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The COVID-19 epidemic declared as a pandemic declared by the World Health Organization (WHO), In March 2020, this provides an opportunity for e-Wallet to continue to grow and meet rapid growth due to new consumer behavior, namely the adaptive shopper. The adaptive shopper is an adaptation behavior of the people to meet needs and wants through online shopping. The purpose of this study is to determine the factors that influence the behavioral intention of using e-Wallets during the COVID-19 pandemic in DKI Jakarta uses the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model. This research data was collected through distributing questionnaires to millennial e-Wallet users with an age range of 24 to 40 years in DKI Jakarta. Using the Structural Equation Modeling method with the Partial Least Square (SEM-PLS) approach and using the SmartPLS version 3 software for data analysis in this study. The results showed that performance expectancy and facilitating conditions had an influence on behavioral intention, while the effort expectancy, social influence, price value, and perceived security have no influence on behavioral intention or behavioral intention of e-Wallet users during the COVID-19 pandemic in DKI Jakarta. Based on the results of this research, it shows that during the COVID-19 pandemic, availability and supporting resources are needed in using e-Wallet and there are benefits that are obtained by users such as increasing productivity, increasing opportunities, and efficiency. This can have an influence on behavioral intention or behavioral intentions of e-Wallet users.

This paper studies an integration of the forward products flow, as in the vehicle routing problem with crossdocking (VRPCD), and the reverse products flow, as in the VRP with reverse cross-docking (VRP-RCD), namely the VRP with forward and reverse cross-docking (VRP-FRCD). It is modelled as a mixed-integer linear programming model that covers a four-level supply chain network, including suppliers, retailers, outlets, and a cross-dock facility. The objective is to determine the appropriate number of vehicles used to complete the delivery process, together with the route sequence of every vehicle, such that the total fixed vehicle and distance-related costs are minimized. In order to solve the problem, a two-phase matheuristic is developed. The first phase focuses on finding as many route combinations as possible. This is implemented by an adaptive large neighborhood search (ALNS) algorithm. Subsequently, a set partitioning formulation is formulated and solved in the second phase to determine the best combination over all routes found in the first phase that minimizes the total cost. The performance of the matheuristic in solving the newly developed benchmark instances is compared against those of CPLEX and a pure ALNS algorithm. Experimental results suggest that the matheuristic is more powerful and beneficial than both ALNS and CPLEX to obtain high-quality solutions within an acceptable computational time. ispartof: pages:1467-1472 ispartof: Proceedings of the IEEE 17th International Conference on Automation Science and Engineering (CASE) pages:1467-1472 ispartof: IEEE 17th International Conference on Automation Science and Engineering (CASE) location:Lyon, France date:23 Aug - 27 Aug 2021 status: Published online

For the last decades, social media has become as one of the learning media used by university students. In this part, the phenomena evolved in many countries. In line with this, early research reported the impact of learning using social media by researchers, both positive and negative impact on the university student. However, the researchers encountered demanding effort to obtain the research report on the causal impact of university student background, such as academic achievement, terms, and student social media behavioural activities. This ceaseless research attempts to explore the gap attributed. Therefore, this quantitative and causal or explanatory research will convey the social media impact of behavioural use for the learning of university student. The research involved 336 university students as the respondent in this research. Technically, SPPS application is used to process the data with Correlation Bivariate technique. The research found 29 correlations regarding the university student background and the activities of using media social for learning and a debatable result emerged from early research eventually.

Cross-docking is considered as a method to manage and control the inventory flow, which is essential in the context of supply chain management. This paper studies the integration of the vehicle routing problem with cross-docking, namely VRPCD which has been extensively studied due to its ability to reduce the overall costs occurring in a supply chain network. Given a fleet of homogeneous vehicles for delivering a single type of product from suppliers to customers through a cross-dock facility, the objective of VRPCD is to determine the number of vehicles used and the corresponding vehicle routes, such that the vehicle operational and transportation costs are minimized. An adaptive large neighborhood search (ALNS) algorithm is proposed to solve the available benchmark VRPCD instances. The experimental results show that ALNS is able to improve 80 (out of 90) best known solutions and obtain the same solution for the remaining 10 instances within short computational time. We also explicitly analyze the added value of using an adaptive scheme and the implementation of the acceptance criteria of Simulated Annealing (SA) into the ALNS, and also present the contribution of each ALNS operator towards the solution quality.

