Demand Classes Research Articles

Robust lower bounds on monopoly profit with [formula omitted]-concave demand

I extend Condorelli’s lower bound on monopoly profit from log-concave demand to a broader class of α-concave demand, with α=0 corresponding to log-concavity and α=1 to concavity. The monopoly profit is at least 1(1+α)1/α of the area under the demand curve. I further derive upper bounds for consumer surplus and deadweight loss relative to monopoly profit and show all three bounds are sharp.

Inventory rationing, admission control, and production capacity allocation in a make‐to‐stock/make‐to‐order manufacturing system

AbstractThis paper considers a manufacturing system in which products are produced in both make‐to‐stock (MTS) and make‐to‐order (MTO) modes. Production of MTS and MTO products is done in batches, incurs a setup cost, and is non‐preemptive. The inventory of MTS products fulfills the demand of multiple classes, and each class demand can be satisfied or rejected. Customer orders for MTO production can be accepted or rejected, and their size is the same as the production batch. The primary goal of this paper is to study a policy that coordinates inventory rationing, admission control, and production capacity allocation to maximize the system's profit. We formulate the problem as a Markov decision process model and identify the structure of optimal control policies. We investigate the effect of inventory rationing on the profit by comparing its performance to that of the system with a first‐come‐ first‐serve policy to allocate inventory to multiple demand classes and study the extent to which the benefit of inventory rationing can be affected by system parameter changes. We also propose a heuristic that manages control decisions from linear threshold functions. Our test results from numerical examples show that the average percentage difference between the optimal and heuristic policies is within 1.2%.

Joint optimization of inventory replenishment and rationing policies for an omnichannel store with both in-store and online demands

Boosted by the COVID-19 pandemic, omnichannel retail has become a popular retail model and is expected to further grow. We study inventory replenishment and rationing of a product in an omnichannel store that fulfils both in-store and online demands. Both demands follow Poisson process. As each in-store demand must be satisfied immediately whereas each online order has a delivery time window, the two demand classes have different behaviors facing stockout: offline demand is lost while online order is backlogged with backorder cost charged only if it is delivered late. The inventory replenishment and rationing between the two demand classes in this omnichannel inventory system are controlled by a continuous review (Q, r) policy and a critical level policy, respectively. The intertwined nature of inventory replenishment and rationing in this system makes its analytical cost evaluation and its joint optimization of the two policies challenging. After deriving an analytical formula for evaluating the expected cost of the system, two heuristic algorithms are proposed to jointly optimize the two policies. Numerical experiments on randomly generated instances demonstrate that the multi-start local search algorithm and the scatter search algorithm can obtain a near-optimal solution quickly with percentage cost gap no larger than 0.81% and 0.18%, respectively, and the critical level policy can yield cost savings up to 10.91% compared to the first-come, first-served policy. Finally, a sensitivity analysis is conducted to evaluate the influences of system parameters on the optimal critical level.

EDC mixtures during pregnancy and body fat at 7 years of age in a Swedish cohort, the SELMA study

BackgroundSome endocrine disrupting chemicals (EDC), are “obesogens” and have been associated with overweight and obesity in children. Daily exposure to different classes of EDCs demands for research with mixtures approach. ObjectivesThis study evaluates the association, considering sex-specific effects, between prenatal exposure to EDC mixture and children's body fat at seven years of age. MethodsA total of 26 EDCs were assessed in prenatal urine and serum samples from first trimester in pregnancy from 737 mother-child pairs participating in the Swedish Environmental Longitudinal, Mother and child, Asthma and allergy (SELMA) study. An indicator for children's “overall body fat” was calculated, using principal component analysis (PCA), based on BMI, percent body fat, waist, and skinfolds measured at seven years of age. Weighted quantile sum (WQS) regression was used to assess associations between EDC mixture and children's body fat. ResultsPrincipal component (PC1) represented 83.6 % of the variance, suitable as indicator for children's “overall body fat”, with positive loadings of 0.40–0.42 for each body fat measure. A significant interaction term, WQS*sex, confirmed associations in the opposite direction for boys and girls. Higher prenatal exposure to EDC mixture was borderline significant with more “overall body fat” for boys (Mean β = 0.20; 95 % CI: −0.13, 0.53) and less for girls (Mean β = −0.23; 95 % CI: −0.58, 0.13). Also, higher prenatal exposure to EDC mixture was borderline significant with more percent body fat (standardized score) for boys (Mean β = 0.09; 95 % CI: −0.04, 0.21) and less for girls (Mean β = −0.10 (−0.26, 0.05). The chemicals of concern included bisphenols, phthalates, PFAS, PAH, and pesticides with different patterns for boys and girls. DiscussionBorderline significant associations were found between prenatal exposure to a mixture of EDCs and children's body fat. The associations in opposite directions suggests that prenatal exposure to EDCs may present sex-specific effects on children's body fat.

