International Transactions in Operational Research最新文献

In the competitive landscape of plastic product manufacturing, optimizing production scheduling is crucial for enhancing efficiency and reducing costs. This paper presents an integrated approach to address the unrelated parallel machine scheduling problem (UPMSP) with a focus on minimizing setup costs and tardiness penalties, while also considering the necessity of preventive maintenance. We incorporate practical constraints such as release time, machine eligibility, and delivery due dates into our model to ensure its applicability to real-world manufacturing scenarios. We propose a mixed-integer linear programming model designed to minimize the total costs associated with setup and tardiness. To solve this complex problem, we develop a constructive heuristic and an adaptive large neighborhood search (ALNS) algorithm, which introduces novel destroy and repair operators. These algorithms are tailored to the specific structure of the UPMSP and are tested through a case study involving a real plastic product manufacturer. Results from the case study demonstrate that our proposed solution method significantly outperforms the company's current planning approach, achieving a reduction in total costs by 73.11%. Furthermore, the ALNS algorithm's performance is validated through numerical experiments on a range of randomly generated instances, showcasing its ability to provide high-quality solutions within practical computation times.

In post-disaster situations, time-critical decision-making is essential. Due to high demand and limited resources, visiting all affected locations is often infeasible. This study presents a novel variant of the covering tour problem that addresses these constraints by integrating location selection and vehicle assignment decisions. In the proposed problem, vehicles depart from selected relief centers, visit a subset of victim locations, and are allowed to complete their tours at any center. The demands of unvisited locations are satisfied through demand transfers from nearby visited nodes, with associated transfer times included in the total operation time. A mixed integer linear programming (MILP) model is formulated to minimize total operation time, incorporating both travel and demand transfer times. Scenario-based analyses are performed to evaluate the model's performance under various operational conditions, including transfer time sensitivity, route flexibility, demand coverage constraints, time-based covering radius, and partial fulfillment policies. To address scalability, a two-stage clustering-based heuristic is developed, offering a practical and computationally efficient solution method. From a humanitarian logistics perspective, the findings emphasize the importance of flexible routing, strategic placement of relief centers, and careful management of coverage thresholds. Additionally, the simplicity and adaptability of the proposed heuristic make it well suited for real-time decision-making in post-disaster response operations.

Integrating green initiatives and digital technology within supply chain management presents notable advantages, enhancing both sustainability and operational efficiency. This study examines the pivotal roles of blockchain and cybersecurity technologies in managing green supply chains under deterministic and stochastic conditions. It explores a three-tier supply chain setup encompassing a supplier, a manufacturer, and a retailer. Through a comparative analysis of traditional and digital implementation scenarios, the study delineates the impacts through numerical illustration. In the deterministic scenario, the classical optimization approach is applied by implementing a Stackelberg leader–follower game using the backwards induction method for optimal pricing and greening. In the stochastic scenario, for the centralized case, an optimization method based on a search technique is employed. The results underscore the indispensability of green investment and blockchain technology to facilitate optimal decision-making, with market variability exerting a significant influence on stakeholder profitability. Moreover, the stochastic nature of demand occasionally yields economic benefits. In the context of random scenarios, the centralized game significantly surpasses the traditional and only blockchain-implemented approaches seen in deterministic situations, indicating the financial resilience of each stakeholder. Managers should adopt green and pricing strategies that leverage blockchain and cybersecurity technologies to enhance transparency, reduce costs, and build customer trust, regardless of whether demand is deterministic or stochastic. By integrating these technologies, firms can optimize supply chain efficiency, mitigate risks, and align with sustainability goals while adapting to varying demand scenarios.

In a sector that needs to work as efficient as possible, artificial intelligence (AI) can guide the efficiency improvements of higher education institutions (HEIs). This paper explores both the AI literature and the efficiency literature as applied to HEIs following the Preferred Reporting Items for Systematic Review guidelines. The goal is to identify the relevant research that uses nonparametric efficiency and AI techniques within the HEI sector by examining articles published up to March 2025. Our findings provide a powerful mix of bibliometric and systematic literature review results that identify the main trends common to these two strands of research. The analysis highlights a long-standing tradition of applying nonparametric efficiency analysis to the sector, as it is attracting the increasing attention of AI scholars. We outline the substantial evidence that reveals much room for improvement in efficiency in the HEI sector, and how the application of AI may be well-suited. This is particularly evident as AI can support efficiency evaluations, particularly in handling tasks that traditional efficiency techniques alone cannot perform. A key contribution of this work is the identification of the opportunities for further research focus within this critical intersection between the two fields, which can inform both HEI administrators and policymakers.

The tree-of-hubs location problem (THLP) is a variant of the classical hub location problem, in which the set of hubs must be connected in a tree topology. The key decision variables involve the selection of hub locations, the allocation of spokes to hubs, and the design of a tree-structured interhub network. In this paper, we introduce a new extension of the THLP that incorporates stopover nodes—intermediate locations situated along the paths between hubs. The inclusion of stopovers helps reduce transportation costs by minimizing unnecessary back-and-forth trips and limiting transshipments at hubs. However, this benefit comes at the expense of potential detours in the interhub connections. We propose a mixed-integer linear programming (MILP) formulation to model this problem, with an objective function that minimizes the total cost of transporting commodities between multiple origins and destinations. Computational experiments are conducted using adapted instances from the AP-200 dataset. We also perform a sensitivity analysis on the discount factors associated with the use of stopovers. The results show that stopovers can lead to logistics cost savings of up to 15%. In addition, we provide managerial insights and quantify the impact of stopovers on network design costs by comparing solutions with and without their inclusion. Finally, we discuss the conditions under which stopovers can be effectively leveraged in practice.

Determining freight rates for heavy trucks involves a detailed analysis of multiple cost factors, including time, distance, fuel, and other operational costs, which collectively contribute to the overall compensation for transportation services. However, actual remuneration is based on more simplified agreements. Often, the standard agreement is based on the loaded driving distance. Such agreements provide an accurate description of the average cost over many transports but can be very unfair in compensation on single transports. This paper presents a pricing model for truck transportation that extends traditional models based on distance. The new model includes a measure of cost driving factors along the route, such as hills, road surface, curves, speed limits, intersections, speed changes, long ascents, and other physical difficulties. This measure is extracted from the Calibrated Route Finder, a route selection support system used for roundwood transportation in Sweden. The suggested price model that combines distance and a weighted resistance measure gives a better match between remuneration and full costing of a transport than a model that concentrates only on distance. The suggested model has been tested on a large annual transport data set and detailed and selected transportations evaluated by five large forest companies.