Risk assessment is an important factor in project cost management. This study addresses the risks associated with a 900 meter long bridge construction project. The risk of a bridge construction project is assessed to limit and quantify the impact on the project. The impact of various risks was investigated to express the impact on the total project contract value at the estimation stage. A project risk analysis is introduced to assess the percentage of risk attributed to the total cost. After assessing the impact of risks on cost using Expected Monetary Value (EMV), a new approach of including uncertainties on risk analyses, using description (C, Q, K), is discussed and its advantages and shortcomings are highlighted. Risk is then assessed at several stages of project execution during the budgeting phase, and risk-based project value (RPV) is used to assess the value of the project at each stage. RPV usually increases as the project progresses towards its goals. Due to this property, the RPV of the entire project can be categorized into the contribution value (CV) of each activity. The CV of an activity is defined as the increase in RPV after each activity completes successfully. The results highlight the positive impact of successfully completing the activities associated with the highest risk. In addition, practical solutions for risk assessment and analysis of bridge construction projects are provided for use by bridge construction contractors, project managers and project management engineers.
Process capability indices play a vital role in evaluating the conformity of the process properties to the required specifications. Process incapability indices are created by transformation in the process capability indices, leading to the separation of information related to the process accuracy and precision. This separation of information can be very beneficial to specify whether the process is capable or not and to detect deviations in the production processes that produce high-tech products, such as the electronics industry. The main goal of this study is to propose a process incapability index by considering the measurement error for processes with multivariate quality characteristics. The efficiency of this index is then examined by a numerical example using Monte Carlo simulation method. Moreover, the performance of proposed approach is compared with the case where there is no measurement error. In addition, as a practical example, this index is compared with a number of recently proposed indices in the literature, and sensitivity analysis is conducted, as well. The simulation results showed that the measurement error has a significant effect on process capability and incapability indices. Therefore, we strongly suggest that the measurement error has to be considered in the process analysis.
Profile monitoring is one of the new statistical quality control methods used to evaluate the functional relationship between the descriptive and response variables to measure the process quality. Most of the studies in this field concern processes whose response variables follow the normal distribution function, but in many industries and services, this assumption is not true. The presence of outliers in the historical data set could have a deleterious effect on phase I parameter estimation. Therefore, in this paper, we propose a robust cluster-based method for estimating the parameters of generalized linear profiles in phase I. In this method, the effect of data contamination on estimating the generalized linear model parameters is reduced and as a result, the performance of T^2 control charts is improved. The performance of this method has been evaluated for two specific modes of generalized linear profiles, including logistic and Poisson profiles, based on a step shift. The simulation results indicate the superiority of this cluster-based method in comparison to the non-clustering method and provide a more accurate estimation of the parameters.
This work presents a quantitative approach on the basis of Dynamic Bayesian Network to model and evaluate the maintenance of multi-state degraded systems and their functional dependencies. The reliability and the availability of system are evaluated taking into account the impact of maintenance repair strategies (perfect repair, imperfect repair and under condition-based maintenance (CBM)). According to transition relationships between the states modeled by the Markov process, a DBN model is established. Using the proposed approach, a DBN model for a separator Z1s system of Sour El-Ghozlane cement plant in Algeria is built and their performances are evaluated. Through the result of diagnostic, for improving the performances of separator, the components E, R and F should given more attention and the results of prediction evaluation show that in comparing with perfect repair strategy, the imperfect repair strategy cannot degrade the performances of separator, whereas the CBM strategy can improve the performances considerably. These results show the utility of this approach and its use in the context of a predictive evaluation process, which allows to offer the opportunity to evaluate the impact of the decisions made on the future performances measurement. In addition, the maintenance managers can optimize and improve maintenance decisions continuously.
The purpose of the current research is to present a location-routing-inventory model for perishable products. The presented model is applied in a two-stage structure. The first-stage decisions confirm the establishment of distribution centers, whereas the second-stage decisions determine the other variables of the problem. For a better management of inventory, it has been used under the names of fresher first and older first policies. In the fresher first policy, the fresher items have a priority to be sent to the customer, whereas in the older first policy, the items with a longer age have the priority to be sent to the customer. The summary of the results of the models demonstrates that among the free, fresher first, and older first policies, it is the free policy that offers a higher profit function to the customer than the other two policies since it is more flexible and general,and encompasses these two extremes. The free policy lets the model determine which items to sell at any given time period in order to maximize profit.Moreover, in the older first policy, since the older items reach the customers sooner than the other items, the number of the expired items is reduced. However, this policy brings the lowest revenue to the customer. In the fresher first policy, since the fresher items are sold first and then the older items are sold, the number of the expired items is increased along the customer horizon. Nevertheless, the customer obtains more revenue compared with the older first policy.
Manufacturing processes can produce imperfect items, and inventory disruptions may occur in the process, causing shortages. We propose a model to explore these real-life scenarios. This paper offers a production inventory model considering the concept of inspection of the produced batches and disposal of defective items in an Economic Production Quantity (EPQ) model with partial backorders and discount on batches that are imperfect but not defective. Furthermore, the proposed model considers the holding cost of these imperfect items while they are not sold. An algorithm for finding the optimal solution is presented, and a numerical example is provided to perform a sensitivity analysis. We conclude that the setup cost followed by the holding cost are the costs that have the most significant impact on the total cost. Although the inspection cost has been added to the model, this cost has little effect on the total cost and does not increase it significantly.