Efficient optimization in stochastic production planning problems with product substitution

We consider the stochastic production planning problem with product substitution, which can be decomposed into several optimization subproblems with sequential decisions. The decision variables in each time period include (1) the product substitution decision and (2) the recipe input quantity decision. The goal is to minimize the total of production cost, holding cost, and shortage cost, while achieving a service level for demand satisfaction.Since this optimization model involves analytically intractable probabilistic formulation, traditional mathematical programming techniques cannot be readily applied. We develop the deterministic SPLINE and the stochastic R-SPLINE algorithms for different scenarios. The probability generating function is embedded into the deterministic algorithm to exactly calculate the desired performance measures, which is reasonable when dealing with independent data (with a small number of product classes as well). The stochastic R-SPLINE algorithm uses simulation to estimate the desired probabilistic measures, allowing correlations between different production recipes as well as between different demand classes. We also present a convergence analysis for the stochastic R-SPLINE algorithm. Experimental results demonstrate the efficiency of the developed algorithms compared to other existing approaches.

Two commodity queueing inventory system with random common lifetime, two demand classes and pool of customers

This paper analyzes a two-commodity perishable queueing inventory system with random common lifetimes commodity-wise, positive service time, and individual ordering policy. We have considered two types of customers: priority customers and non-priority customers. There is a buffer of finite capacity for the priority customers and a pool with infinite capacity made available for the non-priority customers. The arrival of these customers is occurred by two independent Poisson processes. The lifetimes of each of the commodities and service times follow independent exponential distributions. The matrix geometric approach is used to analyze the model's steady-state behavior and we computed various system performance measures. This article examines the effects of the common lifetime parameters on the measures of performance, such as average cycle time, the average number of reorders due to the realization of the common lifetime of commodities, etc. Also, a sensitivity analysis of the system parameters on the performance measures is carried out. Further, a cost function is constructed based on the performance measures, and its convexity is examined numerically.

An operationalization of antifragility in inventory management

PurposeThe paper explores and characterizes antifragility in simple inventory systems exposed to demand variability, providing the initial inroads to operationalizing antifragility in the context of inventory management. Antifragility refers to the feature of a system that can benefit from uncertainty, rather than suffer from it. The paper expands the concept of inventory beyond that of risk mitigation and towards one of enabling antifragility.Design/methodology/approachThe study employs analytical and simulation modelling of an inventory system with two classes of demand. To separate the influence of factors, a simple inventory policy with a range of fixed order quantities is modelled, allowing for the identification of antifragile regions in an experimental space.FindingsOutputs uncover a variety of performance outcomes, ranging from settings where additional inventory yields no benefit, to areas where additional inventory results in increasing normalized profit with increasing uncertainty, demonstrating antifragility. In between these regions, increases in normalized profit are bounded, and confined to specific regions.Research limitations/implicationsThis research expands academic understanding of inventory as a vehicle to achieving antifragile outcomes. Although this paper does not solve for an optimal policy as typical inventory research does, it instead characterizes the antifragile outcomes within simple inventory systems. Further research should be carried out to investigate antifragility in settings of greater complexity and design ordering policies leveraging inventory towards a gain from uncertainty.Practical implicationsTypically, inventory is used to buffer against uncertainty, and, with a given amount of inventory, the performance is expected to degrade with increasing variability. In this paper, the authors demonstrate that certain levels of additional inventory can result in antifragility and increase profitability as uncertainty increases, extending beyond traditional conceptualizations of inventory and uncertainty.Originality/valueEmpirical research into designing antifragile outcomes is limited, with very few examples of increasing performance with increases in uncertainty. This article presents an initial empirical exploration of how additional inventory can result in antifragility.

