International Journal of Industrial Engineering and Productional Research
Volume:30 Issue: 4, Jan 2020

Safety has been established to maintaining and improving the productivity level of aviation industry with derivable benefits in terms of wealth and reputation. Despite, the investment in aviation; safety is generally recognized as an incurred cost, leading to compliance approach. This may be due to dearth of literature on generally agreed proactive safety performance indicator to justify the huge investment. This study therefore developed predictive models that evaluate runway safety investment strategies and predict the overall performance of the aviation system using System Dynamics stock and flow diagram.  An interactive computer programme of the models was written using Java programming language. The set of dynamic equations for predicting number of runway accidents, preventions, monetary savings/losses and safety programme breakeven period were the safety performance measures. Runway safety intervention effectiveness factor and level of budget implementation were system policy parameters used to control the mechanism of the runway safety system. Relevant data were obtained from Federal Aviation Authority, Nigeria to validate the models. Twenty-nine runway safety quantities were identified. The dynamic equations for number of runway accident preventions and monetary savings/losses exhibited exponential growth while number of runway accidents, exhibit exponential decay. The results of the simulation runs showed no significant difference with real life situations; thus the models can serve as useful tools to effectively and efficiently manage the behaviour and performance of runway safety programme.

There is still a great deal of attention in cellular manufacturing systems and proposing capable metaheuristics to better solve these complicated optimization models. In this study, machines are considered unreliable that life span of them follows a Weibull distribution. The intra and inter-cell movements for both parts and machines are determined using batch sizes for transferring parts are related to the distance traveled through a rectilinear distance. The objectives minimize the total cost of parts relocations and maximize the processing routes reliability due to alternative process routing. To solve the proposed problem, Genetic Algorithm (GA) and two recent nature-inspired algorithms including Keshtel Algorithm (KA) and Red Deer Algorithm (RDA) are employed. In addition, the main innovation of this paper is to propose a novel hybrid metaheuristic algorithm based on the benefits of aforementioned algorithms. Some numerical instances are defined and solved by the proposed algorithms and also validated by the outputs of exact solver. A real case study is also utilized to validate the proposed solution and modeling algorithms. The results indicate that the proposed hybrid algorithm is more appropriate than the exact solver and outperforms the performance of individual ones.

One of the key issues in the studies on customer relationship management (CRM) and modalities of marketing budget allocation is to calculate the customer’s lifetime value and applying it to macro-management decisions. A major challenge in this sector pertains to making calculations so as to incorporate the possibility of changes in the behavior of customers with the turn of time in the model. In this article, we first classify the customers of ISACO using clustering techniques and use multilayer neural network to calculate the monetary value of each group of customers during the specific period of time. Then, we use the Markov chain approach to develop a model for calculating the lifetime value of ISACO’s customers by taking into consideration the possibility of changes in their behavior in future time periods. In this study, a new approach has been used to estimate the parameters of the model proposed for calculating the future lifetime value of ISACO’s customers. This method takes into consideration the possibility of changes in the customer behavior throughout their interaction with the company. The results obtained here may be used in the allocation of marketing budget and adoption of macro-management decisions to envisage various projects for customers with different lifetime value.

In this paper healthcare condition of 31 countries that are the members of Organization for Economic and Co-operative Development (OECD) is measured by considering 14 indicators that are relevant to three main pillars of sustainable development.

To estimate the efficiency scores, Principle Component Analysis-Data Envelopment Analysis PCA-DEA additive model in both forms of envelopment and multiplier is used to determine efficiency scores and also to define benchmarks and improvement plan for the inefficient countries. Then Decision Tree Analysis is also used to realize that which factors were the most influential ones to make a county an efficient Decision Making Unit (DMU).

According to the PCA-DEA additive model, among 31 OECD countries, 16 countries have become inefficient, that USA have taken the lowest efficiency score, and among efficient countries Iceland could be considered as a paragon which has the highest frequency between the countries that are defined as the benchmarks. Decision tree analysis also show that exposure to PM2.5 is an influential factor on the efficiency status of countries.

