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This paper addresses a new robust multi objective multi period model for supply chain planning under uncertainty considering quantity discounts. The proposed model maximizes the current proht of the distributor by making a balance between the total costs of the supply chain and the distributor company s revenues of selling products and also maximizes the company s expected profit by introducing brands and taking the risk of loss on it. Considering uncertainty in the purchasing cost, selling fees, and demand fluctuations, the new robust multi objective mixed integer programming model is solved as a single objective mixed integer programming model by utilizing the LP metrics method. By settling regulatory penalty parameters and considering different economic scenarios, the robustness and effectiveness of the developed model are verified with the data from BEH PAKHSH Company, a commodities distributor in Iran. The outcomes show that the proposed model is a promising approach to run an efficient supply chain.

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Despite having a very broad spectrum of applicability in practice, the multi family capacitated lot sizing problem (M FCLSP) has been scarcely studied. The MFCLSP is an extension of the capacitated Iot sizing problem with setup times (CLST) in which items are organized into families based on similar setup structures. In this paper, we propose three formulations for the MFCLSP (MF TRAD, MF ARBNET, and MF EXREQ), and develop a comprehensive comparative analysis to evaluate their performance using a generic solver (CPLEX). Solving large scale problems to optimality has been shown to consume a great amount of computational time, which is very impractical for real life applications. Because of that, this study focuses on analyzing the performance of these formulations in a limited, and reasonable, amount of time. The results show the MF EXREQ model outperforms the other two models in both the time to the first feasible solution and the quality of the solutions generated throughout the solving process.

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This paper focuses on the robust resource constrained project scheduling problem (RCPSP) with discrete time/resource trade offs, in which activity duration and resource are uncertain variables. Combining flexible RCPSP (FRCPSP) with robustness, a discrete mathematical model is developed and resource leveling problem objective is considered to describe the flexible resource allocation comprehensively. In addition, surrogate measures are also introduced, providing an accurate estimate of the schedule robustness. Priority based heuristic methods and resource assignment heuristic are employed to generate and modify the priorities of selected activities, meanwhile obtain the different executing modes. Furthermore, each surrogate measure is compared according to the scheduling performance through the computational experiments. The practicability of proposed multi objective mathematical model and the efficiency of algorithm are verified by a numerical example. Finally, the performance analysis is also presented by the robustness assessment, and the results prove that the proposed approach is more effective than the traditional one.

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Interconnection among systems requires the interaction between different technologies, such as radio frequency identifier, sensors, processing in the cloud and cyber physical systems. The use of these technologies entails transforming the way operations management is carried out within companies, with potential for application in several helds of activity. This article aims to identify the contribution of the adoption of Industry 4.0 technologies for companies operation management (OM). Based on a survey with 38 successful cases of Industry 4.0 implementation, the results achieved the main benefits of the use of these technologies for OM. These results indicate that Industry 4.0s contributions are more concentrated in areas such as Technology management and Just in time manufacturing. Further, the maturity level of the successful cases analyzed is still incipient, indicating a potential for increasing productivity after the application of these technologies.

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Count data are often described by the Poisson distribution, which requires identical mean and variance, namely equi dispersion. However, in practical situations, count data usually exhibit either over dispersion with variance larger than mean, or under dispersion with variance smaller than mean. Therefore, traditional approaches that focus on only mean shifts, such as the c chart, cannot monitor count data with over/under dispersion efficiently.To monitor mean and dispersion of count data simultaneously, this paper adopts Conway MaxwelI Poisson (COMPoisson) distributions to ht count data with over/under dispersion, and constructs a control chart based on the likelihood ratio test. The proposed chart is powerful in detecting both mean and dispersion shifts of count data with either over dispersion or under dispersion. Numerical simulations have demonstrated its performance in various cases.

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In order to minimaze total production time in multi-pass milling process, selecting optimal values of the process parameters is of great importance. the parameter optimazation model of multipass milling process is a constrained non-linear programming formulation. due to nonlinearity and complexity of the mathematical model, developing new solution methodologies , which can provide efficient solutions, is essential. for this purpose, in this paper a novel hybrid alogrithm called sine-cosine whale optimazation alogrithm is propoosed for parameter optimazation problem of multi-pass milling process in order to minimize total production time. the SCWOA uses exploration and exploitation abilities of the two basic alogrithms to achieve better solitions. to show efficiency of the proposed alogrithm, an experimental study is carried out and the result of the proposed alogrithm is compared to the ones in the literature. the fidings reveald that SCWOA provides promising solutions which result in significantly lower production time.

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The Fuzzy Fixed Charge Transportation Problem in which both fixed and transportation cost are fuzzy numbers is considered in this paper. Due to NP-hardness of the problem, we utilize three types of Electromagnetism-like Algorithms (EM), Genetic Algorithm (GA), and Simulated Annealing (SA) which are firstly being proposed and comprised in this research area. Besides, our other novelty approach is the use of new encoding mechanism, namely string representation, for the first time which is employed for the problem and can be used in any extended transportation problems. Also, the last version of EM is being firstly developed and proposed in this paper. The employed operators and parameters are calibrated to ensure the best performance of the algorithms. Besides, different problem sizes are considered at random to study the impacts of the rise in the problem size on the performance of the algorithms.

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This paper considers an M/M/1 queue with second optional service, in which the server is subject to working breakdowns and repairs. All arriving customers require the first essential service, whereas only a portion of them require a second optional service. In case where the server may break down while providing the first (essential) or second (optional) service, the server continues operation at a reduced level rather than ceasing service entirely. The stability condition for this queue is derived in explicit form. The matrix-geometric method is used to compute the stationary probability distribution of the system size and various system performance measures. The genetic algorithm is applied to a cost optimization problem with the aim of optimizing service rates by minimizing the expected cost per unit time. Finally, a numerical example is presented for illustrative purposes.