Single-item Model Research Articles

Building public market opinion indices for electricity consumption prediction of Chinese nonferrous metal industry

Nonferrous metal is an essential natural resource for national economy and industry production. Understanding the supply, demand and price trend of nonferrous metal market is important to economic prosperity analysis and might contribute to electricity power consumption forecast. This research builds related market opinion indices to understand the supply, demand, and price changes in nonferrous metal market. We propose a self-learning graph neural network model-based sentiment index network method for value mining of sentiment-related features, addressing the issue of sentiment loss caused by discontinuities in domain-specific news. Based on the structured indicators and the integration of Internet sentiment indices, we establish two single-item models: Granger Causality-based ARDL Model (G-ARDL) and the Factor Analysis-based Transformer Model (F-Transformer). Finally, we construct a Bagging random sampling integration prediction framework to obtain the weight coefficients for single model predictions. We merge the prediction results in different single models with weighted fusion, resulting in smaller prediction errors compared to single models and equal-weight integration models.

A new approach based on inventory control using interval differential equation with application to manufacturing system

<p style='text-indent:20px;'>Now-a-days, uncertainty conditions play an important role in modelling of real-world problems. In this regard, the aim of this study is two folded. Firstly, the concept of system of interval differential equations and its solution procedure in the parametric approach have been proposed. To serve this purpose, using parametric representation of interval and its arithmetic, system of linear interval differential equations is converted to the system of differential equations in parametric form. Then, a mixing problem with three liquids is considered and the mixing process is governed by system of interval differential equations. Thereafter, the mixing liquid is used in the production process of a manufacturing firm. Secondly, using this concept, a production inventory model for single item has been developed by employing mixture of liquids and the proposed production system is formulated mathematically by using system of interval differential equations.The corresponding interval valued average profit of the proposed model has been obtained in parametric form and it is maximized by centre-radius optimization technique. Then to validate the proposed model, two numerical examples have been solved using MATHEMATICA software. In addition, we have shown the concavity of the objective function graphically using the code of 3D plot in MATHEMATICA. Finally, the post optimality analyses are carried out with respect to different system parameters.</p>

BP Neural Network Combination Prediction for Big Data Enterprise Energy Management System

The energy consumption of an enterprise energy management system (EMS) is a complex process with nonlinearity, time-variance, larger delay, greater inertia and other dynamic characteristics, resulting in the failure of a single-item prediction model to achieve satisfactory prediction results. In this paper, a combination prediction method, based on BP neural network, was proposed to predict the energy consumption of an enterprise EMS for improving the prediction accuracy. The energy consumption of enterprise energy management system (EMS) was predicted and analyzed using gray combination models, i.e., GM (1.1) and pGM (1.1), gray Markov chain, and BP neural network prediction model. These single-item models and their prediction processes were constructed and successfully applied to predict the energy consumption of iron and steel enterprises. The data pertaining to energy consumption of these enterprises from January to December 2018 and January to March 2019 were used for predicting the simulation and testing, respectively. The results showed that the prediction results of our approach has an average relative error of 3.327% and 1.298% respectively, which are extremely lower than the existing approaches for improving the prediction accuracy.

Fixed Order Interval Model for Multi Item Single Supplier Considering Lifetime and Minimum Order Quantity

This paper presents of a material procurement model for multi-item single supplier material considering the life time and minimum order quantity. Procurement of several types of material (multi items) is carried out simultaneously from one supplier with consideration of the savings in ordering costs. The problem is when the order must be performed simultaneously, and how much material must be ordered to obtain a minimum total cost. The cost variables considered in this model are purchase costs, ordering costs, storage costs, and expired cost. This modeling was inspired by a real case found in a manufacturing company located in Bandung. Facts in the field show there were frequently waste due to material has expired. This happens because the material age is relatively short, while the order size must follow the minimum quantity stipulated by the vendor or supplier. Until now the authors have not found a model which fits to this case. However the authors found several articles which have similarities to, and the authors made it as comparison. The developed model departs from the basic model which be developed by several experts. The author modified little bit by adding the cost variable such as the expired cost. Then the authors engaged the model with completely algorithm. This model is a continuation of the single item model developed by Arifin D, and Charisma C, which has been presented and published in the IEOM Conference Proceedings 2018.

