International Transactions in Operational Research最新文献

The manufacturing of large metallic components typically involves cutting, welding, assembly, and machining, which consume a significant number of resources and result in substantial carbon emissions. In addition, there is a wide variety of intermediate parts in the production process, which is prone to the problem of kitting and long waiting times. The traditional single-stage scheduling made it difficult to achieve overall production optimization. Furthermore, due to the large size of the workpiece, transportation equipment also consumes a significant amount of energy and generates carbon emissions during transportation. In heterogeneous hybrid flowshop, there is significant potential for optimizing the collaborative scheduling between processing machines and transportation equipment as well as between different transportation devices. This study presents a green scheduling model that considers the optimization of both the maximum makespan and the carbon emissions generated by the processing machines and transport equipment. The model also takes into account the idle state of the processing machines to further reduce carbon emissions. A green scheduling strategy is proposed to solve this model, along with an enhanced NSGA-III (Non-Dominated Sorting Genetic Algorithm III) that integrates the Moth-Flame Optimization algorithm. Additionally, 15 arithmetic examples are provided to illustrate the manufacturing process of large metallic components of different scales. The effectiveness of the proposed algorithm is demonstrated through comparisons with commonly used intelligent optimization algorithms and non-collaborative scheduling. The findings highlight the efficacy of collaborative scheduling in the heterogeneous multi-stage hybrid flowshop for large metallic components, resulting in reduced manufacturing time and carbon emissions.

Rogaining is an orienteering running sport where participants need to decide what control points to visit and in which order. The objective is to collect the largest possible score associated with the visited controls, without violating the time limit. While extensive literature exists on the orienteering problem from a participants' point of view, there is limited understanding of how to design a rogaining contest where physical abilities do not dominate over cognitive skills in influencing the race outcome. To create these contests, we propose a heuristic bilevel optimization approach where at the upper level organizers assign scores to candidate control points, while at the lower level participants solve the classic orienteering problem. The simulation of the selected courses by the participants results in a provisional ranking that allows to evaluate the score assignment as determined by the organizer at the upper level. We apply our methodology to the 2023 World Rogaining Championships, demonstrating the necessity of thoughtful score allocation to ensure a balanced emphasis on both skills.

In recent decades, choosing sustainable suppliers (SS) within a supply chain (SC) has posed a significant challenge for management. The evaluation and selection of the optimal SS from a pool of suppliers in the SC stands as a pivotal factor in maintaining competitiveness in the market. Hence, decision-makers must seek the most effective method to identify key selection criteria for SS. This study aims to introduce a novel hybrid algorithm for the selection and assessment of the best SSs, encompassing varied criteria such as economic, environmental, sustainability, and human health considerations. The innovative algorithm combines the MEthod based on the Removal Effects of Criteria (MEREC) technique with network data envelopment analysis (NDEA). The enhanced NDEA model incorporates undesirable outputs and environmental emissions like CO₂. Initially, leveraging the MEREC approach, criteria weights are determined in two distinct groups: cost and benefit. Subsequently, inputs, intermediate products, and outputs are defined based on these weights, with NDEA models devised accordingly. The NDEA models effectively identify top suppliers based on efficiency. An efficient supplier is considered the best choice. The NDEA model highlights areas for improvement for inefficient suppliers while also leveraging insights from efficient ones. This leads to a comprehensive ranking of all suppliers, from which the best are selected. A case study at Shahriar Plast Company involving 10 suppliers and 11 criteria demonstrates the methodology's effectiveness. Results indicate that this hybrid algorithm is a reliable solution for supplier selection across various SCs. A comparative analysis with existing models further confirms the stability and reliability of the proposed algorithm, yielding consistent outcomes.

In the last decade, explainability has been attracting much attention in the machine learning community. However, this research topic extends beyond this field to encompass others such as operations research and combinatorial optimization (CO). This paper addresses this issue in the case of the workforce scheduling and routing problem (WSRP), a CO problem involving human resource allocation and routing decisions. We first introduce a novel mathematical framework that models the process of explaining solutions to the end-users of a WSRP-solving system. Then, we present original algorithmic methods to generate explanation texts employing a high-level vocabulary adapted to such end-users. Explanations are user-centered, local, and contrastive. They are triggered by end-user questions about various topics regarding a solution of a WSRP instance. Both questions and explanations are expressed as texts thanks to templates. Numerical experiments show that the algorithms generating explanation texts have execution times that are mostly compatible with the online use of explanations in an interactive system.

Increased awareness of people of the problems caused by CO₂ emissions brings companies to consider environmental issues in their distribution systems. The rapid advance in technology allows logistics companies to tackle with dynamic nature of distribution networks (e.g., a change in the vehicle speed due to unexpected events). The planned routes at the beginning of the time horizon could be subject to modification at any point in time to account for the recent traffic information. This study addresses a dynamic open time-dependent traveling salesman problem. The problem also involves speed optimization that aims to find optimal vehicle speed in a dynamic setting by respecting real-time traffic conditions. We develop a mixed integer linear programming (MILP) formulation for the addressed problem to determine routing and vehicle speed decisions. Furthermore, a MILP-based myopic-clustering decomposition heuristic algorithm has been introduced to solve large-sized instances within reasonable solution times. The use of the heuristic algorithm provides decision-makers with a responsiveness capacity by enabling fast incorporation of dynamically observed data during operations. The numerical analyses demonstrate the potential benefits of employing the proposed tools.

Over time, the focus on supportive and geriatric care has shifted from being predominantly provided in institutional settings like nursing or rest homes to be delivered within the homes of the patients. Trained caregivers now provide home healthcare services by visiting patients in their own homes and carrying out specific services based on each patient's individual needs before moving on to the next patient. Planning such a service involves considering the routing aspect and ensuring synchronization between services and designated time windows for patients. To solve the problem, we propose a local search approach that combines different neighborhood operators guided by the simulated annealing metaheuristic. Additionally, we introduce a realistic and diverse dataset and a robust and flexible file format based on JSON. This dataset and format have the potential to facilitate future comparisons and analyses. Our study shows that by appropriately tuning our algorithm in a statistically rigorous manner, it outperforms existing methods on all benchmarks.

Conflicts among tow trains pose a significant challenge in just-in-time manufacturing systems, impacting both safety and efficiency. This paper proposes an innovative solution to achieve conflict-free tow train routing. In our approach, the production layout is partitioned into “pixels.” The A-star (A*) algorithm is then employed on this pixel-based layout to create a distance matrix between workstations. Subsequently, a simulated annealing heuristic optimizes tow train routes to deliver parts demanded at the workstations. Additionally, a conflict detection algorithm identifies collisions among tow trains on the pixel layout, which are then resolved through two distinct conflict avoidance strategies. The algorithms are implemented and tested on a set of benchmark instances, demonstrating their effectiveness.

Portfolio management is an important research topic in finance and optimization. Drawdown as one of the measures in evaluating portfolios indicates the relative difference between the portfolio value in the current moment and its maximum value during a given time interval in the recent past. In this paper, first, the importance of this measure is discussed and then two mixed-integer nonlinear programming (MINLP) models with the objectives of minimizing the expected drawdown and the maximum drawdown under real-world constraints are presented. Due to the NP-hardness of this problem, by utilizing the problem structure, an efficient cross-entropy-based algorithm is presented to solve it. An effective mechanism is suggested to calibrate the algorithm parameters. Computational results confirm the performance of the proposed algorithm from both solution quality and running time in comparison with MINLP solvers.