A Hybrid Approach for Solving the Flexible Job-Shop Scheduling Problem with Considering Transporting Time and Deteriorating Effect

Immune genetic algorithm has the disadvantage of insufficient local search capability when solving the job-shop scheduling problem, and it is difficult to solve large-scale job-shop scheduling problems well. Aiming at this problem, this paper proposes a hybrid immune genetic algorithm which combines neighborhood search with immune genetic algorithm. After the genetic operation, in addition to the vaccination operation of the poorer individuals in the population, the better individuals will be selected for neighborhood search to improve the local search ability of the algorithm. The neighborhood search operator selects the N5 neighborhood search operator, which can perform a neighborhood search on key paths of high-quality individuals to find a better neighborhood solution. The experimental results show that the hybrid immune genetic algorithm with the neighborhood search method can obtain more standard case optimal solutions.

Current scheduling approach for multiple objective flexible job shop problem (FJSP) cannot construct a precise scheduling model and obtain a satisfactory scheduling result at the same time. To deal with this problem, a simulation optimization scheduling approach was presented which was composed of two basic modules: the Fast non-dominated Sorting Genetic Algorithm (NSGA-II) module and Witness simulation module. Firstly, a multi-objective mathematical model was found for FJSP and NSGA-II was applied to solve. Then, a set of Pareto optimal solution was obtained by NSGA-II module. In order to select the final solution from the Pareto optimal solution for FJSP, the simulation model was set up by Witness, every Pareto solution was as input for simulation model. Finally, the final solution can be selected according other performance indicators.

A single-individual based variable neighborhood search algorithm for the blocking hybrid flow shop group scheduling problem

Analyzing the model of the flexible job-shop scheduling problem(FJSP),an immune genetic algorithm(IGA) is proposed to solve the problem. This algorithm preserves the random global search ability of simple genetic algorithm(SGA), and introduces the immune mechanism by which the necessary vaccine may be extracted with the scheduling vaccinated so as to improve efficiently SGA's low ability for global search because of immature convergence and low local search ability. Thus, the IGA proposed can provide such ability and convergence rate that will implement the global optimum solution. Experimental results showed that the IGA can solve the FJSP effectively.

This paper introduces a new algorithmic nature-inspired approach that uses particle swarm optimization (PSO) with different neighborhood topologies, for successfully solving one of the most computationally complex problems, the permutation flowshop scheduling problem (PFSP). The PFSP belongs to the class of combinatorial optimization problems characterized as NP-hard and, thus, heuristic and metaheuristic techniques have been used in order to find high quality solutions in reasonable computational time. The proposed algorithm for the solution of the PFSP, the PSO with expanding neighborhood topology, combines a PSO algorithm, the variable neighborhood search strategy and a path relinking strategy. As, in general, the structure of the social network affects strongly a PSO algorithm, the proposed method using an expanding neighborhood topology manages to increase the performance of the algorithm. As the algorithm starts from a small size neighborhood and by increasing (expanding) in each iteration the size of the neighborhood, it ends to a neighborhood that includes all the swarm, and it manages to take advantage of the exploration abilities of a global neighborhood structure and of the exploitation abilities of a local neighborhood structure. In order to test the effectiveness and the efficiency of the proposed method, we use a set of benchmark instances of different sizes and compare the proposed method with a number of other PSO algorithms and other algorithms from the literature.

A GRASP embedding a bi-level ELS for solving Flexible Job-shop Problems

Flexible manufacturing systems have many advantages like adaptation to changes and reduction of lateness. But flexible machines are expensive. The scheduling is a central functionality in manufacturing systems. Optimizing the job routing through the system, while taking advantage from the flexibility of the machines, aims at improving the system's profitability. The introduction of the flexibility defines a variant of the scheduling problems known as flexible job shop scheduling. This variant is more difficult than the classical job shop since two sub-problems are to be solved the assignment and the routing. To guarantee the generation of efficient schedules in reasonable computation time, the metaheuristic approach is largely explored. Particularly, much research has addressed the resolution of the flexible job shop problem by genetic algorithms. This chapter presents the different adaptations of the genetic scheme to the flexible job shop problem. The solution encodings and the genetic operators are presented and illustrated by examples.

The application of genetic algorithm is widely, but it is easy to premature convergence and is inadequate about the local searching optimization ability. In this paper, a new immune genetic algorithm (IGA) is proposed. Experiments are done to compare the proposed algorithm with the standard GA, and the results indicate that the proposed IGA's optimization results and converging speed are superior and the proposed IGA overcomes the premature convergence and solves the problem of falling into local optimum solution easily. This paper also combines TSP's encoding characteristics to propose a new crossover operator (DEGX). Experiments shows the DEGX can enhance the local search ability greatly.