Research on Logistics Demand Forecast of Port Based on Combined Model

In order to improve the accuracy, practicability and prediction accuracy of the single prediction model, a logistics demand combination forecasting model based on cargo throughput is established. Based on the original statistical data of throughput, the two models of gray and exponential smoothing are established respectively. On this basis, the weighted assignment method of variance reciprocal is used to construct the combined forecasting model. According to the prediction results of different prediction models analyzed by the three evaluation indexes of average relative error, maximum fitting error and minimum fitting error, the three evaluation indexes of the combined prediction model are respectively 6.344%, 16.345% and 0.343%, which are smaller than the single item model. It indicates that the established combined forecasting model can effectively improve the accuracy of the throughput prediction model based on overcoming the shortcomings of the single-term throughput prediction model.

Automated Item Generation: The Future of Medical Education Assessment

A major innovation in psychometric science, termed automated item generation (AIG), holds the potential to revolutionise assessment in medical education. In short, AIG involves leveraging the expertise of content specialists, item templates, and computer algorithms to create a variety of item permutations, often resulting in hundreds or thousands of new items based on a single item model. AIG may significantly improve item writing capabilities, reduce human error, streamline efficiencies, and reduce costs for individuals in the medical and health professions. Thus, the purpose of this work is to provide readers with a current overview of AIG and discuss its potential advantages, future possibilities, and current limitations.

Stochastic joint replenishment problem with quantity discounts and minimum order constraints

This paper analyzes a logistics system involving a supplier who produces and delivers multiple types of items and a buyer who receives and sells to end customers. The buyer controls the inventory of each item by ordering at a preset time interval, which is an integer multiple of a base cycle, to meet the stochastic demands of the end customers. The supplier makes contracts with the buyer that specify that the ordered amount is delivered at the start of each period at a unit price determined by a quantity discount schedule. The contract also specifies that a buyer’s order should exceed a minimum order quantity. To analyze the system, a mathematical model describing activities for replenishing a single type of item is developed from the buyer’s perspective. An efficient method to determine the base cycle length and safety factor that minimizes the buyer’s total cost is then proposed. The single item model is extended to a multiple items joint replenishment model, and algorithms for finding a cost-minimizing base cycle, order interval multipliers, and safety factors are proposed. The result of computational experiments shows that the algorithms can find near-optimal solutions to the problem.

Heuristics for multi-item two-echelon spare parts inventory control subject to aggregate and individual service measures

We consider a multi-item two-echelon spare parts inventory system in which the central warehouse operates under a (Q, R) policy and local warehouses implement (S−1,S) policy. The objective is to find the policy parameters minimizing expected system-wide inventory holding and fixed ordering subject to aggregate and individual response time constraints. Using an exact evaluation we provide a very efficient and effective heuristic, and also a tight lower bound for real-world, large-scale two-echelon spare parts inventory problems. An extensive numerical study reveals that as the number of parts increases – which is usually the case in practice – the relative gap between the cost of the heuristic solution and the lower bound approaches zero. In line with our findings, we show that the heuristic and the lower bound are asymptotically optimal and asymptotically tight, respectively, in the number of parts. In practice, this means we can solve real-life problems with large numbers of items optimally. We propose an alternative approach between system and item approaches, which are based on setting individual and aggregate service level constraints, respectively. Using our alternative approach, we show that it is possible to keep the cost benefit of using aggregate service levels while avoiding long individual response times. We also show that the well-known sequential determination of policy parameters, i.e., determining the batch sizes first, and then finding the other policy parameters using those batch sizes, which is known for its high performance in single-item models, performs relatively poor for multi-item systems.

