Reconfigurable manufacturing systems (RMS) intend to bridge the gap between dedicated and flexible manufacturing systems. If the literature is mainly focused on the design step and tactical planning of such systems, few research projects have addressed scheduling at the operational level. While setup times may occur in flexible manufacturing systems, reconfiguration times considered in RMS may affect several resources at once, and hence require specific modelling and solving approaches to be considered. This paper first formalises the problem at hand through integer linear programming. An iterative search method is then provided to obtain solutions to larger-scale instances. Results obtained on generated instances show that managing even few possible configurations can yield significant improvements in solutions’ quality. Meanwhile, the extended search space implied by the increase in available configurations hinders the convergence to a good solution in a reasonable computation time, which suggests further investigations.

Nowadays, a large focus is given to mass personalisation, and multiple path shop floors are suited to such production environments. Hence, this paper deals with the Job-shop scheduling problem that is used for modelling a manufacturing system. Meanwhile, a large attention is given to energy consumption of production systems, but few works consider power requirements of the production systems in order to process operations. In order to contribute in filling this gap, this paper considers the problem where the objective is to minimise both the total completion time of all operations and the instant available power required to process these operations. The problem results in the Bi-objective Job-shop Problem with Power Requirements (Bi-JSPPR). The goal of this paper is to provide a Pareto frontier of schedules minimising both criteria, considering that operations may consume a lot of power at the beginning of the process (consumption peak), more than its consumption after a while, which allows to model power profiles of manufacturing operations. To solve the problem two metaheuristic approaches are investigated: a hybrid Non-dominated Sorting Genetic Algorithm (NSGA-II) and an iterated Greedy Randomized Adaptive Search Procedure coupled with an Evolutionary Local Search (iGRASP×ELS). An efficient local search procedure is specifically designed to improve the quality of solutions in the Pareto frontier of the hybrid NSGA-II (hNSGA-II). Computational experiments and statistical tests are conducted to demonstrate the efficiency of the approaches. Results show that both approach are complementary, having the hNSGA-II showing better average performances, while the iGRASP-ELS is better when high peak power consumption are considered.

In this paper, we investigate the use of the scalability property of RMS to reduce the energy cost during the production. The corresponding optimization problem is a new Bilevel Optimization problem which combines a line balancing problem with a planning problem. Aheuristic based on a simulated annealing algorithm and a linear programis proposed. An illustrative example is presented to highlight the poten-tial of this new approach compared to the cost obtained with a classicproduction line.

Individualization as a major driver in societal change forces companies to adapt to continuously changing customer-specific requirements. Therefore, companies incorporate novel enabling technologies such as digitalization, servitization, and reconfigurability in manufacturing systems. Even though the research on reconfigurable systems has continued over the last 20 years, the energy management of such systems has barely been considered. This work represents recent trends connected with energy efficiency and energy flexibility in reconfigurable manufacturing systems. A suitable attempt to model an energy-related system is proposed using scheduling operations which are subject to power requirements, i.e. variable power thresholds. The results of the optimization show that reconfigurable systems support the adaptation of energy consumption to variable thresholds.

Reconfigurable Manufacturing Systems have been introduced in the mid 1990s as an alternative to classical dedicated or flexibles production systems. They are supposed to be more reactive and capable of evolving depending on unpredictable and high-frequency market changes induced by global market competition. While this concept has received a lot of attention in the literature, mainly at the design and conception phase of the production system, only few works are addressing the operational management of such production systems. One of the key features of reconfigurable manufacturing system is the possibility to use different configurations. The objective is to schedule operations efficiently while considering the different configurations of the system that are available. Switching from one configuration to another requires setup times. However, contrary to classical setup times that can be found in literature on scheduling problems, switching from a configuration 𝒊 to 𝒋 may require that some machines are stopped, and then reconfiguration goes beyond classical setups. This paper intends to formalise such a problem in the context of Flow-shop and Job-shop production systems. First results on small case instances are introduced.

In the past years, environmental awareness started to bring new production paradigms based on energy efficiency. If it is possible to improve energy efficiency of existing production systems, it should be even more protable to consider this objective at the design stage. In the context of Paced Production Lines, and given power requirements for operations, it becomes possible to assign more efficiently these operations to stations while respecting other constraints such as maximum takt time and number of workstations. The repetitive nature of paced lines implies that misconceptions implying a high peak power consumption will see this peak power repeated over and over without having large possibilities to correct it. In order to tackle peak power minimization objectives, this implies to consider sequencing of operations in addition to their assignment to workstation which is not classical in line balancing. In this paper, the problem under study is presented with a new specic feature that allows to consider semi-active sequence of operations at each station. In order to address large scale instances, a first metaheuristic approach is implemented and evaluated on an extended dataset from the literature. Results show that it is possible to improve energy efficiency at the design stage of production systems.

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