Robust approach for air cargo freight forwarder selection under disruption Abstract Airlines select a group of strategic freight forwarders as their partners to retain an information advantage and maintain stable sources of air cargo. Cargo supply disruption is a critical factor for airlines when they select freight forwarders. In the present study, we propose a distributional robust model to select freight forwarders under supply disruptions, which allows cargo supply disruptions to be correlated. We first analyze the structural properties of the model and then reformulate it into a tractable form. In a real-world case study, the decision model is executed in the air cargo service chain, which consists of six flights and dozens of forwarders. The case study illustrates how our approach is used by the airline to identify reliable freight forwarders in the service chain and provide comprehensive forwarder selection strategies. We also illustrate the comparative advantages of our new approach over traditional methods: the forwarder selection strategy is capable of considering the bargaining power of freight forwarders and computational efficiency. Given these advantages, this new distributional robust optimization model can serve as a promising approach for solving freight forwarder selection problems.

Extended age maintenance models and its optimization for series and parallel systems Abstract In this paper, extended preventive replacement models for series and parallel system with n independent non-identical components are proposed. It is assumed that the system suffers from two types of failure. One is repairable (type-I) failure, at that time the system can be rectified by minimal repair. Another is non-repairable (type-II) failure, then the whole system is replaced. In the proposed models, the system is replaced at the planned time, at random working time, or at the time when type-II failure occurs, with options whichever occurs first or whichever occurs last. The average cost rate (ACR) function and the failure rate function (FRF) of the series and parallel system under the different cases are obtained respectively. Moreover, the optimal preventive replacement time of models based on minimization of the ACR function is obtained theoretically. Numerical examples are presented to evaluate the cost of the system and verify the performance of our results.

Analysis of tandem polling queues with finite buffers Abstract We analyze a tandem polling queue with two stations operating under three different polling strategies, namely: (1) Independent polling, (2) Synchronous polling, and (3) Out-of-sync polling. Under Markovian assumptions of arrival and service times, we conduct an exact analysis using Matrix Geometric method to determine system throughput, mean queue lengths, and mean waiting times. Through numerical experiments, we compare the performance of the three polling strategies and the effect of buffer sizes on performance. We observe that the independent polling strategy generally performs better than the other strategies, however, under certain settings of product asymmetry, other strategies yield better performance.

Internal auditor selection using a TOPSIS/non-linear programming model Abstract One of the most challenging problems in personnel selection is the multi-attribute nature of the candidates. This problem is magnified during the procedure of selection of sophisticated personnel, such as internal auditors. By definition, an internal auditor must combine a selection of analytical and non-analytical skills, corresponding to specific cognitive and behavioral attributes. In this paper, a framework for internal auditors’ selection using TOPSIS technique is proposed, integrating behavioral and cognitive skills. AHP technique has been used to determine the weights of each criterion. By prioritizing the latter skills, the proposed framework can identify employable and potentially employable candidates. Besides considering the desirable skills in the process of personnel selection, the expected performance is also taken into account. To examine what would be the ideal importance of cognitive and behavioral skills that maximizes candidates’ performance, a non-linear programming method is applied. A real-life application is demonstrated to a sample of internal auditors from the Greek branch of a multi-national company.

A weight-consistent model for fuzzy supplier selection and order allocation problem Abstract Decision support for Supplier Selection and Order Allocation (SSOA) is an important application area of multiple criteria decision making (MCDM) problems. In Amid et al. (Int J Prod Econ 131(1):139–145, 2011) proposed and developed a weighted maximin model to ensure the weight-consistent solution for SSOA in an MCDM problem under an uncertain environment. Essentially, this model is based on a weight-consistent constraint and a maximin aggregation operator. This paper reanalyzes the weighted maximin model in terms of the weight-consistent constraint, and then proposes a general weight-consistent model for SSOA in MCDM problems under uncertainty. In this paper, two existing models are reviewed and compared with the proposed model. Three datasets with different ranges of fuzzy demand and full factorial patterns of criteria weights are used to test the performances of the related models. The results showed that the proposed model always generates a weight-consistent Pareto-optimal solution in all cases, while the other existing models do not.

Two approximations of renewal function for any arbitrary lifetime distribution Abstract The renewal functions (RFs) of most distribution functions do not have closed-form expressions while such expressions are desired for the optimization problems involved RF. Many efforts have been made to develop approximations of RF. However, it seems that no RF approximation is accurate enough in the entire time range. In this paper, we propose two RF approximations. The first approximation is obtained through smoothly connecting two limiting relations and fairly accurate in the entire time range. The second approximation has the same function form as the first part of the first approximation but the model parameter is determined in a different way so as to achieve higher accuracy for small to moderate time range. The expressions of the proposed approximations are simple and applicable for any arbitrary lifetime distribution. Their accuracy is analyzed and, the appropriateness and usefulness are illustrated by a numerical example.

