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This study evaluates the efficiency and productivity change of 39 electricity distribution companies in Iran over the period 2005-2014. For purposes of electricity management and utilization of scarce resources, Iran’s 33 provinces have been classified into five regions by the Ministry of the Interior. Analyzing the efficiency of distribution companies across these regions yields significant understanding of these resources and helps policymakers to generate more informed decisions.

The proposed method of this study develops nonparametric data envelopment analysis (DEA) with the consideration of geographic classification, size and type of company. At the first stage, a DEA model is used to estimate the relative technical efficiency and productivity change of these companies. At the second stage, distributions of efficiency improvements are examined based on geographic classification, size and type of the company type. A stability test is also conducted to verify the proposed model’s robustness.

The results demonstrate that the average technical efficiency of the companies increased during the years 2006-2009, but decreased during 2010-2014. The productivity measurement reveals that low efficiency change was the largest contributor to the small increase in productivity change rather than technology change. In addition, testing the hypothesis that the large and small companies have statistically the same efficiency scores revealed no statistical difference among them. Moreover, another test did not detect a difference among companies at the urban and provincial levels.

By applying this approach, policymakers and practitioners in the power industry at the country and corporate level can effectively compare the efficiency and productivity changes among electricity distribution companies, and therefore generate more informed decisions.

The paper’s novel concept applies DEA to Iran’s electricity distribution companies and analyzes them by examining geographic classification, size and the type of the companies. In addition, a stability test is conducted and productivity changes are estimated.

Integrating project scheduling and material ordering problems is vital in realistically estimating project cost and duration. Also, the quality level of materials is important as one of the key project success factors.

In this paper, a three-objective mathematical model is presented for green project scheduling with materials ordering problems considering rental resources. The first objective is to minimize the total cost of the project site and logistics. The second objective is to minimize the environmental impacts of producing materials and the third objective is to maximize the total quality of materials. Since costs trigger several challenges in projects, cost constraints are considered in this model for the first time and also the cost of delay in supplying of materials by the suppliers has been deducted from the project costs. Subsequently, the model was implemented in a real case and solved by the Lagrangian Relaxation algorithm as an exact method on GAMS software for model validation.

Based on sensitivity analysis of some parameters, the findings indicate that the cost constraint and lead time have considerable effects on the project duration. Also, integrating project scheduling and material ordering improves the robustness of the project schedule.

The primary contributions of the present research can be stated as follows: considering the cost constraints in the project scheduling with material ordering problem, incorporating the rental resources and taking the quality levels of materials as well as the environmental impacts into account.

The purpose of this study is to develop an integrated model for the stochastic multiproject scheduling and material ordering problems, where some of the prominent features of offshore projects and their environmental-degrading effects have been embraced as well. The durations of activities are uncertain in this model. The developed formulation is tri-objective that seeks to minimize the expected time, total cost and CO₂ emission of all projects.

A new version of the multiobjective multiagent optimization (MOMAO) algorithm has been proposed to solve the amalgamated model. To empower the MOMAO, various procedures of this algorithm have been modified based on the multiattribute utility theory (MAUT) technique. Along with the MOMAO, this study has employed four other meta-heuristic methodologies to solve the model as well.

The outputs of the MOMAO have been put to test against four other optimizers in terms of convergence, diversity, uniformity and computation times. The results of the Mean Ideal Distance (MID) metric have revealed that the MOMAO has strongly prevailed its rival optimizers. In terms of diversity of the acquired solutions, the MOMAO has ranked the first among all employed optimizers since this algorithm has offered the best solutions in 56.66 and 63.33% of the test problems regarding the diversification metric and hyper-volume metrics. Regarding the uniformity of results, which is measured through the spacing metric (SP), the MOMAO has presented the best SP values in more than 96% of the test problems. The MOMAO has needed more computation times in comparison to its rivals.

A real case study comprising two concurrent offshore projects has been offered. The proposed formulation and the MOMAO have been implemented for this case study, and their effectiveness has been appraised.

