Algorithm For Flexible Job Shop Research Articles

Knowledge-Based Reinforcement Learning and Estimation of Distribution Algorithm for Flexible Job Shop Scheduling Problem

In this study, a flexible job shop scheduling problem with time-of-use electricity price constraint is considered. The problem includes machine processing speed, setup time, idle time, and the transportation time between machines. Both maximum completion time and total electricity price are optimized simultaneously. A hybrid multi-objective optimization algorithm of estimation of distribution algorithm and deep Q-network is proposed to solve this. The processing sequence, machine assignment, and processing speed assignment are all described using a three-dimensional solution representation. Two knowledge-based initialization strategies are designed for better performance. In the estimation of distribution algorithm component, three probability matrices corresponding to solution representation are provided. In the deep Q-network component, 34 state features are selected to describe the scheduling situation, while nine knowledge-based actions are defined to refine the scheduling solution, and the reward based on the two objectives is designed. As the knowledge for initialization and optimization strategies, five properties of the considered problem are proposed. The proposed mixed integer linear programming model of the problem is validated by exact solver CPLEX. The results of the numerical testing on wide-range scale instances show that the proposed hybrid algorithm is efficient and effective at solving the integrated flexible job shop scheduling problem.

An Improved Harris Hawk Optimization Algorithm for Flexible Job Shop Scheduling Problem

An Improved Harris Hawk Optimization Algorithm for Flexible Job Shop Scheduling Problem

Hybrid Genetic Algorithm for Flexible Job Shop Scheduling Problem

Hybrid Genetic Algorithm for Flexible Job Shop Scheduling Problem

Improved Jaya Algorithm for Flexible Job Shop Rescheduling Problem

Machine recovery is met from time to time in real-life production. Rescheduling is often a necessary procedure to cope with it. Its instability gauges the number of changes to the existing scheduling solutions. It is a key criterion to measure a rescheduling solution’s quality. This work aims at solving a flexible job shop problem with machine recovery, which arises from the scheduling and rescheduling of pump remanufacturing systems. In their scheduling phase, the objective is to minimize makespan. In their rescheduling phase, two objectives are to minimize both instability and makespan. By introducing two novel local search operators into the original Jaya algorithm, this work proposes an improved Jaya algorithm to solve it. It performs experiments on ten different-scale cases of real-life remanufacturing environment. The results show that the improved Jaya is effective and efficient for solving a flexible job shop scheduling and rescheduling problems. It can effectively balance instability and makespan in a rescheduling phase.

Particle Swarm Optimization and Tabu Search Hybrid Algorithm for Flexible Job Shop Scheduling Problem – Analysis of Test Results

Abstract The paper presents a hybrid metaheuristic algorithm, including a Particle Swarm Optimization (PSO) procedure and elements of Tabu Search (TS) metaheuristic. The novel algorithm is designed to solve Flexible Job Shop Scheduling Problems (FJSSP). Twelve benchmark test examples from different reference sources are experimentaly tested to demonstrate the performance of the algorithm. The obtained mean error for the deviation from optimality is 0.044%. The obtained test results are compared to the results in the reference sources and to the results by a genetic algorithm. The comparison illustrates the good performance of the proposed algorithm. Investigations on the base of test examples with a larger dimension will be carried out with the aim of further improvement of the algorithm and the quality of the test results.

Improved Differential Evolution Algorithm for Flexible Job Shop Scheduling Problems

This research project aims to study and develop the differential evolution (DE) for use in solving the flexible job shop scheduling problem (FJSP). The development of algorithms were evaluated to find the solution and the best answer, and this was subsequently compared to the meta-heuristics from the literature review. For FJSP, by comparing the problem group with the makespan and the mean relative errors (MREs), it was found that for small-sized Kacem problems, value adjusting with “DE/rand/1” and exponential crossover at position 2. Moreover, value adjusting with “DE/best/2” and exponential crossover at position 2 gave an MRE of 3.25. For medium-sized Brandimarte problems, value adjusting with “DE/best/2” and exponential crossover at position 2 gave a mean relative error of 7.11. For large-sized Dauzere-Peres and Paulli problems, value adjusting with “DE/best/2” and exponential crossover at position 2 gave an MRE of 4.20. From the comparison of the DE results with other methods, it was found that the MRE was lower than that found by Girish and Jawahar with the particle swarm optimization (PSO) method (7.75), which the improved DE was 7.11. For large-sized problems, it was found that the MRE was lower than that found by Warisa (1ST-DE) method (5.08), for which the improved DE was 4.20. The results further showed that basic DE and improved DE with jump search are effective methods compared to the other meta-heuristic methods. Hence, they can be used to solve the FJSP.

