Research On Scheduling Problem Based On Asynchronous Intelligent Algorithm

Production scheduling is the communication center used to connect layer of decision making and layer of monitor controlling. It delivers the whole operation decision strategy from decision making layer and sends commands to monitor controlling layer and thus ensure the production in normal order. Production scheduling is the key sector to the success of CIMS. Scheduling problem has a close relation with the maximization of the interests of the group and plays a vital role in the development of the modern production process in China. Flow shop scheduling problem is a very typical production scheduling problem. This paper presented a new evolution strategy and a new improved intelligent algorithm to solve the flow shop scheduling problem. The asynchronous intelligent algorithm is very effective for the scheduling problem based on the huge simulation experimental results.An improved particle swarm algorithm is proposed to solve the permutation flow shop scheduling problem with the total flow time objective. This new algorithm use SPV (Smallest Position Value) rule to sort all particle individuals' position vector from lower to bigger order. The position of the particle is mapped on to solution space. An improved VNS (Variable Neighborhood Search) method is adopted to get better solution faster. A difference steps VNS are performed for the particles to enhance diversity. Experimental results show the effectiveness of the algorithm.For the flow shop scheduling problem with the makespan objective, a new intelligent algorithm named asynchronous shuffled frog leaping algorithm (ASFLA) is proposed. The ASFLA is developed based on NSFLA and use asynchronous idea. ASFLA adopts shuffle strategy in global search and use asynchronous idea to enhance population diversity in local search. Experimental results demonstrate the effectiveness of the algorithm.For the permutation flow shop scheduling problem under uncertainty with total flow time objective, an improved intelligent algorithm AGLA (Asynchronous Genetic Local-search Algorithm) is presented. The uncertainty of the process time is denoted by fuzzy mathematic method. In AGLA, an initial solution is generated by a constructive heuristic algorithm and other individuals are randomly yielded. VNS, a simple cross operator and a restart strategy is used in AGLA. The effective of the ALGA is indicated by experimental results.