Solving Job Shop Scheduling Problems Research Articles

Tabu search based on novel neighborhood structures for solving job shop scheduling problem integrating finite transportation resources

As advancements in transportation equipment intelligence continue, the job shop scheduling problem integrating finite transportation resources (JSPIFTR) has attracted considerable attention. Within the domain of shop scheduling, the neighborhood structure serves as a cornerstone for enabling intelligent optimization algorithms to effectively navigate and discover optimal solutions. However, current algorithms for JSPIFTR rely on generalized neighborhood structures, which incorporate operators like insertion and swap. While these structures are tailored to the encoding vectors, their utilization often leads to suboptimal optimization efficacy. To address this limitation, this paper introduces novel neighborhood structures specifically designed to the distinctive properties of JSPIFTR. These innovative structures leverage the intrinsic structural information in integrated scheduling, thereby enhancing the optimization effectiveness of the algorithm. Firstly, two theorems are presented to demonstrate the feasibility of the neighborhood solution. Secondly, different neighborhood structures for critical transportation and processing tasks are subsequently designed based on the analysis of the problem properties and constraints. Thirdly, an efficient fast evaluation method is developed to expediently calculate the objective value of the neighborhood solution. Finally, the novel neighborhood structures are combined with the tabu search (TS_NNS) and compared with other state-of-the-art methods on EX and NEX benchmarks. The comparative results demonstrate the remarkable performance of the neighborhood structure, with the TS_NNS enhancing the best solutions across 23 instances.

An efficient and adaptive design of reinforcement learning environment to solve job shop scheduling problem with soft actor-critic algorithm

Shop scheduling is deeply involved in manufacturing. In order to improve the efficiency of scheduling and fit dynamic scenarios, many Deep Reinforcement Learning (DRL) methods are studied to solve scheduling problems like job shop and flow shop. But most studies focus on using the latest algorithms while ignoring that the environment plays an important role in agent learning. In this paper, we design an effective, robust and size-agnostic environment for job shop scheduling. The proposed design of environment uses centralised training and decentralised execution (CTDE) to implement a multi-agent architecture. Together with the observation space we design, environmental information that is irrelevant to the current decision is eliminated as much as possible. The proposed action space enlarges the decision space of agents, which performs better than the traditional way. Finally, Soft Actor-Critic (SAC) algorithm is adapted to learning within this environment. By comparing with traditional scheduling rules, other reinforcement learning algorithms, and relevant literature, the superiority of the results obtained in this study is demonstrated.

A generic enhanced search framework based on genetic algorithm: Case study on job shop scheduling problem

Optimal allocation of production resources is an urgent need for the development of industrialization. Reasonable production scheduling algorithm and excellent scheduling scheme can efficiently plan production resources, reduce production costs and shorten order completion time. Genetic algorithm has become one of the most popular algorithms for solving job shop scheduling problem because of its simplicity, versatility and good robustness. However, the genetic algorithm for solving NP-hard problems such as job shop scheduling has the problem of falling into local optimum, which leads to the decrease of solution accuracy. This study focused on the problem and proposed a generic enhanced search framework based on genetic algorithm, which named niche adaptive genetic algorithm. The niche selection mechanism and adaptive genetic operators were used to enrich the diversity of population, balance the genetic probability and enhance the global search performance of the algorithm. The working mechanism of this algorithm is analysed by testing data, and the proposed algorithm was tested on job-shop scheduling problem instances. The results show that the performance of the proposed method is 0.79 percentage points higher than that of the standard genetic algorithm, and it has the ability to search for the global optimum.

Solving job shop scheduling problems via deep reinforcement learning

Deep reinforcement learning (DRL), as a promising technique, is a new approach to solve the job shop scheduling problem (JSSP). Although DRL method is effective for solving JSSP, there are still deficiencies in state representation, action space definition, and reward function design, which make it difficult for the agent to learn effective policy. In this paper, we model JSSP as a Markov decision process (MDP) and design a new state representation using the state features of bidirectional scheduling, which can not only enable the agent to capture more effective state information, improve its decision-making ability, but also effectively avoid the phenomenon of multiple optimal action selections in candidate action set. Invalid action masking (IAM) technique is employed to narrow the search space, which helps the agent avoid exploring suboptimal solutions. We evaluate the performance of the policy model on eight public test datasets: ABZ, FT, ORB, YN, SWV, LA, TA, and DMU. Extensive experimental results show that the proposed method on the whole has better optimization ability than the existing state-of-the-art models and priority dispatching rules.

