Omega-international Journal of Management Science最新文献

Logistics delivery is a critical but costly operation in e-commerce. The e-retailer who sells products online may ask the supplier to ship products to customers, and the supplier responds by accepting or rejecting such a delivery proposal. Motivated by these observations, we consider an online distribution channel with two competing suppliers and one common e-retailer and analytically characterize the equilibrium delivery arrangement based on the nonlinear delivery cost in the E-retailer-Stackelberg, Supplier-Stackelberg, and Vertical-Nash games. Our analysis reveals that the market follower might be more willing to deliver products than the market leader in each Stackelberg game. Counterintuitively, the e-retailer prefers undertaking the delivery when there is a large delivery diseconomy under each power structure but prefers asking both suppliers to deliver products when there is a delivery economy (a small delivery diseconomy) in the E-retailer-Stackelberg and Vertical-Nash games (Supplier-Stackelberg game). The suppliers agree to ship products only when the delivery economy is apparent in the E-retailer-Stackelberg game but reject to do so in the Vertical-Nash game; in the Supplier-Stackelberg game, they accept the delivery proposal even though there is a significant delivery diseconomy. Although both suppliers can benefit from refusing to undertake the delivery when there is fierce competition in each Stackelberg game, interestingly, they still agree to do so and trap themselves in a prisoner's dilemma. In each Stackelberg game, only shifting the delivery responsibility to the suppliers might achieve a triple-win situation for the channel members, consumers, and society; in the Vertical-Nash game, however, this situation never occurs, and the consumer surplus and social welfare are invariant to the delivery arrangement.

This study develops a new DEA approach to incorporate such market-level information (i.e., average weights over firms) to create a composite corporate social performance (CSP) measure. The new model addresses the challenge that stakeholder expectations—expressed as the CSP weights of different CSP dimensions—are usually unknown at the firm level, but some reliable estimates of the average weights (across firms) are available at the industry or market level. Distinct from the traditional DEA, the new approach can incorporate constraints on the average values of weight distributions and is built on a computational framework new to the literature. The new model is compared with the traditional CSP approach, using longitudinal investment data by socially responsible funds (SRF). The regression results show evidence that the traditional CSP score, which is widely used in the management literature, may not be a reliable CSP score: firms with higher CSP scores tend to have lower SRF investment (in both the number of SRF and total shares). In contrast, the DEA-based CSP scores show the correct and positive correlation with SRF investment. Overall, this paper contributes to the literature by presenting a more reliable CSP approach and a new, generalizable DEA model. This is also the first empirical work documenting the potential validity issue of the traditional CSP scores.

Protecting critical infrastructure from intentional damage requires foreseeing the strategies of possible attackers. We formulate this problem as a Stackelberg Security Game. A defender must decide which specific targets to protect with limited resources, thus maximizing their expected utility (e.g., minimizing damage value) and considering that a second player (or players), called an attacker, responds in the best possible way.

Since Stackelberg Security Games are generally NP-hard, the main challenge in finding optimal strategies in real applications is to develop efficient methodologies for large instances.

We propose a general methodology to find a Strong Stackelberg Equilibrium for Stackelberg Security Games, exploiting the structure in the defender’s strategy set. This methodology consists of two steps. First, we formulate the problem by using variables representing the probability of defending each target. The formulation must be either a polynomial-size MILP and/or an MILP with an exponential number of constraints that are separable in polynomial time through branch-and-cut. In the second step, we recover the mixed strategies in the original space efficiently (in polynomial time) by using column generation. We apply this methodology to various security applications studied in the last decade. We generalize known examples and propose new examples. Finally, we provide an extensive computational study of the various formulations based on marginal probabilities.

We investigate the impact of diverse market beliefs held by supply chain firms on information flow and channel efficiency. In presentative settings, suppliers sell substitutable products through retailers to a market with uncertain demand and market competition is in quantity. Structure $B$ comprises a monopolistic supplier and a monopolistic retailer. With respect to structure $B$, structure $U$ includes competition between suppliers, structure $D$ includes competition between retailers, while structure $SC$ includes competition between two supply chains. A retailer has access to a demand signal useful for updating the forecast of market uncertainty. A supplier has no signal access but can offer a payment to a retailer, who decides whether to accept the payment and disclose signal to the supplier, termed information sharing. Suppliers and retailers hold diverse beliefs about market conditions. Knowing each other's market beliefs influences firms’ ex-post operations policies and ex-ante profit perception. We demonstrate that holding diverse beliefs about market uncertainty by firms facilitates information sharing except in the presence of only retailer competition, in which case it deters information sharing. Exchanging market beliefs about market uncertainty among firms strengthens their incentive to engage in information sharing, but it exerts mixed effects on their actual profits. By comparison, suppliers and retailers are more likely to benefit from exchanging market beliefs about market size, although it is inconsequential to information sharing. Moreover, we pinpoint the impacts of holding diverse market beliefs by firms on their relative performances across channel structures.

Home package delivery by drones as an alternative or complement to traditional delivery by trucks is attracting considerable attention from the commercial sector as well as academia. While drone delivery may offer considerable benefits, the fundamental issues of how best to deploy drones for small package delivery are still not well understood. Unlike most studies in the literature that consider either truck-drone tandems or drone standalone delivery operations, our research provides a strategic analysis for the optimal design of an integrated drone delivery system in which customers in a service region can be served by a combination of truck-drone (TD) delivery, drone-only (DO) delivery, and truck-only (TO) delivery. We formulate and optimize continuous approximation models to identify the optimal delivery service combination that minimizes the total delivery costs including truck and drone operating and stop costs. We evaluate the impact of a wide range of operating characteristics and delivery environments on the optimal delivery system and the benefits of using drones. Our results suggest that an optimal delivery system design often uses a mix of DO and TD, but TD is the dominant delivery service in most scenarios. The magnitude of the cost savings afforded by the use of drones, relative to truck-only delivery, can be large but strongly depends on the values of key parameters.