Very few studies have focused on presenting an integrated formulation for the stochastic multiproject scheduling and material ordering problems. The model embraces some of the characteristics of the offshore projects which have not been adequately studied in the literature. Limited capacities of the offshore platforms and cargo vessels have been embedded in the proposed model. The offshore platforms have spatial limitations in storing the required materials. The vessels are also capacitated and they also have limited shipment capacities. Some of the required materials need to be transported from the base to the offshore platform via a fleet of cargo vessels. The workforces and equipment can become idle on the offshore platform due to material shortage. Various offshore-related costs have been integrated as a minimization objective function in the model. The cargo vessels release CO₂ detrimental emissions to the environment which are sought to be minimized in the developed formulation. To the best of the authors' knowledge, the MOMAO has not been sufficiently employed as a solution methodology for the stochastic multiproject scheduling and material ordering problems.

The purpose of this paper is to present an integrated algorithm composed of data envelopment analysis (DEA), corrected ordinary least squares (COLS) and principal component analysis (PCA) to estimate efficiency scores of electricity distribution units.

Several DEA and COLS models are prescribed and their results are verified and validated by the algorithm. To calculate efficiency scores, three standard internal consistency conditions between DEA and COLS results are checked by the algorithm. If these conditions are satisfied, DEA is chosen as the superior model because it could be used for optimization as well. Otherwise, the geometric mean of DEA and COLS model is used as the final efficiency scores.

The algorithm of this paper may be easily applied to decision‐making units because of its robustness (combined DEA‐COLS input and output) and validity gained through PCA.

The integrated approach has several unique features which are: verification and validation mechanism by PCA, consideration of internal consistency conditions between DEA and COLS and consolidation of DEA and COLS for improved ranking given consistency conditions are violated.

This paper seeks to present an integrated principal component analysis (PCA) data envelopment analysis (DEA) framework for assessment and ranking of manufacturing systems based on equipment performance indicators.

The integrated framework discussed in this paper is based on PCA and DEA. The validity of the integrated model is further verified and validated by numerical taxonomy (NT) methods.

The results of the integrated PCA DEA framework show the ranking of sectors and weak and strong points of each sector with regard to equipment and machinery. Moreover, a non‐parametric correlation method, namely, Spearman correlation experiment shows high level of correlation among the findings of PCA, DEA and NT. Furthermore, it identifies which indicators have major impacts on the performance of manufacturing sectors.

To achieve the objectives of this study, a comprehensive study was conducted to locate all economic and technical indicators which influence equipment performance. These indicators are related to equipment productivity, efficiency, effectiveness and profitability. Standard factors such as down time, time to repair, mean time between failure, operating time, value added and production value were considered as shaping factors. The manufacturing sectors are selected according to the format of International Standard for Industrial Classification.

The modeling approach of this paper could be used for ranking and analysis of other sectors in particular or countries in general.

The purpose of this paper is to compile and prioritize necessary strategies to produce electrical energy from fossil resources in the Iranian power industry.

In this study, the strengths, weaknesses, opportunities and threats (SWOT) and internal and external (IE) matrix have been applied in order to illustrate the main path long term planning and development. The benchmarking process has been performed by analyzing seven successful developing countries.

The results imply that Iran should implement six main strategies for increasing productivity and efficiency, reducing costs, renewing structure and making private, applying IT, increasing productivity of human resources, and environment protection. Benchmarking study shows that Iran has to speed up the environment protection program.

Although the use of the SWOT and IE matrix provide great advantages, the analysis should apply BSC in developing execution plans.

The results encourage the Iran power industry since five out of six main strategies have already been chosen for long‐term development.

The paper has provided considerable evidence to suggest that the proposed strategies are in line with benchmarked countries policies in the power industry.

The present work seeks to determine the optimal delivery schedule of equipment at a project site in the backdrop of limited storage space, at a minimum cost, and without disturbing the overall project schedule. In addition, the optimized delivery schedule helps in minimizing the fluctuating requirements of space at the project site across the entire project lifespan.

The study is carried out at a Steel plant operating in a constrained space but undergoing a production capacity expansion. The problem motivated us to explore the possibility of postponing the delivery dates of certain equipment closer to the erection dates without compromising on the project schedule. Given the versatility of linear programming models in dealing with such schedule optimization problems, the authors formulated the above problem as a Zero-One Integer Linear Programming problem.

The model is implemented for all the new equipment arriving for two major units – the Hot Strip Mill (HSM) and the Blast Furnace (BF). It generates an optimized delivery schedule by delaying the delivery of some equipment by a certain number of periods, without compromising the overall project schedule and at a minimum storage cost. The average space utilization increases by 25.85 and 14.79% in HSM and BF units respectively. The fluctuations in space requirements are reduced substantially in both units.