Effective Neighborhood Generation Method in Search Algorithm for Flexible Job Shop Scheduling Problem

The flexible job shop scheduling problem (FJSSP) is an extension of the classical job shop scheduling problem (JSSP) that allocates jobs to resources while minimizing the maximum completion time of all the jobs. Machine assignment and job sequence are determined in the FJSSP. To efficiently solve the FJSSP, which is a non-deterministic polynomial-time hard problem, a heuristic method must be used. In previous studies, the FJSSP has been solved using neighborhood algorithms that employ various metaheuristic methods. These approaches constrain the neighborhood operation to jobs on a critical path and simultaneously change the machine assignment and job sequence. Branches on the critical path are easily generated in the FJSSP search processes; this branch structure can improve the efficiency of the FJSSP. This study investigates two neighborhood search algorithms used for changing the machine assignment and job sequence via a critical path. The first method changes the machine assignment and job sequence simultaneously, whereas the second method changes them independently. In this study, we propose an efficient neighborhood generating method using a branch block of critical path.

A Modified Iterated Greedy Algorithm for Flexible Job Shop Scheduling Problem

The flexible job shop scheduling problem (FJSP) is considered as an important problem in the modern manufacturing system. It is known to be an NP-hard problem. Most of the algorithms used in solving FJSP problem are categorized as metaheuristic methods. Some of these methods normally consume more CPU time and some other methods are more complicated which make them difficult to code and not easy to reproduce. This paper proposes a modified iterated greedy (IG) algorithm to deal with FJSP problem in order to provide a simpler metaheuristic, which is easier to code and to reproduce than some other much more complex methods. This is done by separating the classical IG into two phases. Each phase is used to solve a sub-problem of the FJSP: sequencing and routing sub-problems. A set of dispatching rules are employed in the proposed algorithm for the sequencing and machine selection in the construction phase of the solution. To evaluate the performance of proposed algorithm, some experiments including some famous FJSP benchmarks have been conducted. By compared with other algorithms, the experimental results show that the presented algorithm is competitive and able to find global optimum for most instances. The simplicity of the proposed IG provides an effective method that is also easy to apply and consumes less CPU time in solving the FJSP problem.

A green scheduling algorithm for flexible job shop with energy-saving measures

We study how to save energy from the viewpoint of operation management. When to turn-on/off machines and which speed level to choose are two measures we employ to save energy. We focus on the flexible job shop scheduling problem. To begin with, a model is formulated for the flexible job shop scheduling problem when the two energy-saving measures are under consideration. An energy consumption model is proposed to compute the energy consumption for a machine in different states. Then, a non-dominated sorted genetic algorithm is developed to solve the problem. In the non-dominated sorted genetic algorithm, a green scheduling heuristic is presented to optimize the makespan, the energy consumption and the numbers of turning-on/off machines simultaneously. Finally, the comprehensive experiment results prove that the proposed model and the algorithm can solve the problem effectively and efficiently.

Multi Agent model based on Chemical Reaction Optimization with Greedy algorithm for Flexible Job shop Scheduling Problem

Scheduling in production systems consists in assigning operations on a set of available resources in order to achieve defined objectives. The Flexible Job shop Scheduling Problem (FJSP) is one of the scheduling problems and also an extension of classical Job shop Scheduling Problem (JSP) such that each operation can be processed on different machine and its processing time depends on the used machine. The FJSP is classified, as most of scheduling problems, NP-Hard in complexity theory and can be decomposed into two sub-problems: a routing sub-problem, which consists of assigning each operation to a machine out of a set of alternative machines, and a scheduling sub-problem, which consists of sequencing the assigned operations on all selected machines in order to attain a feasible schedule with optimized objectives. In this paper, we propose a decentralized model named Multi Agent model based on Chemical Reaction Optimization with Greedy algorithm (MACROG-FJSP) to solve the FJSP in order to minimize the maximum completion time (Makespan). Experiments are performed on well known benchmark instances proposed in the literature which are Fattahi, Kacem, Brandimarte and Hurink instances to evaluate the performance of our model.

