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In this paper, an efficient O ( n 2 ) algorithm is proposed to solve a special case of single-item lot-sizing problems (SILSP) in which both the production and holding costs are piecewise linear, there is an all-unit discount with one breakpoint for the production cost, and the inventory is bounded. The algorithm is based on a key structural property that may be of more general interest, that of a just-in-time ordering policy. Finally, we show that when the problem is extended to two items, it is NP-complete.

The maintenance of information in visual working memory has been shown to bias the concurrent processing in favor of matching visual input. The present study aimed to examine whether this bias can act at an early stage of processing to enhance target feature perception in single-item displays. Participants were sequentially presented with two distinct colored stimuli as memory samples and a retro-cue indicating which of the two samples should be maintained for subsequent memory test. During the retention interval, they had to discriminate the gap orientation of a Landolt target presented through a single visual stimulus that could match one or neither of the two samples. Across two experiments, we consistently found that discrimination performance was more accurate when the Landolt target was situated within a stimulus that matched the sample being retained in visual working memory, as compared with when the target was not. This effect cannot be attributed to the mechanism of passive priming, because we failed to observe priming effects when the stimulus containing the target matched the sample that was retro-cued to be irrelevant to the working memory task, as compared to when the stimulus matched neither sample. Given the fact that target stimuli were presented in single-item displays wherein external noise was precluded, the present findings demonstrate that the working memory bias of visual attention operating in the absence of stimulus competition facilitates early perceptual processing at the attended location via signal enhancement.

Consider a fundamental problem in microeconomics: selling a single item to a number of potential buyers, who independently draw their values from regular and publicly known distributions. There are four mechanisms widely studied in the literature and widely used in practice: Myerson Auction (OPT), Sequential Posted Pricing (SPM), Second-Price Auction with Anonymous Reserve (AR) , and Anonymous Pricing (AP). OPT is revenue-optimal but complicated, which also experiences several practical issues such as fairness. AP is the simplest mechanism, but also generates the lowest revenue among these four mechanisms. SPM and AR are of intermediate complexity and revenue. A quantitative approach to comparing the relative power of these mechanisms is to study their revenue gaps, each of which is defined as the largest ratio between the revenues from a pair of mechanisms. This letter surveys some recent developments on establishing tight revenue gaps, and highlights some open questions.

Gross substitutes for quasi-linear preferences is characterized by the single improvement property, which says an agent can improve upon a sub-optimal bundle by adding or dropping a single item, or exchanging one item for another. We extend this notion in two ways: by allowing for non-quasi-linear preferences and the exchange of up to Delta items. Our results connect the improvement property with the geometry of the choice correspondence. We derive prices at which the excess demand for each good is at most Delta-1 and provide applications to the design of pseudo-markets for allocating indivisible resources.

We consider the problem of selling a single item to n unit-demand buyers to maximize revenue, where the buyers' values are independently distributed (not necessarily identical) according to publicly known distributions but unknown to the buyers themselves, with the option of allowing buyers to inspect the item at a cost. This problem can be interpreted as a revenue maximizing variant of Weitzman's Pandora's problem with non-obligatory inspection. We present an approximation mechanism that achieves 1/2. The proposed mechanism generalizes to the case of selling k units of an item to unit-demand buyers, obtaining 1-1/√k+3 of the optimal revenue in expectation. The mechanism is sequential and has a simple implementation that works in an online setting where buyers arrive in an arbitrary unknown order, yet achieving the aforementioned approximation with respect to the optimal offline mechanism.

This article reports the results of an experiment comparing branch, grid, and single-item question formats in an internet survey with a nationally representative probability sample. We compare the three formats in terms of administration time, item nonresponse, survey breakoff rates, response distribution, and criterion validity. On average, the grid format obtained the fastest answers, the single-item format was intermediate, and the branch format took the longest. Item nonresponse rates were lowest for the single-item format, intermediate for the grid, and highest for branching, but these results were not statistically significant when modeling the full experimental design. Survey breakoff rates among the formats are not statistically distinguishable. Criterion validity was weakest in the branching format, and there was no significant difference between the grid and single-item formats. This evidence indicates that the branching format is not well suited to internet data collection and that both single-item and short, well-constructed grids are better question formats.