The aim of this paper is to conduct a comparative analysis of three environmental approaches in the context of a bank production framework, considering the presence of nonperforming loans (NPLs). Specifically, we examine banks' inefficiency levels using the "by-production technology," "joint-weak disposable technology," and "material balanced technology." To ensure comparability within a directional slack inefficiency framework, we propose a two-step procedure. The study is based on a sample of 379 prominent banks operating in the United States from 2003 to 2017. Our findings reveal that the material balance and by-production technologies result in estimated inefficiency measures with higher sensitivity compared to the estimator utilizing the joint-weak disposable technology. Additionally, we identify distinct properties among the estimators, emphasizing their unique characteristics for modeling nonperforming loans. Finally, our paper sheds light on the differences between the three estimators in relation to banks' inefficiency levels, considering the incorporation of nonperforming loans in the production process.

As climate change intensifies, more and more people realize that it is necessary and urgent to optimize energy structure through constructing energy asset portfolios. However, most of existing literature on energy asset portfolio models neglects real market restrictions, and the related portfolio performance evaluation approaches ignore the impact of the psychological factors of decision makers (DMs) on evaluation results, which limit their applications. To overcome these limitations, we first present a mean-WMCVaR (MWMC) model for energy stock portfolio selection with real features. Then, we employ the proposed MWMC model and its closely related works to yield a set of investment schemes and determine their characteristic index values including mean, WMCVaR, carbon intensity and ESG. Subsequently, we present a novel regret-rejoice cross-efficiency (NRRCE) approach to evaluate the performance of these investment schemes, and then make a comparison with the ones of the other seven competitive evaluation approaches by using an empirical example. Empirical results show that the proposed MWMC model can yield an optimal investment scheme with a better trade-off between economic and ecological benefits, and our NRRCE approach can effectively provide reasonable and reliable evaluation results for different investment schemes.

The bike-sharing system (BSS) serves as a vital shared mobility mechanism, providing first- and last-mile services in urban transportation. However, one ongoing challenge in the BSS is the spatiotemporal imbalance caused by fluctuating user demand. To address this issue, this paper studies a dynamic bike repositioning problem under scenario-based demand to achieve a balance in inventory levels for electronic fences. Specifically, a multi-period two-stage stochastic model is proposed to make the trade-off between operational costs and service quality. The model incorporates a vehicle no-return (VNR) strategy, which allows vehicles to remain at the last station at the end of each period, awaiting the next repositioning task. To solve the model, an improved rolling horizon framework is introduced to effectively handle the temporal dimension complexity of the multi-period problem. Then a customized hybrid heuristic algorithm based on parallel genetic algorithm and variable neighborhood search is developed to obtain high-quality solutions. To validate the effectiveness and applicability of the proposed method, small-scale and practical-scale numerical experiments are specifically conducted based on real-world BSS data from Beijing, China. The results demonstrate the proposed method (i) creates a substantial reduction in the number of unmet demands when multiple scenarios potentially occur compared to the determined model, (ii) yields an average reduction of 29.04% in operational costs compared to the scheme without the VNR strategy, (iii) overcomes the short-sightedness inherent in the conventional single-period rolling horizon approach, exhibiting closer to optimal performance, and (iv) is superior to baseline approaches in terms of solution quality and efficiency.

Edge computing brings computing and storage resources close to end-users to support new applications and services that require low network latency. It is currently used in a wide range of industries, from industrial automation and augmented reality, to smart cities and connected vehicles, where low latency, data privacy, and real-time processing are critical requirements. The latency of accessing applications in edge computing must be consistently below a threshold of a few tens of milliseconds to maintain an acceptable experience for end-users. However, the latency between users and applications can vary considerably depending on the network load and mode of wireless access. An application provider must be able to guarantee that requests are served in a timely manner by their application instances hosted in the edge despite such latency variations. This article focuses on the placement and traffic allocation problem faced by application providers in determining where to place application instances on edge nodes such that requests are served within a certain deadline. It proposes novel formulations based on robust optimization to provide optimal plans that protect against latency variations in a configurable number of network links. The robust formulations are based on two different types of polyhedral uncertainty sets that offer different levels of protection against variations in latency. Extensive simulations show that our robust models are able to keep the number of chosen edge nodes low while reducing the number of latency violations as compared to a deterministic optimization model that only considers the average latency of network links.

End-of-life (EOL) product recycling has received increasing attention because of potential environmental, social and economic benefits. A well-designed reverse supply chain (RSC) can efficiently handle EOL products. As the critical activity in the RSC, the disassembly process decomposes collected EOL products into components to fulfill the demands of remanufacturing plants. Efficiently coordinating RSC design and disassembly line balancing decisions may improve the whole system performance significantly, especially when facing multiple EOL products and uncertainty. This paper investigates a novel integrated RSC design and disassembly line balancing problem to handle multiple EOL products where the supply of EOL products, the demand for components, and task times in disassembly are stochastic. This complex problem needs to jointly determine the number and locations of disassembly plants, disassembly equipment procurement, disassembly line balancing, and inventory levels of both EOL products collected and components dismantled. The objectives are to maximize the expected profit and minimize the carbon emissions simultaneously. For the problem, a bi-objective two-stage stochastic programming model is formulated and an exact $\varepsilon$-constrained method is proposed, transforming the bi-objective problem into a series of single-objective problems. Especially, an improved Benders decomposition approach is developed to solve each single-objective problem efficiently. Numerical experiments comprising an illustrative case and 200 random instances are conducted to evaluate the performance of proposed methods. Moreover, some managerial insights are drawn.