Abstract
Manufacturing environments commonly present uncertainties and unexpected schedule disruptions. The literature has shown that in these environments simple and fast dynamic dispatching rules are efficient sequencing methods. However, most of the works in the automated designing of these rules have considered deterministic processing times. This work aims to design dispatching rules for problem settings similar to the ones found in real environments such as uncertain processing times and sequence-dependent setup times. We use Genetic Programming to generate efficient rules for stochastic job shops with setup times. We show that the generated rules outperform benchmark dispatching rules, specially in settings with high setup time levels. © 2020, Springer Nature Switzerland AG.

Abstract
Soon, a new generation of Collaborative Robots embodying Human-Robot Teams (HRTs) is expected to be more widely adopted in manufacturing. The adoption of this technology requires evaluating the overall performance achieved by an HRT for a given production workflow. We study this performance by solving the underlying scheduling problem under different production settings. We formulate the problem as a Multimode Multiprocessor Task Scheduling Problem, where tasks may be executed by two different types of resources (humans and robots), or by both simultaneously. Two algorithms are proposed to solve the problem - a Constraint Programming model and a Genetic Algorithm. We also devise a new lower bound for benchmarking the methods. Computational experiments are conducted on a large set of instances generated to represent a variety of HRT production settings. General instances for the problem are also considered. The proposed methods outperform algorithms found in the literature for similar problems. For the HRT instances, we find optimal solutions for a considerable number of instances, and tight gaps to lower bounds when optimal solutions are unknown. Moreover, we derive some insights on the improvement obtained if tasks can be executed simultaneously by the HRT. The experiments suggest that collaborative tasks reduce the total work time, especially in settings with numerous precedence constraints and low robot eligibility. These results indicate that the possibility of collaborative work can shorten cycle time, which may motivate future investment in this new technology.

Abstract
We present a natural probabilistic variation of the multi-depot vehicle routing problem with pickup and delivery (MDVRPPD). In this paper, we present a variation of this deterministic problem, where each pair of pickup and delivery points are present with some probability, and their realization are only known after the routes are computed. We denote this stochastic version by S-MDVRPPD. One route for each depot must be computed satisfying precedence constraints, where each pickup point must appear before its delivery pair in the route. The objective is to find a solution with minimum expected traveling distance. We present a closed-form expression to compute the expected length of an a priori route under general probabilistic assumptions. To solve the S-MDVRPPD we propose an Iterated Local Search (ILS) that uses the Variable Neighborhood Descent (VND) as local search procedure. The proposed heuristic was compared with a Tabu Search (TS) algorithm based on a previous work. We evaluate the performance of these heuristics on a data set adapted from TSPLIB instances. The results show that the ILS proposed is efficient and effective to solve S-MDVRPPD. © 2020 Polish Information Processing Society - as it is since 2011.

Abstract
Supply chains have become one of the most important strategic themes in the aerospace industry in recent years as globalization and deep technological changes have altered the industry at many levels, creating new dynamics and strategies. In this setting, sustainability at the supply chain level is an emerging research topic, whose contributions aim to support businesses into the future. To do so the development of new products and the response to new industry requirements, while incorporating new materials appears as a path to follow, which require more resilient and agile supply chains, while guaranteeing their sustainability. Such supply chains will be better prepared for the future complex challenges and risks faced by the aerospace companies. Such challenges are addressed in this work, where an integrated framework is proposed to contribute to the resilience and sustainability of aerospace supply chains. Using different analysis methods, the framework addresses four important challenges in the context of aerospace supply chain sustainability: evolution and new trends, performance assessment, supplier selection, and supply chain design and planning. © 2019, Springer Nature Switzerland AG.

Abstract
Omnichannel retailing and digitalization result in considerable challenges for the management and optimization of retail operations. The continued demand of quantitative insights, their practical need, and the growing availability of data motivates an increasing number of scientists and practitioners to intensify research on demand and supply-related issues in retailing. This featured cluster provides the state-of-the art literature on forecasting and digitalization technologies, channel structures and delivery concepts as well as logistics in omnichannel and online retailing. The featured cluster contains 17 articles that deal with such topics. © 2021 Elsevier B.V.

Abstract
Technological advances in the last two decades have aroused great interest in the class of dynamic vehicle routing problems (DVRPs), which is reflected in the significant growth of the number of articles published in this period. Our work presents a comprehensive review of the DVRP literature of the last seven years (2015-2021) focusing mainly on applications and solution methods. Consequently, we provide a taxonomy of the problem and a taxonomy of the related solution methods. The papers considered for this review are discussed, analyzed in detail and classified according to the proposed taxonomies. The results of the analysis reveal that 65% of the articles deal with dynamic and stochastic problems (DS) and 35% with dynamic and deterministic problems (DD). With respect to applications, 40% of articles correspond to the transportation of goods, 17.5% to services, 17.5% to the transport of people and 25% to generic applications. Among the solution methods, heuristics and metaheuristics stand out. We discussed the application opportunities associated with DVRPs in recent business models and new concepts of logistical operations. An important part of these new applications that we found in our review is in the segment of business-to-consumer crowd-sourced services, such as peer-to-peer ride-sharing and online food ordering services. In our review many of the applications fall into the stochastic and dynamic category. This means that for many of these applications, companies usually possess historical data about the dynamic and uncertainty sources of their routing problems. Finally, we present the main solution streams associated with DVRPs.