A combined forecasting analysis method based on ARIMA-REGRESSION is proposed in this paper, with the IOWHA operator concept and the forecasting analysis on various deriving as foundation. This paper first constructed the forecasting model of single item, and then used the multiple regression analysis model and the time series ARIMA model to predict the annual civil aviation passenger volume in China. On the basis of the single item, a new IOWHA operator-based combined forecasting model is established, giving an accurate mathematical programming method to determine the weight, and further analyze the forecast. It is proved that the accuracy of prediction can be effectively improved, and the forecasting risk can also be reduced, with the combined forecasting method based on ARIMA-REGRESSION.

This paper studies the design of ex ante efficient mechanisms in environments where a single object is for sale, and agents have positively interdependent values and can covertly acquire information at some cost before participating in a mechanism. We find that ex ante efficient mechanisms discourage agents from acquiring excessive information by pooling or randomization. The optimal pooling regions are those where the semi-elasticity of information acquisition is large. There exists an ex ante efficient mechanism that can be implemented by standard auctions with restrictions on the set of allowable bids. In special cases, this implementation is simple and appealing: standard auctions with discrete bids.

In the present research we provide preliminary evidence of the convergent and divergent validity, test-retest reliability, and the utility of a single-item measure of media immersion. Conve...

Die modellbasierte Prozessüberwachung für die Einzelteilfertigung scheitert häufig an Prozessphasen, welche Beschleunigungsanteile enthalten. Der Beitrag widmet sich der Kompensation von Beschleunigungs- und Reibanteilen im Messsignal durch kennfeldbasierte Ansätze sowie Methoden des maschinellen Lernens. Für die anschließende Modellierung des Gesamtsignals zur Prozessüberwachung werden Regressionsansätze mit Methoden des maschinellen Lernens verglichen, welche sich als besonders geeignet zeigen. Model-based process monitoring often fails to adequately assess process segments that exhibit acceleration. This work evaluates different compensation approaches for non-process-specific parts of the measured signals. For a subsequent modelling of the complete input signal enabling the monitoring of single item processes, conventional regression models are compared to machine learning methods. The latter prove to be very well suited for these requirements.

We investigate a dynamic inventory management problem where a cash-constrained small retailer periodically purchases an item from a supplier and sells it to the customers with non-stationary demands. At each period, the retailer’s available cash restricts the maximum inventory level that it can replenish. There exists a fixed ordering cost for the retailer when ordering and this results in a stochastic lot-sizing problem. We build a stochastic dynamic programming model for this problem and find some characteristics of the optimal ordering pattern.

Inventory routing problem (IRP) is one of the emerging solutions for improving the supply chain efficiency. It gives the vendors the responsibility to monitor and decide the inventory replenishments of their customers. The Inventory Routing Problem (IRP) is an evolved component of VMI policies. Inventory routing provides a win-win situation for both vendors and customers. In this paper we consider a one or more distribution network consisting of a single depot and many geographically dispersed customers where each customer faces demands for a single item which, arise at a deterministic and customer-specific rate. The problem addressed in this paper is based on a finite horizon, multi- period and single product where a fleet of capacitated vehicles transport items from the depot to meet the demand specified by the customers for each period. Here a greedy algorithm is used for finding an optimal route and routing cost associated with it, finding an inventory and transportation cost that minimizes the total coste.

Memorizing time of an event may employ two processes (i) encoding of the absolute time of events within an episode, (ii) encoding of its relative order. Here we study interaction between these two processes. We performed experiments in which one or several items were presented, after which participants were asked to report the time of occurrence of items. When a single item was presented, the distribution of reported times was quite wide. When two or three items were presented, the relative order among them strongly affected the reported time of each of them. Bayesian theory that takes into account the memory for the events order is compatible with the experimental data, in particular in terms of the effect of order on absolute time reports. Our results suggest that people do not deduce order from memorized time, instead people’s memory for absolute time of events relies critically on memorized order of the events.

The current feasibility analysis of specific courses, expert analysis or single item analysis is usually used, and the results are relatively one-sided, so it is difficult to achieve a comprehensive feasibility analysis. Therefore, this paper designs the feasibility analysis model of Wushu Sanda course in universities based on deep learning. First of all, the relationship between curriculum system and training objectives is analyzed, and the relationship matrix between curriculum and objectives is established. Then according to the relationship between training objectives, the importance of the course is analyzed. After that, the training objective factors in the course are analyzed to realize the course process data. At last, use deep learning technology to analyze the marginal characteristics of data, and get the feasibility results of the course. The experiment is designed to analyze the feasibility of Wushu Sanda course in a university. The experimental results show that the model can analyze the feasibility of the course, and get the data for reference to meet the design requirements.

