International Transactions in Operational Research最新文献

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.

Online reviews can help consumers learn that remanufactured products are as good as new products, and how to use online review rebate (ORR) strategies to increase the number of online reviews has become a key issue for remanufacturers. In this study, a supply chain model including the original equipment manufacturer and the independent remanufacturer (IR) was constructed, and three models—no ORR, unconditional ORR, and positive ORR—were compared. The results highlight that the IR prefers to choose the ORR strategy to increase profits. However, if the number of online reviews without rebates is already high, the IR prefers the use of no ORR strategy to prevent the negative effects of excessive rebate costs associated with the production of remanufactured products.

The transformative potential of artificial intelligence (AI) ChatBots, leveraging natural language processing for information retrieval and knowledge synthesis, has garnered attention across diverse fields, including research. Recognizing AI's importance, researchers and policymakers are actively engaged in its development. However, a notable gap persists in understanding the efficacy of various AI-ChatBots specifically within the rigorous process of conducting systematic literature reviews (SLRs). To address this, this study aims to provide a comparative analysis of different AI-ChatBots against traditional manual approaches in executing SLRs. Employing a case study methodology focused on the International Transactions in Operational Research journal, this research evaluates the benefits and pitfalls of integrating AI across various stages of the SLR process. The findings of this study will offer novel empirical evidence regarding the application of AI-ChatBots in SLR processes, thereby informing both researchers and policymakers on the practical implications and future directions of AI integration in academic research.

Districting and routing are crucial components in optimising transportation systems and complex logistics operations. Combining these components helps companies efficiently deliver goods worldwide, reducing costs and improving service levels. The present study contributes to the field by proposing an exact solution method for addressing a bi-objective problem, involving both districting and routing decisions. The problem tackled involves partitioning a set of customers into districts and determining routes to meet their demands. It is known as the capacitated vehicle routing and districting problem (CVRDP), which combines the capacitated vehicle routing problem (CVRP) with districting. There is one route per district, and every route starts and ends at a single depot, visiting the customers of the district exactly once. Vehicles must meet customer demands while respecting capacity constraints. The bi-objective problem aims to minimise the total travel time and district's dispersion. Four mixed-integer linear programming models are proposed, differing in their approach to prevent subtours. These models include the Improved Miller-Tucker-Zemlin (MTZ) and Single Commodity Flow (SCF) models, with both the disaggregated and the aggregated versions. The study applies the augmented $�\epsilon$-constraint method to find the, frequently named as, Pareto front. In the computational experiments, adapted instances from the literature are used in a four-phase analysis, where the number of districts is predetermined. Overall, the disaggregated SCF model outperforms the other models. This model also stands out as the best option for optimising in a single objective problem, specifically in terms of achieving compact districts and minimising the total travel time.

In this paper, we study a problem in the crossover between vehicle routing and scheduling problems. The multi-trip capacitated vehicle routing and scheduling problem with time windows and meal breaks is extended to a heterogeneous fleet of trucks and drivers, with qualifications and different loading patterns. We propose an optimisation model that solves the problem in reasonable time for small instances. Then, we introduce two heuristics in order to deal with real-life cases: a greedy heuristic and a matheuristic extension, the latter building upon exact methods to solve parts of the problem. We tested our algorithms on multiple new real-life instances on U.S. airports. Our computational study compares the effectiveness of each approach and asserts the strength of our matheuristic. Moreover, we implemented our algorithms to help ground handlers specialised in catering operations to speed up the decision-making process of assigning hundreds of tasks every day, with conclusive results.

Livestream selling has experienced quick growth recently, in which brands partner with influencers to promote product sales. Having amassed considerable fan bases and cultivated substantial loyalty among their audiences, some influencers are creating their own brands and becoming their brand customers’ competitors (competitive influencers hereafter). Then, should the brand go on contracting with such competitive influencers? We analyze the pros and cons of their coopetitive relationship by considering the existence of both traditional and fan consumers. Traditional consumers are flexible to purchase any product, while fan consumers solely purchase the influencer's promoted products. We find that the brand benefits when the competitive influencer's commission rate is moderate. Taking the influencer's livestream accidents into account, we find that the supply chain resilience performs better in the no cooperation scenario, and the livestream accidents do not necessarily hurt the brand. Interestingly, we find that either a high or a low commission rate will compel the brand and the competitive influencer to set higher selling prices. This may hurt consumer surplus, but the competitive influencer can significantly be benefited, even confronting a low commission rate.

The introduction of the European Union Artificial Intelligence (AI) Act, the NIST AI Risk Management Framework, and related international norms and policy documents demand a better understanding and implementation of novel risk analysis issues when facing systems with AI components: dealing with new AI-related impacts; incorporating AI-based assets within the cyber architecture; considering AI-based security and recovery controls within the cybersecurity portfolio; and managing novel AI-based targeted attacks. This paper suggests solutions to such issues and integrates them within a broad novel framework to support risk analysis in systems with AI components and services. An example concerning automated driving systems illustrates the framework validating it conceptually.

For many retailers with an existing consumer base, direct marketing, particularly, one-to-one marketing actions, is crucial. However, to decide which consumer should receive which kind of advertising at which point in time is challenging, particularly if the individual consumer's demand is history dependent, that is, influenced by his/her previous sales as well as previously received advertising. To effectively manage a heterogeneous consumer base over time, in this paper, we propose a relaxed dynamic programming-based consumer selection strategy optimizing long-term objectives under budget constraints. To be able to apply our model under unknown consumer behavior, we integrate exemplary predictions for history-dependent demand based on observable data. Using different synthetic consumer behavior models, we analyze under which conditions history-dependent consumer behavior can be estimated accurately from historic data. Finally, we evaluate the performance of our approach based on data-driven demand predictions. Our evaluations show that our model is general applicable and outperforms established consumer selection heuristics as well as solutions for greedy short-term models by up to 20%. The selection policies obtained are discussed and managerial insights are inferred.