Using pairwise associations for multi-item markdown optimisation

ABSTRACTForecasting is an essential task conducted regularly by competitive retailers around the world. Most retail decisions particularly markdowns are made based on the demand forecasts which may or may not be accurate in the first place. In this study, we propose a framework for forecasting weekly demands of retail items via linear regression models within multi-item groups that incorporate both positive and negative item associations. We then utilise dynamic pricing models to optimise markdown decisions based on the forecasts within multi-item groups. Grouping items can be considered as a form of variable selection to prevent the overfitting in prediction models. We report regression results from multi-item groupings besides results from single-item regression model on a real-world data-set provided by an apparel retailer. We then report markdown optimisation results for the single items and multi-item groupings that multi-item forecasting models are built upon. The results show that the regression models provide better estimates within multi-item groups compared to the single-item model. Moreover, the overall revenue achieved in multi-item markdown optimisation across all grouping schemes are higher than the total revenue yielded by single-item markdown optimisation scheme.

Allocating service parts in two-echelon networks at a utility company

We study a multi-item, two-echelon, continuous-review inventory problem at a Dutch utility company. We develop a model for the optimal allocation of service parts in a two-echelon network under an aggregate waiting time constraint. Specific model aspects are emergency shipments in case of stockout, and batching for regular replenishment orders at the central warehouse. We use column generation to solve this problem with various building blocks for single-item models as columns. Further, we derive simple classification rules from the solution of our multi-item, two-echelon service part optimization problem using statistical techniques. Application of our models at Liander yields a cost reduction of 15% and a decrease in the impact of waiting time for service parts by 52%.

Service differentiation through selective lateral transshipments

We consider a multi-item spare parts problem with multiple warehouses and two customer classes, where lateral transshipments are used as a differentiation tool. Specifically, premium requests that cannot be met from stock at their preferred warehouse may be satisfied from stock at other warehouses (so-called lateral transshipments). We first derive approximations for the mean waiting time per class in a single-item model with selective lateral transshipments. Next, we embed our method in a multi-item model minimizing the holding costs and costs of lateral and emergency shipments from upstream locations in the network. Compared to the option of using only selective emergency shipments for differentiation, the addition of selective lateral transshipments can lead to significant further cost savings (14% on average).

Demand Forecasting based on Pairwise Item Associations

Forecasting is an essential task conducted regularly by competitive retailers around the world. Most retail decisions are made based on the demand forecasts which may or may not be accurate in the first place. In this study, a framework for forecasting weekly demands of retail items is proposed via linear regression models within item groups that incorporate both positive and negative item associations. In addition to pairwise item associations found by utilizing transactional data, our framework incorporates item similarities based on weekly sales figures to group the similar items. Grouping items can be regarded as a form of variable selection to prevent the overfitting in the prediction models. The regression results of the framework and benchmark linear regression models are reported for a real world dataset provided by an apparel retailer. The results show that the regression models provide better estimates within multi-item groups compared to the single item models.

Instructional Topics in Educational Measurement (ITEMS) Module: Using Automated Processes to Generate Test Items

Changes to the design and development of our educational assessments are resulting in the unprecedented demand for a large and continuous supply of content‐specific test items. One way to address this growing demand is with automatic item generation (AIG). AIG is the process of using item models to generate test items with the aid of computer technology. The purpose of this module is to describe and illustrate a template‐based method for generating test items. We outline a three‐step approach where test development specialists first create an item model. An item model is like a mould or rendering that highlights the features in an assessment task that must be manipulated to produce new items. Next, the content used for item generation is identified and structured. Finally, features in the item model are systematically manipulated with computer‐based algorithms to generate new items. Using this template‐based approach, hundreds or even thousands of new items can be generated with a single item model.

The Role of Item Models in Automatic Item Generation

Automatic item generation represents a relatively new but rapidly evolving research area where cognitive and psychometric theories are used to produce tests that include items generated using computer technology. Automatic item generation requires two steps. First, test development specialists create item models, which are comparable to templates or prototypes, that highlight the features or elements in the assessment task that must be manipulated. Second, these item model elements are manipulated to generate new items with the aid of computer-based algorithms. With this two-step process, hundreds or even thousands of new items can be created from a single item model. The purpose of our article is to describe seven different but related topics that are central to the development and use of item models for automatic item generation. We start by defining item model and highlighting some related concepts; we describe how item models are developed; we present an item model taxonomy; we illustrate how item models can be used for automatic item generation; we outline some benefits of using item models; we introduce the idea of an item model bank; and finally, we demonstrate how statistical procedures can be used to estimate the parameters of the generated items without the need for extensive field or pilot testing.

