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In response to the challenges posed by economic globalisation and increasing customer demands, enterprises are compelled to adapt and refine their production models and operational objectives, which motivates this paper to address the integrated production scheduling and vehicle routing problem in the distributed manufacturing environment (IPSVRP-DME). The objective is to simultaneously minimise both total energy consumption and total earliness/tardiness. Initially, a mixed integer programming model is proposed to address small-scale problems using Gurobi. Considering the NP-hardness of the problem, a novel Self-adapted Gaussian Mutation-based Arithmetic Optimiser Algorithm (SGMAOA) is developed to handle medium-scale and large-scale instances. To address the complexity of decision-making, an innovative two-level encoding method that encompasses four decision dimensions is introduced. Additionally, an incremental repair strategy is devised to rectify infeasible solutions caused by unreasonable delivery batching, while a time relaxation strategy is proposed to further enhance service levels without compromising energy consumption. Comparative experiments are conducted to demonstrate the effectiveness of SGMAOA, which is benchmarked against five prominent metaheuristics. In addition, a specific example is applied for the discussion of managerial applications and to illustrate the practicality of the proposed model and solution method. [ABSTRACT FROM AUTHOR]

Energy efficiency has become a major concern for manufacturing systems, due to industry being the largest user of scarce, finite energy sources, and also to recent events which have pushed energy prices to alarming levels. In the present Industry 4.0 context, Reconfigurable Manufacturing Systems (RMS) are therefore one of the most promising manufacturing paradigm. In this paper, we investigate the suitability of one of the most common types of RMS, the Parallel-Serial manufacturing line with Crossover, to help minimise the peak of the electric power consumption. More specifically, the balancing of such a production line is studied, so as to integrate power peak minimisation from the design stage. Thus, we define the Parallel-Serial-with-Crossover Assembly Line Balancing Problem with Power Peak Minimization, a new combinatorial NP-hard problem. We also propose a suitable time-indexed Integer Linear Program that integrates balancing and scheduling decisions and a matheuristic algorithm designed to tackle large-size instances. Both approaches are tested on a wide set of instances. The computational results show that relevant power peak reductions can be achieved (33% on average), opening up promising perspectives from both algorithmic and managerial viewpoints. [ABSTRACT FROM AUTHOR]

Changes to components, known as engineering changes (ECs), rarely occur on their own. In fact, in complex assembly systems, most ECs are introduced in batches to ensure that changed components match. As a result, to implement ECs optimally, multiple component's stock must be considered until the change is executed on the EC effectivity date. This problem is known as the EC effectivity date optimisation problem, a variation of the general inventory control problem with deterministic and dynamic demand. As optimisation and monitoring of this problem is computationally expensive, research has suggested to investigate whether metaheuristics can provide adequate support. To fill this research gap, we present the results of a benchmark on basic metaheuristics for EC effectivity date optimisation. To do so, we have compared five common metaheuristics in their basic form (Ant Colony Optimisation, Genetic Algorithm, Particle Swarm Optimisation, Tabu Search, and Simulated Annealing) on a real-world test set. Of the tested algorithms the Genetic Algorithm identified most best solutions and returned good average results for the test cases. However, as its reliability was comparatively low, our research suggests a sequential application of the Genetic Algorithm and Tabu Search. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]