Background: A number of theoretical accounts have been put forward to explain the ability to simultaneously track multiple visually indistinguishable objects over a period of time. Serial processing models of visual tracking focus on the maintenance of the spatial locations of every single item over time. A more recent mechanism describes multiple object tracking as the ability to maintain a higher order representation of an abstract spatial configuration built by the illusory connection of the tracked items through their transition. Objective: The current study investigates the correspondence between these serial and parallel tracking accounts and the right hemispheric specialization for the space-based vs. left hemispheric for object-based attentional processing. Methods: Electrophysiological brain responses were recorded in two groups of patients with right- and left hemispheric lesions while performing in a multiple object tracking task. Results: The results suggest a failure to distinguish single item information for the right hemispheric patients accompanied by the absence of a known electrophysiological marker associated with single item tracking. Importantly, left hemispheric patients showed a graded behavioral and electrophysiological response to probe stimuli as a function of the congruence of the probe with the relevant target stimuli. Conclusions: The current data suggest that the differential contribution of serial and parallel tracking mechanisms during object tracking can partly be explained by the different functional contributions of the right and left brain hemispheres.

The international Space Station (ISS) to date is the most expensive single item ever built.

We consider the sale of a single item to multiple buyers by a revenue-maximizing seller. Recent work of Akbarpour and Li formalizes \emph{credibility} as an auction desideratum, and prove that the only optimal, credible, strategyproof auction is the ascending price auction with reserves (Akbarpour and Li, 2019). In contrast, when buyers' valuations are MHR, we show that the mild additional assumption of a cryptographically secure commitment scheme suffices for a simple \emph{two-round} auction which is optimal, strategyproof, and credible (even when the number of bidders is only known by the auctioneer). We extend our analysis to the case when buyer valuations are $\alpha$-strongly regular for any $\alpha > 0$, up to arbitrary $\varepsilon$ in credibility. Interestingly, we also prove that this construction cannot be extended to regular distributions, nor can the $\varepsilon$ be removed with multiple bidders.

For item selection in cognitive diagnostic computerized adaptive testing (CD-CAT), ideally, a single item selection index should be created to simultaneously regulate precision, exposure status, and attribute balancing. For this purpose, in this study, we first proposed an attribute-balanced item selection criterion, namely, the standardized weighted deviation global discrimination index (SWDGDI), and subsequently formulated the constrained progressive index (CP_SWDGDI) by casting the SWDGDI in a progressive algorithm. A simulation study revealed that the SWDGDI method was effective in balancing attribute coverage and the CP_SWDGDI method was able to simultaneously balance attribute coverage and item pool usage while maintaining acceptable estimation precision. This research also demonstrates the advantage of a relatively low number of attributes in CD-CAT applications.

Puzzle-based storage systems consist of densely stored unit loads on a square grid. The problem addressed in this paper is to retrieve a stored unit load from a puzzle-based storage using the minimum number of item moves. While previous research contributed optimal algorithms for only up to two empty locations (escorts), our approach solves configurations where multiple empty locations are arbitrarily positioned in the grid. The problem is formulated as a state space problem and solved to optimality using an exact search algorithm. To reduce the search space, we derive bounds on the number of eligible empty locations and develop several search-guiding estimate functions. Furthermore, we present a heuristic variant of the search algorithm to solve larger problem instances. We evaluate both solution algorithms on a large set of problem instances. Our computational results show that the algorithms clearly outperform existing approaches where they are applicate and solve more general configurations, which could not be solved to optimality before. The heuristic variant efficiently yields high-quality solutions for significantly larger instances of practically relevant size.

