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The purpose of this paper is to focus on disruption management responding to large‐area flight delays (LFD). It is urgent for airways to reschedule the disrupted flights so as to relieve the negative influence and minimize losses. The authors try to reduce the risk of airline company's credit and economic losses by rescheduling flights with mathematic models and algorithm.

Based on flight classifications of real‐time statuses and priority indicators, all flights are prioritized. In this paper, two mathematic programming models of flight rescheduling are proposed. For the second model, an optimum polynomial algorithm is designed.

In practice, when LFD happens, it is very important for the airline company to pay attention to real‐time statuses of all the flights. At the same time, the disruption management should consider not only the economic loss but also other non‐quantitative loss such as passengers' satisfaction, etc.

In this paper, two mathematic programming models of flight rescheduling are built. An algorithm is designed and it is proved to be an optimum polynomial algorithm and a case study is given to illustrate the algorithm. The paper provides a theory support for airways to reduce the risk brought by LFD.

The purpose of this paper is to present a four‐phase dynamic feedback model for supply partner selection in agile supply chains (ASCs). ASCs are commonly used as a response to increasingly dynamic markets. However, partner selection in ASCs is inherently more complex and difficult under conditions of uncertainty and ambiguity as supply chains form and re‐form.

The model draws on both quantitative and qualitative techniques, including the Dempster‐Shafer and optimisation theories, radial basis function artificial neural networks (RBF‐ANN), analytic network process‐mixed integer multi‐objective programming (ANP‐MIMOP), Kraljic's supplier classification matrix and principles of continuous improvement. It incorporates modern computer programming techniques to overcome the information processing difficulties inherent in selecting from amongst large numbers of potential suppliers against multiple criteria in conditions of uncertainty.

The model enables decision makers to make efficient and effective use of the vastly increased amount of data that is available in today's information‐driven society and it offers a comprehensive, systematic and rigorous approach to a complex problem.

The model has two main drawbacks. First, practitioners may find it difficult to match supplier evaluation criteria with the strategic objectives for an ASC. Second, they may perceive the model to be too complex for use when speed is of the essence.

The main contribution of this paper is that, for the first time, it draws together work from previous articles that have described each of the four stages of the model in detail to present a comprehensive overview of the model.

This paper aims to develop a bi-objective mixed integer non-linear programing model to optimize the supply chain networks consisting of raw material providers, final product manufacturers and distribution centers. Raw material substitution caused by varying raw material supply amounts, prices and carbon emissions and final product substitution due to different product prices and carbon emissions are considered.

The proposed model aims to achieve total profit maximization and total carbon emission minimization. The objective function of carbon emissions is converted into a maximization problem by changing minimum to maximum. The composite objective function is the weighted sum of the bias value of each objective function. The model is then solved using software Lingo12.

Numerical analysis results show that an increase in the number of alternate raw materials for original raw material helps improve supply chain network performance, and variation in that number causes detectable but not significant changes in downstream final product substitution results.

The major differences between this work and existing research are as follows: first, although previous research considered carbon emissions in supply chain network optimization, it has not considered the substitution effects of products or raw materials. This paper considers the substitution of both raw material and productions. Second, the item substitution considered by previous research is derived from inventory shortage or price difference of original items. However, the substitution considered in the present paper is a response to differences in purchase price, production cost and carbon emissions for items.

This paper aims to investigate the part feeding scheduling problem with electric vehicles (EVs) for automotive assembly lines. A point-to-point part feeding model has been formulated to minimize the number of EVs and the maximum handling time by specifying the EVs and sequence of all the delivery tasks.

First, a mathematical programming model of point-to-point part feeding scheduling problem (PTPPFSP) with EVs is presented. Because the PTPPFSP is NP-hard, an improved multi-objective cuckoo search (IMCS) algorithm is developed with novel search strategies, possessing the self-adaptive Levy flights, the Gaussian mutation and elite selection strategy to strengthen the algorithm’s optimization performance. In addition, two local search operators are designed for deep optimization. The effectiveness of the IMCS algorithm is verified by dealing with the PTPPFSP in different problem scales.

Numerical experiments are used to demonstrate how the IMCS algorithm serves as an efficient method to solve the PTPPFSP with EVs. The effectiveness and feasibility of the IMCS algorithm are validated by approximate Pareto fronts obtained from the instances of different problem scales. The computational results show that the IMCS algorithm can achieve better performance than the other high-performing algorithms in terms of solution quality, convergence and diversity.

This study is applicable without regard to the breakdown of EVs. The current research contributes to the scheduling of in-plant logistics for automotive assembly lines, and it could be modified to cope with similar part feeding scheduling problems characterized by just-in-time (JIT) delivery.

Both limited electricity capacity and no earliness and tardiness constraints are considered, and the scheduling problem is solved satisfactorily and innovatively for an efficient JIT part feeding with EVs applied to in-plant logistics.

As a traditional area, engineering management has always been at the core of the research study in academia. In the current uncertain world, more and more attention is paid to risk management. New risks in many practical engineering management problems require new computational methods and tools. The purpose of this paper is to introduce the state‐of‐the‐art research in engineering management and risk management in China.