The interplay of computational complexity and memory load during quantifier verification

ABSTRACTFormal analysis of the minimal computational complexity of verification algorithms for natural language quantifiers implies that different classes of quantifiers demand the engagement of different cognitive resources for their verification. In particular, sentences containing proportional quantifiers, e.g. “most”, provably require a memory component, whereas non-proportional quantifiers, e.g. “all”, “three”, do not. In an ERP study, we tested whether previously observed differences between these classes were modulated by memory load. Participants performed a picture-sentence verification task while they had to remember a string of 2 or 4 digits to be compared to a second string at the end of a trial. Relative to non-proportional quantifiers, proportional quantifiers elicited a sentence-internal sustained negativity. Additionally, an interaction between Digit-Load and Quantifier-Class was observed at the sentence-final word. Our results suggest that constraints on cognitive resources deployed during human sentence processing and verification are of the same nature as formal constraints on abstract machines.

Hed: Cal Poly's multi‐year journey to overhaul course offerings and boost student graduations

Cem Sunata was feeling helpless. As the University Registrar at California Polytechnic State University in San Luis Obispo he could see which high demand classes ran out of seats quickly. He could discern the patterns and inefficiencies at the 22,000‐student school that kept people from graduating, but he felt powerless to make the needed changes.

Cal Poly's multi‐year journey to overhaul course offerings and boost student graduations

Cem Sunata was feeling helpless. As the University Registrar at California Polytechnic State University in San Luis Obispo he could see which high demand classes ran out of seats quickly. He could discern the patterns and inefficiencies at the 22,000‐student school that kept people from graduating, but he felt powerless to make the needed changes.

Learning from Imbalanced Datasets: The Bike-Sharing Inventory Problem Using Sparse Information

In bike-sharing systems, the inventory level is defined as the daily number of bicycles required to optimally meet the demand. Estimating these values is a major challenge for bike-sharing operators, as biased inventory levels lead to a reduced quality of service at best and a loss of customers and system failure at worst. This paper focuses on using machine learning (ML) classifiers, most notably random forest and gradient tree boosting, for estimating the inventory level from available features including historical data. However, while similar approaches adopted in the context of bike sharing assume the data to be well-balanced, this assumption is not met in the case of the inventory problem. Indeed, as the demand for bike sharing is sparse, datasets become biased toward low demand values, and systematic errors emerge. Thus, we propose to include a new iterative resampling procedure in the classification problem to deal with imbalanced datasets. The proposed model, tested on the real-world data of the Citi Bike operator in New York, allows to (i) provide upper-bound and lower-bound values for the bike-sharing inventory problem, accurately predicting both predominant and rare demand values; (ii) capture the main features that characterize the different demand classes; and (iii) work in a day-to-day framework. Finally, successful bike-sharing systems grow rapidly, opening new stations every year. In addition to changes in the mobility demand, an additional problem is that we cannot use historical information to predict inventory levels for new stations. Therefore, we test the capability of our model to predict inventory levels when historical data is not available, with a specific focus on stations that were not available for training.

Latent classes and related predictors of demand for home-and community-based integrated care for older Chinese adults.

Home-and community-based integrated care has been developing rapidly in China in recent years. However, empirical research on the demand from older people is insufficient. Most studies have failed to identify or differentiate the heterogeneity of older people, resulting in poor understanding of their needs and fragmentation of services. This study seeks to identify latent classes of demand for home-and community-based integrated care among older Chinese adults and the predictors that differentiate these demands. From January to March 2021, a questionnaire was administered to older people (aged ≥60 years) in community-based service centers for older people in six districts of Changsha City, Hunan Province. Participants were selected through purposive and incidental sampling. Latent profile analysis was used to categorize older people's demand for home-and community-based integrated care. By extending Andersen's behavioral model of health service use, and running multinomial logistic regression analyses, we explored which factors influenced the latent classes of demand. A total of 382 older people were included in the analyses: 64.4% were women and 33.5% were aged 80-89. The demand from older people for home-and community-based integrated care was classified into four latent classes: high health and social interaction demand (30% - 115/382); high comprehensive demand (23% - 88/382); high care service demand (26% - 100/382), and high social participation and low care demand (21% - 79/382). Taking this last class as the reference group, the other three latent classes differed significantly in the factors of predisposition, enabling, need, and perception of aging. The demand from older people for home-and community-based integrated care is multifaceted and heterogeneous. Services for older people should be designed with different sub-models of integrated care.

