Dispatching Vehicles in a Mega Container Terminal

Port Authority of Singapore partialResearch was supported in part by the Port of Singapore Authority (PSA).</p

Introduction to the special issue : management science in the fight against Covid-19

At the time of writing of this Editorial in April 2021, Covid-19 continues to ravage our planet, with an official global death toll now exceeding three million, and a horrendous legacy of economic and human damage. The roll-out of vaccination has given hope that we will soon reach the end of this chapter of history. However, it will take years for the world to overcome this calamity and many individuals whose health or livelihoods have been destroyed will never fully recover. This failure of the world to effectively respond to the challenge of Covid-19 is all the more bitter because the outbreak of a novel pathogen was entirely predictable; the spread, preventable; and the suffering, avoidable. The experience of different countries around the world shows that the ability to plan, and to execute plans in a disciplined fashion, can make all the difference between relative security and catastrophe. The challenge for Management Scientists is to show that our discipline can have a role – a critical role – as a part of this planning. Epidemiological models of disease dynamics have been prominent through this crisis but do not fully capture the constraints in the health system and cannot directly support many of the management decisions which have to be made as part of the response. As Management Scientists, our perspective and our modelling tools have the potential to address those shortcomings; but if our profession cannot demonstrate our ability to add value, others will do so in our place

Optimal demand shaping for a dual-channel retailer under growing e-commerce adoption

The e-commerce adoption level within our society has been growing in the past decade, leading to dynamically evolving demand patterns across retailing channels. In this work, we study a dual-channel retailer’s optimal demand shaping strategy, through e-commerce marketing efforts and store service levels, in the presence of this dynamic evolution. Our stylized model integrates the growing adoption of e-commerce within society with individual consumers’ channel choice, and explicitly models the reference effects of the retailer’s prior decisions on consumer decision-making in a multi-period setting. This model allows us to characterize the settings in which e-commerce marketing is beneficial for the retailer, and to show that the retailer’s optimal demand shaping strategy depends on the product’s e-commerce adoption phase. Interestingly, we find that if the retailer provides the consumers with information on store availability levels, then the retailer’s optimal service levels stay constant over time, even if e-commerce adoption in the society grows. KEYWORDS: Dual-channel retailing, e-commerce adoption, adaptive learning, consumer choic

Optimal demand shaping for a dual-channel retailer under growing e-commerce adoption

The e-commerce adoption level within our society has been growing in the past decade, leading to dynamically evolving demand patterns across retailing channels. In this work, we study a dual-channel retailer’s optimal demand shaping strategy, through e-commerce marketing efforts and store service levels, in the presence of this dynamic evolution. Our stylized model integrates the growing adoption of e-commerce within society with individual consumers’ channel choice, and explicitly models the reference effects of the retailer’s prior decisions on consumer decision-making in a multi-period setting. This model allows us to characterize the settings in which e-commerce marketing is beneficial for the retailer, and to show that the retailer’s optimal demand shaping strategy depends on the product’s e-commerce adoption phase. Interestingly, we find that if the retailer provides the consumers with information on store availability levels, then the retailer’s optimal service levels stay constant over time, even if e-commerce adoption in the society grows.\u3cbr/\u3e\u3cbr/\u3eKEYWORDS: Dual-channel retailing, e-commerce adoption, adaptive learning, consumer choic

Allocation of flexible and indivisible resources with decision postponement and demand learning

We consider a firm that uses two perishable resources to satisfy two demand types. Resources are flexible such that each resource can be used to satisfy either demand type. Resources are also indivisible such that the entire resource must be allocated to the same demand type. This type of resource flexibility can be found in different applications such as movie theater complexes, cruise lines, and airlines. In our model, customers arrive according to independent Poisson processes, but the arrival rates are uncertain. Thus, the manager can learn about customer arrival rates from earlier demand figures and potentially increase the sales by postponing the resource allocation decision. We consider two settings, and derive the optimal resource allocation policy for one setting and develop a heuristic policy for the other. Our analysis provides managerial insights into the effectiveness of different resource allocation mechanisms for flexible and indivisible resources

