Optimal Order Batching in Warehouse Management: A Data-Driven Robust Approach

Optimizing warehouse processes has direct impact on supply chain responsiveness, timely order fulfillment, and customer satisfaction. In this work, we focus on the picking process in warehouse management and study it from a data perspective. Using historical data from an industrial partner, we introduce, model, and study the robust order batching problem (ROBP) that groups orders into batches to minimize total order processing time accounting for uncertainty caused by system congestion and human behavior. We provide a generalizable, data-driven approach that overcomes warehouse-specific assumptions characterizing most of the work in the literature. We analyze historical data to understand the processes in the warehouse, to predict processing times, and to improve order processing. We introduce the ROBP and develop an efficient learning-based branch-and-price algorithm based on simultaneous column and row generation, embedded with alternative prediction models such as linear regression and random forest that predict processing time of a batch. We conduct extensive computational experiments to test the performance of the proposed approach and to derive managerial insights based on real data. The data-driven prescriptive analytics tool we propose achieves savings of seven to eight minutes per order, which translates into a 14.8% increase in daily picking operations capacity of the warehouse.

A Space-Time Interior Penalty Discontinuous Galerkin Method for the Wave Equation

A new higher-order accurate space-time discontinuous Galerkin (DG) method using the interior penalty flux and discontinuous basis functions, both in space and in time, is presented and fully analyzed for the second-order scalar wave equation. Special attention is given to the definition of the numerical fluxes since they are crucial for the stability and accuracy of the space-time DG method. The theoretical analysis shows that the DG discretization is stable and converges in a DG-norm on general unstructured and locally refined meshes, including local refinement in time. The space-time interior penalty DG discretization does not have a CFL-type restriction for stability. Optimal order of accuracy is obtained in the DG-norm if the mesh size h and the time step $$\Delta t$$ Δ t satisfy $$h\cong C\Delta t$$ h ≅ C Δ t , with C a positive constant. The optimal order of accuracy of the space-time DG discretization in the DG-norm is confirmed by calculations on several model problems. These calculations also show that for pth-order tensor product basis functions the convergence rate in the $$L^\infty$$ L ∞ and $$L^2$$ L 2 -norms is order $$p+1$$ p + 1 for polynomial orders $$p=1$$ p = 1 and $$p=3$$ p = 3 and order p for polynomial order $$p=2$$ p = 2 .

Intelligent Traffic Signal Phase Distribution System Using Deep Q-Network

Traffic congestion is a worsening problem owing to an increase in traffic volume. Traffic congestion increases the driving time and wastes fuel, generating large amounts of fumes and accelerating environmental pollution. Therefore, traffic congestion is an important problem that needs to be addressed. Smart transportation systems manage various traffic problems by utilizing the infrastructure and networks available in smart cities. The traffic signal control system used in smart transportation analyzes and controls traffic flow in real time. Thus, traffic congestion can be effectively alleviated. We conducted preliminary experiments to analyze the effects of throughput, queue length, and waiting time on the system performance according to the signal allocation techniques. Based on the results of the preliminary experiment, the standard deviation of the queue length is interpreted as an important factor in an order allocation technique. A smart traffic signal control system using a deep Q-network , which is a type of reinforcement learning, is proposed. The proposed algorithm determines the optimal order of a green signal. The goal of the proposed algorithm is to maximize the throughput and efficiently distribute the signals by considering the throughput and standard deviation of the queue length as reward parameters.

An EOQ model for deteriorating items analyzing retailer’s optimal strategy under trade credit and return policy with nonlinear demand and resalable returns

This paper presents an EOQ model where demand is dependent upon time and selling price. In the proposed model of inventory, the retailer allows its unsatisfied customers to return their product whereas the manufacturer offers a full trade credit policy to the retailer. To make our model realistic, we have assumed that the product returned can be resold with the same selling price. Number of returns is a function of demand. In this proposed inventory model considering deterioration, the retailer does not fully reimburse its customers for the returned product. The primary purpose of this inventory model is to determine the optimal selling price, optimal order quantity, and optimal replenishment cycle length in order to maximize the retailer’s total profit earned per unit time. A numerical example is also presented and a sensitivity analysis is carried to highlight the findings of the suggested inventory model.

A C 0 conforming dg finite element method for biharmonic equations on triangle/tetrahedron

A C 0 conforming discontinuous Galerkin (CDG) finite element method is introduced for solving the biharmonic equation. The first strong gradient of C 0 finite element functions is a vector of discontinuous piecewise polynomials. The second gradient is the weak gradient of discontinuous piecewise polynomials. This method, by its name, uses nonconforming (non C 1) approximations and keeps simple formulation of conforming finite element methods without any stabilizers. Optimal order error estimates in both a discrete H 2 norm and the L 2 norm are established for the corresponding finite element solutions. Numerical results are presented to confirm the theory of convergence.

