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Many transportation systems for routing flows between several origin-destination pairs of demand nodes have been widely designed as hub-and-spoke networks. To improve the provided service level of these networks, service time requirements are here considered during modeling, giving rise to a multiple allocation incomplete hub location problem with service time requirements. The problem consists of designing a hub and spoke network by locating hubs, establishing inter-hub arcs, and routing origin-destination demand flows at minimal cost while meeting some service time requirements. As travel times are usually uncertain for most real cases, the problem is approached via a binary linear programming robust optimization model, which is solved by two specialized Benders decomposition algorithms. The devised Benders decomposition framework outperforms a general purpose optimization solver on solving benchmark instances of the hub location literature. The achieved results also show how the probability of violating the travel time requirements decreases with the prescribed protection level, at the expense of the higher costs of the optimal solution for the robust optimization model. [ABSTRACT FROM AUTHOR]

Uncertainty is frequently present in logistics and transportation, where vehicle routing problems play a crucial role. However, due to the complexity inherent in dealing with uncertainty, most research has been devoted to deterministic problems. This paper considers a robust version of the vehicle routing problem with hard time windows, in which travel times are uncertain. A budget polytope uncertainty set describes the travel times, to limit the maximum number of sailing legs that can be delayed. This makes sure that improbable scenarios are not considered, while making sure that solutions are immune to delays on a given number of sailing legs. Existing exact methods are only able to solve small instances of the problem and can be computationally demanding. With the aim of solving large instances with reduced running times, this paper proposes an efficient heuristic based on adaptive large neighborhood search. The computational study performed on instances with different uncertainty levels compares and analyzes the performance of four versions of the heuristic and shows how good quality solutions can be obtained within short computational times. [ABSTRACT FROM AUTHOR]

Highlights: [•] The robust optimization model of OVRP with uncertain demands was proposed. [•] Four robust strategies to cope with the uncertain demands were proposed. [•] An improved differential evolution algorithm (IDE) was presented to solve the ROVRP. [•] The performances of four different robust strategies were analyzed. [ABSTRACT FROM AUTHOR]

Highlights: [•] We developed the stochastic MOWs for unreliable manufacturing systems. [•] We accounted for system uncertainties to achieve more robust maintenance performance. [•] We modeled a two-machine one-buffer system with random failure and repair actions. [ABSTRACT FROM AUTHOR]

Abstract: Conventional super-efficiency data envelopment analysis (DEA) models require the exact information of inputs or outputs. However, in many real world applications this simple assumption does not hold. Stochastic super-efficiency is one of recent methods which could handle uncertainty in data. Stochastic super-efficiency DEA models are normally formulated based on chance constraint programming. The method is used to estimate the efficiency of various decision making units (DMUs). In stochastic chance constraint super-efficiency DEA, the distinction of probability distribution function for input/output data is difficult and also, in several cases, there is not enough data for estimating of distribution function. We present a new method which incorporates the robust counterpart of super-efficiency DEA. The perturbation and uncertainty in data is assumed as ellipsoidal set and the robust super-efficiency DEA model is extended. The implementation of the proposed method of this paper is applied for ranking different gas companies in Iran. [Copyright &y& Elsevier]

Abstract: It is well-known that robustness refers to the ability dealing with unknown uncertainties or unmodeled dynamics of the model-based control theory. However, this kind of robustness no longer has any significance for model free or data-driven control theory because the controller is designed only using I/O data of the controlled plant. In this paper, a novel robustness of the model free control or data-driven control is presented when the system is controlled by the model free adaptive control (MFAC) scheme. It is assumed that an MFAC scheme is implemented via a network control system and that data dropout occurs due to a failing sensor, actuator or network failure, resulting in what it is called intermittent MFAC. The stability of such a MFAC scheme is analyzed by the statistical approach. It is shown that the MFAC system is still stable when data dropout occurs, and the data dropout impacts the convergence speed. [Copyright &y& Elsevier]

Abstract: Hierarchical Genetic Algorithms (HGA) as a tool for a search and optimizing methodology has now reached a mature stage. In a cellular network, the call-arrival rate, the call duration and the communication overhead between the base stations and the control center are vague and uncertain. Whether the criteria of concern be nonlinear, constrained, discrete or NP hard. In this paper, the HGA is used to tackle the neural network (NN) topology as well as the fuzzy logic controller for the dynamic channel-borrowing scheme in wireless cellular networks. Therefore, we propose a new efficient HGA Channel Borrowing (HGACB) in distributed cellular networks. The proposed scheme exhibits better learning abilities, optimization abilities, robustness, and fault-tolerant capability thus yielding a better performance than other algorithms. It aims to efficiently satisfy their diverse quality-of-service (QoS) requirements of multimedia traffic. The results show that our algorithm has lower blocking rate, lower dropping rate, less update overhead, and shorter channel-acquisition delays than previous methods. [Copyright &y& Elsevier]