Optimal capacity for substitutable products under operational postponement

We consider a monopolist producing two substitutable products with one flexible (shared) capacity. The demand of each product is a linear function of the prices of both products, and is subject to an additive shock. We study the impact of two key drivers, namely the degree of substitution between the products and the level of operational postponement, on the optimal capacity and the resulting expected profit. We show that the relationship between the optimal capacity and the degree of product substitution is not impacted by the different postponement strategies the firm can utilize or by the different settings (forced clearance versus holdback) considered in the previous literature. On the other hand, how capacity is affected by postponement critically depends on how closely substitutable the products are. In particular, we show that the well-known result that operational postponement and capacity are strategic complements in a single-product setting (Van Mieghem and Dada, 1999) no longer holds in our setting, because the two substitutable products are now linked through consumer-driven substitution, which the firm can influence through pricing. In particular, capacity and operational postponement (in the form of quantity postponement) can be either strategic substitutes or strategic complements, and this depends on both the firm's cost structure and the degree of substitution between the products. We also study the impact of forced clearance on the firm's expected profit and find that clearance deteriorates the firm's earnings more when the products it offers are highly differentiated.Capacity planning Pricing Flexible capacity Price/quantity postponement Product substitution Uncertainty

A robust pooled testing approach to expand COVID-19 screening capacity.

Limited testing capacity for COVID-19 has hampered the pandemic response. Pooling is a testing method wherein samples from specimens (e.g., swabs) from multiple subjects are combined into a pool and screened with a single test. If the pool tests positive, then new samples from the collected specimens are individually tested, while if the pool tests negative, the subjects are classified as negative for the disease. Pooling can substantially expand COVID-19 testing capacity and throughput, without requiring additional resources. We develop a mathematical model to determine the best pool size for different risk groups, based on each group's estimated COVID-19 prevalence. Our approach takes into consideration the sensitivity and specificity of the test, and a dynamic and uncertain prevalence, and provides a robust pool size for each group. For practical relevance, we also develop a companion COVID-19 pooling design tool (through a spread sheet). To demonstrate the potential value of pooling, we study COVID-19 screening using testing data from Iceland for the period, February-28-2020 to June-14-2020, for subjects stratified into high- and low-risk groups. We implement the robust pooling strategy within a sequential framework, which updates pool sizes each week, for each risk group, based on prior week's testing data. Robust pooling reduces the number of tests, over individual testing, by 88.5% to 90.2%, and 54.2% to 61.9%, respectively, for the low-risk and high-risk groups (based on test sensitivity values in the range [0.71, 0.98] as reported in the literature). This results in much shorter times, on average, to get the test results compared to individual testing (due to the higher testing throughput), and also allows for expanded screening to cover more individuals. Thus, robust pooling can potentially be a valuable strategy for COVID-19 screening

Adaptive risk-based pooling in public health screening

<p>Pooled testing is commonly used in public health screening for classifying subjects in a large population as positive or negative for an infectious or genetic disease. Pooling is especially useful when screening for low-prevalence diseases under limited resources. Although pooled testing is used in various contexts (e.g., screening donated blood or for sexually transmitted diseases), a lack of understanding of how an optimal pooling scheme should be designed to maximize classification accuracy under a budget constraint hampers screening efforts.</p> <p> We propose and study an <i>adaptive risk–based pooling</i> scheme that considers important test and population level characteristics often over looked in the literature (e.g., dilution of pooling and heterogeneous subjects). We characterize important structural properties of optimal subject assignment policies (i.e., assignment of subjects, with different risk, to pools) and provide key insights. Our case study, on chlamydia screening, demonstrates the effectiveness of the proposed pooling scheme, with the expected number of false classifications reduced substantially over policies proposed in the literature.</p