Solving a job shop scheduling problem

ABSTRACT Job shop scheduling is a highly nondeterministic polynomial combinatorial issue. In this regard, optimization is essential for reducing the time consumed to perform a task. This research paper proposes an evolved social spider optimization method to deal with the job shop scheduling problem. The evolved social spider optimization method was developed by combining both the differential evolutionary and the social spider optimizations. The key proposal is to minimize the makespan time and solve Job shop scheduling problems to improve productivity. The differential evolutionary algorithm is integrated into the spider position update to boost the exploration capabilities of the social spider optimization algorithm. The time taken for ‘‘i’ jobs and ‘‘j’ machines to perform their tasks is studied. Consequently, 23 benchmark problems were prosperously studied utilizing the proposed techniques, and the computational results were compared with previous Meta heuristics methods. An all-inclusive comparison process was carried out to rate the efficiency of the existing optimization techniques in solving job shop scheduling problems. The proposed method of evolved social spider optimization has emerged as the most promising methodology in solving the job shop scheduling problem by consuming minimum makespan time.

Solve job shop scheduling problem with sequence dependent setup times using a local search algorithm with different neighborhood search structure

In this study examines job shop scheduling problems with sequence dependent setup times under objective function minimization of makespan (JSSP/SDST/ ). An effective meta-heuristic, local search is a meta-heuristic method for solving computationally hard optimization problems. Local search can be used on problems that can be formulated as finding a solution maximizing or minimizing a criterion among a number of candidate solutions. Local search algorithms move from solution to solution in the space of candidate solutions (the search space) by applying local changes, until a solution deemed optimal is found or a time bound is elapsed. The performance of the local search depends on its neighborhood search structure (NSS). We used five methods from neighborhood search: Swap, Migration Mechanism (MM), Inversion, shift, and a proposed robust neighborhood search method. The results showed that the new PNS method gives less makespan value with different problems size (15x15, 20x15, 20x20, 30x15, 30x20, 50x15, 50x20 and 100x20) taken from the OR- library compared to previous well known neighborhood search methods.

Solving the Job Shop Scheduling Problem by the Multi-Hybridization of Swarm Intelligence Techniques

The industry is subject to strong competition, and customer requirements which are increasingly strong in terms of quality, cost, and deadlines. Consequently, the companies must improve their competitiveness. Scheduling is an essential tool for improving business performance. The production scheduling problem is usually an NP-hard problem, its resolution requires optimization methods dedicated to its degree of difficulty. This paper aims to develop multi-hybridization of swarm intelligence techniques to solve job shop scheduling problems. The performance of recommended techniques is evaluated by applying them to all well-known benchmark instances and comparing their results with the results of other techniques obtainable in the literature. The experiment results are concordant with other studies that have shown that the multi hybridization of swarm intelligence techniques improve the effectiveness of the method and they show how these recommended techniques affect the resolution of the job shop scheduling problem.

An Adaptive Job Shop Scheduler Using Multilevel Convolutional Neural Network and Iterative Local Search

This manuscript proposed an effective hybrid method based on multi-level convolutional neural network (ML-CNN) and iterative local search (ILS) to solve job shop scheduling problems (JSSP) with small-scale training samples and less time. In the proposed method, ML-CNN is proposed to find the global path of JSSP instance from the genetic algorithm (GA); ML-CNN learned global path is fed into ILS to search for the best local path, it is the key to attach excellent adaptivity. In order to find the global path from optimal solutions, the proposed ML-CNN treated the JSSP as some sub-classification tasks first. Each subclass corresponding to one sub-operation prioritizes a particular machine according to the production environment. Significantly, the proposed ML-CNN designed two-level inputs to represent production statutes. The detailed-level input channel records fourteen detailed statutes, while the system-level input channel records four systematic statutes. Two-level inputs are fed into one dimensional (1-D) CNN to extract rich hidden features to predict the priority of each suboperation using a support vector machine (SVM) classifier. At last, the global path (priority sequences) could be obtained, which is encoded as the input of ILS to find the best local path. The authors trained and tested the proposed method on 82 public JSSP instances. The results indicated that the proposed method could obtain optimal solutions for small-scale instances and outperform others regarding makespan and computation time for large-scale JSSP instances.

