Omega-international Journal of Management Science最新文献

Crowd-shipping is a last-mile delivery concept in which commuters pick up and deliver parcels on their pre-existing paths. In urban areas, crowd-shipping circumvents problems that traditional last-mile delivery systems suffer from, such as road congestion and lack of parking spaces, especially if more sustainable modes of transport are utilized, like bikes or e-bikes. Using transfers between crowd-shippers allows for expanding the service area and improving the overall performance. However, as this requires synchronization over space and time, it makes the problem more complex. In this work, we develop a model that can encompass fully heterogeneous crowd-shippers and parcels. Thereby, it allows for both direct time-synchronized transfers as well as intermediate storage at designated parcel lockers. We design a column generation algorithm to solve large-scale realistic instances to optimality. We extend the problem to allow crowd-shippers to carry multiple parcels at the same time and for this, we extend the algorithm to simultaneous column and row generation. We evaluate the performance of our algorithm as well as the potential of crowd-shipping with transfers on a realistic case study of a bike-based crowd-shipping system in Washington DC. Our methods solve realistic instances with 1000 crowd-shippers and 1000 parcels within minutes. The results show that a gain in revenue and service level of 30% can be obtained by allowing transfers. By letting part of the population of crowd-shippers carry two or three parcels at the same time, the revenue and service level can be further increased by 30 to 50%. Maximum locker capacities are shown to be reasonable and are the highest in areas where there is a large gap between the moment when parcels are dropped off and when they are picked up from parcel points, which are mainly in the city center.

Once effective vaccines are discovered for a pandemic and initial distribution infrastructure, and mechanisms are implemented, the next concern is an optimal vaccination campaign to stop the disease or minimize the casualties. This paper studies the optimization of a Vaccine Distribution Network (VDN) through a single-objective Mixed-Integer Non-Linear Programming (MINLP) model for minimizing the total cost in terms of governmental aid (to maintain the economy), logistics, vaccination, and hospitalization costs. The proposed model develops the optimal joint vaccination-quarantine strategy under equity considerations to (1) determine the optimal allocation of available vaccine stockpiles to different population classes in different regions and (2) seek to impose quarantine restrictions to different regions optimally. The proposed model was applied to a real case study, the vaccine distribution network in France. Through a comprehensive numerical analysis, the optimal vaccine allocation is calculated, and the impact of equity and quarantine on the performance of the distribution network is investigated. Moreover, fractional dosing on the number of infections is examined. The results show that equity considerations, counter-intuitively, lead to an increased number of infections. Furthermore, it is illustrated that fractional dosing helps policymakers to control the pandemic better under a limited supply of vaccines.

This study examines how customer firms’ digital transformation affects the performance and value creation of their suppliers in the Chinese context. We use text mining to construct digital transformation indicators for customer firms based on data from Chinese A-share-listed firms from 2011 to 2021. We find that customer firms’ digital transformation has a positive and significant impact on supplier performance, and this effect is robust to various sensitivity tests. We also find that customer firms’ digital transformation influences supplier performance through two mechanisms: demand push and innovation spillover. Moreover, the heterogeneity analysis reveals that the effect of customer firms’ digital transformation on supplier performance varies by different factors, such as geographic distance, customer concentration, ownership type, and industry type. This study contributes to the literature on supply chain value from a customer-driven perspective and provides practical implications for supply chain firms to leverage digital transformation to enhance their performance and competitive advantage.

Online retailers throw out food that has not yet expired. This gives rise to the question whether online retailers generate more food waste than offline retailers, who typically throw out food only after it has expired. We focus on the food waste at the retailer which inherently ensues from the logistical set-up. We provide a theoretical discussion of the potential increase in food waste as a result of the inventory control policy and throwing out food that has not yet expired, as is the case for online retailers. Note that the online retailer has some advantages over offline retailers as well. Online retailers benefit from full control of order picking, which is instead often done by the consumer in offline retail. Moreover, the online retailer often benefits from the pooling effect, as offline retailers might use multiple stores to satisfy the same demand volume as an online retailer from a single warehouse. Our numerical experiments with a base-stock policy suggests that online retail actually yields less food waste for many products, despite throwing out food before expiration.

Among recent methodological proposals for efficiency measurement, machine learning methods are playing an important role, particularly in the reduction of overfitting in classical statistical methods. In particular, Support Vector Frontiers (SVF) is a method which adapts Support Vector Regression (SVR) to the estimation of production technologies through stepwise frontiers. The SVF estimator is convexified in a second stage to deal with convex technologies. In this paper, we propose SVF-Splines, an extension of SVF for the estimation of efficiency in multi-input multi-output production processes which uses a transformation function generating linear splines to directly estimate convex production technologies. The proposed methodology reduces the computational complexity of the original SVF and does not require a two-step estimation process to obtain convex production technologies. A simulated experiment comparing SVF-Splines with standard DEA and (convexified) SVF indicates better performance of the proposed methodology, with improvements of up to 95 % in mean squared error when compared with DEA. The computational advantages of SVF-Splines are also observed, with runtime over 70 times faster than SVF in certain scenarios, with better scaling as the size of the problem increases. Finally, an empirical illustration is provided where SVF-Splines is calculated with respect to various typical technical efficiency measures of the literature.

