Omega-international Journal of Management Science最新文献

The robotic mobile fulfillment system (RMFS), with wide application in warehousing and logistics, requires many robots powered by electricity, which significantly impacts energy consumption. This paper investigates the energy consumption in the RMFS under a classic e-business environment, which classifies the orders into regular orders and expedited orders. We evaluate the impact of three dynamic priority policies (the earliest deadline first policy, waiting time-dependent policy, and weighted waiting time first policy) on throughput time and energy consumption. This paper proposes multi-class semi-open queuing network models (SOQN) with dynamic priority policies to investigate energy consumption. We validate the accuracy of the analytical models by simulation models. This paper makes the following contributions: (1) In methodology, we propose new methods to solve the SOQN with dynamic priority policies. (2) In operational planning and control, we are among the earliest to investigate the impact of dynamic priority policies on order throughput time and energy consumption in an RMFS. (3) In design optimization, we propose a decision tool to optimize the robot number for realizing the required throughput time with minimal energy consumption. Our model can also decide the optimal warehouse shape to minimize energy consumption. (4) In system analysis, we estimate the energy consumption per transaction in an RMFS, providing logistics managers insights into energy saving of warehouses.

In the context of cooperation distribution among multiple wholesale markets, each customer can place orders with multiple agricultural wholesale markets, and each agricultural wholesale market can supply different products. To improve their distribution efficiency and avoid traffic congestion, cargoes need to be transshipped among collaborative distribution of different agricultural wholesale markets owing to their product heterogeneity. Each agricultural wholesale market undertakes the cost of completing the task for the reassigned customers of finding a balance among them, respectively. Fairness of allocation is achieved through workload balance and individual rationality constraints instead of monetary transfer payments. Aimed at the collaboration problem, a two-echelon collaborative many-to-many pickup and delivery problem with workload balance (2E-MPDP-WB) is presented in this study. A mixed-integer programming model for 2E-MPDP-WB is established, and a two-stage iterative algorithm combining branch-and-bound algorithm and adaptive large neighborhood search algorithm is proposed according to the problem structure. Some valid inequalities are also proposed. Finally, computational experiments show the correctness of the model and effectiveness of the algorithm, and sensitivity analysis is performed from four aspects, namely, costs before and after collaboration, workload balance, market geographical distribution, and demand mixing degree. The findings provide management insights for the collaborative distribution of multiple wholesale markets.

One fundamental issue in finance is portfolio selection, which seeks the best strategy for assigning capital among a group of assets. There has been growing interest in online portfolio selection where the investment strategy is frequently readjusted in a short time as new financial market data arrives constantly. Numerous effective algorithms have been extensively examined both in terms of theoretical analysis and empirical evaluation. Previous online portfolio selection algorithms that incorporate transaction costs are limited by the fact that they often approximate the transaction remainder factor instead of calculating it precisely. This could lead to suboptimal investment performance. To address this issue, we present an innovative method that considers transaction costs and resolves the accurate transaction remainder factor and the optimal portfolio allocation simultaneously for each period. In addition, we take into account the open-end fund, which permits constant cash inflows, and develop a framework for online portfolio selection. We also incorporate the uncertainty set to minimize the impact of the prediction error during the prediction process. Utilizing the framework presented in this innovative model, we develop a novel algorithm for online portfolio selection that incorporates transaction costs and continuous cash inflows with the objective of maximizing cumulative wealth. Numerical experiments show that the proposed algorithms are able to handle transaction costs and constant cash inflows effectively.

This paper uses concepts taken from Cooperative Game Theory to model the incentives to join forces among a group of agents involved in collaborative provision of a mobile app under uncertainty around an open source platform. Demand uncertainty leads the agents to reach a noncooperative equilibrium by offering low quality apps. This can be avoided by introducing a coordination scheme through a common platform that eliminates the effects of lack of information. Coordination is achieved by providing a revenue sharing scheme enforcing the stability of the collaboration but also defined in a “fair” way, depending on the importance of the resources that each provider supplies to the app. To this aim, we introduce the concept of Stochastic Provision Games. This coordination leads both to higher app quality and improved profitability for the participants.

In response to the challenges posed by high costs and rapid degradation of electric vehicle (EV) batteries, Battery as a Service (BaaS) is introduced as a new EV battery supply model, alongside the traditional model of Battery as a Product (BaaP). Under this background, how to choose a battery supply model is a major strategic problem. Through game theory approach, we analyze and compare the BaaS and BaaP models under a durable goods framework. Firstly, we find that the BaaS model is more profitable than the BaaP model when battery cost or degradation rate is excessively high. The firm under BaaS can relieve cost pressures by selectively retaining used batteries and reducing the price threshold for consumers to acquire batteries. Secondly, through premature disposal of used batteries, a firm can endogenously determine the quality of battery service, making the BaaS model more profitable when battery cost or degradation rate is excessively low. Thirdly, we show that the firm under BaaS always benefits from the decrease of battery degradation rate. However, a lower battery degradation rate may reduce the firm’s profit under BaaP because of the cannibalization effect from the secondary market.