Dynamic deformation data analysis and prediction is a complex systematic project. Aimed at the shortcoming of the traditional prediction model, a method to design the BP neural network based on Immune Genetic Algorithm(IGA) was proposed. The mechanisms of diversity maintaining and antibody density regulation exhibited in a biological immune system were introduced into IGA based on genetic algorithm. The proposed algorithm overcame the problems of GA on search efficiency, individual diversity and prematur, and enhanced the convergent performance effectively. The results show that the BP neural network designed by IGA have better performance in convergent speed and global convergence, and the forecasting accuracy is improved, which illustrates IGA-BP neural network has certain of value on dynamic deformation monitoring forecasting.

In this paper we propose a hybrid algorithm that can overcome the typical drawback of an artificial immune algorithm, namely, the propensity to runs slowly and experience a slower speed of convergence is than a genetic algorithm. Our hybrid algorithm combines the steepest descent algorithm with an artificial immune adaptive algorithm based on Euclidean distance. The hybrid algorithm fully displays global search ability and the global convergence of the immune algorithm. At the same time, the hybrid algorithm inserts a steepest descent operator to strengthen the local search ability. Experimental results show that the hybrid algorithm successfully improves the operational speed and convergence performance. In addition, this paper proves the convergence of the hybrid algorithm with a quasi-descent method.

Automated guided vehicles (AGVs) are widely used for transportation in flexible job shop (FJS) systems, and their transportation task scheduling has the same substantial impact on production efficiency as machine scheduling does. However, traditional FJS scheduling methods often prioritize job sequencing and machine selection while ignoring the impact of AGV transportation, resulting in suboptimal scheduling solutions and even difficulties in implementation. To address this issue, this paper formulates a cooperative scheduling model by introducing the AGV scheduling problem into the classical FJS scheduling problem, abbreviated as the FJS-AGV problem, with the objective of minimizing the makespan. With respect to the FJS-AGV problem, a heuristic-assisted deep Q-network (HA-DQN) algorithm is proposed, which leverages heuristic rules to enable the decision agent to perform multiple actions at each decision point, which includes determining the responses to the following questions: Which operation should be processed next? On which machine? By which AGV? This decision mechanism enables the agent to make more informed decisions, leading to improved performance and resource allocation in the FJS-AGV system. The practicability of the proposed FJS-AGV model and the efficiency of the HA-DQN algorithm in solving the FJS-AGV problem are verified through various international benchmarks. Specifically, when solving instances in a large benchmark, the HA-DQN algorithm yields a significant 12.63% reduction in makespan compared with that when traditional heuristics are employed.

First, a multi-objective immune genetic algorithm integrating immune algorithm and genetic algorithm for flexible job shop scheduling is designed. Second, Markov chain is used to analyze quantitatively its convergence. Third, a simulation experiment of the flexible job shop scheduling is carried out. Running results show that the proposed algorithm can converge to the Pareto frontier quickly and distribute evenly along the Pareto frontier.

Based on Latin Hypercube Sampling method, the crossover operation in GA is redesigned; combined with immune mechanism, chromosome concentration is defined and clonal selection strategy is designed, thus an Immune Genetic Algorithm is given based on Latin Hypercube Sampling for solving the Maximum Clique Problem in this paper. The examples shows the new algorithm in solution quality, convergence speed, and other indicators is better than the classical genetic algorithm and good point set genetic algorithm. On the other hand, the new algorithm is not inferior to such classical algorithms as dynamic local search, and it got better solutions to some examples.KeywordsMaximum Clique Problem(MCP)Genetic Algorithm (GA)Latin Hypercube Sampling(LHS)Artificial Immune Systems(AIS)Immune Genetic Algorithm based on Latin Hypercube Sampling(LIGA)

Modified Self-adaptive Immune Genetic Algorithm for Optimization of Combustion Side Reaction of p-Xylene Oxidation

As a classic issue in military operation research, weapon target assignment (WTA) has been of interest to researchers for a long time. The purpose of WTA is to determine the best assignment scheme to gain the largest benefit while satisfying a number of constraints deriving from the capability of available platforms and munitions (or weapons), target characteristics, timings for operation and so on. In order to overcome the drawbacks of easily falling into premature convergence and local optimum of existing heuristic algorithms when they are employed to optimize WTA problems, this paper proposes a hybrid genetic algorithm (HGA) which combines an adaptive genetic algorithm (AGA) with an adaptive variable neighborhood search algorithm (AVNSA) to balance the exploration and exploitation ability. In the framework of HGA, AGA is used for wide scope search in the solution space to avoid premature convergence while AVNSA for local search to jump out of the local optimal space. The simulation result demonstrates the effectiveness and feasibility of the proposed HGA in small-scale WTA optimization problems and through a comparison analysis, advantages of the algorithm over the adaptive genetic algorithm, the immune genetic algorithm and the standard genetic algorithm in large-scale WTA optimization problems are revealed.