This paper proposes an innovative reverse logistics network (RLN) to manage kitchen waste (KW) transportation and resource treatment. The network employs battery electric (BE) trucks for transportation, and the challenge lies in determining the distribution of various KW treatment centers and establishing the optimal transportation routes for KW and its residues. The proposed RLN is self-sufficient, because the electricity produced by the centers within the network is adequate to power the BE trucks. We develop a matched mixed-integer programming model to optimize the entire process, with the goal of minimizing the total potential economic and environmental costs. Notably, the model considers comprehensive cost components and employs a carbon trading policy to translate carbon emissions into carbon costs. We use robust optimization to generate optimal solutions that remain viable even under the worst-case scenario concerning uncertain parameters. We then test the feasibility of the proposed methodology in a real-world case. We conduct specific scenario analyses on capacity and mode of trucks to offer practical KW transportation strategies and recommendations. We found that the larger the capacity of a BE truck, the greater the economic and environmental benefits for the KW RLN. The self-sufficient KW RLN using BE trucks proved to be the least costly, followed by the ordinary KW RLN using BE trucks, while the KW RLN using diesel trucks was the most expensive and environmentally detrimental.

With the booming development of social e-commerce platforms, an increasing number of brand owners are embarking on short video marketing activities through these channels. Nevertheless, brand owners encounter numerous quandaries in determining the optimal short video creation effort and traffic investment when they opt to rely on their own capabilities or enlist Key Opinion Leaders (KOLs) for their short video marketing campaigns. Hence, this paper delves into the sensitive demand function for short video creation effort and traffic investment. Subsequently, it formulates optimization decision frameworks based on the brand owner's self-built or KOL's short video channel. Accordingly, we delineate the brand owner's short video channel preference, and further design coordination mechanisms for the KOL short video channel. The research results first indicate that a stronger KOL influence and a lower signing fee will help the brand owner gain more profit after introducing the KOL short video channel. Secondly, the KOL is inclined to invest less in short video creation and traffic investment when engaging in short video marketing compared to the brand owner. Fortunately, the ex-ante traffic investment cost-sharing and cooperative decision can incentivize them to intensify their endeavors in short video creation and investing in traffic. Finally, we design the ex-post traffic investment cost-sharing and co-creation value distribution mechanisms under the cooperative decision situations, both of which can realize Pareto improvement, and it suggests that using the latter one would be fairer for the brand owner and KOL.

Evaluating the performance of public service providers is often complicated by the fact that they must choose input levels before demands for their services are known. We consider an even more complicated situation in which service providers have no opportunity to directly influence demands. This means that their predetermined inputs may be more than what is required to meet realised demands. In such cases, conventional measures of revenue efficiency will generally mis-classify rational and efficient managers as inefficient. We develop a more appropriate measure of revenue efficiency that accounts for exogenously-determined demands. We explain how data envelopment analysis (DEA) methods can be used to estimate our measure. The methodology is applied to hospital and health service networks in Queensland (Australia). We find that most of these networks were able to maximise the revenue they could obtain from their predetermined inputs.

Customer returns are a major problem for online retailers due to their economic and environmental impact. This paper investigates a new concept for handling online returns: customer-to-customer (C2C) returns logistics. The idea behind the C2C concept is to deliver returned items directly to the next customer, bypassing the retailer’s warehouse. To incentivize customers to purchase C2C return items, retailers can promote return items on their webshop with a discount. We build the mathematical models behind the C2C concept to determine how much discount to offer to ensure enough customers are induced to purchase C2C return items and to maximize the retailer’s expected total profit. Our first model, the base model (BM), is a customer-based formulation of the problem and provides an easy-to-implement constant-discount-level policy. Our second model formulates the real-world problem as a Markov decision process (MDP). Since our MDP suffers from the curse of dimensionality, we resort to simulation optimization (SO) and reinforcement learning (RL) methods to obtain reasonably good solutions. We apply our methods to data collected from a Dutch fashion retailer. We also provide extensive numerical experiments to claim generality. Our results indicate that the constant-discount-level policy obtained with the BM performs well in terms of expected profit compared to SO and RL. With the C2C concept, significant benefits can be achieved in terms of both expected profit and return rate. Even in cases where the cost-effectiveness of the C2C returns program is not pronounced, the proportion of customer-to-warehouse returns to total demand becomes lower. Therefore, the system can be defined as more environmentally friendly. The C2C concept can help retailers financially address the problem of online returns and meet the growing need for reducing their environmental impact.

This paper considers a supplier who offers a customized product to multiple potential customers, each with uncertain demand. The supplier’s end items consume common raw materials, which must be ordered far in advance of the selling season due to long procurement lead times, limiting the supplier’s capacity to meet realized demands. As a result of the customization, each customer negotiates an individual agreement with the supplier, leading to customer-specific prices and loss of goodwill costs in case of unsatisfied demands. The supplier aims to select a portfolio of customers and a raw material procurement quantity to maximize its expected profit. We formulate the problem based on echelon stockout costs, and establish optimality conditions and bounds for the newsvendor solution. This formulation, moreover, elegantly generalizes the analysis of the so-called selective newsvendor problem to address settings with lost sales and an uncertain spot market price for expediting. As customer requirements are oftentimes interdependent, we further extend our models to handle correlated customer demands. Lastly, we analyze the setting in which capacity can be reserved with the supplier prior to the selling season. Such contracts may serve as coordination mechanisms to improve overall supply chain profitability. Since the resulting models are in general difficult to solve exactly, we propose conic quadratic programming-based heuristics as well as sampling-based methods. We validate our approach through an extensive numerical study, illustrating the effectiveness of the proposed solution methods, the importance of explicitly considering correlation among customers due to risk pooling effects, and the impact of capacity reservation on both supplier and customer decisions. Finally, to support real-world applicability, we demonstrate the remarkable performance of the solutions obtained assuming normally distributed demands even when the true distributions deviate from this assumption.