Sawmills are an important part of the forest supply chain, and as at any company, their production planning is highly complex. Planning in the remanufacturing area, in terms of its economic contribution to the sawmill and the supply chain, has not been studied in the scientific literature. The goal of this study was to develop and solve a mixed-integer linear programming model by employing an efficient allocation of cutting patterns on in-stock logs to maximize profits. To quantify the impact of an appropriate use of raw materials in the remanufacturing area in a sawmill, real and generated data were used. The model considers fixed and variable production costs, the availability of raw material, the capacity of the processes, the sale price of the products and the demand, for a process period of one month. The proposed compact mixed-integer linear programming model was solved using the commercial solver IBM ILGO CPLEX12.8. It was determined that the additional margin in USD earned in the remanufacturing area for the considered scenarios amounted to an average of 21.6%. The proposed method facilitates evaluating the economic contribution of remanufacturing while identifying bottlenecks and assessing proposed scenarios.
Inappropriate gate assignment has some consequences such as flight delays, inefficient usage of resources and customer’s dissatisfaction. Airports are service sectors and provide services to their customers. Passengers and airlines are two main customers of an airport. Current research presents a novel mathematical model for gate assignment problem to minimize customers’ dissatisfaction. In other words, passengers walking time as well as flight delays are minimized in as objective function. In this model, transit and non-transit passengers and also arrival and departure flights are considered. Moreover, operational safety constraints are inserted to avoid collision of large aircraft. The proposed model is solved by Benders decomposition approach. The model is applied in San Francisco International Airport for a 24-hour horizon. The result shows that walking time of non-transit passengers, walking time of transit passengers, delay in departure flights and delay in arrivals include 74.55, 23.32, 0.96 and 1.17 percent of the objective function, respectively.
During the last three decades, the concept of integrated decision making in the supply chain becomes one of the essential aspects of supply chain management (SCM). This concept explores the interdependence between facility location, flow allocation between facilities, transportation system structure, and inventory control system. This study presents a new form of the location-routing problem of facilities under uncertainty in a supply chain network for deteriorating items through taking environmental considerations, cost, and procurement time and customer satisfaction into account, to simultaneously minimize total system costs, maximal delivery time and emissions across the entire network and maximize customer satisfaction. The research problem is formulated in a mixed-integer nonlinear multi-objective programming. In order to solve the model, the combination of the two Benders decomposition algorithm and Lagrange multiplier liberalization, as well as the combination of red deer algorithm and annealing simulation, was proposed. For validation, the results of the proposed algorithm in different size examples are compared with the results of the exact method solution by MATLAB software. The mean error of the proposed algorithm for the objective function is less than 4% compared to the exact method in solving the sample problems. Besides, the results of the algorithm performance are investigated based on standard indices. The computational results show the efficiency of the algorithm for a wide range of problems with different sizes. The location decisions are interdependent, and the process of determining the optimal values of these variables interact together, which can lead to an optimal system.
In this paper, a data mining approach is proposed for duration prediction of the town trips (travel time) in New York City. In this regard, at first, two novel approaches, including a mathematical and a statistical approach, are proposed for grouping categorical variables with a huge number of levels. The proposed approaches work based on the cost matrix generated by repetitive post-hoc tests for different pairs. Then, a random forest model is constructed for the prediction of the type of trips, short or long. Finally, based on the trip type and each of the mathematical and statistical approaches, separate artificial neural networks (ANN) are developed to predict the duration time of the trips. According to the results, the mathematical approach performs better and provides more accurate results than the statistical approach. In addition, the proposed methods are compared with some other methods in the literature in which the results show that they perform better than all other methods. The RMSE of mathematical and statistical approaches is, respectively, 4.23 and 4.27 minutes for short trips, and the related value is 9.5 minutes for long trips. In addition, a modified version of the nearest neighborhood approach, entitled modified nearest neighborhood (MNN), is proposed for the prediction of the trip duration. This model resulted in accurate predictions where its RMSE is 4.45 minutes.
This paper analyzes the labor–employer relations during conditions that lead to strike using an evolutionary game and catastrophe theory. During a threat to strike, the employers may accept the whole or only a part of the demands of labors and improve the work conditions or decline the demands, and each selected strategies has its respective costs and benefits. The threat to strike action causes the formation of a game between the strikers and employers that in which, as time goes on, different strategies are evaluated by the players and the effective variables of strike faced gradual and continuous changes, which can lead to a sudden jump of the variables and push the system to very different conditions such as dramatic increase or decrease in the probability of selecting strategies. So the alliance between labors could suffer or reinforce. This discrete sudden change is called catastrophe. In this study after finding evolutionary stable strategies for each player, the catastrophe threshold analyzed by nonlinear evolutionary game and the managerial insight is proposed to employers to prevent the parameters from crossing the border of the catastrophe set that leads to a general strike.
