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Mass customized and mass personalized production has become facilitated by the fourth industrial revolution. The resulting industrial environments require the development of information systems able to take the specifications of customers and convey them to the production system in such a way as to contribute to the coordination of all the stakeholders and activities required to fulfill the orders of the customers. This is beyond the capabilities of traditional systems based on MRP and ERP, since the information should be managed in a flexible and decentralized way to exploit the Smart Manufacturing facilities of Industry 4.0. Blockchain, instead, is a technology that provides those features constituting a sound information supporting basis for mass customized/personalized production. Consequently, we explore the potential of blockchain as an information technology able to support industries that base their business models on mass customized/personalized production processes. This survey allows us to identify important challenges for further developments, highlighting three issues in the production setting: (i) to deepen the interoperability of systems, (ii) to generate more implementations, and (iii) to develop efficient consensus protocols. As a response to these insights we provide a conceptual design of how blockchain contributes to managing efficiently mass customized production systems. In our design the information of customer specifications can be fused with data from the production process to generate a plan to fulfill the demand. This design arises as a solution approach to three stated problem, which are faced by mass customized production systems.

Industry 4.0 proposes the incorporation of information technologies at all levels of the production process. By incorporating these technologies, Industry 4.0 provides new tools for production planning processes, allowing to address problems in an innovative and efficient manner. From these technologies and tools, it is that in this work a One-of-a-Kind Production (OKP) process is approached, where the products tend to be highly customized. OKP implies working with a very large variability within production, demanding very efficient planning systems. For this, a planning model based on CONWIP-type strategies was proposed, which seeks to level the production of a shop floor configured in the form of a job shop. Even more, for having a more realistic shop-floor representation, machine failures have been included in the model. In turn, different dispatching rules were proposed to study the performance and analyze the behaviour of the system. From the results obtained, it is observed that, when the production demand is very exigent in relation with the capacity of the system, the dispatching rules that analyze the workload generated by each job tend to perform better. However, when the demand on the capacity of the production system is less intense, the rules associated with due dates are the ones that obtain the best results.

In this paper we introduce a novel approach to the combinatorial analysis of flow shop scheduling problems for the case of two jobs, assuming that processing times are unknown. The goal is to determine the dominance properties between permutation flow shop (PFS) and non-permutation flow shop (NPFS) schedules. In order to address this issue we develop a graph-theoretical approach to describe the sets of operations that define the makespan of feasible PFS and NPFS schedules (critical paths). The cardinality of these sets is related to the number of switching machines at which the sequence of the previous operations of the two jobs becomes reversed. This, in turn, allows us to uncover structural and dominance properties between the PFS and NPFS versions of the scheduling problem. We also study the case in which the ratio between the shortest and longest processing times, denoted ρ, is the only information known about those processing times. A combinatorial argument based on ρ leads to the identification of the NPFS schedules that are dominated by PFS ones, restricting the space of feasible solutions to the NPFS problem. We also extend our analysis to the comparison of NPFS schedules (with different number of switching machines). Again, based on the value of ρ, we are able to identify NPFS schedules dominated by other NPFS schedules. Fil: Rossit, Daniel Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina Fil: Vásquez, Óscar C.. Universidad de Santiago de Chile; Chile Fil: Tohmé, Fernando Abel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina Fil: Frutos, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones Económicas y Sociales del Sur. Universidad Nacional del Sur. Departamento de Economía. Instituto de Investigaciones Económicas y Sociales del Sur; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones Económicas y Sociales del Sur. Universidad Nacional del Sur. Departamento de Economía. Instituto de Investigaciones Económicas y Sociales del Sur; Argentina Fil: Safe, Martin Dario. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina

Cloud Manufacturing (CMFg) is a novel production paradigm that benefits from Cloud Computing in order to develop manufacturing systems linked by the cloud. These systems, based on virtual platforms, allow direct linkage between customers and suppliers of manufacturing services, regardless of geographical distance. In this way, CMfg can expand both markets for producers, and suppliers for customers. However, these linkages imply a new challenge for production planning and decision-making process, especially in Scheduling. In this paper, a systematic literature review of articles addressing scheduling in Cloud Manufacturing environments is carried out. The review takes as its starting point a seminal study published in 2019, in which all problem features are described in detail. We pay special attention to the optimization methods and problem-solving strategies that have been suggested in CMfg scheduling. From the review carried out, we can assert that CMfg is a topic of growing interest within the scientific community. We also conclude that the methods based on bio-inspired metaheuristics are by far the most widely used (they represent more than 50% of the articles found). On the other hand, we suggest some lines for future research to further consolidate this field. In particular, we want to highlight the multi-objective approach, since due to the nature of the problem and the production paradigm, the optimization objectives involved are generally in conflict. In addition, decentralized approaches such as those based on game theory are promising lines for future research. Fil: Halty, Agustín. Universidad de la República; Uruguay Fil: Sánchez, Rodrigo. Universidad de la República; Uruguay Fil: Vázquez, Valentín. Universidad de la República; Uruguay Fil: Viana, Víctor. Universidad de la República; Uruguay Fil: Piñeyro, Pedro. Universidad de la República; Uruguay Fil: Rossit, Daniel Alejandro. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina

The literature on flow shop scheduling has extensively analyzed two classes of problems: permutation and non-permutation ones (PFS and NPFS). Most of the papers in this field have been just devoted on comparing the solutions obtained in both approaches. Our contribution consists of analyzing the structure of the critical paths determining the makespan of both kinds of schedules for the case of 2 jobs and m machines. We introduce a new characterization of the critical paths of PFS solutions as well as a decomposition procedure, yielding a representation of NPFS solutions as sequences of partial PFS ones. In structural comparisons we find cases in which NPFS solutions are dominated by PFS solutions. Numerical comparisons indicate that a wider dispersion of processing times improves the chances of obtaining optimal non-permutation schedules, in particular when this dispersion affects only a few machines. Fil: Rossit, Daniel Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina Fil: Tohmé, Fernando Abel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina Fil: Frutos, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones Económicas y Sociales del Sur. Universidad Nacional del Sur. Departamento de Economía. Instituto de Investigaciones Económicas y Sociales del Sur; Argentina Fil: Safe, Martin Dario. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina Fil: Vásquez, Óscar C.. Universidad de Santiago de Chile. Departamento de Ingeniería Industrial; Chile

In this work, the flow shop scheduling problem with energy awareness is approached with lot-streaming strategies. Energy consumption is modeled within the objective function, together with the makespan, by means of a normalized and weighted sum. Thus, reducing energy consumption guides the optimization process. For lot streaming approaches mathematical models are provided and assessed. The results showed that lot-streaming is an efficient strategy to address this problem, allowing to improve both makespan and total energy consumption compared to the problem without lot-streaming. In turn, the selection of processing speeds for each sublot was incorporated, which improved the strategy yielding the best quality solutions. Fil: Florencia D'Amico. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina Fil: Rossit, Daniel Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina Fil: Frutos, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones Económicas y Sociales del Sur. Universidad Nacional del Sur. Departamento de Economía. Instituto de Investigaciones Económicas y Sociales del Sur; Argentina

“Digital twining” is one the main ways of establishing data channels in cyber-physical systems using both the outputs of running a virtual model and real time data collected by sensors. The purpose to this paper is to outline the digital twin of a cyber-physical production system. We apply the System Dynamics paradigm to the case of a shop-floor factory devoted to cloud manufacturing. The digital twin uses data from the real production line, providing assistance to maintenance procedures triggered by inconsistencies between the real and the virtual processes.

Industry 4.0 is a modern approach that aims at enhancing the connectivity between the different stages of the production process and the requirements of consumers. This paper addresses a relevant problem for both Industry 4.0 and flow shop literature: the missing operations flow shop scheduling problem. In general, in order to reduce the computational effort required to solve flow shop scheduling problems only permutation schedules (PFS) are considered, i.e., the same job sequence is used for all the machines involved. However, considering only PFS is not a constraint that is based on the real-world conditions of the industrial environments, and it is only a simplification strategy used frequently in the literature. Moreover, non-permutation (NPFS) orderings may be used for most of the real flow shop systems, i.e., different job schedules can be used for different machines in the production line, since NPFS solutions usually outperform the PFS ones. In this work, a novel mathematical formulation to minimize total tardiness and a resolution method, which considers both PFS and (the more computationally expensive) NPFS solutions, are presented to solve the flow shop scheduling problem with missing operations. The solution approach has two stages. First, a Genetic Algorithm, which only considers PFS solutions, is applied to solve the scheduling problem. The resulting solution is then improved in the second stage by means of a Simulated Annealing algorithm that expands the search space by considering NPFS solutions. The experimental tests were performed on a set of instances considering varying proportions of missing operations, as it is usual in the Industry 4.0 production environment. The results show that NPFS solutions clearly outperform PFS solutions for this problem. Fil: Rossit, Daniel Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina Fil: Toncovich, Adrián Andrés. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina Fil: Rossit, Diego Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina Fil: Nesmachnow, Sergio. Facultad de Ingeniería; Uruguay

We address an extension of the discrete lot-sizing and scheduling problem in which the demand of products can be also satisfied by remanufacturing used products returned to the origin. A mixed-integer linear programming formulation is provided for the problem, assuming dynamic demand and returns values, and time-invariant costs of setup and holding inventory. We then present a numerical experimentation carried out with the mathematical model for the case of a single product, in order to evaluate the efficiency in both costs and solving times compared to the traditional problem without returns. From the results obtained we conclude that the problem with recovery options can lead to economic benefits only under certain assumptions on the costs and amounts of returns. In addition, according to the runtimes obtained for large instances of the problem, it seems to be much more difficult to solve than its traditional version without returns.