Inventory control for point-of-use locations in hospitals

Most inventory management systems at hospital departments are characterised by lost sales, periodic reviews with short lead times, and limited storage capacity. We develop two types of exact models that deal with all these characteristics. In a capacity model, the service level is maximised subject to a capacity restriction, and in a service model the required capacity is minimised subject to a service level restriction. We also formulate approximation models applicable for any lost-sales inventory system (cost objective, no lead time restrictions etc). For the capacity model, we develop a simple inventory rule to set the reorder levels and order quantities. Numerical results for this inventory rule show an average deviation of 1% from the optimal service levels. We also embed the single-item models in a multi-item system. Furthermore, we compare the performance of fixed order size replenishment policies and (R, s, S) policies.

Inventory management with random supply and imperfect information: A hidden Markov model

In most of the papers on inventory models operating in a random environment, the state of the environment in each period is assumed to be fully observed with perfect information. However, this assumption is not realistic in most real-life situations and we provide a remedy in this paper by assuming that the environment is only partially observed with imperfect information. We accomplish this by analyzing two formulations of single-item models with periodic-review and random supply in a random environment. In the first one, supply is random due to random capacity of production and random availability of transportation. We show that state-dependent base-stock policy is optimal if the capacity and all costs are observed, while demand and availability are unobserved. In the second model, we consider a model with random availability only with fixed-ordering cost. We show that state-dependent (s,S) policy is optimal if the availability process is observable.

Inventory Control and Regenerative Processes: Theory

In this paper we will discuss a general framework for single item inventory models based on the theory of regenerative processes. After presenting without proof the main theorems for regenerative processes we analyze in detail how the different single item models can be embedded within this general theory. This facilitates to write down the expressions for the average cost associated with an arbitrary costrate function $f$, and some of the service measures, which appear most frequently in the literature.

Modeling of inventory control with regenerative processes

In this paper we will discuss a general framework for single-item inventory models based on the theory of regenerative processes. After presenting without proof the main theorems for regenerative processes we analyze in detail how the different single-item models can be embedded within this general theory. This facilitates to write down the expressions for the average cost associated with an arbitrary costrate function f. Since these expressions are still complicated, involving convolutions, we use a recently developed numerically stable Laplace inversion algorithm to compute these objective functions in MATLAB©. This enables us to compute the costs instead of using approximations.

An evaluation of vendor selection models from a total cost of ownership perspective

Many different vendor selection models have been published in the purchasing literature. However there has been no systematic approach to compare the relative efficiency of the systems. In this paper we propose to use the concept of Total Cost of Ownership as a basis for comparing vendor selection models. We illustrate the comparison with a real life data set of the purchasing problem of ball bearings at Cockerill Sambre, a Belgian multinational company in the steel industry. From a Total Cost of Ownership perspective mathematical programming models outperform rating models and multiple item models generate better results than single item models for this specific case study.

The deterministic multi-item dynamic lot size problem with joint business volume discount

This paper considers the multi-item dynamic lot size model where joint business volume discount is applied for all items purchased whenever the total dollar value of an order reaches a certain level. Multi-item discounts are prevalent in practical applications, yet the literature has only considered limited instances of single-item models. We establish the mathematical formulation and design an effective dynamic programming based heuristic. Computational results disclose our approach obtains high quality solutions that dominate the best known heuristic for the simplified one-item case, and that proves vastly superior to the state-of-the-art CPLEX MIP code for the multi-item case (for which no alternative heuristics have been devised). We obtained significantly better solutions than CPLEX for the more complex problems, while running from 4800 to over 100,000 times faster. Enhanced variants of our method improve these outcomes further.