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Problem definition: A significant percentage of online consumers place consecutive orders within a short duration. To reduce the total order arrangement cost, an online retailer may consolidate consecutive orders from the same consumer. We investigate how long the retailer should hold the consumer's orders before sending them to a third-party logistics provider (3PL) for processing. In this order-holding problem, we optimize the holding time to balance the total order arrangement cost and the potential delay in delivery. Methodology/results: We model the order-holding problem as a Markov decision process. We show that the optimal order-holding decisions follow a threshold-type policy that is straightforward to implement: Hold any pending orders if the holding time is within a threshold or send them to the 3PL otherwise. Whenever the consumer places a new order, the holding time is reset, and the threshold is updated based on a cumulative set of the past consecutive orders in the consumer's shopping journey. Using a consumer's sequential decision model, we personalize the threshold by finding its closed-form expression in the consumer's order features. We determine the model's coefficients and evaluate the threshold-type policy using the data of the 2020 MSOM Data Driven Research Challenge. Extensive numerical experiments suggest that the personalized threshold-type policy outperforms two commonly used benchmarks by having fewer order arrangements or shorter holding times. Furthermore, personalizing the order-holding decisions is significantly more valuable for "enterprise" customers. Managerial implications: Our research suggests a higher threshold for consumers who are more likely to place consecutive orders within a short duration. The consumers' demographic information has a significant effect on the threshold. Specifically, the threshold is higher for "plus" consumers, female consumers, and consumers in the age group of 16–25 years. The threshold for tier 1 cities is lower than that for tier 2 to tier 4 cities but higher than that for tier 5 cities. History: This paper has been accepted as part of the 2020 MSOM Data Driven Challenge. Funding: This work was supported by the National Natural Science Foundation of China [Grants 71931009, 72201237, and 72231009], the Research Grants Council of Hong Kong [Grants 15501920 and 15501221], the Singapore Ministry of Education Academic Research Fund [Tier 1, Grant RG17/21; Tier 2, Grant MOE2019-T2-1-045], the Association of South-East Asian Nations Business Research Initiative Grant [Grant G17C20421], and the Neptune Orient Lines [Fellowship NOL21RP04]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.1201. [ABSTRACT FROM AUTHOR]

We consider a periodic‐review perishable inventory system with multiple demand classes, each characterized by a different lost‐sales cost and the least freshness requirement. Demands of different classes in the same period could be correlated, while demands across periods are independent but not necessarily identical. In each period, the firm jointly makes the decisions regarding demand fulfillment, production/ordering, and disposal. The objective is to minimize the total discounted expected cost over the entire planning horizon including linear ordering cost, inventory holding/lost‐sales cost, expiration cost, and disposal cost. By establishing new properties of multimodularity, we explore some monotonicity and bounded sensitivity properties of the optimal policies. The optimality analysis enables us to propose a novel approximation approach, called adaptive approximation approach, which can be recursively calculated through a single‐dimension dynamic program. Numerical studies demonstrate that our proposed approximation approach is nearly optimal with the average optimality gap 0.30% and significantly outperforms the existing heuristics in the literature. [ABSTRACT FROM AUTHOR]

Manufacturing servitization has become a major trend in industry that is implemented by so called integrated production and service systems (IPSSs) to offer not only products but also their associated services. In this study, we explore the capacity allocation policy for an IPSS that consists of a manufacturing facility and a service center. The IPSS serves two classes of customers, each demanding a specific product produced at the manufacturing facility and, subsequently, the associated service offered by the service center. We formulate this problem of resource allocation as a Markov decision process. Our analysis suggests that it is optimal to serve the customer with the larger cost saving rate and slower service rate, where the cost saving rate equals service rate times the sum of holding cost and waiting cost. Then we explore production control policies coupled with optimal service ones. We demonstrate that, with light service traffic, the system under the optimal production policy becomes similar to a make‐to‐stock system. Such a system can effectively hedge the system uncertainty. Therefore, the optimal production policy for the IPSS is also a hedging point policy. Under this policy, switching and idling curves split the state space into three regions: one without any production, the two others for production of two type products, respectively. We also discuss the optimal control policies of the two extensions of the IPSS (one with multiple types of product and customers, and the other with a single product and two classes of customers) and find that the characters of the optimal production and service policy still hold. Finally, we develop three heuristic integrated scheduling policies. Numerical experiments show that one is very effective. [ABSTRACT FROM AUTHOR]

