Omega-international Journal of Management Science最新文献

To improve the operating efficiency of container terminals, we investigate a closed-loop scheduling method in an autonomous inter-terminal system that employs unmanned shipment vessels (USVs) to transport containers among operational berths (Dedicated to USVs) in seaport terminals. Our USVs scheduling model is developed by considering energy replenishment, time windows, and berth restrictions, aiming to obtain cost-saving USV transportation solutions and conflict-free paths. To solve this optimization model more efficiently, we propose the multi-attention reinforcement learning (MARL) algorithm by integrating an encoder-decoder framework and an unsupervised auxiliary network. The MARL algorithm provides instant problem-solving capabilities and benefits from extensive offline training. Experimental results demonstrate that our method can obtain efficient solutions for our USVs scheduling problem, and our algorithm outperforms other compared algorithms on computing time and solution accuracy.

As one of the 17 Sustainable Development Goals, reducing carbon emissions is crucial to combat climate change. It has also prompted companies to comply with emission regulations and evaluate the environmental impacts of their supply chains. Yet, news and reports occasionally highlight industrial instances of noncompliance. In particular, supplier’s noncompliance is often mistakenly attributed to its downstream manufacturers. Due to this misconception, manufacturers might conduct audits to protect their reputation and sales. Moreover, because a supplier may provide components to multiple competing manufacturers, they may collaborate to share audit findings regarding the common supplier’s compliance with carbon emissions regulations. However, studies do not reveal how this audit cooperation affects stakeholder interests. Here, we introduce a stylised model to examine the effects of carbon audit cooperation on the environment, competing manufacturers, and their supplier. We identify two main effects: the free-riding and amplifying effects. The former benefits the supplier but harms the environment and competing manufacturers, while the latter presents the opposite effect. The net impact depends on the balance between these two effects, which challenges conventional beliefs about carbon emissions compliance and highlights the importance of sustainability in the industry. Finally, we explore various extensions to validate the robustness of our findings.

Co-creation has become popular in experience good markets as it can resolve the uncertainty of the mismatch between product attributes and customer preferences. This paper delves into an examination of how firms strategize product co-creation with their customers in a competitive duopoly context, all the while taking into account the potential product return stemming from mismatch uncertainty. Results show that when the launching cost is moderate and the customer’s participation cost is relatively small, an asymmetric equilibrium with one firm launching the project and the other not is reached; otherwise, both firms adopt co-creation or not, conditional on the launching cost. Besides, firms may fall into a prisoner’s dilemma in which both firms launch the project when the launching cost is moderate. Furthermore, it is shown that co-creation functions as an effective way for firms to escape the Bertrand trap and reap the benefits of the product’s high valuation. Additionally, there is a mutual influence between co-creation and product return. A higher product return cost incentivizes firms to embrace co-creation, but meanwhile, it makes them more susceptible to falling into a prisoner’s dilemma scenario. In a state of equilibrium, co-creation consistently diminishes the product return for the firm engaging in it. Particularly, the firm not providing co-creation can take a free ride on the competitor’s co-creation benefits in reducing product return when the return cost is relatively large. Lastly, the value of co-creation can be amplified through the implementation of a price discrimination strategy, particularly when the participation cost is relatively large.

In this study we investigate whether the preference for deliberate and deep information processing, also known as epistemic motivation, affects an important feature of model-driven decision support practice, namely, to serve as an aid to surface and manage cognitive conflict within the group. We report on an experimental study that adopts a process-oriented methodology to examine the conflict management behaviours and trajectories of groups high and low in epistemic motivation. Our findings reveal that groups exhibited a variety of patterns of conflict management despite using the same model-driven decision support approach. In addition, three facets of epistemic motivation (discomfort with ambiguity, decisiveness, preference for order and structure) were significant co-variates of the variance in groups’ conflict management trajectories. Notably, only a small proportion of groups high in epistemic motivation were able to confront their conflict and resolve it in positive ways. Our findings have significant implications for the theory and practice of model-driven decision support.

The popularity of bike-sharing systems has constantly increased throughout the recent years. Most of such success can be attributed to their multiple benefits, such as user convenience, low usage costs, health benefits and their contribution to environmental relief. However, satisfying all user demands remains a challenge, given that the inventories of bike-sharing stations tend to be unbalanced over time. Bike-sharing system operators must therefore intervene to rebalance station inventories to provide both available bikes and empty docks to the commuters. Due to limited rebalancing resources, the number of stations to be rebalanced often exceeds the system’s rebalancing capacity, especially close to peak hours. As a consequence, operators are forced to manually select a subset of stations that should be prioritized for rebalancing. While most of the literature has concentrated either on predicting optimal station inventories or on the rebalancing itself, the identification of critical stations that should be prioritized for rebalancing has received little attention. Given the importance of this step in current operating practices, we propose three strategies to select the stations that should be prioritized for rebalancing, using features such as the predicted trip demand and the inventory levels at the stations themselves. Two sets of computational experiments aim at evaluating the performance of the proposed prioritization strategies on real-world data from Montreal’s bike-sharing system operator. The first set of experiments focuses on both the 2019 and 2020 seasons, each of which exhibits distinct travel patterns given the restrictive measures implemented in 2020 to prevent the spread of COVID-19. One of these strategies significantly improves by reducing the estimated lost demand by up to 65%, while another strategy reduces the estimated number of required rebalancing operations by up to 33% when compared to the prioritization scheme currently in use at the considered bike-sharing system. The second set of experiments evaluates the performance of the proposed strategies when rebalancing decisions are optimized in a rolling horizon planning. The results highlight various benefits of the proposed strategies, which are efficiently solved as transportation problems and improve lost demand over two intuitive baselines.