A green mixed fleet vehicle routing with realistic energy consumption and partial recharges problem (GMFVRP-REC-PR) is addressed in this paper. This problem involves a fixed number of electric vehicles and internal combustion vehicles to serve a set of customers. The realistic energy consumption which depends on several variables is utilized to calculate the electricity consumption of an electric vehicle and fuel consumption of an internal combustion vehicle. Partial recharging policy is included into the problem to represent the real life scenario. The objective of this problem is to minimize the total travelled distance and the total emission produced by internal combustion vehicles. This is a new variant of problem which is developed from a mixed fleet of electric and internal combustion vehicles, full recharging policy, and operational cost minimization. A mixed integer programming model is then developed to address this problem and commercial software is utilized to solve the model.

—This paper studies the Multi-Vehicle Cyclic Inventory Routing Problem (MV-CIRP) as the extension of the Single-Vehicle CIRP (SV-CIRP). The objective is to minimize both distribution and inventory costs at the customers and to maximize the collected rewards simultaneously. The problem is treated as a single objective optimization problem. A subset of customers is selected for each vehicle including the quantity to be delivered to each customer. For each vehicle, a cyclic distribution plan is developed. We construct a mathematical programming model and propose a simulated annealing (SA) metaheuristic for solving both SV-CIRP and MV-CIRP. For SV-CIRP, experimental results on benchmark instances show that SA is comparable to the state-of-the-art algorithms and it is able to improve 12 best known solutions. For the MV-CIRP, the results show that SA performs better than an Iterated Local Search algorithm. ispartof: pages:691-696 ispartof: Proceedings of the 15th IEEE International Conference on Automation Science and Engineering. pages:691-696 ispartof: 15th IEEE International Conference on Automation Science and Engineering (IEEE CASE 2019) location:Vancouver, Canada date:22 Aug - 26 Aug 2019 status: published

Research on Economic Order Quantity (EOQ) models has been developed intensively to get practical models. One of the models is considering imperfect quality items where a fraction of imperfect quality items will be found after 100% screening a batch items. However, in many models, they only consider deterministic demand and there was no discussion about stochastic demand models. In this paper, stochastic demands are considered and we assume that a shipment is coming from a distant supplier and therefore it’s not feasible to replace the imperfect quality items with an additional order to the same supplier. The stochastic demand needs to be considered because of the fact that demand is uncertain. Since the model is hard to solve mathematically, simulation and evolutionary algorithm are used to solve the model. The results show that periodic review systems have less total inventory cost than continuous review system. The sensitivity analysis shows that s,S system is more robust than S,T system in difference standard deviation.

Small signal stability is the ability of a system to maintain stability when subjected to small disturbances. Small signal analysis gives valuable data about the dynamic properties of the system and helps in its design, operation, and control. Time domain simulation and eigenvalue analysis are the two main ways to study system stability. Eigenvalue analysis are widely utilized to perform small signal stability analysis. The dynamic property of a system in response to small perturbations can be predicted by computing the eigenvalues and eigenvectors of the system matrix. The location of the eigenvalues can be used to study the system's performance. In addition eigenvectors can be used to predict the relative participation of the respective states in the corresponding disturbance modes. This paper described a new method and algorithm for the numerical solution of eigenvalues with the largest real part of essentially positive matrices. The method is based on a numerical implementation of Collatz's eigenvalue inclusion theorem for non-negative irreducible matrices. Finally, a numerical example is given to show the speed of convergence of the new algorithm.

This paper discusses the stress-induced mismatch for MOS transistors for analog circuits. Hot carrier aging and NBTI stress have been performed on nmos and pmos transistor pairs respectively to study transistor's matching properties. While there is no evident increase of drain current mismatch for transistor pairs under the exactly same stress conditions, a slight difference in gate or drain voltage or in stress time will aggravate saturation current mismatch which is very critical to the analog circuits. The stress-induced transistor mismatch may be the limiting factor for the mismatch and therefore for the reliability of the analog circuits.

This second-generation partial-response maximum-likelihood (PRML) read channel device implements an extended partial-response class 4 (EPR4) architecture by adding an EPR4 post-processor to the PR4 channel previously described. Only a single reference resistor and power-supply decoupling are required to form a complete read, write, and servo demodulation solution. Additions to the PR4 architecture include adaptive MR head asymmetry (MRA) cancellation and thermal asperity (TA) detection and correction. The servo demodulator supports 20 MHz bursts and includes a 9 b analog to digital converter (ADC) interface.