Research on Crude Oil Trade Procurement Model Based on DEA-Malmquist Algorithm

To retain valuable information to the maximum extent and enhance the ability to mine the crude oil trade purchase price demand, this paper proposes a crude oil trade purchase model based on the DEA-Malmquist algorithm. The intranet of the management and control platform shall share the same database, and the intranet shall only allow managers to access and manage the system and only allow all registered users to access and realize data exchange between the intranet and the intranet through two-dimensional code scanning; moreover, due to the resource sharing between the intranet and the intranet for crude oil trade procurement, suppliers and other registered users can immediately grasp the procurement trends of enterprises. Under the DEA-Malmquist algorithm, the uncertainty of procurement management is analyzed by fuzzy theory, and the refined procurement decision model with fuzzy parameters is established. The optimal order time and purchase quantity are determined through the symbol distance and the method of the center of gravity. Experimental results show that the method can effectively retain valuable information in the initial sequence and has better practical application value of material procurement demand intelligent mining. The proposed model obtained the highest accuracy of 98.62%.

Optimal Order of Time-Domain Adaptive Filter for Anti-Jamming Navigation Receiver

Adaptive filtering algorithms can be used on the time-domain processing of navigation receivers to suppress interference and maintain the navigation and positioning function. The filter length can affect the interference suppression performance and hardware utilization simultaneously. In practical engineering, the filter length is usually set to a large number to guarantee anti-jamming performance, which means a high-performance receiver requires a high-complexity anti-jamming filter. The study aims at solving the problem by presenting a design method for the optimal filter order in the time-domain anti-jamming receiver, with no need for detailed interference information. According to interference bandwidth and jam-to-signal ratio (JSR), the approach designed a band-stop filter by Kaiser window for calculating the optimal filter order to meet interference suppression requirements. The experimental results show that the time-domain filtering processing has achieved good interference suppression performance for engineering requirements with optimal filter order in satellite navigation receivers.

Inventory Policy Control Using a Continuous Review Model Based on Information Systems: A Case Study in a Restaurant Industry

The development of information technology has become a necessity for every company, one of which is the optimization of inventory planning. The joint warehouse owned by the Chickenpedia Restaurant which has to supply various types of raw materials to the three restaurant branches often experiences stock outs and has to make emergency orders resulting in losses. For this reason, it is necessary to control the inventory of raw materials in the shared warehouse so that stock outs can be overcome and losses that arise can be minimized. One of them is by using the Continuous Review method. By using this method, the optimal order quantity and reorder point will be obtained. The weakness is iterative calculation and monitoring of raw material stock manually. For this reason, it is necessary to support an information system to speed up and make it easier to calculate when to place an order and how many orders of raw materials to suppliers so as to reduce the occurrence of running out of raw materials. The Information System can also monitor the stock of raw materials every day in real time based on the number of incoming and outgoing goods that enter the application.

SIMULATION OF AN ADARTIVE FILTER BASED ON THE RECURSIVE LEAST SQUARES METHOD

Modern electronics systems are high-speed, compact and require the use of energy-efficient digital electronics devices (DED’s) such as microcontrollers, programmable logic integrated circuits (FPGA’s), digital signal processors. Application of the DED’s is a hardware implementation of high - performance digital signal processing (DSP) algorithms based on the target architecture of the electronic device. In order to accellarate of the design process in the direct hardware implementation of DSP algorithms, simulation models are created to enable optimizing the design process at the stage of a creation of the programming part for FPGA. The paper presents the results of a study of the adaptive filter (AF) model based on the recursive least squares method (RLS). According to the analysis of time and frequency parameters of the AF model has been conducted during simulation it was found that the qualitative filtering process starting from the 24th order and further increasing the AF order does not significantly improve signal filtering, but only increases the required hardware resources. In process of the verification of the proposed simulation model, the AF-based noise reduction system has been modeled and the THD level of 7.103 % was obtained for the built-in AF unit, which is more than one and a half times higher than the proposed AF unit 4.323 %, which confirmed the efficiency of the developed AF unit. Thus, during the study, the optimal order of AF has been determined, which will allow more efficient use of FPGA resources during the hardware implementation of AF. In accordance with the results of the study, the correctness and efficiency of the created hardware-oriented simulation model has been proved, as well as the hardware-oriented structure of the adaptive RLS filter for future implementation on FPGA nas been shown.