In this paper, we introduce the problem of minimizing total implementation cost of a 2-D indoor localization system for underground mine tunnels, guaranteeing a constant accuracy. To address this problem, we propose a system based on Cell-ID technique and visible light communication (VLC) technology with square panels of light-emitting diode (LED) lights, which fixes the maximum position error in each cell by considering the distribution of overlapping and non-overlapping cells. Formally, our system is mainly defined by an easy-to-implement algorithm based on a simple order rule. In order to illustrate the usefulness of the proposal, an example is provided. Finally, potential applications in the industrial environments are discussed and future research is proposed.

It is possible to obtain greater productivity of a production system by overlapping the operations required to process a manufacturing order. This methodology, known as lot streaming, requires divi...

We present a new scheduling model with the capacity of addressing autonomously reschedules of production plans. The proposed model allows to consider the evolution of an optimized schedule (pseudo optimized) and to carry out corrective actions during the execution of the schedule. These corrective actions guarantee the fulfillment of the objectives of the initial schedule with respect to the tolerance of the schedule defined by the scheduler. These tolerances are established by calculating the tolerance scheduling problem. Therefore, the rescheduling actions, instead of being triggered by events, will be activated based on the reception of realtime data collected during the production run. The data is analyzed by the logic controller in terms of scheduling efficiency using the scheduling model proposed in this paper. This model is based on Cyber-Physical Systems, proper of Industry 4.0 systems. Fil: Rossit, Daniel Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina Fil: Tohmé, Fernando Abel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina Fil: Frutos, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones Económicas y Sociales del Sur. Universidad Nacional del Sur. Departamento de Economía. Instituto de Investigaciones Económicas y Sociales del Sur; Argentina

Smart Manufacturing and Industry 4.0 production environments integrate the physical and decisional aspects of manufacturing processes into autonomous and decentralised systems. One of the main aspects in these systems is production planning, in particular scheduling operations on machines. We introduce here a new decision-making schema, Smart Scheduling, intended to yield flexible and efficient production schedules on the fly, taking advantage of the features of these new environments. The ability to face unforeseen and disruptive events is one of the main improvements in our proposed schema, which uses an efficient screening procedure (Tolerance Scheduling) to lessen the need of rescheduling in the face of those events. Fil: Rossit, Daniel Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina Fil: Tohmé, Fernando Abel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Economía; Argentina Fil: Frutos, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones Económicas y Sociales del Sur. Universidad Nacional del Sur. Departamento de Economía. Instituto de Investigaciones Económicas y Sociales del Sur; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina

Supply chain management problems are widespread across all economic activities. We analyze here how to address these in the case of the forest industry, which in emerging economies such as Argentina is subject to high logistic costs and faces problems of biological and economic sustainability. In this work, we analyze a management model covering from the schedule of harvesting activities and the transportation of raw materials to the final transformation at several industrial plants. Since this involves more than one objective, single-criterion mathematical programming methods are not appropriate. Here, instead, we introduce an extended goal programming formulation of the problem, able to yield good solutions in a computationally efficient way. We consider four goals: the maximization of the net present value of the production, the minimization of interannual variations in harvests, the maximization of carbon capture in the form of forest biomass, and the minimization of variations in the mean annual distance covered in transportation to the industrial plants. We apply this theoretical model to derive solutions for an actual Argentinean company. We show that the model reaches the target levels of the different goals, except for carbon balance, which is negative in all of the scenarios under evaluation.

This chapter introduces the Tolerance Scheduling problem, which involves the decision-making issues in rescheduling processes. The solutions to this problem can be incorporated in the design of Decision Support Systems (DSS) in Industry 4.0 environments. We present here the mathematical foundations for the solutions of the Tolerance Scheduling problem as well as the technical requirements of their embodiment in Industry 4.0’s DSS. We illustrate these ideas in a case study with a single machine, in which we analyze the performance of the model at different tolerances.

