Abstract
In a housing market of Shapley and Scarf, each agent is endowed with one indivisible object and has preferences over all objects. An allocation of the objects is in the (strong) core if there exists no (weakly) blocking coalition. We show that, for strict preferences, the unique strong core allocation respects improvement-if an agent's object becomes more desirable for some other agents, then the agent's allotment in the unique strong core allocation weakly improves. We extend this result to weak preferences for both the strong core (conditional on nonemptiness) and the set of competitive allocations (using probabilistic allocations and stochastic dominance). There are no counterparts of the latter two results in the two-sided matching literature. We provide examples to show how our results break down when there is a bound on the length of exchange cycles. Respecting improvements is an important property for applications of the housing markets model, such as kidney exchange: it incentivizes each patient to bring the best possible set of donors to the market. We conduct computer simulations using markets that resemble the pools of kidney exchange programs. We compare the game-theoretical solutions with current techniques (maximum size and maximum weight allocations) in terms of violations of the respecting improvement property. We find that game-theoretical solutions fare much better at respecting improvements even when exchange cycles are bounded, and they do so at a low efficiency cost. As a stepping stone for our simulations, we provide novel integer programming formulations for computing core, competitive, and strong core allocations.

Abstract
In a world facing unprecedented challenges, such as climate changes and growing social problems, the pharmaceutical industry must ensure that its supply chains are environmentally sustainable and resilient, guaranteeing access to key medications even when faced with unanticipated disruptions or crises. The core goal of this work is to develop an innovative simulation-based approach to support more informed and effective decision making, while establishing reasonable trade-offs between supply chain robustness and resiliency, operational efficiency, and environmental and social concerns. Such a decision-support system will contribute to the development of more resilient and sustainable pharmaceutical supply chains, which are, in general, critical for maintaining access to essential medicines, especially during times of crises or relevant disruptions. The system will help companies to better manage and design their supply chains, providing a valuable tool to achieve higher levels of resilience and sustainability. The study we conducted has two primary contributions that are noteworthy. Firstly, we present a new advanced approach that integrates multiple simulation techniques, allowing for the modeling of highly complex environments. Secondly, we introduce a new conceptual framework that helps to comprehend the interplay between resiliency and sustainability in decision-making processes. These two contributions provide valuable insights into understanding complex systems and can aid in designing more resilient and sustainable systems.

Abstract
This work addresses the flexible job shop scheduling problem with transportation (FJSPT), which can be seen as an extension of both the flexible job shop scheduling problem (FJSP) and the job shop scheduling problem with transportation (JSPT). Regarding the former case, the FJSPT additionally considers that the jobs need to be transported to the machines on which they are processed on, while in the latter, the specific machine processing each operation also needs to be decided. The FJSPT is NP-hard since it extends NP-hard problems. Good-quality solutions are efficiently found by an operation-based multistart biased random key genetic algorithm (BRKGA) coupled with greedy heuristics to select the machine processing each operation and the vehicles transporting the jobs to operations. The proposed approach outperforms state-of-the-art solution approaches since it finds very good quality solutions in a short time. Such solutions are optimal for most problem instances. In addition, the approach is robust, which is a very important characteristic in practical applications. Finally, due to its modular structure, the multistart BRKGA can be easily adapted to solve other similar scheduling problems, as shown in the computational experiments reported in this paper.

Abstract
Energy-efficient scheduling has become a new trend in industry and academia, mainly due to extreme weather conditions, stricter environmental regulations, and volatile energy prices. This work addresses the energy-efficient Job shop Scheduling Problem with speed adjustable machines. Thus, in addition to determining the sequence of the operations for each machine, one also needs to decide on the processing speed of each operation. We propose a multi-population biased random key genetic algorithm that finds effective solutions to the problem efficiently and outperforms the state-of-the-art solution approaches. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
This work addresses a variant of the job shop scheduling problem in which jobs need to be transported to the machines processing their operations by a limited number of vehicles. Given that vehicles must deliver the jobs to the machines for processing and that machines need to finish processing the jobs before they can be transported, machine scheduling and vehicle scheduling are intertwined. A coordi-nated approach that solves these interrelated problems simultaneously improves the overall performance of the manufacturing system. In the current competitive business environment, and integrated approach is imperative as it boosts cost savings and on-time deliveries. Hence, the job shop scheduling problem with transport resources (JSPT) requires scheduling production operations and transport tasks simultane-ously. The JSPT is studied considering the minimization of two alternative performance metrics, namely: makespan and exit time. Optimal solutions are found by a mixed integer linear programming (MILP) model. However, since integrated production and transportation scheduling is very complex, the MILP model can only handle small-sized problem instances. To find good quality solutions in reasonable com-putation times, we propose a hybrid particle swarm optimization and simulated annealing algorithm (PSOSA). Furthermore, we derive a fast lower bounding procedure that can be used to evaluate the perfor-mance of the heuristic solutions for larger instances. Extensive computational experiments are conducted on 73 benchmark instances, for each of the two performance metrics, to assess the efficacy and efficiency of the proposed PSOSA algorithm. These experiments show that the PSOSA outperforms state-of-the-art solution approaches and is very robust.(c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )

Abstract
This work formulates a mixed-integer linear programming (MILP) model and proposes a bi-objective multi-population biased random key genetic algorithm (mp-BRKGA) for the joint scheduling of quay cranes and speed adjustable vehicles in container terminals considering the dual-cycling strategy. Under such a strategy, a combination of loading and unloading containers are handled by a set of cranes (moved between ships and vehicles) and transported by a set of vehicles (transported between the quayside and the storage area). The problem consists of four components: crane scheduling, vehicle assignment, vehicle scheduling, and speed assignment both for empty and loaded journey legs. The results show that an approximated true Pareto front can be found by solving the proposed MILP model and that the mp-BRKGA finds uniformly distributed Pareto fronts, close to the true ones. Additionally, the results clearly demonstrate the advantages of considering speed adjustable vehicles since both the makespan and the energy consumption can be considerably reduced.