Yazar "Weber, Gerhard Wilhelm" seçeneğine göre listele

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    Foreword
    (IGI Global, 2019) Weber, Gerhard Wilhelm; Aydın, Nadi Serhan; Baltas, Ioannis; Savku, Emel
    Foreword
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    Fuzzy mathematical programming and self-adaptive artificial fish swarm algorithm forjJust-in-time energy-aware flow shop scheduling problem with outsourcing option
    (Institute of Electrical and Electronics Engineers Inc., 2020) Tirkolaee, Erfan Babaee; Goli, Alireza; Weber, Gerhard Wilhelm
    Flow shop scheduling (FSS) problem constitutes a major part of production planning in every manufacturing organization. It aims at determining the optimal sequence of processing jobs on available machines within a given customer order. In this article, a novel biobjective mixed-integer linear programming (MILP) model is proposed for FSS with an outsourcing option and just-in-time delivery in order to simultaneously minimize the total cost of the production system and total energy consumption. Each job is considered to be either scheduled in-house or to be outsourced to one of the possible subcontractors. To efficiently solve the problem, a hybrid technique is proposed based on an interactive fuzzy solution technique and a self-adaptive artificial fish swarm algorithm (SAAFSA). The proposed model is treated as a single objective MILP using a multiobjective fuzzy mathematical programming technique based on the ?-constraint, and SAAFSA is then applied to provide Pareto optimal solutions. The obtained results demonstrate the usefulness of the suggested methodology and high efficiency of the algorithm in comparison with CPLEX solver in different problem instances. Finally, a sensitivity analysis is implemented on the main parameters to study the behavior of the objectives according to the real-world conditions.
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    Human paradigm and reliability for aggregate production planning under uncertainty
    (Elsevier, 2022) Gütmen, Selma; Weber, Gerhard Wilhelm; Goli, Alireza; Tirkolaee, Erfan Babaee
    Aggregate production planning (APP) within a supply chain is known as one of the main activities in general planning of large and leading companies throughout the world. In the present study, a survey is conducted on the importance of three main factors: (1) human paradigm, (2) reliability, and (3) uncertainty in the APP. To do so, these three factors are investigated and reviewed and the most significant challenges are discussed accordingly. Moreover, the most relevant studies performed recently are reviewed to find the major gaps in the literature. Finally, the survey is concluded through discussing the main challenges, limitations, and recommendations for future research.
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    An integration of neural network and shuffled frog-leaping algorithm for CNC machining monitoring
    (Sciendo, 2021) Goli, Alireza; Tirkolaee, Erfan Babaee; Weber, Gerhard Wilhelm
    This paper addresses Acoustic Emission (AE) from Computer Numerical Control (CNC) machining operations. Experimental measurements are performed on the CNC lathe sensors to provide the power consumption data. To this end, a hybrid methodology based on the integration of an Artificial Neural Network (ANN) and a Shuffled Frog-Leaping Algorithm (SFLA) is applied to the data resulting from these measurements for data fusion from the sensors which is called SFLA-ANN. The initial weights of ANN are selected using SFLA. The goal is to assess the potency of the signal periodic component among these sensors. The efficiency of the proposed SFLA-ANN method is analyzed compared to hybrid methodologies of Simulated Annealing (SA) algorithm and ANN (SA-ANN) and Genetic Algorithm (GA) and ANN (GA-ANN).
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    Preface
    (Springer Science and Business Media Deutschland GmbH, 2021) Molamohamadi, Zohreh; Babaee Tirkolaee, Erfan; Mirzazadeh, A.; Weber, Gerhard Wilhelm
    [No Abstract Available]
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    A robust bi-objective mathematical model for disaster rescue units allocation and scheduling with learning effect
    (Elsevier Ltd, 2020) Tirkolaee, Erfan Babaee; Aydın, Nadi Serhan; Ranjbar-Bourani, Mehdi; Weber, Gerhard Wilhelm
    This paper proposes a novel bi-objective mixed-integer linear programming (MILP) model for allocating and scheduling disaster rescue units considering the learning effect. When a natural phenomenon (e.g., earthquake or flood) occurs, the presented decision support model is expected to help decision-makers of emergency relief centers to provide efficient planning for rescue units to minimize the total weighted completion time of rescue operations, as well as the total delay in rescue operations. The problem has some features in common with unrelated parallel machine scheduling (UPMS) problem and traveling salesman problem (TSP). To deal with the inherent uncertainty and bi-objective nature of the problem, an uncertainty-set based robust optimization technique and multi-choice goal programming (MCGP) with utility functions are applied. To demonstrate the applicability of the proposed model, a real case study in Mazandaran province in Iran is presented. The computational results confirm the high complexity of the problem and the significant impacts of the uncertainty on the solution. Moreover, the analytical results provide useful insights to decision-makers for disastrous situations.