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Implementation of mixed-model U-shaped assembly lines (MMUL) is emerging and thriving in modern manufacturing systems owing to adaptation to changes in market demand and application of just-in-time production principles. In this study, the line balancing and model sequencing (MS) problems in MMUL are considered simultaneously, which results in the NP-hard mixed-model U-line balancing and sequencing (MMUL/BS) problem. A colonial competitive algorithm (CCA) is developed and modified to solve the MMUL/BS problem. The modified CCA (MCCA) improves performance of original CCA by introducing a third type of country, independent country, to the population of countries maintained by CCA. Implementation details of the proposed CCA and MCCA are elaborated using an illustrative example. Performance of the proposed algorithms is tested on a set of test-bed problems and compared with that of existing algorithms such as co-evolutionary algorithm, endosymbiotic evolutionary algorithm, simulated annealing, and genetic algorithm. Computational results and comparisons show that the proposed algorithms can improve the results obtained by existing algorithms developed for MMUL/BS.

Computer-aided process planning (CAPP) is an essential component of computer integrated manufacturing (CIM) system. A good process plan can be obtained by optimizing two elements, namely, operation sequence and the machining parameters of machine, tool and tool access direction (TAD) for each operation. This paper proposes a novel optimization strategy for process planning that considers different dimensions of the problem in parallel. A multi-dimensional tabu search (MDTS) algorithm based on this strategy is developed to optimize the four dimensions of a process plan, namely, operation sequence (OperSeq), machine sequence (MacSeq), tool sequence (ToolSeq) and tool approach direction sequence (TADSeq), sequentially and iteratively. In order to improve its efficiency and stability, tabu search, which is incorporated into the proposed MDTS algorithm, is used to optimize each component of a process plan, and some neighbourhood strategies for different components are presented for this tabu search algorithm. The proposed MDTS algorithm is employed to test four parts with different numbers of operations taken from the literature and compared with the existing algorithms like genetic algorithm (GA), simulated annealing (SA), tabu search (TS) and particle swarm optimization (PSO). Experimental results show that the developed algorithm outperforms these algorithms in terms of solution quality and efficiency. process planning, cooperative tabu search, genetic algorithm, simulated annealing, particle swarm optimization Citation:Lian K L, Zhang C Y, Shao X Y, et al. A multi-dimensional tabu search algorithm for the optimization of process planning.

Process planning is an essential part for a Computer Aided Process Planning (CAPP) system in the dynamic workshop environment. It is a combinatorial optimization problem to conduct operations selection and operations sequencing simultaneously with various constraints deriving from practical workshop environment as well as the part to be processed. In this paper, a hybrid genetic simulated annealing algorithm has been developed, which combined the strengths of genetic algorithm (GA) and simulated Annealing (SA), to solve this problem. The GA is carried out as a main frame of this hybrid algorithm while SA is used as a local search strategy to help GA jump out of local optima. A case study is employed to verify the performance and efficiency of the hybrid genetic simulated annealing algorithm (GASA) and the experiment results show that the developed hybrid GASA can generate satisfactory solutions.

To improve production efficiency and reduce makes pan, this paper investigates lot scheduling with multiple process routes in job shop. Equal sublots are adopted and integrated with parallel translation model to optimize production cycle. Based on improved genetic algorithm (GA), a novel initialization method is used for chromosome encoding rationally. Then a new crossover operation is proposed for the problem. The computation results show that the improved genetic algorithm is feasible and effective.

Assembly sequence planning (ASP) refers to taking the related constraint factors such as assembly features, assembly tools and machines into consideration to generate a low-cost feasible sequence. In this paper, a mathematical model of assembly sequence planning problem based on connectors is constructed, and a hybrid honey-bees mating optimization (HBMO) algorithm is proposed for solving this ASP problem. The proposed algorithm has two main innovative features compared to the conventional HBMO algorithm. Firstly, a crossover operator, called Multipoint Precedence Crossover (MPX), is proposed, which can avoid the generation of infeasible solutions and preserve the meaningful characteristics of the queen and broods. Secondly, worker bees utilize the simulated annealing (SA) algorithm as a local search method to improve the broods, which makes the proposed algorithm achieve the right balance between intensification and diversification. The hybrid HBMO algorithm is tested on three practical instances and compared with other approaches, such as Guided-GAs, MAs (memetic algorithm) and AIS (artificial immune systems). The superior results on these practical instances validate the effectiveness of the proposed algorithm.