The rapid expansion of the cold chain market is a key supply chain trend, but its high energy consumption conflicts with low-carbon goals. To address this, the paper proposes a multi-objective lot sizing procurement (LSP) model for managing the procurement of perishable products in the cold chain. This model, constrained by limited inventory and transportation capacity, aims to optimize multi-period procurement plans and order allocation and minimize total costs and carbon emissions. The proposed multi-objective LSP model adopts a posteriori mode, which contributes to enhancing the model's applicability, especially in countries where carbon tax and trading systems are not fully developed. To enhance decision makers’ decision efficiency and preserve the diversity of the original Pareto solutions as much as possible, the K-means++ algorithm is employed to prune the original Pareto solution set, providing decision-makers with three representative solutions (cost priority, balanced, and carbon priority solutions). In addition, the paper conducts sensitivity analysis, stability experiments, and compares the multi-objective LSP model with the benchmark model (relaxed carbon emission constraints). Experiments show that the multi-objective LSP model quickly and stably provides decision-makers with lot sizing purchasing plans for numerical examples of different scales and effectively controls the total carbon footprint of the entire cold chain at a low cost.

The waning efficiency of natural pollution sinks and the predicted consequence of their irreversible transformation into pollution sources within the current century call into question the environmental sustainability of economic activity and, more specifically, its basic engine: horizontal competition. Among factors likely to cast doubt on the environmental sustainability of competition, competing firms’ approaches to pollution control deserve particular attention because myopic approaches that ignore pollution dynamics accelerate such transformations. Another potentially harmful factor is the cost of adjusting production, which can hinder firms’ ability to reduce their polluting emissions in a timely way. In the context of a Cournot market structure, we consider competing firms that incur a cost when adjusting production and are subject to pollution emission taxes. We analyze how production adjustment costs and firms’ choice of myopic or farsighted pollution control policies affect their competitiveness and the long-run sustainability of the environmental system. We show that competition is not compatible with environmental sustainability under a myopic pollution control policy when the maximum threshold of pollution absorption has already been exceeded. Myopic policies cannot neutralize history dependency, and extremely large production adjustment costs exacerbate the problem. In contrast, farsighted pollution control policies can eliminate history-dependency as long as the tax policy is not too restrictive, though they lead to a more polluted environment than myopic policies operating below the maximum absorption efficiency threshold.

In centrally managed systems (CMSs), the need for incentivization systems at the local management level is crucial to optimize overall performance. Three alternative incentive systems have emerged within the centralized resource allocation (CRA) framework, aiming to measure the contribution of decision-making units (DMUs) in CMSs. However, we identify inconsistencies within these approaches and present them through illustrative examples. First, existing methods may struggle to effectively distinguish between CRA-efficient and CRA-inefficient DMUs, potentially resulting in inappropriate penalties or rewards for some the DMUs. Second, they may encounter difficulty in differentiating among CRA-efficient DMUs, especially when dealing with non-extreme DMUs or masked data within the dataset. Third, these methods may lack precision in measuring the impact of non-extreme CRA-efficient DMUs on overall performance. To address these limitations, we first highlight certain misconceptions related to individual efficiency within CMSs in the existing literature. Subsequently, we establish a fundamental characterization of individual efficient DMUs by outlining necessary and sufficient conditions for categorizing a DMU as CRA-efficient. For the second and third limitations, we adopt an endogenous perspective to quantify the influence of each CRA-efficient DMU. This involves calculating the maximum potential contribution of the DMU under evaluation in constructing the projection points of other DMUs. Furthermore, we propose a new method to handle masked data well in differentiating among CRA-efficient DMUs. We show the validity and applicability of our approaches using a real dataset.

Innovation in the sharing economy continues to thrive within healthcare systems. Emerging internet platforms and traditional hospitals/alliances have created diverse healthcare sharing scenarios, integrating a wide range of dispersed specialized medical resources to provide more convenient and efficient solutions to meet diverse demands. We adopt a classification framework based on staff, supplies, information, and alliances, guided by the concepts and scenarios of healthcare sharing. We mainly review research issues in the fields of operations management and information systems over the past decade, encompassing service pricing and matching, patient flow management, inventory management, multiple resources scheduling and allocation, network and mechanism design, quality evaluation, technology impact and cost-effectiveness analysis. Combining the advancements of digital intelligence technology and the evolution of healthcare sharing modes, we present some new problems, scenarios, and approaches, which offer potential opportunities for future research.

Targeted advertising has been extensively researched and utilized, yet it remains a challenge for the majority of small and medium-sized enterprises (SMEs) to effectively implement. To tackle this issue, e-commerce platforms have established member-oriented marketing windows that attract high-quality merchants and consumers, creating new opportunities for SMEs. Unlike traditional mass advertising (MA) in public windows, SMEs can achieve targeted advertising (TA) marketing to member and non-member groups separately by joining the membership marketing system. Two typical advertising cost-bearing structures are included for the platform’s membership marketing system: the Platform-Bearing-Cost (PBC) and the Merchant-Bearing-Cost (MBC) mechanisms. The results show that, regardless of the cost-bearing mechanism, the narrow fee gap between the two marketing windows leads to a higher advertising level and advertising investment for members under TA. Moreover, we identify the criteria under which the merchant adopts TA: a limited cross-purchase for the members in public windows. Furthermore, a sufficiently low members’ cross-purchase coefficient in public windows contributes to a TA win–win outcome for the platform and the merchant. Nevertheless, the platform does not always benefit from the merchant’s adoption of TA, which has inspired us to refine the platform’s membership marketing system further. Two viable options are proposed for consideration: discount adjustment and product screening. The former, which is a strategic adjustment of member-only discounts, is designed to induce merchants to adopt the ad format that aligns with the platform’s expectations. The latter, which is a set of rigorous entry criteria, is intended to permit only those products that are in high demand to participate.