A linear programming-based method for job shop scheduling

We present a decomposition heuristic for a large class of job shop scheduling problems. This heuristic utilizes information from the linear programming formulation of the associated optimal timing problem to solve subproblems, can be used for any objective function whose associated optimal timing problem can be expressed as a linear program (LP), and is particularly effective for objectives that include a component that is a function of individual operation completion times. Using the proposed heuristic framework, we address job shop scheduling problems with a variety of objectives where intermediate holding costs need to be explicitly considered. In computational testing, we demonstrate the performance of our proposed solution approach

Shop-floor scheduling as a competitive advantage:A study on the relevance of cyber-physical systems in different manufacturing contexts

The aim of this paper is to analyse the relevance of cyber-physical systems (CPS) in different manufacturing contexts and to study whether CPS could provide companies with competitive advantage by carrying out a better scheduling task. This paper is developed under the umbrella of contingency theory which states that certain technologies and practices are not universally applicable or relevant in every context; thus, only certain companies will benefit from using particular technologies or practices. The conclusion of this paper, developed through deductive reasoning and supported by preliminary simulation experiments and statistical tests, is that factories with an uncertain and demanding market environment as well as a complex production process could benefit the most from implementing a CPS at shop-floor level since a cyber-physical shop-floor will provide all the capabilities needed to carry out the complex scheduling task associated with this type of context. On the other hand, an increase in scheduling performance due to a CPS implementation in factories with simple production flows and stable demand could not be substantial enough to overcome the high cost of installing a fully operational CPS

Solving a flow shop scheduling problem with missing operations in an Industry 4.0 production environment

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; ArgentinaFil: Toncovich, Adrián Andrés. Universidad Nacional del Sur. Departamento de Ingeniería; ArgentinaFil: 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; ArgentinaFil: Nesmachnow, Sergio. Facultad de Ingeniería; Urugua

Exploring the benefits of scheduling with advanced and real-time information integration in Industry 4.0: A computational study

The technological advances recently brought to the manufacturing arena (collectively known as Industry 4.0) offer huge possibilities to improve decision-making processes in the shop floor by enabling the integration of information in real-time. Among these processes, scheduling is often cited as one of the main beneficiaries, given its data-intensive and dynamic nature. However, in view of the extremely high implementation costs of Industry 4.0, these potential benefits should be properly assessed, also taking into account that there are different approaches and solution procedures that can be employed in the scheduling decision-making process, as well as several information sources (i.e. not only shop floor status data, but also data from upstream/downstream processes). In this paper, we model various decision-making scenarios in a shop floor with different degrees of uncertainty and diverse efficiency measures, and carry out a computational experience to assess how real-time and advance information can be advantageously integrated in the Industry 4.0 context. The extensive computational experiments (equivalent to 6.3 years of CPU time) show that the benefits of using real-time, integrated shop floor data and advance information heavily depend on the proper choice of both the scheduling approach and the solution procedures, and that there are scenarios where this usage is even counterproductive. The results of the paper provide some starting points for future research regarding the design of approaches and solution procedures that allow fully exploiting the technological advances of Industry 4.0 for decision-making in scheduling.Ministerio de Ciencia e Innovación PID2019-108756RB-I0Junta de Andalucía P18-FR-1149, 5835 and US-12645

Scheduling of non-repetitive lean manufacturing systems under uncertainty using intelligent agent simulation

World-class manufacturing paradigms emerge from specific types of manufacturing systems with which they remain associated until they are obsolete. Since its introduction the lean paradigm is almost exclusively implemented in repetitive manufacturing systems employing flow-shop layout configurations. Due to its inherent complexity and combinatorial nature, scheduling is one application domain whereby the implementation of manufacturing philosophies and best practices is particularly challenging. The study of the limited reported attempts to extend leanness into the scheduling of non-repetitive manufacturing systems with functional shop-floor configurations confirms that these works have adopted a similar approach which aims to transform the system mainly through reconfiguration in order to increase the degree of manufacturing repetitiveness and thus facilitate the adoption of leanness. This research proposes the use of leading edge intelligent agent simulation to extend the lean principles and techniques to the scheduling of non-repetitive production environments with functional layouts and no prior reconfiguration of any form. The simulated system is a dynamic job-shop with stochastic order arrivals and processing times operating under a variety of dispatching rules. The modelled job-shop is subject to uncertainty expressed in the form of high priority orders unexpectedly arriving at the system, order cancellations and machine breakdowns. The effect of the various forms of the stochastic disruptions considered in this study on system performance prior and post the introduction of leanness is analysed in terms of a number of time, due date and work-in-progress related performance metrics

A data-driven scheduling approach to smart manufacturing

Traditional methods of scheduling are mostly based on the use of pieces of information directly related to the performance of schedules, as for instance processing times, delivery dates, etc., assuming that the production system is operating normally. In the case of malfunctions, the literature concentrates on the ensuing corrective operations, like scheduling with machine breakdowns or under remanufacturing considerations. These event-driven approaches are mainly used in dynamic scheduling or rescheduling systems. Unlike those, Smart Manufacturing and Industry 4.0 production environments integrate the physical and decision-making aspects of manufacturing processes in order to achieve their decentralization and autonomy. On these grounds we propose a data-driven architecture for scheduling, in which the system has real time access to data. Then, scheduling decisions can be made ahead of time, on the basis of more information. This promising approach is based on the architecture of cyber-physical systems, with a data-driven engine that uses, in particular, Big Data techniques to extract vital information for Industry 4.0 systems.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; ArgentinaFil: Tohmé, Fernando Abel. Universidad Nacional del Sur. Departamento de Economía; 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; ArgentinaFil: Frutos, Mariano. Universidad Nacional del Sur. Departamento de Ingeniería; 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; Argentin

Improving just-in-time delivery performance of IoT-enabled flexible manufacturing systems with AGV based material transportation

Autonomous guided vehicles (AGVs) are driverless material handling systems used for transportation of pallets and line side supply of materials to provide flexibility and agility in shop-floor logistics. Scheduling of shop-floor logistics in such systems is a challenging task due to their complex nature associated with the multiple part types and alternate material transfer routings. This paper presents a decision support system capable of supporting shop-floor decision-making activities during the event of manufacturing disruptions by automatically adjusting both AGV and machine schedules in Flexible Manufacturing Systems (FMSs). The proposed system uses discrete event simulation (DES) models enhanced by the Internet-of-Things (IoT) enabled digital integration and employs a nonlinear mixed integer programming Genetic Algorithm (GA) to find near-optimal production schedules prioritising the just-in-time (JIT) material delivery performance and energy efficiency of the material transportation. The performance of the proposed system is tested on the Integrated Manufacturing and Logistics (IML) demonstrator at WMG, University of Warwick. The results showed that the developed system can find the near-optimal solutions for production schedules subjected to production anomalies in a negligible time, thereby supporting shop-floor decision-making activities effectively and rapidly