Abstract
Taking resource allocation into account, flexible job shop problem (FJSP) is a class of complex scheduling problem in manufacturing system. In order to utilize the machine resources rationally, multi-objective particle swarm optimization (MOPSO) integrating with variable neighborhood search is introduced to address FJSP efficiently. Firstly, the assignment rules (AL) and dispatching rules (DR) are provided to initialize the population. And then special discrete operators are designed to produce new individuals and earliest completion machine (ECM) is adopted in the disturbance operator to escape the optima. Secondly, personal-best archives (cognitive memories) and global-best archive (social memory), which are updated by the predefined non-dominated archive update strategy, are simultaneously designed to preserve non-dominated individuals and select personal-best positions and the global-best position. Finally, three neighborhoods are provided to search the neighborhoods of global-best archive for enhancing local search ability. The proposed algorithm is evaluated by using Kacem instances and Brdata instances, and a comparison with other approaches shows the effectiveness of the proposed algorithm for FJSP.
Highlights
Production planning and scheduling problems arise in many production manufacturing systems
The multi-objective particle swarm optimization (MOPSO) algorithm we developed an effective MOPSO algorithm and the details of the proposed algorithm is described as follows: “Initialization” section describes population initialization and “The details of MOPSO algorithm” section presents the extended position update formula in discrete Particle Swarm Optimization (PSO)
In this paper, a multi-objective flexible job shop problem (FJSP) with three criteria is investigated to meet the requirements in manufacturing system and MOPSO algorithm is developed to address this problem
Summary
Production planning and scheduling problems arise in many production manufacturing systems. The machine assignment vectors remain the same, and the procedure of f2 (IPOX) on operation sequence vector is as follows: Step 1: Select the operation sequence vectors of the parents F1 and F2, and all the jobs are randomly divided into two set J1 and J2. To obtain high-quality and high-diversity solutions, a selective strategy of non-dominated individuals’ positions should be developed to update the global-best archive and the personal-best archive. Many selection mechanisms, such as NSGA-II (Deb et al 2002), MOEA/D (Li and Zhang 2009), and SPEA2 (Zitzler et al 2002) have already been used to sort the non-dominated individuals. For the 15 jobs × 10 machines instance, the solutions obtained by the HTSA, Xing algorithm and MOPSO algorithm are the same and dominate some solutions obtained by AIA, MOGA and P-DABC
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