Flexible job shop scheduling using Jaya-Tabu search algorithm

In this paper, a multi-objective flexible dynamic job shop scheduling problem (MO-FDJSPM) with maintenance constraint is studied. The objectives of the scheduling are maximizing the completion time, mean job rotation time and mean components' tardiness. Also, in order to adapt with the internal disruptions of the manufacturing system, such as breakdown of existing machines, we consider the machines availability (so called maintenance) as a constraint. The multi-objective mathematical model is formulated and a genetic algorithm (GA) with dynamic bidimensional chromosomes along with a heuristic algorithm to handle maintenance sub-problem is developed as solution approach. In proposed algorithm, since the control parameters change intelligently and dynamically during implementation and optimization process, the early convergence and trapping in local optimum are reduced leading to performance improvement. The performance of the proposed approach is evaluated with respect to convergence speed and solutions quality. The results of computations verify and confirm both two evaluation criteria.

Flexible job shop scheduling is very important in both fields of production management and combinatorial optimization. Owing to the high computational complexity, it is quite difficult to achieve an optimal solution to this problem with traditional optimization approaches. Motivated by some empirical knowledge, we propose an efficient search method for the multi-objective flexible job shop scheduling problems in this paper. Through the work presented in this work, we hope to move a step closer to the ultimate vision of an automated system for generating optimal or near-optimal production schedules. The final experimental results have shown that the proposed algorithm is a feasible and effective approach for the multi-objective flexible job shop scheduling problems.

Scheduling choice method for flexible job shop problems using a fuzzy decision maker

This paper addresses the dual-resource constrained flexible job shop scheduling problem (DRCFJSP) with minimizing energy consumption. It is the first to study the energy-conscious DRCFJSP with turn OFF/ ON strategy. Different from the classical FJSP, the worker flexibility is considered in DRCFJSP. First, in order to solve this problem, we propose two mixed integer linear programming (MILP) models based on two modeling ideas, namely, idle time and idle energy. Because DRCFJSP is NP-hard, then we propose an efficient variable neighborhood search (VNS) algorithm. In the proposed VNS algorithm, eight neighborhood structures are designed to generate neighboring solutions. In addition, four energy-saving decoding approaches are specifically designed, in which two energy-saving strategies, namely, postponing strategy and turn OFF/ ON strategy are designed. Finally, the MILP model, the energy-conscious decoding methods, and the VNS are evaluated on numerical tests, whose effectiveness is shown by the experimental results. The experimental results show that the MILP model based on idle energy performs better than the model based on idle time idea, and the greedy hybrid decoding method outperforms the other three decoding methods. Moreover, the proposed VNS with eight neighborhood structures is a very competitive algorithm for the energy-conscious DRCFJSP.

Flexible Job shop Scheduling Problem (FJSP) is an extension of the classical job shop scheduling problem. Although the traditional optimization algorithms could obtain preferable results in solving the mono-objective FJSP, they are very difficult to solve multi-objective FJSP very well. In this chapter, a Particle Swarm Optimization (PSO) algorithm and a Tabu Search (TS) algorithm are combined to solve the multi-objective FJSP with several conflicting and incommensurable objectives. PSO which integrates local search and global search scheme possesses high search efficiency. And TS is a meta-heuristic which is designed for finding a near-optimal solution of combinatorial optimization problems. Through reasonably hybridizing the two optimization algorithms, an effective hybrid approach for the multi-objective FJSP has been proposed. The computational results have proved that the proposed hybrid algorithm is an efficient and effective approach to solve the multi-objective FJSP, especially for the problems on a large scale.

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.

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.

Calculating idle time (IDT) based on different definitions for solving flexible job shop scheduling problems (FJSSPs), may lead to dissimilar results. This is valid for both offline and online scheduling. Therefore, it is important to clarify the description of IDT. In this study, the differences between offline and online scheduling concepts are first explained. The advantages and disadvantages of these two approaches are analyzed in detail. The details of an offline scheduling method are illustrated through a step-by-step example, which is solved manually to be elucidative. Two definitions for IDT are then given over the waiting of the operations and machines, and an FJSSP is solved offline with a priority rule defined based on them. The differences in the results of the two definitions are demonstrated through the illustrative example. In addition, a source code is written in MATLAB for offline scheduling, with which some benchmarks are solved. Details of benchmarks are presented and the results are discussed.KeywordsOffline schedulingIdle timeFlexible job shop schedulingPriority rules

This work proposes a hybrid monkey search algorithm (HMSA) to solve the flexible job shop scheduling problem (FJSP) to minimize the makespan. The FJSP is a simple scheduling model that resembles numerous industrial production processes and the FJSP has been proved to be strongly NP-hard. Due to both theoretical and practical significance of FJSP, numerous researchers tackled the FJSP using different approaches. In this paper, the variable neighbourhood search (VNS) algorithm is combined with the monkey search algorithm (MSA) to enhance the solution quality. Benchmark problems are considered for validating the performance of the proposed algorithm. The computational results confirm the supremacy of the proposed HMSA for the benchmark problem.

