Mixed integer programming models for concurrent configuration design and scheduling in a reconfigurable manufacturing system

Abstract

A reconfigurable manufacturing system can evolve its configuration to offer exactly the capacity and functionality needed for every demand period. For the reconfigurable manufacturing system with multi-part flow-line configuration simultaneously producing multiple parts within the same family, the production cost and the delivery time are closely related to its configuration and corresponding scheduling for certain demand period. Although studies on multi-part flow-line configuration design are abundant, studies on concurrent optimization of configuration design and scheduling for reconfigurable manufacturing system are scarce. First, a generic mixed integer nonlinear programming model for concurrent configuration design and scheduling is established to relax the limitation of the existing model, and then a mixed integer linear programming model is derived. The decisions of the two generalized models are to decide the amount of stations, the amount of identical machines and machines’ configuration for every station, and assign parts to machines along the multi-part flow line together with sequencing assigned parts for each machine. Based on the mixed integer linear programming model, an exact ε-constraint method is developed to obtain the Pareto optimal solutions with tradeoffs between cost and tardiness. The validation of two models and the ε-constraint method is verified against two cases adapted from the literature.

RETRACTED: The role of industry 4.0 technologies in overcoming pandemic challenges for the manufacturing sector

“…In pandemics, sudden and unexpected rise in demand of certain goods and products lead to poor response from the industries in terms of meeting the demand. It is known that Reconfigurable Manufacturing Systems (RMS) can help firms to be more responsive to the market demands ( Dou et al, 2020 ). With help of RFID technology, industries which produce several products can adjust their production units and machines to respond to the increase or decrease in demand for certain goods.…”

Modularity-based quality assessment of a disruptive reconfigurable manufacturing system-A hybrid meta-heuristic approach

“…The model was implemented through exact and adaptive meta-heuristic approaches. Dou et al [32] developed a mixed integer linear programming model to optimize the cost and tardiness of RMS. The objective function of cost contained capital cost and reconfiguration cost of a reconfigurable flow line.…”

“…The variation in quality can be attributed to the assignable causes of manufacturing system which are discussed below. ■ ■ e-constraint NSGA-II Dou et al [32] ■ ■ e-constraint NSGA-II MOPSO Prasad and Jayswal [44] ■ AHP Khezri et al [33] ■ ■ ■ ■ AUGECON SPEA NSGA-II Moghadddam et al [24] ■ GAMS Cost components: P, production cost; E, exploitation cost of machine; C, configuration cost; S, scrap cost; R, re-work cost GA genetic algorithm, NSGA-II non-sorting genetic algorithm, AMOSA archived multi-objective simulated annealing, AHP analytical hierarchical process, MOPSO multi-objective particle swarm optimization, SPEA strength Pareto evolutionary algorithm, RSUPP repetitive single unit process plan meta-heuristics, ILSSUPP iterated local search on single-unit process plan meta-heuristic, ABILS archive-based iterated local search meta-heuristic, AUGECON augmented e-constraint.…”

Reconfigurability improvement in Industry 4.0: a hybrid genetic algorithm-based heuristic approach for a co-generation of setup and process plans in a reconfigurable environment

“…In their work integration is classified into three classes as non-linear, distributed, and close loop process planning and scheduling. Dou et al (2020) proposed a mathematical modelling for integrated configuration design and scheduling considering multiple processing plans for every product, to minimise the total cost (comprising capital cost and reconfiguration cost), and the total tardiness. Wang et al (2009) proposed a genetic algorithm (GA) based approach for cross-machine adaptive setup planning of the part.…”