The study shows a timeline in the form of a Gantt chart for the delivery of equipment, storage of equipment across different periods, and the number of periods for which the delivery of certain equipment needs to be postponed. The study uses linearly increasing storage costs with the increase in the number of periods for storage of the equipment in the temporary shed.

1. Determined the optimal delivery schedule of the equipment in a project environment in the backdrop of limited storage space in the project site.

2. Formulated the above problem as a Zero-One Integer Linear Programming (ILP) problem.

3. The average space utilization has increased by 25.85 and 14.79% in HSM and BF units respectively.

4. The optimized delivery schedule helps in reducing the fluctuations in space requirements substantially across the entire lifespan of the project.

5. The timeline of delivery of equipment, storage of equipment across different periods and periods of postponement of the equipment are shown in the form of a Gantt Chart.

Determined the optimal delivery schedule of the equipment in a project environment in the backdrop of limited storage space in the project site.

Formulated the above problem as a Zero-One Integer Linear Programming (ILP) problem.

The average space utilization has increased by 25.85 and 14.79% in HSM and BF units respectively.

The optimized delivery schedule helps in reducing the fluctuations in space requirements substantially across the entire lifespan of the project.

The timeline of delivery of equipment, storage of equipment across different periods and periods of postponement of the equipment are shown in the form of a Gantt Chart.

This paper aims to investigate the performance of OECD countries' long-term productivity during the period of 1975–2018.

This study employed different approaches to evaluate how efficiency scores vary with changes in inputs and outputs: Data Envelopment Analysis (CRS, VRS and FDH), TOPSIS and TOPSIS of these scores.

The findings suggest that, during the period of this study, countries with higher freedom of religion and with Presidential democracy regimes are positively associated with higher productivity.

To the best of the authors’ knowledge, this is the first study that uses efficiency models to assess the productivity levels of OECD countries based on several contextual variables that can potentially affect it.

Food manufacturing supply chain systems are the most relevant wheels of the world economy since they provide essential products supporting daily life. Nevertheless, various supply inefficiencies have been reported to compromise food safety in different regions. Sustainable supplier management and digitalization practices have become cornerstone activities in addressing these shortcomings. Therefore, this paper proposes an integrated method for sustainability management in digital manufacturing supply chain systems (DMSCS) from the food industry.

The Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP) was used to weigh the criteria and subcriteria under uncertainty. Second, the Intuitionistic Fuzzy Decision-Making Trial and Evaluation Laboratory (IF-DEMATEL) was applied to determine the main DMSCS sustainability drivers whilst incorporating the expert's hesitancy. Finally, the Combined Compromise Solution (CoCoSo) was implemented to pinpoint the weaknesses hindering DMSCS sustainability. A case study from the pork supply chain was presented to validate this method.

The most important criterion for DMSCS sustainability management is “location” while “manufacturing capacity” is the most significant dispatcher.

This paper presents a novel approach integrating IF-AHP, IF-DEMATEL, and CoCoSo methods for sustainability management of DMSCS pillaring the food industry.

The purpose of this study is to benchmark the performance of state-owned coal-fired power plants (CFPPs) and test whether plant-specific knowledge in terms of quality of coal, size, age and make of plant contribute to an improvement in plant efficiency.

The methodology that is utilized in the study follows a nonparametric approach of data envelopment analysis (DEA) with sensitivity analysis and Tobit regression model. The input-oriented DEA models are applied to evaluate the overall, pure technical and scale efficiencies of the CFPPs. Further, slack analysis is conducted to identify modes to improve the efficiency of the inefficient plants. Sensitivity analysis based on peer count and the removal of variables is carried out to identify the benchmark power plant. Through Tobit and bootstrap-truncated regression model, the paper investigates whether a plant's specific knowledge influences its efficiency.

The DEA analysis demonstrates that nine plants are technically purely efficient.The slack analysis reveals that reducing the consumption of oil is the most effective way to improve the efficiency of inefficient plants. Mattur plant is the benchmark for most of the inefficient plants. Regression result suggests that quality of coal and size of plant significantly affect the inefficiency of the sample plants. Bharat Heavy Electrical Limited MAKE plant achieved higher efficiency in comparison to mixed MAKE.

This study is one of the few published studies that benchmark the performance of state-owned CFPPs. This research carried out taking some new uncontrollable parameters of power plant utilities of India. Research work also identifies the possible causes of inefficiency and provides measures to improve the efficiency of the inefficient power plant.