This study explores the segmentation algorithm of item text data, especially of single long length data in health examination. In the specific implementation, a large amount of historical health examination data is analysed. Using the method of character statistics, the connection tightness values T(AB)s between two adjacent characters are calculated. Three parameters, the candidate number N, the best position BP, and balance weight BW are set. The total segmentation indexes SIs are calculated, thus determined the segmentation position Pos. The optimal parameter values are determined by the method of information measurement. Experimental results show that the accuracy rate is 78.6% and reaches 82.9% in the most frequently appeared text item. The complexity of the algorithm is O(n). Using no existing domain knowledge, it is very simple and fast. By executed repeatedly, it is convenient to obtain the characteristics of each single item of text data, furthermore, to distinguish respective express preference of different physicians to the same item. The assumption is verified that without professional domain knowledge, a large amount of historical data can provide valuable clues for the text understanding. The results of this research are being applied and verified in the following research works in the field of health examination.

Retro-cues (i.e., cues presented between the offset of a memory array and the onset of a probe) have consistently been found to enhance performance in working memory tasks, sometimes ameliorating the deleterious effects of increased memory load. However, the mechanism by which retro-cues exert their influence remains a matter of debate. To inform this debate, we applied a hierarchical diffusion model to data from 4 change detection experiments using single item, location-specific probes (i.e., a local recognition task) with either visual or verbal memory stimuli. Results showed that retro-cues enhanced the quality of information entering the decision process-especially for visual stimuli-and decreased the time spent on nondecisional processes. Further, cues interacted with memory load primarily on nondecision time, decreasing or abolishing load effects. To explain these findings, we propose an account whereby retro-cues act primarily to reduce the time taken to access the relevant representation in memory upon probe presentation, and in addition protect cued representations from visual interference. (PsycINFO Database Record

This paper proposes an integrated model for multi-machines dynamic lot sizing aiming to produce a single item, considering the energy consumption during the production horizon. The objective is to ...

An increased interest in sequential recommendation has been observed in recent years. Many models have been proposed to leverage the sequential user-item interaction data, which includes those based on Markov Chain or recurrent neural networks. Most of these models are designed for the scenario where each historical record composed of single item. However, the records could be a subset of items (or session) such as music playlists and baskets in e-commerce applications. How to leverage the session structure to improve the effectiveness of the recommendation system is a challenge. To this end, we propose a MEmory-augmented Attention Network for Sequential recommendation (MEANS), to effectively recommend next items given the sequential session data. The most recent sessions are stored into external memory after a max-pooling operation. The long-term user preference are learned through an attention network which is stacked on the memory layer. Finally, the mixture of long-term and short-term preference is feeded into the prediction layer to make recommendations. Extensive experiments on four real datasets show that MEANS outperforms various state-of-the-art sequential recommendation models.

We focus on a canonical Bayesian mechanism design setting: a seller wants to sell a single item to n bidders, whose values are drawn i.i.d. from a monotone-hazard-rate distribution. In the literature, three mechanisms receive particular attention: the revenue-optimal mechanism Myerson Auction (OPT), the welfare-optimal mechanism Second-Price Auction (SPA), and the most widely-used mechanism Anonymous Pricing (AP). In terms of revenue, we investigate how well the later two mechanisms can approximate Myerson Auction.

International audience; Renewable energy sources play an important role to decrease the dependency to the traditional energy sources which are responsible for the high carbon emission levels in the world. The main challenge in the use of the renewable energy sources in the industrial sector, which has the highest energy consumption share among the others, is to plan the production considering the risk of discontinuity in the supply of the renewable energy sources due to their stochastic nature. In this paper, a single item lot sizing problem under uncertain renewable energy availability is addressed. The aim is to determine the optimal production plan and energy contract option which minimizes the total production and energy costs by guaranteeing to satisfy the external demand with a given service level. To do so, two types of chance constraints are introduced to cope with the stochastic availability of the renewable energy sources. The developed model is explained in detail and illustrative examples are presented and the findings are discussed briefly.

In the context of voice shopping, fifteen experiments demonstrate that information presented by voice can be more difficult to process than the same information presented in writing. Consequently, auditory consumers who shop by voice are less able to differentiate between choice options, leading them to choose recommended items more often, but also defer choice at higher rates compared to when the options are presented visually. This auditory choice difficulty stems from greater difficulty comparing auditory options and is related to an increased burden on working memory. Because the difficulty is related to making comparisons, a joint-evaluation—where an option is considered in the context of others—is more likely to be impacted by voice presentation than a separate-evaluation, and joint versus separate preference reversals are less likely by voice. However, voice presentation can negatively affect the evaluation of even a single item when a consumer compares it against a remembered item, a common scenario in the actual marketplace for tasks such as re-orders. Describing choice options in a way that reduces the burden on memory for auditory consumers can reduce processing difficulty and its downstream consequences.

