Abstract

Abstract
The Prisoner Transportation Problem is an NP-hard combinatorial problem and a complex variant of the Dial-a- Ride Problem. Given a set of requests for pick-up and delivery and a homogeneous fleet, it consists of assigning requests to vehicles to serve all requests, respecting the problem constraints such as route duration, capacity, ride time, time windows, multi-compartment assignment of conflicting prisoners and simultaneous services in order to optimize a given objective function. In this paper, we present anew solution framework to address this problem that leads to an efficient heuristic. A comparison with computational results from previous papers shows that the heuristic is very competitive for some classes of benchmark instances from the literature and clearly superior in the remaining cases. Finally, suggestions for future studies are presented.

Abstract
The Prisoner Transportation Problem is an NP-hard combinatorial problem and a complex variant of the Dial-a-Ride Problem. Given a set of requests for pick-up and delivery and a homogeneous fleet, it consists of assigning requests to vehicles to serve all requests, respecting the problem constraints such as route duration, capacity, ride time, time windows, multi-compartment assignment of conflicting prisoners and simultaneous services in order to optimize a given objective function. In this paper, we present a new solution framework to address this problem that leads to an efficient heuristic. A comparison with computational results from previous papers shows that the heuristic is very competitive for some classes of benchmark instances from the literature and clearly superior in the remaining cases. Finally, suggestions for future studies are presented.

Abstract
This study focuses on improving the picking processes within a Picking-by-Line (PBL) warehouse through the development of a simulation model to assess different layouts and new operational rules. Utilizing a combination of Discrete Event Simulation (DES) and Agent-Based Modeling (ABS) in AnyLogic, the simulation model was validated against real-world Key Performance Indicators (KPIs) to ensure accuracy. The study identified three primary improvement opportunities. To address these opportunities, four scenarios were tested. The results showed varying impacts on productivity, with three of the four scenarios yielding improvements in picking productivity. Pilot testing confirmed the simulation model's predictions. The findings indicate that balancing travel distance reduction with congestion management is key to increasing picking productivity. This study reaffirms the value of simulation modeling in warehouse management, providing a robust framework for free-risk testing. © 2025 Elsevier B.V., All rights reserved.

Abstract
Complex systems, characterised by high interconnectedness and unpredictability, demand structured approaches to support decision-making, system integration, and operational efficiency. This study aims to develop a comprehensive understanding of these systems to facilitate the implementation of Digital Twins (DTs) in Complex Operations Environments (COEs). A Systematic Literature Review (SLR), following PRISMA methodology, is conducted and complemented by a grounded theory approach to identify, organise, and synthesise system characteristics. The result is a hierarchical conceptualisation comprising eight core categories that capture the essential dimensions of complexity, including: Process and System Interoperability, Human Relationships, Behaviour/Nature, Products and Services, Multi-processes, Performance, System Structure, and Management and Skill Requirements. This framework offers a structured foundation for designing and implementing DT-based decision support in COEs.

Abstract
This paper studies the process of cutting steel bars in a truck suspension factory with the objective of reducing its inventory costs and material losses. A mathematical model is presented that focuses on decisions for a medium-term horizon (4 periods of 2 months). This approach addresses the one-dimensional 3-level integrated lot sizing and cutting stock problem, considering demand, inventory costs and stock level limits for bars (objects-level 1), springs (items-level 2) and spring bundles (final products-level 3), as well as the acquisition of bars as a decision variable. The solution to the proposed mathematical model is reached through an optimization package, using column generation along with a method for achieving integer solutions. The results obtained with real data demonstrate that the method provides significantly better solutions than those carried out at the company, whilst using reduced computational time. Additionally, the application of tests with random data enabled the analysis of both the effect of varying parameters in the solution, which provides managerial insights, and the overall performance of the method.