Non-Crossover and Multi-Mutation Based Genetic Algorithm for Flexible Job-Shop Scheduling Problem

Non-Crossover and Multi-Mutation Based Genetic Algorithm for Flexible Job-Shop Scheduling Problem

Priority Rule-Based Construction Procedure Combined with Genetic Algorithm for Flexible Job-Shop Scheduling Problem

The heuristic method we propose solves the flexible job-shop scheduling problem (FJSP) using a solution construction procedure with priority rules. FJSP is more complex than classical scheduling problems in that operations are processed on one of multiple candidate machines, one of which must be selected to get a feasible solution. The solution construction procedure with priority rules is implemented on top of the efficient existing method for solving the FJSP which consists of a genetic algorithm and a local search method. The performance of the proposed method is analyzed using various benchmark problems and it is confirmed that our proposed method outperforms the existing method on problems with particular conditions. The conditions are further investigated by applying the proposed method on newly created benchmark.

An Improved Shuffled Frog-Leaping Algorithm for Flexible Job Shop Scheduling Problem

The flexible job shop scheduling problem is a well-known combinatorial optimization problem. This paper proposes an improved shuffled frog-leaping algorithm to solve the flexible job shop scheduling problem. The algorithm possesses an adjustment sequence to design the strategy of local searching and an extremal optimization in information exchange. The computational result shows that the proposed algorithm has a powerful search capability in solving the flexible job shop scheduling problem compared with other heuristic algorithms, such as the genetic algorithm, tabu search and ant colony optimization. Moreover, the results also show that the improved strategies could improve the performance of the algorithm effectively.

An Improved Genetic Algorithm for Flexible Job-Shop Scheduling Problems

The flexible job-shop scheduling algorithm with the high computational complexity is important in both fields of production management and combinatorial optimization. We developed an improved genetic approach for the flexible job-shop scheduling problem (FJSP), since it is quite difficult to achieve an optimal solution to this problem within reasonable computation time. A chromosome representation combines both routing and sequencing information is represented with genetic algorithms (GAs) commonly optimize the problem by minimizing an objective, makespan. Our algorithm selects the chromosome according to different objectives, and crossover each other to maintain the species diversity. New crossover and the mutation operators utilize the critical path with certain probability to modify assignment and sequence for preventing being trapped in a local optimum. The results obtained from the computational study have shown that the proposed algorithm is an effective approach for the FJSP.

An Evolutionary Neighborhood Search Algorithm for Flexible Job-Shop Scheduling Problem in Steel Tube Production

This paper focuses on the tube processing context in steel tube production and constructs a flexible job-shop scheduling problem (FJSP) model with consideration of the typical features and constraints. An evolutionary neighborhood search algorithm which consists of process consolidation (PC) strategy, production line adjustment (LA) strategy and elimination mechanism (EM) strategy is proposed. The computational tests show that the proposed algorithm has good performance.

Hybrid Genetic Algorithm for Flexible Job Shop Scheduling with Overlapping in Operations

Flexible job shop scheduling problem )FJSP) is an extension of the classical job shop scheduling problem which allows an operation to be processed by any machine from a given set. FJSP is NP-hard and mainly presents two difficulties. The first one is to assign each operation to a machine out of a set of capable machines, and the second one deals with sequencing the assigned operations on the machines. However, it is quite difficult to achieve an optimal solution to this problem in medium and large size problems with traditional optimization approaches. In this paper a memetic algorithm )MA) for flexible job shop scheduling with overlapping in operation is proposed that solves the FJSP to minimize makespan time. The experimental results show that the proposed algorithm is capable to achieve the optimal solution for small size problems and near optimal solutions for medium and large size problems

A Hybrid Artificial Bee Colony Algorithm for Flexible Job Shop Scheduling Problems

<p>In this paper, we propose a hybrid Pareto-based artificial bee colony (HABC) algorithm for solving the multi-objective flexible job shop scheduling problem. In the hybrid algorithm, each food sources is represented by two vectors, i.e., the machine assignment vector and the operation scheduling vector. The artificial bee is divided into three groups, namely, employed bees, onlookers, and scouts bees. Furthermore, an external Pareto archive set is introduced to record non-dominated solutions found so far. To balance the exploration and exploitation capability of the algorithm, the scout bees in the hybrid algorithm are divided into two parts. The scout bees in one part perform randomly search in the predefined region while each scout bee in another part randomly select one non-dominated solution from the Pareto archive set. Experimental results on the well-known benchmark instances and comparisons with other recently published algorithms show the efficiency and effectiveness of the proposed algorithm.</p>