scholarly journals Metaheuristics for a Flow Shop Scheduling Problem with Urgent Jobs and Limited Waiting Times

Algorithms ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 323
Author(s):  
BongJoo Jeong ◽  
Jun-Hee Han ◽  
Ju-Yong Lee
Keyword(s):  
Manufacturing Systems ◽  
Simulated Annealing Algorithm ◽  
Flow Shop ◽  
Waiting Times ◽  
Optimal Solution ◽  
Computation Time ◽  
Metaheuristic Algorithms ◽  
Flow Shop Scheduling ◽  
Mixed Integer ◽  
Scheduling Problem

This study considers a scheduling problem for a flow shop with urgent jobs and limited waiting times. The urgent jobs and limited waiting times are major considerations for scheduling in semiconductor manufacturing systems. The objective function is to minimize a weighted sum of total tardiness of urgent jobs and the makespan of normal jobs. This problem is formulated in mixed integer programming (MIP). By using a commercial optimization solver, the MIP can be used to find an optimal solution. However, because this problem is proved to be NP-hard, solving to optimality requires a significantly long computation time for a practical size problem. Therefore, this study adopts metaheuristic algorithms to obtain a good solution quickly. To complete this, two metaheuristic algorithms (an iterated greedy algorithm and a simulated annealing algorithm) are proposed, and a series of computational experiments were performed to examine the effectiveness and efficiency of the proposed algorithms.

scholarly journals A Reinforcement Learning Method for a Hybrid Flow-Shop Scheduling Problem

Algorithms ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 222 ◽  
Author(s):  
Han ◽  
Guo ◽  
Su
Keyword(s):  
Reinforcement Learning ◽  
Flow Shop ◽  
Optimal Solution ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
First Choice ◽  
Hybrid Flow Shop Scheduling

The scheduling problems in mass production, manufacturing, assembly, synthesis, and transportation, as well as internet services, can partly be attributed to a hybrid flow-shop scheduling problem (HFSP). To solve the problem, a reinforcement learning (RL) method for HFSP is studied for the first time in this paper. HFSP is described and attributed to the Markov Decision Processes (MDP), for which the special states, actions, and reward function are designed. On this basis, the MDP framework is established. The Boltzmann exploration policy is adopted to trade-off the exploration and exploitation during choosing action in RL. Compared with the first-come-first-serve strategy that is frequently adopted when coding in most of the traditional intelligent algorithms, the rule in the RL method is first-come-first-choice, which is more conducive to achieving the global optimal solution. For validation, the RL method is utilized for scheduling in a metal processing workshop of an automobile engine factory. Then, the method is applied to the sortie scheduling of carrier aircraft in continuous dispatch. The results demonstrate that the machining and support scheduling obtained by this RL method are reasonable in result quality, real-time performance and complexity, indicating that this RL method is practical for HFSP.

Optimization Techniques for No-Wait Manufacturing Scheduling: A Review

2012 ◽  
Vol 488-489 ◽  
pp. 1114-1118 ◽  
Author(s):  
Sagar U. Sapkal ◽  
Dipak Laha ◽  
Dhiren Kumar Behera
Keyword(s):  
Manufacturing Systems ◽  
Flow Shop ◽  
Optimization Methods ◽  
Performance Measure ◽  
Optimization Techniques ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Manufacturing Scheduling ◽  
Academic Fields ◽  
No Wait

This paper deals with a general continuous or no-wait manufacturing scheduling problem. Due to its applications in advanced manufacturing systems, no-wait scheduling has gained much attention in both practical and academic fields. Due to its NP-hard nature, most of the contributions focus on development of approximation based optimization methods or heuristics for the problem. Several heuristic procedures have been developed to solve this problem. This paper presents a survey of various methodologies developed to solve no-wait flow shop scheduling problem with the objective of minimizing single performance measure

scholarly journals The Discrete Particle Swarm Optimization Algorithms For Permutation Flowshop Scheduling Problem

2019 ◽  
Vol 20 (2) ◽  
pp. 105
Author(s):  
Ikhlasul Amallynda
Keyword(s):  
Particle Swarm Optimization ◽  
Flow Shop ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Flow Shop Scheduling ◽  
Computational Time ◽  
Scheduling Problem ◽  
Discrete Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Discrete Particle

In this paper, two types of discrete particle swarm optimization (DPSO) algorithms are presented to solve the Permutation Flow Shop Scheduling Problem (PFSP). We used criteria to minimize total earliness and total tardiness. The main contribution of this study is a new position update method is developed based on the discrete domain because PFSP is represented as discrete job permutations. In addition, this article also comes with a simple case study to ensure that both proposed algorithm can solve the problem well in the short computational time. The result of Hybrid Discrete Particle Swarm Optimization (HDPSO) has a better performance than the Modified Particle Swarm Optimization (MPSO). The HDPSO produced the optimal solution. However, it has a slightly longer computation time. Besides the population size and maximum iteration have any impact on the quality of solutions produced by HDPSO and MPSO algorithms 

