International Transactions in Operational Research最新文献

We are pleased to present this special issue of International Transactions in Operational Research, which showcases the latest advancements in metaheuristics, as presented at the Metaheuristics International Conference (MIC 2022). This conference was held in the beautiful city of Syracuse, in Sicily, Italy, on July 11–14, 2022. The collection of papers in this issue reflects the breadth and depth of current research efforts, demonstrating both algorithmic innovation and practical applications.

This volume consists of ten papers, some of which were presented at the conference and some that were not. Together, they represent a significant contribution to the field of metaheuristics and operational research.

The papers cover a wide range of topics, including advances in quantum-inspired optimization, hybrid approaches for healthcare logistics, and bi-objective job shop scheduling with energy constraints. Other contributions focus on enhancing local search algorithms within the MOEA/D framework, applying adaptive iterated local search to location problems, and addressing the multi-objective traveling salesman–repairman problem with profits.

Further contributions explore iterated greedy algorithms for the obnoxious p-median problem, comparing QUBO models for quantum annealing, variable neighborhood search methodologies for the median location problem, and metaheuristics for flexible flow-shop scheduling with s-batching machines.

This special issue represents a significant contribution to the field of metaheuristics and operational research. We hope that the insights and innovations presented herein will inspire further research and development, driving advancements in both theory and practice.

We extend our gratitude to the authors for their outstanding contributions and to the reviewers for their meticulous evaluations. Together, they have ensured the high quality of this special issue.

This research develops an initial attack plan for combating forest fires in any wildland areas susceptible to fire outbreaks. To be eligible for such a plan, the landscape must have been previously mapped and modelled concerning spatial and topographic data and fuel levels. Thus, when ignition occurs, one can predict the expected fire behaviour in terms of spread direction and rate of spread. With such information, decisions can be taken on where and when to position the suppression resources. This paper extends a recent contribution to this subject, generalising each node's resource requirement, allowing a more precise modelling of non-homogeneous landscapes. Moreover, we treat the cases where the estimated number of resources may not be sufficient to deal with the fire intensity, which becomes revealed only at the fire scene. In such cases, additional resources may be needed to contain the fire effectively. This worst-case approach is modelled with the support of the robust optimisation paradigm. We propose a deterministic mathematical programming model, a robust optimisation counterpart, and a robust tabu search (RoTS) algorithm. We adapt instances from the literature, which are optimally solved by a commercial solver and used for assessing the quality of the RoTS. The proposed algorithm could optimally solve 94 of 96 instances. Finally, we conducted a Monte Carlo simulation as part of a risk analysis assessment of the generated solutions.

Artificial intelligence (AI) as a disruptive technology is not new. However, its recent evolution, engineered by technological transformation, big data analytics, and quantum computing, produces conversational and generative AI (CGAI/GenAI) and human-like chatbots that disrupt conventional operations and methods in different fields. This study investigates the scientific landscape of CGAI and human–chatbot interaction/collaboration and evaluates use cases, benefits, challenges, and policy implications for multidisciplinary education and allied industry operations. The publications trend showed that just 4% (n = 75) occurred during 2006–2018, while 2019–2023 experienced astronomical growth (n = 1763 or 96%). The prominent use cases of CGAI (e.g., ChatGPT) for teaching, learning, and research activities occurred in computer science (multidisciplinary and AI; 32%), medical/healthcare (17%), engineering (7%), and business fields (6%). The intellectual structure shows strong collaboration among eminent multidisciplinary sources in business, information systems, and other areas. The thematic structure highlights prominent CGAI use cases, including improved user experience in human–computer interaction, computer programs/code generation, and systems creation. Widespread CGAI usefulness for teachers, researchers, and learners includes syllabi/course content generation, testing aids, and academic writing. The concerns about abuse and misuse (plagiarism, academic integrity, privacy violations) and issues about misinformation, danger of self-diagnoses, and patient privacy in medical/healthcare applications are prominent. Formulating strategies and policies to address potential CGAI challenges in teaching/learning and practice are priorities. Developing discipline-based automatic detection of GenAI contents to check abuse is proposed. In operational/operations research areas, proper CGAI/GenAI integration with modeling and decision support systems requires further studies.

This paper considers a manufacturing system in which products are produced in both make-to-stock (MTS) and make-to-order (MTO) modes. Production of MTS and MTO products is done in batches, incurs a setup cost, and is non-preemptive. The inventory of MTS products fulfills the demand of multiple classes, and each class demand can be satisfied or rejected. Customer orders for MTO production can be accepted or rejected, and their size is the same as the production batch. The primary goal of this paper is to study a policy that coordinates inventory rationing, admission control, and production capacity allocation to maximize the system's profit. We formulate the problem as a Markov decision process model and identify the structure of optimal control policies. We investigate the effect of inventory rationing on the profit by comparing its performance to that of the system with a first-come- first-serve policy to allocate inventory to multiple demand classes and study the extent to which the benefit of inventory rationing can be affected by system parameter changes. We also propose a heuristic that manages control decisions from linear threshold functions. Our test results from numerical examples show that the average percentage difference between the optimal and heuristic policies is within 1.2%.