Currently, in terms of electric load, single item prediction method is mainly applied based on traditional research methods or artificial neural networks technology. This paper proposes a new method of electric load prediction based on BP neural networks and ARIMA combination model, which first extracts time series data sample in terms of the variation trend of the electric load of Shiqu County, GanZi State, China, and then applies BP neural networks to make correction to the prediction errors, and finally adds the corrected value with ARIMA predicted value. The results show that the mean relative error of the combined prediction method is 0.042, which is of better prediction precision compared to single BP neural networks & ARIMA model prediction method. Thus, to use combination model in the electric load prediction is feasible.

This paper analyses a multi-period single-item repairable inventory system with stochastic new and warranty demands. We assume that newly-procured items are indistinguishable from items repaired from repairable warranty returns and new demand has higher priority than warranty demand within a period. Our model also captures the “repair loss” in repairing returns in each period. We formulate the problem by a dynamic programme, and prove that the optimal policy is completely defined by three period-dependent thresholds: the purchase-up-to level, the repair-up-to level, and the scrap-down-to level. Numerical results demonstrate the effectiveness of the optimal policy and show that our optimal strategy outperforms the repair-all strategy and no-repair strategy. Managerial insights are also generated.

The goal of this research is to formulate an inventory control problem of a single item with variable demand dependent on displayed stock level and selling price of the commodity. The item deterior...

Concert recordings of Carnatic music are often continuous and unsegmented. At present, these recordings are manually segmented into items for making CDs. The objective of this paper is to develop algorithms that segment continuous concert recordings into items using applause as a cue. Owing to the ‘here and now’ nature of applauses, the number of applauses exceeds the number of items in the concert. This results in a concert being fragmented into different segments. In the first part of the paper, applause locations are identified using time, and spectral domain features, namely, short-time energy, zero-crossing rate, spectral flux and spectral entropy. In the second part, inter-applause segments are merged if they belong to the same item. The main component of every item in a concert is a composition. A composition is characterised by an ensemble of vocal (or main instrument), violin (optional) and percussion. Inter-applause segments are classified into three segments, namely, vocal solo, violin solo, composition and thaniavarthanam using tonic normalised cent filter-bank cepstral coefficients. Adjacent composition segments are merged into a single item, if they belong to the same melody. Meta-data corresponding to the concert in terms of items, available from listeners, are matched to the segmented audio. The applauses are further classified based on strength using Cumulative Sum. The location of the top three highlights of every concert is documented. The performance of the proposed approaches to applause identification, inter-applause classification and mapping of items is evaluated on 50 live recordings of Carnatic music concerts. The applause identification accuracy is 99%, and the inter- and intra-item classification is 93%, while the mapping accuracy is 95%.

The capacity for survey-based research to advance theoretical knowledge is heavily dependent on the degree to which the measures used capture the constructs that they are intended to represent. Despite the importance of construct validity, the management accounting and control (MAC) literature tends to devote less attention to construct validity than other areas of organizational research. In this article, we discuss contemporary thinking about construct validity and examine how this compares to established practice in MAC research through a systematic review of survey studies from 1996 to 2015. Based on this review, we identify four areas where greater attention is warranted: specifying construct dimensionality, justifying the choice of the measurement model, distinguishing between causal indicators and composite indicators in formative measurement models, and being judicious in the use of single item measures. We discuss the implications of each issue and provide suggestions on how they can be addressed.

One of the most celebrated results in mechanism design is Myerson’s characterization of the revenue optimal auction for selling a single item. However, this result relies heavily on the assumption that buyers are indifferent to risk. In this paper we investigate the case where the buyers are risk-loving, i.e. they prefer gambling to being rewarded deterministically. We use the standard model for risk from expected utility theory, where risk-loving behavior is represented by a convex utility function.