Development of maze puzzle algorithm for the job shop scheduling

Maze puzzle concept has been introduced for solving job shop scheduling problem. Maze puzzle algorithm (MPA) is based on rotation and random jumping which explores the solution space as well as exploits the solution near to optimum. Coding is done using MATLAB software, and benchmark problem is evaluated for assessing efficiency of the algorithm. The algorithm can be used for optimisation of makespan for the given problem. The results are compared with other methods like GA, SA, SBI, SBII, PSO, BBO and TS, and found better than GA, SA, SBI, PSO, BBO but poor than SB-2 and TS.

Solving the Job Shop Scheduling Problem with an Enhanced Artificial Bee Colony Algorithm through Local Search Heuristic

Aims: In this paper, we propose an enhanced artificial bee colony (ABC) algorithm for the job shop scheduling problem. Background: Meta-heuristic approaches are widely applied to solve job shop scheduling problem and find the near optimal solutions in polynomial time. Objective: Improving performance of ABC algorithm to solve job shop scheduling problem. Methods: Using local search heuristics in ABC algorithm. Results: The experimental results showed that the proposed algorithm improves the efficiency. Conclusion: Proposed algorithm improves efficiency.

Intelligent Scheduling with Reinforcement Learning

In this paper, we present and discuss an innovative approach to solve Job Shop scheduling problems based on machine learning techniques. Traditionally, when choosing how to solve Job Shop scheduling problems, there are two main options: either use an efficient heuristic that provides a solution quickly, or use classic optimization approaches (e.g., metaheuristics) that take more time but will output better solutions, closer to their optimal value. In this work, we aim to create a novel architecture that incorporates reinforcement learning into scheduling systems in order to improve their overall performance and overcome the limitations that current approaches present. It is also intended to investigate the development of a learning environment for reinforcement learning agents to be able to solve the Job Shop scheduling problem. The reported experimental results and the conducted statistical analysis conclude about the benefits of using an intelligent agent created with reinforcement learning techniques. The main contribution of this work is proving that reinforcement learning has the potential to become the standard method whenever a solution is necessary quickly, since it solves any problem in very few seconds with high quality, approximate to the optimal methods.

Crossover and Mutation Strategies applied in Job Shop Scheduling Problems

This paper reviews modern genetic algorithm based approaches for solving job shop scheduling problems. This paper elaborates the types of crossover methods such as Partially Mapped crossover (PMX), Order One Crossover (OX), etc involved in Genetic Algorithm (GA) that are used for Job Shop Scheduling problems. Job Shop Scheduling represents one of the hardest combinatorial optimization problems where number of possible schedules drastically increases with the number of operations and machines. Normal crossover operators will often lead to inadmissible solution . Many specialized combining order or adjacency information from the two parents. The crossover method operators builds an offspring by choosing a subsequence of elements from one parent and preserving the order and position of as many elements as possible from the other parent. A subsequence of elements is selected by choosing two random cut points, which serve as boundaries for the swapping operations. There are few distinct mutation operators widely used for JSS problems such as swap, inversion, insertion (shift) and displacement mutation. Here some modern genetic algorithm-based approaches from the literature are also discussed as well.

The DABC and TLBO Algorithms for Solve Job Shop Scheduling Problem

The DABC and TLBO Algorithms for Solve Job Shop Scheduling Problem

Analysis of Tabu List Length on Job Shop Scheduling Problem

Tabu search is one of the meta-heuristics methods that can solve job shop scheduling problem because it has the ability to direct other methods to exit or avoid from local optimal solutions by utilizing the memory structure that records the trace of the search process that has been done that called tabu list. The size of tabu list is too small causes the local optimum and the size is too large causes the algorithm does not work effectively and will be slower for finding the best solution. In this research, the authors analyse the influence of the length of tabu list on the performance of tabu search algorithm on job shop scheduling problem. The size of tabu list is obtained by finding the median value of the minimum tabu list and the size of tabu list that obtained from the method of tabu search. The result of experiment shows that the length of tabu list that obtained by finding the median value is ideal for the small and large size job.