As a humanity crisis, the tragedy of forced displacement entails relief aid distribution efforts among en route refugee to alleviate their migration hardships. This study aims to assist humanitarian organizations in cost-efficiently optimizing the logistics of capacitated mobile facilities utilized to deliver relief aid to transiting refugees in a multi-period setting. The problem is referred to as the Capacitated Mobile Facility Location Problem with Mobile Demands (CMFLP-MD). In CMFLP-MD, refugee groups follow specific paths, and meanwhile, they receive relief aid at least once every fixed number of consecutive periods, maintaining continuity of service. To this end, the overall costs associated with capacitated mobile facilities, including fixed, service provision, and relocation costs, are minimized. We formulate a mixed integer linear programming (MILP) model and propose two solution methods to solve this complex problem: an accelerated Benders decomposition approach as an exact solution method and a matheuristic algorithm that relies on an enhanced fix-and-optimize agenda. We evaluate our methodologies by designing realistic instances based on the Honduras migration crisis that commenced in 2018. Our numerical results reveal that the accelerated Benders decomposition excels MILP with a 46% run time improvement on average while acquiring solutions at least as good as the MILP across all instances. Moreover, our matheuristic acquires high-quality solutions with a 2.4% average gap compared to best-incumbents rapidly. An in-depth exploration of the solution properties underscores the robustness of our relief distribution plans under varying migration circumstances. Across several metrics, our sensitivity analyses also highlight the managerial advantages of implementing CMFLP-MD solutions.

The Pairwise Comparison Table for the Deck of Cards Method enables the elicitation of preference parameters through an interactive process between an analyst and a Decision-Maker (DM). As in other preferences elicitation processes in Multiple Criteria Decision Aiding (MCDA) problems, this methodology can result in several inconsistent judgments. One way to address this concern is to use Linear Programming (LP) techniques to find solutions that minimize the number of initial inconsistent judgments that must be modified to restore consistency. However, since this approach does not allow further differentiating solutions, it can be inefficient in decision problems with multiple inconsistent information and several distinct solutions to overcome such inconsistencies. To enhance the decision-making process and to suggest solutions based on enriched information, we propose a new procedure for addressing inconsistent judgments based on two additional criteria to complement the minimum cardinality criterion. While the first additional criterion concerns the confidence level of the DM in their initial judgments, the second seeks to minimize the modifications made to the initial judgments to distort them as little as possible. Solving the three criteria in a specific LP-based lexicographic order defined by the DM makes it possible to determine a comprehensive ranking of eligible solutions to restore consistency. Proposing these solutions to the DM according to their position in the ranking enables revising the initial inconsistent judgments more efficiently. The new methodology also considers the possibility of missing confidence information, which can decrease the cognitive effort of the DM in real-world decision problems when eliciting preferences.

Additive manufacturing technology can enable low-cost, efficient production of low-demand, highly-complex customized items with reduced lead time if production is judiciously planned. This paper addresses the assignment of requested items into batches and the scheduling of the batches onto 3D printers. The objective is to minimize the manufacturing makespan while satisfying items’ compatibility, two-dimensional no-overlap and containment packing constraints within a batch, and machine’s capability to manufacture a batch. The problem is modeled as a mixed integer linear program (MIP) that solves instances up to 100 items. For hard and large instances, this paper proposes a matheuristic that fathoms packings using a step-wise check procedure. Computational results reveal that the proposed heuristic improves the makespan of MIP solutions of hard instances by 12% on average, with improvements reaching up to 72% for instances with 150 items. They further show that the proposed heuristic finds the best makespan for 88% of all cases. Finally, we provide useful managerial insights for production flexibility and scheduling policies.

In dense urban rail networks with high passenger demands, uncertain disturbances occur frequently, and the resulting train delays will likely spread over the whole network rapidly, hence degrading the service quality offered to passengers. To cope with the uncertainties of frequent disturbances in urban rail networks, this paper proposes a robust train regulation strategy based on the information gap decision theory, which allows the operators to adjust the conservativeness of adjustment schemes flexibly by varying system performances but without the need for prior knowledge of uncertain disturbances. Specifically, considering the coupling relationship between train dynamic flows and passenger dynamic flows, a mixed integer quadratically constrained programming (MIQCP) model is constructed for the robust train regulation problem to generate solutions with immunity against disturbance uncertainties, in which the envelope-bound model is used to characterizing the uncertain sets of disturbances. To meet the real-time requirements of train operation adjustment, a tailored outer approximation algorithm incorporating a two-phase heuristics method is devised to effectively solve the developed robust train regulation model, thereby quickly generating high-quality solutions. Moreover, the warm start technique and domain reduction technique are carefully developed to accelerate the solving procedure. Numerical experiments based on the Beijing metro network illustrate the robustness of the proposed train regulation strategies and the effectiveness of the designed solution approach.

Although the benchmarking literature has amassed both theoretical developments and empirical evidence on the performance of DMUs, the void related to the learning perspective of benchmarking remains to be filled. Further required methodological and empirical justifications aiming to fill this gap, are presented in this paper. Specifically, the mechanisms of knowledge transfer among the examined entities and the identification of the most influential source of learning require further investigation. We introduce a novel heuristic algorithm based on the Peer Removal to Improve Mean Efficiency in a Data Envelopment Analysis context to explore knowledge transfer within a benchmarking set. Based on sequential re-modifications of the technology following the removal of knowledge transmitters, a taxonomy arises including the role models, the knowledge receivers and the minimum efficiency DMU. Knowledge transfer is quantified by the calculation of the learning trace, following the removal of knowledge transmitters. We employ the most productive scale size to identify the most influential unit in terms of knowledge contribution. Findings from an illustrative example and a case study on European regions indicate that knowledge flows are not equally strong across benchmarking rounds.