Stochastic flexible flow shop scheduling problem with limited buffers and fixed interval preventive maintenance: a hybrid approach of simulation and metaheuristic algorithms

SIMULATION ◽  
2018 ◽  
Vol 95 (6) ◽  
pp. 509-528 ◽  
Author(s):  
R Rooeinfar ◽  
S Raissi ◽  
VR Ghezavati
Keyword(s):  
Computer Simulation ◽  
Preventive Maintenance ◽  
Flow Shop ◽  
Metaheuristic Algorithms ◽  
Flow Shop Scheduling ◽  
Computer Simulation Model ◽  
Scheduling Problem ◽  
Shop Scheduling ◽  
Limited Buffers ◽  
Flexible Flow Shop Scheduling

This study focused on the uncertain flexible flow shop scheduling problem with limited buffers when preventive maintenance is applied at fixed intervals. This issue has not been addressed in spite of widespread applications, due to complexity arising in solving such a stochastic decision making problem. To this aim, a novel optimization model is presented along with two types of solving methods using metaheuristic algorithms with and without a computer simulation model. The proposed hybrid method, named HSIM-META, integrates the computer simulation model along with the three most common metaheuristic algorithms, i.e., genetic algorithm (GA), simulated annealing (SA) algorithm, and particle swarm optimization (PSO), which offer better solution quality according to the literature. For this purpose, the simulation outputs are applied as an initial population for the tuned metaheuristic parameters to look for the next improved solution by investigating different approaches. Different numerical examples are discussed to examine the performance of the proposed method. The computational results of the proposed method, including hybrid simulation with GA (HSIM-GA), SA (HSIM-SA), and PSO (HSIM-PSO), are compared with the just applying GA, SA, and PSO. The results reveal that the suggested method acts more efficiently in terms of accuracy and speed in solving the problem.

scholarly journals Solving a flow shop scheduling problem with missing operations in an Industry 4.0 production environment

2021 ◽  
pp. 33-44 ◽  
Author(s):  
Daniel Alejandro Rossit ◽  
Adrián Toncovich ◽  
Diego Gabriel Rossit ◽  
Sergio Nesmachnow
Keyword(s):  
Industry 4.0 ◽  
Simulated Annealing Algorithm ◽  
Flow Shop ◽  
Mathematical Formulation ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Production Environment ◽  
Modern Approach ◽  
The Real ◽  
Shop Scheduling

Industry 4.0 is a modern approach that aims at enhancing the connectivity between the different stages of the production process and the requirements of consumers. This paper addresses a relevant problem for both Industry 4.0 and flow shop literature: the missing operations flow shop scheduling problem. In general, in order to reduce the computational effort required to solve flow shop scheduling problems only permutation schedules (PFS) are considered, i.e., the same job sequence is used for all the machines involved. However, considering only PFS is not a constraint that is based on the real-world conditions of the industrial environments, and it is only a simplification strategy used frequently in the literature. Moreover, non-permutation (NPFS) orderings may be used for most of the real flow shop systems, i.e., different job schedules can be used for different machines in the production line, since NPFS solutions usually outperform the PFS ones. In this work, a novel mathematical formulation to minimize total tardiness and a resolution method, which considers both PFS and (the more computationally expensive) NPFS solutions, are presented to solve the flow shop scheduling problem with missing operations. The solution approach has two stages. First, a Genetic Algorithm, which only considers PFS solutions, is applied to solve the scheduling problem. The resulting solution is then improved in the second stage by means of a Simulated Annealing algorithm that expands the search space by considering NPFS solutions. The experimental tests were performed on a set of instances considering varying proportions of missing operations, as it is usual in the Industry 4.0 production environment. The results show that NPFS solutions clearly outperform PFS solutions for this problem.

Surgical Scheduling Based on Hybrid Flow-Shop Scheduling

2012 ◽  
Vol 201-202 ◽  
pp. 1004-1007 ◽  
Author(s):  
Guo Xun Huang ◽  
Wei Xiang ◽  
Chong Li ◽  
Qian Zheng ◽  
Shan Zhou ◽  
...  
Keyword(s):  
Parallel Machines ◽  
Flow Shop ◽  
Elective Surgery ◽  
Optimal Solution ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Scheduling Problem ◽  
Shop Scheduling ◽  
Surgical Scheduling ◽  
Hybrid Flow Shop Scheduling

The efficient surgical scheduling of the operating theatre plays a significant role in hospital’s income and cost. Currently surgical scheduling only considered the surgery process in operating room and ignored other stages which should not be left out in real situations. The surgical scheduling problem is regarded as the hybrid flow-shop scheduling problem in this study. Each elective surgery which need local anesthesia has to go through a two-stage surgery procedure. Beds and operating rooms are represented as parallel machines. A mathematical model for such surgical scheduling problem is proposed and solved by LINGO. A case study with its optimal solution is also presented to verify the model.

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