Every time the classification of causes of death is changed, time series of deaths by cause are disrupted in more or less profound ways. When changes involve only the merging of several items or splitting a single item into several new categories, the problems caused by these ruptures are not too difficult to solve. A more or less severe imbroglio occurs, however, each time a new item results from recombining portions of different split items. Sometimes, but very rarely, some countries proceed to a bridge coding during the year of transition, which can help reconstruct coherent time series. This article first summarizes the general principles of the method developed for France by Meslé and Vallin to reconstruct complete series for France from 1925 to 1999 in the detailed list of the 9th WHO International Classification of Diseases (ICD), doing so by successively bridging a posteriori the five versions of the ICD that were in use during that period. Second, it reports on several methodological improvements that have been developed with the aim to reconstruct and analyze mortality trends by cause in sixteen industrialized countries. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s10680-017-9453-1) contains supplementary material, which is available to authorized users.

International audience; Following our previous paper (Brahimi, N., Dauzère-Pérès, S., Najid, N. M., and Nordli, A. Single item lot sizing problems. European Journal of Operational Research, 168(1):1-16, 2006), we present an updated and extended survey of Single-Item Lot-Sizing Problems with focus on publications from 2004 to 2016. Exact and heuristic solution procedures are surveyed. A concise and comprehensive summary of different extensions of the problem is given. The classification of the extensions is based on different characteristics such as resource limitations, assumptions on demand and cost structure. The large number of surveyed papers shows the increased interest of researchers in lot-sizing problems in general and in single-item problems in particular. The survey and the proposed classification should help researchers to identify new research topics, to propose relevant problems and/or novel solution approaches.

The efficiency of the k-Nearest Neighbour classifier depends on the size of the training set as well as the level of noise in it. Large datasets with high level of noise lead to less accurate classifiers with high computational cost and storage requirements. The goal of editing is to improve accuracy by improving the quality of the training datasets. To obtain such datasets, editing removes noise and mislabeled data as well as smooths the decision boundaries between the discrete classes. On the other hand, prototype abstraction aims to reduce the computational cost and the storage requirements of classifiers by condensing the training data. This paper proposes an editing algorithm called Editing through Homogeneous Clusters (EHC). Then, it extends the idea by introducing a prototype abstraction algorithm that integrate the EHC mechanism and is capable of creating a small noise-free representative set of the initial training data. This algorithm is called Editing and Reduction through Homogeneous Clusters (ERHC). Both are based on a fast and parameter free iterative execution of k-means clustering that forms homogeneous clusters. Both consider as noise and remove clusters consisting of a single item. In addition, ERHC summarizes the items of the remaining clusters by storing the mean item for each one in the representative set. EHC and ERHC are tested on several datasets. The results show that both run very fast and achieve high accuracy. In addition, ERHC achieves high reduction rates.

This paper presents a mathematical model for an inventory control system in which customers' demands and suppliers' service time are considered as stochastic parameters. The proposed problem is solved through queuing theory for a single item. In this case, transitional probabilities are calculated in steady state. Afterward, the model is extended to the case of multi-item inventory systems. Then, to deal with the complexity of this problem, a new heuristic algorithm is developed. Finally, the presented bi-level inventory-queuing model is implemented as a case study in Electroestil Company.

This research addresses a batch scheduling problem for single item parts with multi due date on m heterogeneous batch processors. The objective is to minimise total actual flowtime of parts through the shop. The total actual flowtime of parts in a batch is the multiplication of the interval between batch arrival time and the due date by the number of parts in the batch. Using the actual flowtime as the objective means it is oriented to satisfy the due date as a commitment to customers, and simultaneously to minimise the length of time of the parts spending in the shop. An algorithm to solve this problem is proposed.

The properties defining the target purpose of functional clothing in addition to the construction and external appearance requirements are dominant factors in its fabrication. For example, sports apparel should even out temperature drops of the body and wick water away from it well. An analysis showed that such attire could be produced if the sides of the item were made of a different kind of thread or yarn. Items made of bicomponent cloths can economize raw material by combining different cloths in a single item. The material outlays for the items could be reduced by using bicomponent cloths in several of their parts.

This paper proposes an integrated model for single item dynamic lot-sizing problem and Quality Inspection Planning (QIP). The objective is to provide a model of production planning that takes into ...

The inventory models discussed in the previous chapters assumed a single item being managed. Inventory managers at warehouses and retail shops manage a large number of items in their inventory. Inventory decisions – such as timing of replenishment orders, determining order quantities – are not made for each item independently. Decisions are often made jointly for several items managed at one location. Such decisions may be constrained by either the value of inventory they can hold in stock, or the availability of space to stock the items in their warehouse, or some other similar scarce resource. In this chapter, we discuss multi-item inventory models that are subject to one or more resource constraints such as budget, space, or number of orders. We also discuss methods of treating inventory problems that are subject to more than one constraint. Later in this chapter, we discuss another point of interest to inventory managers – that of optimizing the inventory costs by replenishing jointly to derive the benefits of economies of scale.