Memetic chicken swarm algorithm for job shop scheduling problem

<p>This paper presents a Memetic Chicken swarm optimization (MeCSO) to solve job shop scheduling problem (JSSP). The aim is to find a better solution which minimizes the maximum of the completion time also called Makespan. In this paper, we adapt the chicken swarm algorithm which take into consideration the hierarchical order of chicken swarm while seeking for food. Moreover, we integrate 2-opt method to improve the movement of the rooster. The new algorithm is applied on some instances of ORLibrary. The empirical results show the forcefulness of MeCSO comparing to other metaheuristics from literature in term of run time and quality of solution.</p>

A novel discrete PSO algorithm for solving job shop scheduling problem to minimize makespan

In this work, a discrete version of PSO algorithm is proposed to minimize the makespan of a job-shop. A novel schedule builder has been utilized to generate active schedules. The discrete PSO is tested using well known benchmark problems available in the literature. The solution produced by the proposed algorithms is compared with best known solution published in the literature and also compared with hybrid particle swarm algorithm and variable neighborhood search PSO algorithm. The solution construction methodology adopted in this study is found to be effective in producing good quality solutions for the various benchmark job-shop scheduling problems.

Fast simulated annealing hybridized with quenching for solving job shop scheduling problem

Various heuristic based methods are available in literature for optimally solving job shop scheduling problems (JSSP). In this research work a novel approach is proposed which hybridizes fast simulated annealing (FSA) with quenching. The proposed algorithm uses FSA for global search and quenching for localized search in neighborhood of current solution, while tabu list is used to restrict search from revisiting previously explored solutions. FSA is started with a relatively higher temperature and as search progresses temperature is gradually reduced to a value close to zero. The overall best solution (BS) is maintained throughout execution of the algorithm. If no improvement is observed in BS for certain number of iterations then quenching cycle is invoked. During quenching cycle current temperature is reduced to nearly freezing point and iterations are increased by many folds, as a result of this change search becomes nearly greedy. The strength of the proposed algorithm is that even in quenching mode escape from local optima is possible due to use of Cauchy probability distribution and non-zero temperature. At the completion of quenching cycle previous values of search parameters are restored and FSA takes over, which moves search into another region of solution space. Effectiveness of proposed algorithm is established by solving 88 well known benchmark problems taken form published work. The proposed algorithm was able to solve 45 problems optimally to their respective best known values in reasonable time. The proposed algorithm has been compared with 18 other published works. The experimental results show that the proposed algorithm is efficient in finding solution to JSSP.

A Performance Study for the Multi-objective Ant Colony Optimization Algorithms on the Job Shop Scheduling Problem

Most of the research on job shop scheduling problem are concerned with minimization of a single objective. However, the real world applications of job shop scheduling problems are involved in optimizing multiple objectives. Therefore, in recent years ant colony optimization algorithms have been proposed to solve job shop scheduling problems with multiple objectives. In this paper, some recent multi-objective ant colony optimization algorithms are reviewed and are applied to the job shop scheduling problem by considering two, three and four objectives. Also in this study, four criteria: makespan, mean flow time, mean tardiness and mean machine idle time are considered for simultaneous optimization. Two types of models are used by changing the number of ants in a colony and each multi-objective ant colony optimization algorithm is applied to sixteen benchmark problem instances of up to 20 jobs × 5 machines, for evaluating the performances of these algorithms. A detailed analysis is performed using the performance indicators, and the experimental results have shown that the performance of some multi-objective ant colony optimization algorithms depend on the number of objectives and the number of ants.

Research of an Improved Genetic Algorithm for Job Shop Scheduling

job shop scheduling is one of the most difficult NP-hard combinatorial optimize problems, in order to solve this problem, an improved Genetic Algorithm with three- dimensional coded model was put forward in this paper. In this model, the gene was coded with 3-D space, and self-adapting plot was drawn into conventional GA, then the probability of crossover and mutation can automatic adjust by fit degree. The instance shows that this algorithmic is effective to solve job shop scheduling problem.

Job Scheduling Problem with Fuzzy Neural Network by using the MapReduce Model in a Cloud Environment

Cloud computing is a solution for processing large amounts of data. Therefore, Google introduced map reduce as a programming model for large scale data applications in the cloud environment. Map reduce is used for data processing and parallel computing. The Apache Hadoop is an open source implementation of mapreduce. However job shop scheduling problem (JSSP) is an important issues that is one of the most popular NP hard, it is necessary to find a faster solution for large scale problems. For this purpose, fuzzy neural network must be use to solve this kind of optimization problem. In this paper, we proposed new novel method by using a fuzzy neural network with map reduce model to solve job shop scheduling problem, implementation and results are presented. The experiments of our proposed method are performed for well-known problem instances from job scheduling. The results show our method has high convergence speed and less execution time compared with Genetic algorithm.