This research gives an insight about the sustainable development and healthcare system and show the impressive effect of environmental and social factors like: exposure to PM2.5 and water quality, population insurance coverage, and AIDS on the healthcare efficiency of OECD countries

In this research, we analyze a supply chain involving two competing manufacturers that sells their product through two common competing retailers. The manufacturers’ products are the same but with different brand in market. The retailers face stochastic demand where demand is a decreasing function of price with additive uncertain part. Manufacturers compete on supplying orders where retailers compete on selling price. Each manufacturer set wholesale price contract with retailers similarly. We examine supply chain coordination with wholesale price contract under competition and demand uncertainty. The analytical results show that under coordination condition, manufacturers do not obtain any positive profit and consequently the retailers intend to increase wholesale prices. On the other hand, manufacturers can increase wholesale prices until the retailers’ profit becomes zero. Hence, with a numerical study for actual cases, it is found that changing demand sensitivity and competition intensity affect the optimal decisions of ordering and pricing. Moreover, increasing in competition sensitivity, increase the supply chains’ efficiency, stocking level and selling price. The concluding remarks show that further investigation is required for possibility of coordination under competition by other contractual mechanisms.

The number of nonconforming items in a sample is monitored using the fraction defective known as the np-chart. The performance of the np-chart in Phase II depends on the accuracy of the estimated parameter in Phase I. Although taking large sample sizes ensures the accuracy of the estimated parameter, it can be impractical for attributes in some cases. Recently, the traditional c-chart and the np-chart with some adjustments have been studied to guarantee the in-control performance. Due to technology progresses, researchers have faced high-quality processes with a very low rate of nonconformity, for which traditional control charts are inadequate. To ameliorate such inaccuracy, this study develops a new method for designing the np-chart, such that the in-control performance is guaranteed with a pre-defined probability. The proposed method uses Cornish-Fisher expansions and the bootstrap method to guarantee the desired conditional in-control average run length. Through a simulation study, this study shows that the proposed adjustments improve the np-charts’ in-control performance.

The present research aims at investigating feasible improvements by determining optimal number of stations and required workforce using a simulation system, with the ultimate goal of reaching optimal throughput while respecting the problem constraints in an attempt to achieve maximum feasible performance in terms of production rate. For this purpose, similar research works were investigated by reviewing the relevant pieces of the literature on simulation model, car signoff station, and techniques for optimizing the station, and the model of the car signoff unit was designed using data gathering tools, existing documents, and actual observations. Subsequently, the model was validated by means of descriptive statistics and analysis of variance (ANOVA). Furthermore, available data was analyzed using ARENA and SPSS software tools. In a next step, potential improvements of the unit were identified and the model was evaluated accordingly. The results indicated that some 80% of the existing problems could be addressed by appropriately planning for human resources, on-time provision of the required material at reworking units, and minimization of transportation at the stations that contributed the most to the working queue. Thus, the waiting time per station could be minimized while increasing the production rate.

Sustainability performance assessment is a significant aspect of making sustainable decisions for organizations. Measuring sustainability performance of firms in a time span, covered in several periods, leads to more rational decision-making and planning by managers. Furthermore, in many application fields, there are discrete and bounded measures. However, there has been no systematic effort to analyze sustainability performance of Decision-Making Units (DMUs) in multiple periods of time when discrete and bounded factors are presented. Therefore, approaches based on Data Envelopment Analysis (DEA) are proposed in this paper to tackle this problem. To illustrate this issue in more detail, the performance of systems is measured for all dimensions, including economic, social, and environmental ones and for each period. Moreover, the overall multi-period sustainability performance and sustainability performance of each period are estimated using the suggested one-stage methods. Then, the sustainability performance is investigated for conditions in which internal relationships among economic, social, and environmental indicators are presented. Moreover, sustainability performance changes and performance changes of dimensions are addressed. An example and a case study are provided to explain our proposed approach. Results show that the introduced ideas are practical and effective.