A comparison of physiological intensity and psychological perceptions during three different group exercise formats.

Past research highlighted that group fitness is an ideal format to meet exercise prescription guidelines. To add, a group enhances exertion, enjoyment, and satisfaction. In the last five years, streaming (live classes on screen with other participants visible) and on demand (pre-recorded classes on screen without other participants visible) formats have grown in popularity. Our goal is to compare the physiological intensity and psychological perceptions of live group, live streaming, and non-live on demand classes. We hypothesize that live classes will have the greatest cardiovascular intensity, enjoyment, and satisfaction followed by streaming and finally on demand. Fifty-four adults between 18-63 years, who regularly participate in group fitness classes, recorded their heart rate with a chest transmitter during a mixed-martial arts cardiovascular class on consecutive weeks in random order. We calculated the mean, identified the max, and extracted the top 300 values (5 min) for comparison between conditions. Following each class, the participants completed an online survey to evaluate their rate of perceived exertion, enjoyment, and satisfaction. Confirming our hypothesis, mean class heart rate and mean heart rate for the five minutes at the highest intensity were 9% greater during the live group format compared to both live streaming and non-live on demand (all values, p < 0.01). However, there was no difference in any heart rate variables between the streaming and on demand formats. Also, rate of perceived exertion, enjoyment, and satisfaction were all significantly greater during the live session compared to the home collections (all values, p < 0.05). Streaming and on demand group fitness formats are viable options for meeting exercise prescription guidelines. But physiological intensity and psychological perceptions were greater during the live class format.

A complete algebraic solution to the optimal dynamic rationing policy in the stock-rationing queue with two demand classes.

In this paper, we study a stock-rationing queue with two demand classes by means of the sensitivity-based optimization, and develop a complete algebraic solution to the optimal dynamic rationing policy. We show that the optimal dynamic rationing policy must be of transformational threshold type. Based on this finding, we can refine three sufficient conditions under each of which the optimal dynamic rationing policy is of threshold type (i.e., critical rationing level). To do this, we use the performance difference equation to characterize the monotonicity and optimality of the long-run average profit of this system, and thus establish some new structural properties of the optimal dynamic rationing policy by observing any given reference policy. Finally, we use numerical experiments to demonstrate our theoretical results of the optimal dynamic rationing policy. We believe that the methodology and results developed in this paper can shed light on the study of stock-rationing queue and open a series of potentially promising research.

Оцінка пропускної спроможності міської дорожньої мережі з урахуванням пропозиції паркування

In order to improve the estimation of network capacity for a given urban road system, it is assumed that the impact of destination parking management strategies should be considered in the network capacity estimation model. This study presents an extended road network capacity model that takes into account both the number of parking spaces and parking rates in each traffic zone. The network capacity model is formulated as a two-level programming problem with maximizing the total trip generation at the upper level and the combined trip distribution and traffic assignment (CTDTA) problem at the lower level. In order to reasonably characterize the impact of parking pricing and parking delays due to congestion, the CTDTA model involves two classes of travel demand. An efficient and practical algorithm for solving the two-level network capacity model is presented. Numerical experiments show the advantages of the proposed model and demonstrate the impact of parking supply and pricing on the results of road network capacity assessment.

Periodic blood inventory system with two supplies and two priority demand classes

Managing blood inventory is challenging due to the perishable and unstable nature of the product needed for transfusions in healthcare facilities. In this paper, we consider a periodic review blood inventory model with two priority demand classes, namely emergency and regular patients. We propose a dynamic programming model for determining the optimal ordering policy at the hospital given the uncertainty regarding received donated blood units. The optimal policy deals with placing orders for blood units that will expire within a fixed period. The objective is to minimize total expected costs within a planning horizon while maintaining a specified expected service level. Our model considers uncertain demands and donated blood units with discrete probability following known distributions. A tabu search algorithm is developed for large-scale problems. The performance of these ordering policies is compared against the optimal fixed order quantity and the order up-to-level policies using real-life data. The numerical results show the benefit of our model over the optimal fixed order quantity and the order up-to-level policies. We measure the total expected cost and the expected service level obtained from the optimal and near-optimal policies and provide a sensitivity analysis on parameters of interest.

