International Transactions in Operational Research最新文献

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.

This study examines how environmental, social, and governance practices impact firms' technical efficiency, focusing on operational capabilities often overlooked in financial performance measures. Using a nonparametric frontier analysis with robust order-m estimators, we analyzed 6033 firms across Asia from 2015 to 2022. Results show a nonlinear, “U”-shaped relationship: initial environmental, social, and governance investments may decrease efficiency, but surpassing a threshold improves it. This suggests that while early environmental, social, and governance efforts may lead to inefficiency due to adjustments, long-term benefits include better stakeholder investment and innovation. Firms also showed greater resilience during the COVID-19 pandemic. The sector-specific analysis supports this “U”-shaped trend but highlights the need for detailed, sector-specific research.

Sustainability has become one of the main objectives in all human activities and, in particular, in manufacturing environments. In this paper, we consider the flexible job shop scheduling problem with the objective of minimizing energy consumption. As it is known that a considerable part of the energy consumption occurs when the machines are on and idle, the addressed problem includes the possibility of turning the machines off and on between processing operations. To bring the problem closer to the large variety of real-world problems it encompasses, we include two relevant factors: nonlinear routes and position-based learning effect. The treated problem is formally described through a mixed integer linear programming model. We propose constructive heuristics, two types of neighborhoods with which we construct local search schemes and three metaheuristics, namely, general variable neighborhood search, greedy randomized adaptive search procedure, and simulated annealing. We conduct a large number of experiments to evaluate the performance of the introduced methods on small-sized and large-sized instances. In the large-sized instances, the general variable neighborhood search that combines the two neighborhoods into a single method is particularly effective. In the small-sized instances with known optimal solutions, the greedy randomized adaptive search procedure finds solutions that, on average, are within 0.22% of the optimal solution.

This paper studies so-called connection scheduling problems, a type of interactive operations research problem. A connection scheduling problem combines aspects from the minimum cost spanning tree and sequencing problems. Given a graph, we aim to first establish a connection order on the players such that the total cost of connecting them to a source is minimal and second to find a fair cost allocation of such an optimal order among the players involved. We restrict our attention to connection scheduling problems on trees and propose a recursive method to solve these tree connection scheduling problems integrated with an allocation approach. This latter mechanism consistently and recursively uses benchmark endogenous myopic orders to determine potential cost savings, which will then be appropriately allocated. Interestingly, the transition process from a benchmark myopic order to an optimal one will be smooth using the switching of blocks of agents based on the basic notion of merge segments.