Abstract: This paper addresses a new uncertainty set – interval random uncertainty set for worst-case value-at-risk and robust portfolio optimization. The form of interval random uncertainty set makes it suitable for capturing the downside and upside deviations of real-world data. These deviation measures capture distributional asymmetry and lead to better optimization results. We also apply our interval random chance-constrained programming to robust worst-case value-at-risk optimization under interval random uncertainty sets in the elements of mean vector and covariance matrix. Numerical experiments with real market data indicate that our approach results in better portfolio performance. [Copyright &y& Elsevier]

Abstract: Dealing with uncertainties and lack of knowledge about problems and situations, there is a perpetual difficulty to evaluate the situations and action values in all time steps. On the other hand, the design of critics which delicately guide the agent even with reinforcement rewards and punishments in these complicated or blurred environments is laborious and cumbersome. In this study, we propose a framework for concurrent learning of control of attention to the sensory space, attention to the various critics to evaluate the selected motor actions, and the motor actions themselves. Previous works include the implementation of the control of attention for selecting the most important parts of data and/or reducing the dimensionality of the input space. However, decision making can depend not only on objective sensory data, but also upon mental states or subjective inputs as well. Specifically, we examine attention for the evaluations of selected action by various local critics, as well as sensory inputs. Each local critic evaluates the agent’s actions regarding to its standpoints on task domain. Our agent tries to learn the degree of importance of each local critic’s assessment, using sparse super-critic punishing revisions. So the agent learns the way of combining or even disregarding some of local critics while it learns to focus on the appropriate subset of features, and learns physical actions concurrently. By discovering the effective combination of local critics, the agent does not need any prior accurately designed critic. The mathematical formulation of proposed learning method is developed. Also, in order to evaluate the proposed method, two benchmarks are discussed. The effect of using attention control on robustness is analyzed via Monte Carlo analysis. The experimental results show the efficiency of proposed formulation in presence of uncertainties. [Copyright &y& Elsevier]

Abstract: Paper is an example of a valuable material that can be recycled and recovered. In this study, a two-stage stochastic revenue-maximization model is presented to determine a long-term strategy under uncertainty for a large-scale real-world paper recycling company. This network-design problem includes optimal recycling center locations and optimal flow amounts between the nodes in the multi-facility environment. The proposed model is formulated with two-stage stochastic mixed-integer and robust programming approaches. The models are solved by commercial software GAMS 21.6/CPLEX 9.0 and the results are compared. The study is followed by the analyses of the results. [Copyright &y& Elsevier]

Abstract: In this paper, fuzzy threshold values, instead of crisp threshold values, have been used for optimal reliability-based multi-objective Pareto design of robust state feedback controllers for a single inverted pendulum having parameters with probabilistic uncertainties. The objective functions that have been considered are, namely, the normalized summation of rising time and overshoot of cart (S RO–C) and the normalized summation of rising time and overshoot of pendulum (S RO–P) in the deterministic approach. Accordingly, the probabilities of failure of those objective functions are also considered in the reliability-based design optimization (RBDO) approach. A new multi-objective uniform-diversity genetic algorithm (MUGA) is presented and used for Pareto optimum design of linear state feedback controllers for single inverted pendulum problem. In this way, Pareto front of optimum controllers is first obtained for the nominal deterministic single inverted pendulum using the conflicting objective functions in time domain. Such Pareto front is then obtained for single inverted pendulum having probabilistic uncertainties in its parameters using the statistical moments of those objective functions through a Monte Carlo simulation (MCS) approach. It is shown that multi-objective reliability-based Pareto optimization of the robust state feedback controllers using MUGA with fuzzy threshold values includes those that may be obtained by various crisp threshold values of probability of failures and, thus, remove the difficulty of selecting suitable crisp values. Besides, the multi-objective Pareto optimization of such robust feedback controllers using MUGA unveils some very important and informative trade-offs among those objective functions. Consequently, some optimum robust state feedback controllers can be compromisingly chosen from the Pareto frontiers. [Copyright &y& Elsevier]

Abstract: The prioritization of engineering characteristics (ECs) provides an important basis for decision-making in QFD. However, the prioritization results in the conventional QFD may be misleading since it does not consider the uncertainty of input information. This paper develops two robustness indices and proposes the notion of robust prioritization that ensures the EC prioritization to be robust against the uncertainty. The robustness indices consider robustness from two perspectives, namely, the absolute ranking of ECs and the priority relationship among ECs. Based on the two indices, robust prioritization seeks to identify a set of ECs or a priority relationship among ECs in such a way that the result of robust prioritization is stable despite the uncertainty. Finally, the proposed robustness indices and robust prioritization are demonstrated in a case study conducted on the ADSL-based high-speed internet service. [Copyright &y& Elsevier]