Because the actual values of the parameters of the items in a test are unknown, one of the tasks performed when a test is analyzed under item response theory is to estimate these parameters. The obtained item parameter estimates then provide information as to the technical properties of the test items. To keep matters simple in the following presentation, the parameters of a single item will be estimated under the assumption that the examinees ability scores are known. In reality, these scores are not known, but it is easier to explain how item parameter estimation is accomplished if this assumption is made.

We argue that although the framework put forward by Hulleman & Olivers (H&O) can successfully explain much of visual search behaviour, it appears limited to tasks without precise target identification demands. In particular, we contend that the unit of selection may be larger than a single item in standard detection tasks, whereas the unit may mandatorily be item-based in compound tasks.

This paper presents a bi-objective VMI problem in a single manufacturer-single vendor multi-retailer (SM-SV-MR) supply chain, which a redundancy allocation problem is incorporated. In the hybridized problem, a manufacturer produces a single item using several machines that work in series, and stores it in a warehouse to replenish one vendor who delivers it to several retailers using the shortest possible route. A novel meta-heuristic, called hybrid bat algorithm (HBA), with calibrated parameters is utilized to find a near-optimum solution. To show the efficiency of HBA, the results are compared to the ones using the traditional BA and a genetic algorithm.

Asymmetries between movement types have standardly been derived by theories of improper movement that preclude certain configurations where different kinds of movement steps are mixed in the course of displacement of a single item. However, closer inspection reveals that none of the existing accounts of improper movement can be maintained under a strictly derivational, local approach to displacement in which syntactic structure is generated bottom-up, by successive application of structure-building operations (such as internal or external Merge), and only very small parts of the structure are accessible at any given point in the derivation (cf. Chomsky, 2001). In view of this state of affairs, the present paper pursues a fairly modest goal: it implements a specific constraint against improper movement going back to Williams (1974, 2003) – viz, what I will refer to the Williams Cycle – in a local way, without a need for backtracking or look-ahead

Set-valued database publication has been increasing its importance recently due to its benefit for various applications such as marketing analysis and advertising. However, publishing a raw set-valued database may cause individual privacy breach such as the leakage of sensitive information like personal tendencies when data recipients perform data analysis. Even though imposing data anonymization methods such as suppression-based methods and random data swapping methods to such a database can successfully hide personal tendency, it induces item loss from records and causes significant distortion in record structure that degrades database utility. To avoid the problems, we proposed a method based on swapping technique where an individual&rsquo, s items in a record are swapped to items of the other record. Our swapping technique is distinct from existing one called random data swapping which yields much structure distortion. Even though the technique results in inaccuracy at a record level, it can preserve every single item in a database from loss. Thus, data recipients may obtain all the item information in an anonymized database. In addition, by carefully selecting a pair of records for item swapping, we can avoid excessive record structure distortion that leads to alter database content immensely. More importantly, such a strategy allows one to successfully hide personal tendency without sacrificing a lot of database utility.

In practice, single item support cannot comprehensively address the complexity of items in large datasets. In this study, we propose a big data analytics framework (named Multiple Item Support Frequent Patterns, MISFP-growth algorithm) that uses Hadoop-based parallel computing to achieve high-efficiency mining of itemsets with multiple item supports (MIS). The proposed architecture consists of two phases. First, in the counting support phase, a Hadoop MapReduce architecture is employed to determine the support for each item. Next, in the analytics phase, sub-transaction blocks are generated according to MIS and the MISFP-growth algorithm identifies the frequency of patterns. To facilitate decision makers in setting MIS, we also propose the concept of classification of item (COI), which classifies items of higher homogeneity into the same class, by which the items inherit class support as their item support. Three experiments were implemented to validate the proposed Hadoop-based MISFP-growth algorithm. The experimental results show approximately 38% reduction in the execution time on parallel architectures. The proposed MISFP-growth algorithm can be implemented on the distributed computing framework. Furthermore, according to the experimental results, the enhanced performance of the proposed algorithm indicates that it could have big data analytics applications.

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.