market survey was also carried out to estimate the cost of raw foods that are frequently consumed. All linear programming analyses were done using Excel Solver to generate optimal dietary patterns. Our findings showed that the menus designed from diet optimization models using locally available foods would improve dietary adequacy for the seven food groups based on the Malaysian Dietary Guidelines 2010 (MDG 2010) and the 14 nutrients based on Recommended Nutrient Intake 2017 (RNI 2017) in pregnant women. However, inadequacies remained for iron and niacin, indicating that these nutrients may require supplementation.

Matched MeSH terms: Programming, Linear
Fulltext Diet optimization using linear programming to develop low cost cancer prevention food plan for selected adults in Kuala Lumpur, Malaysia

Alaini R, Rajikan R, Elias SM

BMC Public Health, 2019 Jun 13;19(Suppl 4):546.
PMID: 31196148 DOI: 10.1186/s12889-019-6872-4

BACKGROUND: Poor dietary habits have been identified as one of the cancer risks factors in various epidemiological studies. Consumption of healthy and balance diet is crucial to reduce cancer risk. Cancer prevention food plan should consist of all the right amounts of macronutrients and micronutrients. Although dietary habits could be changed, affordability of healthy foods has been a major concern, as the price of healthy foods are more expensive the unhealthy counterparts.
METHODS: Therefore, using linear programming, this study is aimed to develop a healthy and balanced menu with minimal cost in accordance to individual needs that could in return help to prevent cancer. A cross sectional study involving 100 adults from a local university in Kuala Lumpur was conducted in 3 phases. The first phase is the data collection for the subjects, which includes their socio demographic, anthropometry and diet recall. The second phase was the creation of a balanced diet model at a minimum cost. The third and final phase was the finalization of the cancer prevention menu. Optimal and balanced menus were produced based on respective guidelines of WCRF/AICR (World Cancer Research Fund/ American Institute for Cancer Research) 2007, MDG (Malaysian Dietary Guidelines) 2010 and RNI (Recommended Nutrient Intake) 2017, with minimum cost.
RESULTS: Based on the diet recall, most of subjects did not achieve the recommended micronutrient intake for fiber, calcium, potassium, iron, B12, folate, vitamin A, vitamin E, vitamin K, and beta-carotene. While, the intake of sugar (51 ± 19.8 g), (13% ± 2%) and sodium (2585 ± 544 g) was more than recommended. From the optimization model, three menus, which met the dietary guidelines for cancer prevention by WCRF/AICR 2007, MDG 2010 and RNI 2017, with minimum cost of RM7.8, RM9.2 and RM9.7 per day were created.
CONCLUSION: Linear programming can be used to translate nutritional requirements based on selected Dietary Guidelines to achieve a healthy, well-balanced menu for cancer prevention at minimal cost. Furthermore, the models could help to shape consumer food choice decision to prevent cancer especially for those in low income group where high cost for health food has been the main deterrent for healthy eating.

Matched MeSH terms: Programming, Linear*