Contemporary assembly line systems are characterized by an increasing capability to offer each client a different product, more tuned to her needs and preferences. These assembly systems will be heavily influenced by the advent of Industry 4.0 technologies, enabling the proposal of business models that allow the late customization of the products, i.e., the customer can modify attributes of its product once the production of it is started. This business model requires that the manufacturing tools are able to make decisions online and negotiate with the customer the changes that can be carried out, according to the workload flowing through the production system. In this work, we analyze the possibilities and limitations of this new paradigm. First, we show that Industry 4.0 systems can autonomously manage the production management process, and then, we present a framework based on tolerance planning strategies (tolerance scheduling problem), to determine which changes can be carried out. The ability of resequencing the production process is also implemented in the case that the operations associated with late customization allow it (i.e., when intermediate buffers are available). This establishes a parallelism with the problem of non-permutation flow shop. We finally discuss future developments necessary to implement these procedures. Fil: Rossit, Daniel Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina Fil: Tohmé, Fernando Abel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina Fil: Frutos, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones Económicas y Sociales del Sur. Universidad Nacional del Sur. Departamento de Economía. Instituto de Investigaciones Económicas y Sociales del Sur; Argentina

In this paper, we present a new decision-making tool aimed at improving the efficiency of the operational planning of pick-up processes in logistic distribution centers. It is based on a memetic algorithm (MA) solving both the Order Batching Problem (OBP) and the Order Picking Problem (OPP). The result yields a sequence of simultaneous pick up operations of lots for different clients in a storing facility, satisfying a previously defined distribution plan. The objective is the minimization of the operational cost of the entire process, which is directly proportional to the time spent on different activities involved. The failure to satisfy the conditions, either leads to overstocking, delays in delivery or creates inefficiency costs. The analysis of the results obtained with our algorithmic tool indicates that it has a good performance in comparison with other known algorithms used to solve this kind of problem. Fil: Miguel, Fabio. Universidad Nacional de Río Negro; Argentina Fil: Frutos, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones Económicas y Sociales del Sur. Universidad Nacional del Sur. Departamento de Economía. Instituto de Investigaciones Económicas y Sociales del Sur; Argentina Fil: Tohmé, Fernando Abel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina Fil: Rossit, Daniel Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina

The competition manufacturing companies face has driven the development of novel and efficient methods that enhance the decision making process. In this work, a specific flow shop scheduling problem of practical interest in the industry is presented and formalized using a mathematical programming model. The problem considers a manufacturing system arranged as a work cell that takes into account the transport operations of raw material and final products between the manufacturing cell and warehouses. For solving this problem, we present a multiobjective metaheuristic strategy based on simulated annealing, the Pareto Archived Simulated Annealing (PASA). We tested this strategy on two kinds of benchmark problem sets proposed by the authors. The first group is composed by small-sized problems. On these tests, PASA was able to obtain optimal or near-optimal solutions in significantly short computing times. In order to complete the analysis, we compared these results to the exact Pareto front of the instances obtained with augmented ε-constraint method. Then, we also tested the algorithm in a set of larger problems to evaluate its performance in more extensive search spaces. We performed this assessment through an analysis of the hypervolume metric. Both sets of tests showed the competitiveness of the Pareto Archived Simulated Annealing to efficiently solve this problem and obtain good quality solutions while using reasonable computational resources. Fil: Toncovich, Adrián Andrés. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina Fil: Rossit, Daniel Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina Fil: Frutos, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones Económicas y Sociales del Sur. Universidad Nacional del Sur. Departamento de Economía. Instituto de Investigaciones Económicas y Sociales del Sur; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina Fil: Rossit, Diego Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones Económicas y Sociales del Sur. Universidad Nacional del Sur. Departamento de Economía. Instituto de Investigaciones Económicas y Sociales del Sur; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina

The Non-Permutation Flow-Shop scheduling problem (NPFS) is a generalization of the traditional Permutation Flow-Shop scheduling problem (PFS) that allows changes in the job order on different machines. The flexibility that NPFS provides in models for industrial applications justifies its use despite its combinatorial complexity. The literature on this problem has expanded largely in the last decade, indicating that the topic is an active research area. This review is a contribution towards the rationalization of the developments in the field, organizing them in terms of the objective functions in the different variants of the problem. A schematic presentation of both theoretical and experimental results summarizes many of the main advances in the study of NPFS. Finally, we include a bibliometric analysis, showing the most promising lines of future development. Fil: Rossit, Daniel Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina Fil: Tohmé, Fernando Abel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Economía; Argentina Fil: Frutos, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones Económicas y Sociales del Sur. Universidad Nacional del Sur. Departamento de Economía. Instituto de Investigaciones Económicas y Sociales del Sur; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina

The incorporation of high technology to production systems is bringing the advent of Industry 4.0. One of the mainstays of Industry 4.0 is the application of Cyber-Physical Production Systems (CPPS). CPPSs will redefine decision-making processes in manufacturing environments, integrating traditionally disparate functionalities in a single system. One of the questions to be answered is how will the process of scheduling activities be redefined in this scenario. We examine the advances in the scheduling literature and analyze which aspects should be taken into account in future designs. Among them, we focus on topics as dynamic scheduling, distributed scheduling and inverse scheduling. Fil: Rossit, Daniel Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina Fil: Tohmé, Fernando Abel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Economía; Argentina