Abstract
Job shop scheduling problem (JSSP) has high theoretical and practical significance in academia and manufacturing respectively. Therefore, scholars in many different fields have been attracted to study this problem, and many meta-heuristic algorithms have been proposed to solve this problem. As a meta-heuristic algorithm, particle swarm optimization (PSO) has been used to optimize many practical problems in industrial manufacturing. This paper proposes a hybrid PSO enhanced with nonlinear inertia weight and and Gaussian mutation (NGPSO) to solve JSSP. Nonlinear inertia weight improves local search capabilities of PSO, while Gaussian mutation strategy improves the global search ability of NGPSO, which is beneficial to the population to maintain diversity and reduce probability of the algorithm falling into the local optimal solution. The proposed NGPSO algorithm is implemented to solve 62 benchmark instances of JSSP, and the experimental results are compared with other algorithms. The results obtained by analyzing the experimental data show that the algorithm is better than other comparison algorithms in solving JSSP.
Highlights
Job Shop Scheduling Problem (JSSP) is a simplified model of many practical scheduling problems, including aircraft carrier scheduling, airport dispatching, high-speed rail scheduling, automobile pipeline, etc
T +1 t t +1 xid where, ω is the inertia weight, which is an important parameter affecting the search performance of the algorithm [57], its value indicates the amount of particles inheriting the current individual speed. c1 and c2 are called acceleration factors, c1 is called cognitive coefficient, which represents the self cognitive experience of particles, c2 is called social coefficient, which represents the capability of particles to learn from the current global optimal solution; r1 and r2 are two independent random numbers with sizes between [0, 1] [37]; pbestid is the extreme value of particle i in the d-th dimension, gbestid is the global extremum of all particles in the d-th dimension
OVCK in Table 3 is the optimal value currently known, the best is the optimal value in 30 runs, the optimal solution is marked in bold, and Avg is the average value in 30 runs
Summary
Job Shop Scheduling Problem (JSSP) is a simplified model of many practical scheduling problems, including aircraft carrier scheduling, airport dispatching, high-speed rail scheduling, automobile pipeline, etc. A meta-heuristic algorithm needs to find some strategies to balance the local search capability (exploitation) and global search capability (exploration) of the algorithm [24]. When solving practical problems, the population size cannot reach the infinite in theory, and the fitness function cannot fully reflect the real adaptability of individuals, and the behavior of individuals can not perfectly reproduce the intelligence of individuals in nature, the above factors limit the performance of algorithms It has become a hot issue in the field of meta-heuristic algorithm research to more effectively balance exploration and exploitation by mixing different strategies in the algorithm. Lu and Jiang [26] divided the bat optimization algorithm (BA) into two subpopulation, and added parallel search mechanism, communication strategy, and improved population discrete update method in the algorithm to solve the low-carbon JSSP.
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