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Part selection and machine loading are two major problems in the production planning phase of the flexible manufacturing systems. The problems have a combinatorial structure and usually, in practice, it is difficult to deal with this kind of problems using a mathematical programming model. In this paper, the above problems are formulated as a mixed-integer programming model which is handled sequentially and solved by a diversification-strategy-added version of the hybrid tabu search/simulated annealing algorithm of Zhang et al. ( Comput Oper Res 35:282-294, 2008) presented in 2008. The performance of the algorithm is tested on eight random-generated problems with different sizes. The results are compared with those of the mathematical programming model, the original version of Zhang et al.'s ( Comput Oper Res 35:282-294, 2008) algorithm and also a tabu search algorithm developed earlier by the authors. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the optimization of process planning in which various flexibilities are considered. The objective is to minimize total weighted sum of manufacturing costs. Various flexibilities, including process flexibility, sequence flexibility, machine flexibility, tool flexibility, and tool access direction (TAD) flexibility, generally exist in process planning and consideration of these flexibilities is essential for improving production efficiency and system flexibility. However, process planning is strongly NP-hard due to the existence of various flexibilities as well as complex machining precedence constraints. To tackle this problem, an imperialist competitive algorithm (ICA) is employed to find promising solutions with reasonable computational cost. ICA is a novel socio-politically motivated metaheuristic algorithm inspired by imperialist competition. It starts with an initial population and proceeds through assimilation, position exchange, imperialistic competition, and elimination. Computational experiments on three sets of process planning problem taken from literature are carried out, and comparisons with some existing algorithms developed for process planning are presented. The results show that the algorithm performs significantly better than existing algorithms like genetic algorithm (GA), simulated annealing (SA), tabu search (TS), and particle swarm optimization (PSO). [ABSTRACT FROM AUTHOR]

Generating schedules such that all operations are repeated every constant period of time is as important as generating schedules with minimum delays in all cases where a known discipline is desired or obligated by stakeholders. In this paper, a periodic job shop scheduling problem (PJSSP) based on the periodic event scheduling problem (PESP) is presented, which deviates from the cyclic scheduling. The PESP schedules a number of recurring events as such that each pair of event fulfills certain constraints during a given fixed time period. To solve such a hard PJSS problem, we propose a hybrid algorithm, namely PSO-SA, based on particle swarm optimization (PSO) and simulated annealing (SA) algorithms. To evaluate this proposed PSO-SA, we carry out some randomly constructed instances by which the related results are compared with the proposed SA and PSO algorithms as well as a branch-and-bound algorithm. In addition, we compare the results with a hybrid algorithm embedded with electromagnetic-like mechanism and SA. Moreover, three lower bounds (LBs) are studied, and the gap between the found LBs and the best found solutions are reported. The outcomes prove that the proposed hybrid algorithm is an efficient and effective tool to solve the PJSSP. [ABSTRACT FROM AUTHOR]

This paper considers group scheduling problem in hybrid flexible flow shop with sequence-dependent setup times to minimize makespan. Group scheduling problem consists of two levels, namely scheduling of groups and jobs within each group. In order to solve problems with this context, two new metaheuristics based on simulated annealing (SA) and genetic algorithm (GA) are developed. A design procedure is developed to specify and adjust significant parameters for SA- and GA-based metaheuristics. The proposed procedure is based on the response surface methodology and two types of objective function are considered to develop multiple-objective decision making model. For comparing metaheuristics, makespan and elapsed time to obtain it are considered as two response variables representing effectiveness and efficiency of algorithms. Based on obtained results in the aspect of makespan, GA-based metaheuristic is recommended for solving group scheduling problems in hybrid flexible flow shop in all sizes and for elapsed time SA-based metaheuristic has better results. [ABSTRACT FROM AUTHOR]