Usually, in monitoring a proportion p, the binary observations are considered independent; however, in many real cases, there is a continuous stream of autocorrelated binary observations in which a two-state Markov chain model is applied with first-order dependence. On the other hand, the Bernoulli CUSUM control chart which is not robust to autocorrelation can be applied two-sided control chart to able to detect either increases or decreases in the process parameter. In this paper, a two-sided Bernoulli-based CUSUM control chart is proposed based on a log-likelihood-ratio statistic using a Markov chain model and average run length relationship. The average run length relationship is set using the corresponding upper and lower Bernoulli CUSUM charts. Simulation studies show the superior performance of the proposed monitoring scheme. Numerical results show the superior performance of the proposed control chart.
In this paper, we consider the problem of scheduling on two-machine permutation flowshop with minimal time lags between consecutive operations of each job. The aim is to find a feasible schedule that minimizes the total tardiness. This problem is known to be NP-hard in the strong sense. We propose two mixed-integer linear programming (MILP) models and two types of valid inequalities which aim to tighten the models’ representations. One of them is based on dominance rules from the literature. Then, we provide the results of extensive computational experiments used to measure the performance of the proposed MILP models. They are shown to be able to solve optimally instances until the size 40-job and even several larger problem classes, with up to 60 jobs. Furthermore, we can distinguish the effect of the minimal time lags and the inclusion of the valid inequalities in the basic MILP model on the results.
One of the main tasks facing all European countries for the next few years is the creation of the most dynamically organized transport sector. The constant passenger and freight traffic lead to congestions and pollutions at the transport highways, having negative impact on a person. Thus, introduction of new technologies, addressing the interrelated problems of optimizing transport flows and improving the environmental footprint of transport, is an overriding priority. In this respect, approaches that allow analyzing the reliability of a vehicle as an object characteristic, reflecting the ability of a product to operate without sudden changes in its quality in real time, are of considerable interest. This is reflected in the development of preventive diagnostic systems (warning the driver of a possible failure of the systems and the car as a whole). The concept for formation of normative and methodological support for research in the field of reliability of complex systems (including vehicles), which can be adopted as a basis for the development of a database of a preventive diagnostic system, is proposed.
This paper deals with construction of confidence intervals for process capability index using bootstrap method (proposed by Chen and Pearn in Qual Reliab Eng Int 13(6):355–360, 1997) by applying simulation technique. It is assumed that the quality characteristic follows type-II generalized log-logistic distribution introduced by Rosaiah et al. in Int J Agric Stat Sci 4(2):283–292, (2008). Discussed different bootstrap confidence intervals for process capability index. Maximum likelihood method is considered for obtaining the estimators of the parameter. Monte Carlo simulation technique is applied to find out the coverage probabilities and average widths of the bootstrap confidence intervals. The results are illustrated with real data sets.
This paper develops an economic production quantity model in a three-echelon supply chain composing of a supplier, a manufacturer and a wholesaler under two scenarios. As the first scenario, we consider a return contract between the outside supplier and the supplier and also between the manufacturer and the wholesaler, but in the second one, the return policy between the manufacturer and the wholesaler is not applied. Here, it is assumed that shortage is permitted and demand is price-sensitive. The principal goal of the research is to maximize the total profit of the chain by optimizing the order quantity of the supplier and the selling prices of the manufacturer and the wholesaler. Nash-equilibrium approach is considered between the chain members. In the end, a numerical example is presented to clarify the applicability of the introduced model and compare the profit of the chain under two scenarios.
In this paper, Burr-type XII X̅ synthetic schemes are proposed as an alternative to the classical X̅ synthetic schemes when the assumption of normality fails to hold. First, the basic design of the Burr-type XII X̅ synthetic scheme is developed and its performance investigated using exact formulae. Secondly, the non-side-sensitive and side-sensitive Burr-type XII X̅ synthetic schemes are introduced and their zero-state and steady-state performances, in terms of the average run-length and expected extra quadratic loss values, are investigated using a Markov chain approach. Thirdly, the proposed schemes are compared to the existing classical runs-rules and synthetic X̅ schemes. It is observed that the proposed schemes have very interesting properties and outperform the competing schemes in many cases under symmetric and skewed underlying process distributions. Finally, an illustrative real-life example is given to demonstrate the design and implementation of the proposed Burr-type XII X̅ synthetic schemes.
In this paper, instead of the classical approach to the multi-criteria location selection problem, a new approach was presented based on selecting a portfolio of locations. First, the indices affecting the selection of maintenance stations were collected. The K-means model was used for clustering the maintenance stations. The optimal number of clusters was calculated through the Silhouette index. The efficiency of each cluster of stations was determined using the Charnes, Cooper and Rhodes input-oriented data envelopment analysis model. A bi-objective zero one programming model was used to select a Pareto optimal combination of rank and distance of stations. The Pareto solutions for the presented bi-objective model were determined using the invasive weed optimization method. Although the proposed methodology is meant for the selection of repair and maintenance stations in an oil refinery Company, it can be used in multi-criteria decision-making problems.