Flexible job-shop scheduling problem (FJSSP) is an extension of the classical job shop scheduling problem (JSSP) with practical applications. FJSSP has been proven as an NP-hard problem. Many researchers have focused on FJSSP in recent years. FJSSP includes two sub-problems: 1. machine assignment that is to select a machine from a set of candidate machines for operations; 2. operation sequencing that is to schedule the operations on machines to obtain a feasible solution. In the classical JSSP problem, one operation can be processed on only one machine. Hence, the FJSSP is more difficult than the classical JSSP. There is a great variety of real-world problems that can be modeled as FJSSP, e.g., optimization of crane operations, simulation and optimization of transport systems, and scheduling of manufacturing and remanufacturing systems. In this thesis, the scheduling problems in the remanufacturing industry are modeled as FJSSP with multiple constraints. The constraints are new job insertion and uncertain processing time. The uncertain processing time constraint is built into two models, one is based on most probable processing time and another is based on fuzzy processing time. A rescheduling operator is executed when a new job is inserted or processing time becomes larger than the most probable processing time. The problem size may change dynamically during scheduling and rescheduling stages. The objectives include minimizing maximum completion time (Makespan), minimizing maximum machine workload, minimizing the average of earliness and tardiness and so on. Compared to exact methods, approximate methods, especially meta-heuristics, are better for solving FJSSP because meta-heuristics can obtain satisfactory solutions in a reasonable time. Meta-heuristics are more suitable for large-scale FJSSP. A music-inspired algorithm, discrete harmony search (DHS), and a nature-inspired algorithm, artificial bee colony (ABC), are investigated and improved to solve FJSSP with constraints. Two encoding and decoding methods are developed for solution representation. Several simple and ensemble heuristics are employed to initialize population. An effective heuristic, named MinEnd heuristic is proposed to generate a high quality initial solution. Dynamic grouping strategies of harmony memory and new crossover method for improvising new harmonies are proposed. Effective local search operators are proposed to improve algorithms’ performance. For the rescheduling operator, three re-scheduling strategies are proposed and compared. A two-stage discrete harmony search algorithm and a two-stage artificial bee colony algorithm are proposed for rescheduling FJSSP with remanufacturing constraints. Extensive experiments have been conducted to test the performance of the proposed algorithms for solving FJSSP with remanufacturing constraints. The instance sets used in this thesis include three sets of benchmark instances and two sets from remanufacturing industry. Single objective, multi-objective and Pareto-based multi-objective formulations…

Competition among companies leads to a race in order to improve the management system of their production with respect to the delivery time. In the last few decades, the optimization of production scheduling has attracted the interest of numerous researchers. This article deals with the Flexible Jobshop Scheduling Problem (FJSP) as one of the most challenging combinatorial optimization problems. FJSP is an extension of the classical jobshop scheduling problem, in which an operation can be processed by several different machines. This article presents a new decomposition based artificial bee colony algorithm for the multi-objective FJSP. The proposed algorithm is tested and compared to other algorithms from the literature on benchmarks of the multi-objective FJSP and its superior performance and robust results have been proved.

Flexible job shop scheduling problem (FJSP), widely prevalent in many intelligent manufacturing industries, is one of the most classic problems of production scheduling and combinatorial optimization. In actual manufacturing enterprises, the setup of machines and the handling of jobs have an important impact on the scheduling plan. Furthermore, there is a trend for a cluster of machines with similar functionalities to form a work center. Considering the above constraints, a new order-driven multi-equipment work center FJSP model with setup and handling including multiple objectives encompassing the minimization of the makespan, the number of machine shutdowns, and the number of handling batches is established. An improved shuffled frog leading algorithm is designed to solve it through the optimization of the initial solution population, the improvement of evolutionary operations, and the incorporation of Pareto sorting. The algorithm also combines the speed calculation method in the gravity search algorithm to enhance the stability of the solution search. Some standard FJSP data benchmarks have been selected to evaluate the effectiveness of the algorithm, and the experimental results confirm the satisfactory performance of the proposed algorithm. Finally, a problem example is designed to demonstrate the algorithm’s capability to generate an excellent scheduling plan.

A hybrid harmony search algorithm for the flexible job shop scheduling problem

An immune-based multi-agent system for flexible job shop scheduling problem in dynamic and multi-objective environments

Flexible job shop scheduling problem is one of the most important topics in production management and is one of the most complex topics in combinatorial optimization. This problem is a generalization of job shop and parallel machines scheduling problem. Since the efficient allocation of resources can improve the performance of manufacturing, here, to reduce the processing time of jobs, additional resources are assigned to machines. In fact, in this paper, the effect of flexible resources in the flexible job shop scheduling problem with unrelated parallel machines and sequence-dependent setup time is investigated. Also, by presenting a mixed-integer linear programming model, an attempt has been made to minimize the costs of makespan, total weighted tardiness, delivery time and inventory. After solving this model by the GAMS, due to the NP-hardness of the problem, a tabu search (TS) algorithm is utilized for large-size instances. Finally, the obtained results are compared with the genetic algorithm (GA). To verify the statistical validity of the computational experiments and confirm which the best algorithm between the TS algorithm and GA is, a Kruskal–Wallis test is used. The results show that the TS algorithm is better than the GA.