Abstract
The present study consists in the comparison of two metaheuristics in a scheduling problem (SP), in particular in the minimization of the makespan in flowshop problem. The two selected metaheuristics were DABC (Discrete Artificial Bee Colony) and ACO (Ant Colony Optimization). For the performance analysis, the metaheuristics were tuned with an extensive DOE study, subsequently, several tests were performed. Thirty-one evenly distributed instances were generated for a in-depth analysis and each one was subjected to three runs for each metaheuristic. Through the results obtained, it was possible to concluded that the DABC has a better performance when compared to SA and ACO. SA and ACO have a similar performance in the chosen problem. These conclusions were supported by descriptive statistics and statistical inference. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
The purpose of this study is to describe the construction process and the evidence of content validity of SCARA, a prototype of a technological system to support language and communication rehabilitation in people with aphasia, providing a tool that serves both patients and health professionals who accompany the respective recovery process. The process followed four stages: internal phase of the program's organization, with research in the literature and analysis of the materials available in the Portuguese market; construction of the SCARA prototype; evaluation by experts; and data analysis. A Content Validity Index was calculated to determine the level of agreement between the experts. The level of agreement between experts showed the validity of SCARA. SCARA has shown to help the work of the speech-language pathologist and persons with aphasia, contributing to a higher therapeutic quality, enhancing linguistic recovery, and compensating for the impossibility of direct support more frequently and/or prolonged intervention. © 2022 ACM.

Abstract
Objective: A systematic literature review was conducted to identify relevant ergonomic and safety factors for designing collaborative workspaces in industrial settings. Background: The growing use of smart and collaborative robots in manufacturing brings some challenges for the human-robot interaction design. Human-centered manufacturing solutions will improve physical and mental well-being, performance, productivity and sustainability. Method: A systematic review of the literature was performed based on the protocol of Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Results: After a search in the databases Scopus and Web of Science, applying inclusion and exclusion criteria, 33 publications in the English language, published between the years 2010 and 2020, remained in the final analysis. Publications were categorized in cognitive ergonomic factors (13), safety factors (10), physical ergonomic factors (6) and organizational ergonomic factors (4). The analysis of results reinforced that to optimize the design of collaborative workstations it is imperative to have a holistic perspective of collaboration, integrating multiple key factors from areas such as engineering, ergonomics, safety, sociology and psychological as well as manufacturing efficiency and productivity. Application: Considering the advantages of the use of cobots in manufacturing, the results of this review will be useful to support companies in implementing human-robot collaboration. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
This study investigates the potential of dynamically adjusting WIP cap levels to maximize the throughput (TH) performance and minimize work in process (WIP), according to real-time system state arising from process variability associated with low volume and high-variety production systems. Using an innovative approach based on state-of-the-art deep reinforcement learning (proximal policy optimization algorithm), we attain WIP reductions of up to 50% and 30%, with practically no losses in throughput, against pure-push systems and the statistical throughput control method (STC), respectively. An exploratory study based on simulation experiments was performed to provide support to our research. The reinforcement learning agent's performance was shown to be robust to variability changes within the production systems.

Abstract
This paper proposes a new problem by integrating the job shop scheduling, the part feeding, and the automated storage and retrieval problems. These three problems are intertwined and the performance of each of these problems influences and is influenced by the performance of the other problems. We consider a manufacturing environment composed of a set of machines (production system) connected by a transport system and a storage/retrieval system. Jobs are retrieved from storage and delivered to a load/unload area (LU) by the automated storage retrieval system. Then they are transported to and between the machines where their operations are processed on by the transport system. Once all operations of a job are processed, the job is taken back to the LU and then returned to the storage cell. We propose a mixed-integer linear programming (MILP) model that can be solved to optimality for small-sized instances. We also propose a hybrid simulated annealing (HSA) algorithm to find good quality solutions for larger instances. The HSA incorporates a late acceptance hill-climbing algorithm and a multistart strategy to promote both intensification and exploration while decreasing computational requirements. To compute the optimality gap of the HSA solutions, we derive a very fast lower bounding procedure. Computational experiments are conducted on two sets of instances that we also propose. The computational results show the effectiveness of the MILP on small-sized instances as well as the effectiveness, efficiency, and robustness of the HSA on medium and large-sized instances. Furthermore, the computational experiments clearly shown that importance of optimizing the three problems simultaneous. Finally, the importance and relevance of including the storage/retrieval activities are empirically demonstrated as ignoring them leads to wrong and misleading results.

Abstract
Problem definition: Marketplace platforms such as Amazon or Farfetch provide a convenient meeting point between customers and suppliers and have become an important element of e-commerce. This sales channel is particularly interesting for suppliers that sell seasonal goods under a tight time frame because they provide expanded reach to potential customers even though it entails lower margins. In this dyadic relationship, a supplier needs to optimize when to share inventory with the platform, and the platform needs to set the right commission structure during the season. Academic/practical relevance: We characterize supplier participation into the platform in a dynamic setting and link it to inventory levels, demand rates, time left in the season, and commission structure. This directly drives the commission structure decision made by the platform. We, thus, provide a framework to evaluate platform commission fee policies, taking into account supplier responses. Methodology: We use an optimal control framework with limited inventory supply and a stochastic demand process. We study the conditions under which the supplier accepts participation and use the platform as a sales channel. We also study the optimal commission structure that the platform should employ and the supplier procurement response. Results: We find that suppliers only participate if inventory is high relative to the time left to sell the items. As a result, the platform can only offer limited supply at the beginning of the season. Given this behavior, we find that the platform and the system are always better off with flexible pricing via fully dynamic commissions, which hurts the supplier the most (better off with less flexible commission fees). Interestingly, when the inventory decision is contingent on the platform pricing policy, the platform often finds it beneficial to commit to a static fee to incentivize the supplier to stock up, highlighting that inability to commit to fixed commissions may destroy value through double marginalization effects. Managerial implications: Our work suggests that short-term profit for the platform is maximized with fully dynamic commission fees at the expense of supplier profit. If inventory is endogenous, suppliers can retaliate by reducing their commitment at the start of the season. Despite the increased revenue obtained with the fully dynamic commission fee, the lost sales from the inventory drop incentivize the platform to opt for supplier-friendly commission fees, which are better for long-term-profit.