A finite-source M/G/1 retrial queue with outgoing calls Abstract In this paper we deal with a single-server, finite-source retrial queue where the server not only accepts incoming calls but after some exponentially distributed idle time makes outgoing calls. The service times of incoming and outgoing calls follow two distinct arbitrary distributions. The outgoing calls are directed not to the customers in the system but outside it, which implies that the model can be considered as a model with vacations or with customers of two types. Along with the standard retrial queue where all customers are allowed to join the orbit we consider also the corresponding queue with restriction on the orbit size. We derive formulas for computing the stationary system state distribution and investigate the influence of the system input parameters on the main macro characteristics of the system performance.

A new inherent reliability modeling and analysis method based on imprecise Dirichlet model for machine tool spindle Abstract The factors that influence the inherent reliability of machine tool spindle are a mixture of various uncertainties and it leads that the reliability modeling and analysis of machine tool spindle can’t be dealt with by one mathematical theory. Meanwhile, the reliability data of the machine tool spindle for reliability modeling and analysis is often insufficient, and data of different types such as accumulated historical data, expert opinions, simulation data, etc. are used to make up for the lack of data. Thus, the unified quantification of mixed uncertainties and the data characterization of different types are the major premises for reliability modeling and analysis of machine tool spindle. By considering this, this paper makes use of the advantage of imprecise probability theory in quantizing the multiple types of data and the advantage of the Bayes theory in data fusion, and proposes a new inherent reliability modeling and analysis method based on imprecise Dirichlet model. In the proposed method, imprecise probability theory is used to quantify mixed uncertainties, imprecise Dirichlet model is built to characterize the different types of reliability data. After analyzing the inherent reliability variation regularity, an inherent reliability model is built, and the proposed method is verified by the inherent reliability calculation of a certain heavy-duty CNC machine tool’s milling spindle. This study can provide new method, theory and reference for reliability modeling and analysis when there are various uncertainties mixed and multiple data existed.

A biobjective chance constrained optimization model to evaluate the economic and environmental impacts of biopower supply chains Abstract Generating electricity by co-combusting biomass and coal, known as biomass cofiring, is shown to be an economically attractive option for coal-fired power plants to comply with emission regulations. However, the total carbon footprint of the associated supply chain still needs to be carefully investigated. In this study we propose a stochastic biobjective optimization model to analyze the economic and environmental impacts of biopower supply chains. We use a life cycle assessment approach to derive the emission factors used in the environmental objective function. We use chance constraints to capture the uncertain nature of energy content of biomass feedstocks. We propose a cutting plane algorithm which uses the sample average approximation method to model the chance constraints and finds high confidence feasible solutions. In order to find Pareto optimal solutions we propose a heuristic approach which integrates the \(\epsilon \) -constraint method with the cutting plane algorithm. We show that the developed approach provides a set of local Pareto optimal solutions with high confidence and reasonable computational time. We develop a case study using data about biomass and coal plants in North and South Carolina. The results indicate that, cofiring of biomass in these states can reduce emissions by up to 8%. Increasing the amount of biomass cofired will not result in lower emissions due to biomass delivery.

A two-stage approach for the critical chain project rescheduling Abstract The fundamental principle of critical chain project management is to use the critical chain instead of a traditional critical path, to insert a project buffer at the end of the project and to insert feeding buffers wherever non-critical chains join the critical chain to protect a timely project completion. Due to the complexity of project, inserting feeding buffers may cause a conflict, such as precedence conflict or resource conflict, which can be solved by rescheduling. However, after rescheduling some new problems may arise: non-critical chain may start earlier than critical chain (non-critical chain overflow), or a gap may occur between activities on the critical chain (critical chain break-down). This paper is aiming to solve these new problems by a two-stage approach combined with feeding buffer for rescheduling. In the first stage, a first-stage rescheduling based on priority rules together with a backward-recursive procedure is proposed for rescheduling to solve resource and precedence conflicts, resulting in a critical chain break-down or a non-critical chain overflow. In the second stage, a second-stage rescheduling based on a heuristic algorithm is proposed to eliminate new problems and generate a better rescheduling scheme. Finally, we do simulations on the 110 Patterson instances set to verify the feasibility, effectiveness and applicability of our two-stage approach for rescheduling. Simulation results show that, it is an effective approach to generate reliable rescheduling schemes in most projects with excellent performances, i.e. the average project length, timely project completion probability and etc.