An effective backtracking search algorithm for multi-objective flexible job shop scheduling considering new job arrivals and energy consumption

Caldeira, Rylan H.; Gnanavelbabu, A.; Vaidyanathan, T.

doi:10.1016/j.cie.2020.106863

Cited by 88 publications

(32 citation statements)

References 58 publications

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“…Several variations and extensions of the FSP, with multiple objective function criteria, have been studied in the literature [8][9][10]. Among the variants of the FSP, the Permutation Flow-shop Scheduling Problem (PFSP), in which jobs have the same sequence on all machines, plays a key role.…”

Section: Related Workmentioning

confidence: 99%

An Effective Decomposition-Based Stochastic Algorithm for Solving the Permutation Flow-Shop Scheduling Problem

Amirghasemi

2021

Algorithms

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This paper presents an effective stochastic algorithm that embeds a large neighborhood decomposition technique into a variable neighborhood search for solving the permutation flow-shop scheduling problem. The algorithm first constructs a permutation as a seed using a recursive application of the extended two-machine problem. In this method, the jobs are recursively decomposed into two separate groups, and, for each group, an optimal permutation is calculated based on the extended two-machine problem. Then the overall permutation, which is obtained by integrating the sub-solutions, is improved through the application of a variable neighborhood search technique. The same as the first technique, this one is also based on the decomposition paradigm and can find an optimal arrangement for a subset of jobs. In the employed large neighborhood search, the concept of the critical path has been used to help the decomposition process avoid unfruitful computation and arrange only promising contiguous parts of the permutation. In this fashion, the algorithm leaves those parts of the permutation which already have high-quality arrangements and concentrates on modifying other parts. The results of computational experiments on the benchmark instances indicate the procedure works effectively, demonstrating that solutions, in a very short distance of the best-known solutions, are calculated within seconds on a typical personal computer. In terms of the required running time to reach a high-quality solution, the procedure outperforms some well-known metaheuristic algorithms in the literature.

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Section: Related Workmentioning

confidence: 99%

An Effective Decomposition-Based Stochastic Algorithm for Solving the Permutation Flow-Shop Scheduling Problem

Amirghasemi

2021

Algorithms

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“…The objective of their work was to minimize the energy consumption and the productivity simultaneously. Another form of unpredictable events that gets a lot of attention lately is the new job arrivals: [12] developed an energy-conscious FJSSP with new job arrivals, where the minimization of makespan and energy consumption and instability were considered. To solve the scheduling problem, they proposed a discrete improved backtracking search algorithm (BSA), and for the rescheduling they used a novel slack-based insertion algorithm.…”

Section: Flexible Job Shop Energy-efficient Schedulingmentioning

confidence: 99%

A Q-Learning Rescheduling Approach to the Flexible Job Shop Problem Combining Energy and Productivity Objectives

2021

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The flexible job shop problem (FJSP) has been studied in recent decades due to its dynamic and uncertain nature. Responding to a system’s perturbation in an intelligent way and with minimum energy consumption variation is an important matter. Fortunately, thanks to the development of artificial intelligence and machine learning, a lot of researchers are using these new techniques to solve the rescheduling problem in a flexible job shop. Reinforcement learning, which is a popular approach in artificial intelligence, is often used in rescheduling. This article presents a Q-learning rescheduling approach to the flexible job shop problem combining energy and productivity objectives in a context of machine failure. First, a genetic algorithm was adopted to generate the initial predictive schedule, and then rescheduling strategies were developed to handle machine failures. As the system should be capable of reacting quickly to unexpected events, a multi-objective Q-learning algorithm is proposed and trained to select the optimal rescheduling methods that minimize the makespan and the energy consumption variation at the same time. This approach was conducted on benchmark instances to evaluate its performance.

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“…To solve the nonlinear complex optimization problems, the backtracking search algorithm (BSA) [ 1 ] is one of the most efficient and powerful tools because of the simplicity and user-friendly approach of the algorithm. Due to these advantages, numerous attempts have been made towards further improving the BSA, and at the same time, these improved versions have been applied to solve various optimization problems in the past few years [ 2 – 9 ]. For example, based on self-adaptive strategy design [ 10 – 13 ] and hybridization mechanism [ 2 , 3 , 11 , 14 ], many variants of BSA have been studied by several authors in the recent past.…”

Section: Introductionmentioning

confidence: 99%

A Bio-Inspired Multi-Population-Based Adaptive Backtracking Search Algorithm

Nama

Saha

2022

Cogn Comput

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Backtracking search algorithm (BSA) is a nature-based optimization technique extensively used to solve various real-world global optimization problems for the past few years. The present work aims to introduce an improved BSA (ImBSA) based on a multi-population approach and modified control parameter settings to apprehend an ensemble of various mutation strategies. In the proposed ImBSA, a new mutation strategy is suggested to enhance the algorithm’s performance. Also, for all mutation strategies, the control parameters are updated adaptively during the algorithm’s execution. Extensive experiments have been performed on CEC2014 and CEC2017 single-objective benchmark functions, and the results are compared with several state-of-the-art algorithms, improved BSA variants, efficient differential evolution (DE) variants, particle swarm optimization (PSO) variants, and some other hybrid variants. The nonparametric Friedman rank test has been conducted to examine the efficiency of the proposed algorithm statistically. Moreover, six real-world engineering design problems have been solved to examine the problem-solving ability of ImBSA. The experimental results, statistical analysis, convergence graphs, complexity analysis, and the results of real-world applications confirm the superior performance of the suggested ImBSA.

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An effective backtracking search algorithm for multi-objective flexible job shop scheduling considering new job arrivals and energy consumption

Cited by 88 publications

References 58 publications

An Effective Decomposition-Based Stochastic Algorithm for Solving the Permutation Flow-Shop Scheduling Problem

An Effective Decomposition-Based Stochastic Algorithm for Solving the Permutation Flow-Shop Scheduling Problem

A Q-Learning Rescheduling Approach to the Flexible Job Shop Problem Combining Energy and Productivity Objectives

A Bio-Inspired Multi-Population-Based Adaptive Backtracking Search Algorithm

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