Spatial-Temporal Changes in Water Supply and Demand in the Citarum Watershed, West Java, Indonesia Using a Geospatial Approach

Balancing water supply demand is vital for sustaining livelihoods. Spatial mapping and calculating water yield dynamics due to land use changes over decades are needed to manage land resources and formulate ecological protection policies. This study mapped the supply, demand, and matching status of water product service using the Integrated Valuation of Ecosystem Service and Tradeoff (InVEST) biophysical models in the Citarum Watershed (CW) in 2000, 2010, and 2020. Moreover, this study used Exploratory Spatial Data Analysis (ESDA) and Geographic Information System (GIS) techniques to study the agglomeration characteristics and evolutionary trajectories of supply–demand over two decades. The results showed that between 2000–2010 and 2010–2020, the water supply decreased by 19.01 × 108 m3 (18.28%) and 12.97 × 108 m3 (15.27%), respectively. However, the water demand in the same period increased by 6.17 × 108 m3 (23%) and 15.74 × 108 m3 (47%), respectively. Over the decades, the contribution of land use land cover (LULC) changes to variations in water supply has yielded values ranging from 2.87% to 6.37%. The analysis of the water supply–demand imbalance indicated that the entire CW experienced water shortage, and the type of spatial matching for supply and demand is dominated by a high supply and high demand class (16.09% of the total area). Based on the level of water deficit calculation, the upstream and downstream areas were identified as zones that require ecological conservation, while the middle CW area requires ecological restoration or ecological improvement.

A sufficient statistics approach for welfare analysis of oligopolistic third‐degree price discrimination

This paper proposes a sufficient statistics approach to studying the welfare effects of third-degree price discrimination in differentiated oligopoly. Specifically, our sufficient conditions for price discrimination to increase or decrease social welfare simply entail a cross-market comparison of multiplications of such sufficient statistics as pass-through, conduct, and profit margin that are functions of first-order and second-order elasticities of the firm’s demand. Notably, these results are derived under a general class of market demand, and can be readily extended to accommodate heterogeneous firms. These features suggest that our approach has potential for conducting welfare analysis without a full specification of an oligopoly model.

Periodic review inventory models with multiclass demands and fixed order costs

We consider a periodic review inventory system with multiclass demands and fixed setup cost. Demand arrivals of each class are assumed to be a Poisson process, and a lost-sales setting is adopted. The demand classes are classified by the cost of their unsatisfied demands. We consider two cases: the leftover inventory at the end of a restocking interval is entirely discarded or entirely carried over to the next period. We obtain the optimal rationing policy, the optimal ordering policy and the optimal duration of the periodic review interval that minimize the average cost per unit time. We derive the differential equations satisfied by the value function characterized by the on-hand inventory level and the residual restocking time. This value function does not have the traditional modularity and convexity properties. Hence, the optimal policy is derived directly based on the ordinary differential equations satisfied by the value function. Moreover, some structural properties of the optimal policy such as the monotone property of the optimal rationing policy are obtained.

오피스텔 건축물의 문제점과 개선방안에 관한 연구

In this study, these officetels are one of the mixed-use buildings that well reflect the pluralistic and complex characteristics of the current society, and are being constructed particularly remarkably. Recognizing that it is a suitable type of housing, we propose that it is a downtown residential space that can accommodate the new demand class of 'one-two-person households', and seek the basis for planning directions and guidelines for 'officetel'. In this process, through conceptual analysis on the characteristics and lifestyle changes of consumers, literature research and case studies centered on 'officetel', and demand for residential space of consumers and experts' recognition of the importance of residential elements, It was intended to derive research results on officetels. Through this study, it is expected that the foundation will be prepared that can be used in the design of regional officetels reflecting the propensities of planning elements centered on users and experts with high quality satisfaction in the future. The scope of the study was spatially and temporally limited to single-block buildings among officetels built after 2000 in the Daegu area. This is because, with the exception of the strengthening of regulations on officetels in 2004, regulations are being eased, and the recently built facilities reflect the diverse needs of modern people as much as possible.