The present study investigates the benefit of joint decision making regarding whole blood collection and platelet production at a blood center. We consider a blood center that faces two types of platelet demands, differing in their freshness requirements and shortage penalty costs. We fully characterize the structure of the optimal policy regarding whole blood collection, platelet production, and inventory issuing, rationing and disposal. We find that the optimal platelet production quantity in each period is nonincreasing in the inventory levels of platelets and whole blood but that interestingly, the optimal blood collection effort may increase with the on‐hand platelet inventory level. We demonstrate with a real dataset that joint decision making leads to significant cost savings compared with separate decision making. The benefit is mainly derived from reduced blood collection and platelet production, better utilization of the collected whole blood, and reduced platelet shortage. For practical implementation, we develop a lookahead heuristic, which is shown to be very effective by numerical experiments. [ABSTRACT FROM AUTHOR]

In order to improve the utilization rate of spectroscopic data and texture information, this study proposes a method for optimal selection of spectrum and texture features based on automatic subspace division and rough set theory. This method takes advantage of rough set reduct ideology in order to realize the reduction of different types of ground object spectral features on the basis of the conventional subspace division method. In using this method, the primary spectral band based on spectral information can be determined. Then, the grey-level co-occurrence matrix method can be used to calculate the texture information of the primary spectral band and determine the reduction and optimization in order to obtain the final band based on the spectrum and texture information. Verification of this method is made by using CASI data of Heihe Region, China, and AVIRIS data of the Indiana Region, USA, and also using Support Vector Machine (SVM) classification of the original spectral, primary spectral, and final bands. The results indicate the following. (1) The method for optimal selection of the critical spectral band and texture band, based on the rough set theory, can efficiently improve the classification accuracy of high-spatial resolution remote-sensing images. However, the effects for the low-spatial resolution images are minimal. (2) For high-spatial-resolution remote-sensing images, such as roads, trenches, buildings, and other types of object with obvious textural features, the addition of image texture information can increase the degree of distinction of these different types and thereby improve the classification accuracy. However, the addition of the textural information for some objects with similar texture features will cause misclassification and reduce the classification accuracy for these types of images. (3) This method can realize the optimal selection of spectrum and texture bands of a hyperspectral image and has a certain universality. Also, the texture information will be richer and this method will be more practical through increasing the spatial resolution of images. [ABSTRACT FROM AUTHOR]

Wavelet neural networks have been successfully applied to object classification due to their unique various advantages. The wavelet neural network used in this paper is a type of back-propagation algorithm-learning wavelet neural network. The log-sigmoid function and wavelet basis function satisfying the frame condition are employed as an activation function in the output and hidden layers, respectively, and the entropy error function is also used to accelerate the learning speed. The log-sigmoid function has two saturated values, 0 and 1, which are the value of the function at a point whose value changes slightly as the independent variable changes at a somewhat wide range. Using this property of the saturated values and simplifying the mathematical model of neural network classification, we may mathematically prove that using different saturated values to encode the modes can affect the training error, generalization ability, and anti-noise ability of the wavelet neural network, in turn resulting in differences in classification accuracy. The saturated and unsaturated value-encoding modes will both decrease the generalization ability of the network and reduce the classification accuracy due to excessively strong or weak anti-noise ability. Therefore, we propose a type of moderate saturated-value encoding mode, in which the anti-noise ability, the gradient, and error in training process are more moderate than the other two encodings, so that this kind of encoding mode can facilitate a stronger generalization ability and higher classification accuracy for the wavelet neural network, and which have been affirmed in the classification experiments of CHRIS remote-sensing imagery of the Huanghe estuary coastal wetland and SIR-C remote-sensing image of sea ice in the Labrador Gulf, and reaffirmed in classification experiments where noise was added to the test data. [ABSTRACT FROM PUBLISHER]