The collected papers are briefly reviewed and positioned in existing work.

The special issue includes a variety of perspectives on engineering management and risk management from the Great China area.

The paper shows that all the papers address interesting research problems.

The purpose of this paper is to review the literature on logistics and supply chain management (SCM) in Africa over the last few decades. It provides a comprehensive assessment of theory application regarding the research articles published between 1994 and 2016.

In this structured systematic literature review, a set of 110 articles on SCM research in Africa is assessed.

The authors present the state-of-the-art review on logistics and SCM research in Africa. Extant literature shows that most research works focused on operational aspects of logistics and SCM and that papers drew heavily on theories inspired by strategic management, marketing, micro/macroeconomics and organizational behavior theories. Also, most of the papers with theoretical background can be categorized into theory matching and theory dressing and only a minority of theoretical papers belongs to theory suggestion category. Furthermore, based on the findings, the authors present a framework to characterize the peculiar aspects of Africa-based SCM and logistics practices and provide research propositions related to underexplored aspects of logistics and SCM in Africa.

This study has a number of implications. Practitioners and researchers will gain a greater understanding of how logistics and SCM are carried out in Africa and the type of issues that have been addressed. Furthermore, researchers will be able to identify areas that need greater research attention in Africa.

This study is one of the first literature reviews of publications on logistics and SCM in Africa. It presents an overarching map of the research to date and a series of propositions to inform future research.

The purpose of this paper is to establish the portfolio models of contained grey profit under uncertainty, and the results are applied to solve uncertain investment problem.

In investment problems, uncertainties may exist in model parameters and input data. For the investment problems contained grey profit and incomplete information about natural world state, according to the portfolio theory, the grey systems theory and the uncertainty decision theory, the paper puts forward portfolio models and the methods.

Traditional uncertainty decision is researched for incomplete information about natural world state, in reality, investment problems are not only uncertain state information, but income are uncertain.

Because the investment problems have been widely used in economic analysis, decision analysis and economic management, examples are provided at the end to verify its feasibility.

The paper successfully combined the portfolio theory, the gray system theory and uncertainty decision theory and new uncertainty investment decision-making models and methods are presented.

Airdrop operation has become an important transportation mode due to its mobility and rapidity and mission planning is one of the critical steps in the preparation of an airdrop operation. The purpose of this paper is to propose an efficient mission planning method for airdrop operation using multiple vehicles.

A hierarchical mission planning method is proposed. According to the objectives of the action, the mission planning is divided into three planning levels to form the hierarchical structure and the constraints are distributed among them. By doing so, the proposed approach converts the original mission planning problem to a constrained optimization problem, which is solvable using existing mathematical methods.

On the basis of analysis, the mathematic models of three planning levels are established. Each level has its own optimization objective, taking part of constraints into account. The integrated mission scheme had been obtained step by step.

This paper systematically tackles the complicated multiple vehicles airdrop mission planning problem, and it provides a platform for optimizing the outcomes. The mathematical models established in this paper could apply in a variety of more complex mission scenarios.

This paper fulfils an urgent need to study how the advantages of airdrop operation can be maximized through planning airdrop mission schemes carefully.

The purpose of this paper is to provide a new computational method based on the polynomial chaos (PC) expansion to identify the uncertain parameters of load sensing proportional valve (LSPV), which is commonly used to improve the efficiency of brake system in heavy truck.

For this investigation, the mathematic model of LSPV is constructed in the form of state space equation. Then the estimation process is implemented relying on the experimental measurements. With the coefficients of the PC expansion obtained by the numerical implementation, the output observation function can be transformed into a linear and time-invariant form. The uncertain parameter recursively update functions based on Newton method can therefore be derived fit for computer calculation. To improve the estimation accuracy and stability, the Newton method is modified by employing the acceptance probability to escape from the local minima during the estimation process.

The accuracy and effectiveness of the proposed parameter estimation method are confirmed by model validation compared with other estimation methods. Meanwhile, the influence of measurement noise on the robustness of the estimation methods is taken into consideration, and it is shown that the estimation approach developed in this paper could achieve impressive stability without compromising the convergence speed and accuracy too much.

The model of LSPV is originally developed in this paper, and then the authors propose a novel effective strategy for recursively estimating uncertain parameters of complicate pneumatic system based on the PC theory.

The aim of this paper is to study reverse logistics network design in order to better facilitate the location of factories, online retailers, and the third party logistics in the context of e‐commerce.

Drawing on types of third party collections of returned products, the paper proposes a 0‐1 mixed integrate linear programming (0‐1MILP) mathematic model for reverse logistics networks in e‐business and a further mathematical model is discussed in relation to determining the market demands and returns. Furthermore, a case study is developed and described with the intention of illustrating the value of this model and network.

The paper identifies possibilities for the application of the reverse logistics network models to deal with returned products from customers in companies using e‐business.

There is scope for future research to build on the present research and consider further factors in relation to the influences on return logistics.

This paper provides novel insights for e‐companies setting up and operating a reverse logistics network. The application of these models could decrease costs and allow optimal decisions on price and return price in order to increase efficiency and profit levels.