Abstract
The hybrid flow shop is a typical discrete manufacturing system. A novel method is proposed to solve the shop scheduling problem featured with uncertain processing times. The rolling horizon strategy is adopted to evaluate the difference between a predictive plan and the actual production process in terms of job delivery time. The genetic regulatory network-based rescheduling algorithm revises the remaining plan if the difference is beyond a specific tolerance. In this algorithm, decision variables within the rolling horizon are represented by genes in the network. The constraints and certain rescheduling rules are described by regulation equations between genes. The rescheduling solutions are generated from expression procedures of gene states, in which the regulation equations convert some genes to the expressed state and determine decision variable values according to gene states. Based on above representations, the objective of minimizing makespan is realized by optimizing regulatory parameters in regulation equations. The effectiveness of this network-based method over other ones is demonstrated through a series of benchmark tests and an application case collected from a printed circuit board assembly shop.
Highlights
The Hybrid Flow Shop (HFS) is a typical discrete manufacturing system in which a set of jobs passes through a series of production stages to complete required operations
In terms of minimizing the maximum completion time, i.e., makespan, Mirsanei et al [20] proposed a simulated annealing algorithm to solve the HFS scheduling problem featured with sequence-dependent setup times
The IACO method achieves better results than the Genetic Regulatory Network (GRN)-based methods for the benchmarks “6 × 2”, “30 × 2” and “100 × 2”. Because these benchmarks are featured with a small-scale solution space, the IACO method is possibly to search out the optimal solutions via its global searching procedure, whereas the GRN-based method might fail to find an optimal one owing to the predetermined rules embedded in its regulation equations
Summary
The Hybrid Flow Shop (HFS) is a typical discrete manufacturing system in which a set of jobs passes through a series of production stages to complete required operations. The operation processing time varies with different machines because capacities of parallel machines are normally unrelated at each stage [4,5]. This type of workshop exists in various industries, which include Printed. Different kinds of methods (e.g., exact methods, heuristics and metaheuristics) were proposed to minimize a variety of objectives, which include the maximum completion time, the maximum flow time, the number of late jobs [16,17,18,19]. Wang et al [22]
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