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To develop a methodology for faculty salary adjustment through market adjustment based on market demand for business PhDs and merit adjustment based on faculty members' performance levels in the areas of teaching, research and service.

The methodology is composed of two models: one for market adjustment and the other for merit adjustment. The market adjustment is handled through goal programming and the merit adjustment through data envelopment analysis (DEA).

The approach when applied to a sample of faculty salaries shows that the adjusted salary of each faculty member is higher than his/her current actual salary, and each faculty member in the particular discipline deserves a salary increase that reflects market demand and merit factors.

The DEA model used in this research does not impose restrictions on the weights. Realistically, one may impose bounds on the weights and exclude unreasonable solutions from DEA analysis and also set multiple goals instead of the single goal used in the goal programming model.

Based on a goal programming model that addresses the market demand and a DEA model that addresses the merit‐based performances, this methodology may be implemented as a solution procedure for restructuring faculty salaries.

The novelty in this approach is that DEA is being used as a benchmarking technique for merit adjustment of faculty salaries. In that sense, this research work may be the first, where benchmarking has been used in “faculty salary equity adjustment.”

Automakers are engaged in manufacturing both efficient and inefficient green cars. The purpose of this paper is to categorize efficient green cars and inefficient green cars followed by improving efficiencies of identified inefficient green cars for distribution fitting.

The authors have used 2014 edition of secondary data published by the Automotive Research Centre of the Automobile Club of Southern California. The paper provides the methodology of applying data envelopment analysis (DEA) consisting of 50 decision-making units (DMUs) of green cars with six input indices (emission, braking, ride quality, acceleration, turning circle, and luggage capacity) and two output indices (miles per gallon and torque) integrated with Monte Carlo simulation for drawing significant statistical inferences graphically.

The findings of this study showed that there are 27 efficient and 23 inefficient DMUs along with improvement matrix. Additionally, the study highlighted the best distribution fitting of improved efficient green cars for respective indices.

This study suffers from limitations associated with 2014 edition of secondary data used in this research.

This study may be useful for motorists with efficient listing of green cars, whereas automakers can be benefitted with distribution fitting of improved efficient green cars using Monte Carlo simulation for calibration.

The paper uses DEA to empirically examine classification of green cars and applies Monte Carlo simulation for distribution fitting to improved efficient green cars to decide appropriate range of their attributes for calibration.

The purpose of this paper is to propose two performance-based capital budgeting (PBCB) approaches. The proposed approaches aim to assign limited capital within different firms based on their performance. These approaches have been applied to capital budgeting process of the technical and vocational training schools of Semnan Province, Iran for the fiscal year 2014–2016. Although the current capital of each decision-making unit (DMU) is secured in the first approach, the second approach provides possibility of sharing the capital between DMUs.

Data envelopment analysis which is a broadly used mathematical programming technique for assessing performance of DMUs is utilized for the first phase of both approaches. The proposed models are based on linear programming. Different scenarios are presented and their pros and cons for the capital budgeting process are discussed.

The proposed approaches are applied to capital budgeting process for a fiscal year of technical and vocational training schools of Semnan Province, Iran. The budget allocation of the previous year has been found to be non-optimal in terms of budget consuming. This emphasizes reconsideration of budget allotment within schools. The results show a high potential for producing more outputs. The second approach that provides the possibility of sharing and realloting of budget between schools based on their performance may be crucial for those schools that are not performing efficiently because there is possibility of losing budget in each given year in contrast with the previous years.

This paper proposes two linear programming-based approaches for the PBCB. The author not only deals with static framework but also proposes dynamic structured models. Using performance-based budgeting in organizations has been emphasized by authorities in Iran for many years. Using the proposed approaches, different suggestion and policy recommendation for decision makers in process of capital budgeting process within period of study are provided for technical and vocational training schools of Semnan Province, Iran.