Lean Six Sigma is systematic and excellent operation management approach aimed to en-hance overall production through the elimination of waste and improve customer satisfaction. In the present case study, automobile part manufacturing unit has been selected and found 22% wastage in manufacturing steps resulting reduction in production. The primary objective of this research is to find the main reasons of production wastage and recommend corresponding remedies to counter the wastage reasons. For this purpose, Lean Six Sigma approach with the help of DMAIC methodology is implemented to observe and examine different root causes of the frame lugs production losses and rejection problems. The result reveals that the movement of material and employee are critical issues for wastage of resources, ultimately affecting production of industry. Lastly, the possible solutions are to be advised to tackling these issues. The successful execution of the proposed solutions show 7% growth in the production of the component which saves annually INR 15, 64, 056.

The application of material handling robots (MHRs) has been commonly observed in flexible manufacturing systems (FMS) for efficient material handling activities. In order to gain maximum throughput, minimum tardiness from the minimum investment of funds for the material handling activities, it is important to determine the optimum numbers of MHRs required for efficient production of jobs in the FMS. In the present work, the requirement of MHRs is optimized for different FMS layouts by using a heuristic procedure. Initially, a mathematical model is proposed to identify the MHRs requirement to perform the material handling activities in the FMS, later on, the model is optimized by simulating a novel heuristic procedure to find the required optimum number of MHRs in the FMS. The proposed methodology is found to be generic enough and can also be applied in various industries employing the MHRs.

Construction operations are blamed as one of important causes of green gas effects. During the past few decades, there has been tremendous efforts to reduce the negative effects of construction operations on environment. This paper presents an application of data envelopment analysis to measure the relative efficiencies of the researches accomplished by various countries around the world on green construction or green management. The study expects countries that produce higher amount of CO2 accomplish more quality research articles. To do this, the study performs a survey using three keywords; namely “green construction”, “green building” and “sustainable building” on Scopus database and found 8692 articles over the period 1965-2019 where one of these keywords, at least, was used in their abstracts, keywords or titles. We also use three measures of h-index, I-10 and total publications repre-sentative of quality and quantity of the outputs produced by researchers. The study considers 28 countries responsible for at least 90% of CO2 emission for measuring the relative efficiency program using data envelopment analysis. The results indicate that Hong Kong was the only efficient country followed by Singapore with relative efficiency of 0.67.

In this paper, a backward approach is proposed for maximizing net present value (NPV) in multi-mode resource constrained project scheduling problem while assuming discounted positive cash flows (MRCPSP-DCF). The progress payment method is used and all re-sources are considered as pre-emptible. The proposed approach maximizes NPV using unscheduled resources through resource calendar in backward mode. For this purpose, a Genetic Algorithm is applied to solve experimental cases with 50 variables and the results are compared with forward serial programming method. The remarkable results reveal that the backward approach is an effective way to maximize NPV in MRCPSP-DC while activity splitting is allowed. The algorithm is flexible enough to be used in real project.

Maintenance engineering plays an important role in management of oil and gas projects. This paper presents a dynamic programming approach for resource allocation in oil and gas projects. The study presents a dynamic programming approach to allocate human resources for repairment of oil and gas equipment. Many heavy equipment normally needs to be repaired on predetermined scheduled and the process normally takes days or even weeks. The process can be divided into three stages of disassembling the equipment, executing the repairment and assembling the equipment. The proposed model of this paper models the problem into a classical dynamic programming and using a real-world case study, the implementation of the proposed model is described.

An important function of the project management is to optimize the project in various phases and at different levels. From sourcing and allocation to scheduling and even dealing with uncertainties, the science of operation research (OR) has played an important role in this area. So far, many papers have been published using the optimization science to make various decisions regarding the project management. This study aims to investigate all papers published on the application of optimization in the project management from 1940 to 2019 and shows: a) how the trend has changed over this 79 years period, b) to what direction the trend has changed, c) determines the interesting topics of the recent years, and d) which subjects are more attractive as future studies as the applications of the optimization techniques in the project management.

The numerous studies on comparing and ranking fuzzy numbers clear that this task is still young. However, the observation that many papers do not hesitate to use some incorrect ranking methods in risk analysis problems, encouraged the author to point out the shortcoming of some of these methods. In this note, we review briefly the methods for ranking fuzzy number in risk analysis.

The purpose of this paper is to examine the existing risks for the offshore project and risk weighting using the fuzzy best worst method (FBWM). In offshore oil projects, we face six major risks. Each of these risks is divided into smaller risks leaving us to have a total of 34 risks. Some of these risks are internal and some are external risks. In this method, first, the experts of this field determined the best and the worst type of risk. Then, using the experts’ opinions, the study compared the remaining risks with the two selected risks and the other weights are determined. In our survey, “Technical Risk and Project Execution” is the most important risk factor followed by “Political Risk and Sanctions”, “Market risk”, “Manage-ment risk”, “Financial risk and currency fluctuations” and “Environmental risk”.

Project portfolio selection is a major issue in organizations, which involves a complex pro-cess from the first step to the end step of project selection. In order to pursue the organizations’ financial and physical constraints, choosing the most suitable portfolio of projects is necessary. Organizations have a number of constraints to select a portfolio of projects that must be considered. Different interactions are considered between the proposed projects. For example, resource constraints, the possibility of transferring liquidity resources that are not consumed over a period to the next, the interdependence between projects, and the synergistic impact of the projects are important. In this paper, an exponential function is considered for synergistic impact of the projects that make the problem more similar to the real-world problems. An illustrative example is used to demonstrate the appropriate application of the proposed model.

This paper addresses the stochastic permutation flow shop problem (SPFSP) in which the stochastic parameters are the processing times. This allows the modeling of setups and machine breakdowns. Likewise, it is proposed a multi-objective greedy randomized adaptive search procedure (GRASP) coupled with Monte-Carlo Simulation to obtain expected makespan and expected tardiness. To manage the bi-objective function, a sequential combined method is considered in the construction phase of the meta-heuristic. Moreover, the local Search combines 2-optimal interchanges with a Pareto Archived Evolution Strategy (PAES) to obtain the Pareto front. Also, some Taillard benchmark instances of deterministic permutation flow shop problem were adapted in order to include the variation in processing times. Accordingly, two coefficients of variation (CVs) were tested: one depending on expected processing times values defined as twice the expected processing time of a job, and a fixed value of 0.25. Thus, the computational results on benchmark instances show that the variable CV provided lower values of the expected makespan and tardiness, while the con-stant CV presented higher expected measures. The computational results present insights for further analysis on the behavior of stochastic scheduling problems for a better approach in real-life scenarios at industrial and service systems.