Stochastic mixed-model assembly line sequencing problem: Mathematical modeling and Q-learning based simulated annealing hyper-heuristics

Mosadegh, Hadi; Ghomi, S. M. T. Fatemi; Süer, Gürsel A.

doi:10.1016/j.ejor.2019.09.021

Cited by 57 publications

(25 citation statements)

References 23 publications

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“…Solution approaches meta-heuristics and hyper-heuristics have been developed to tackle the NP-hard combinatorial optimization problem [72]. Recently, hyperheuristics arise in this context as efficient methodologies for selecting or generating (meta) heuristics to solve NP-hard optimization problems.…”

Section: Deep Rl For Solving Np-hard Problemsmentioning

confidence: 99%

“…The developed meta-algorithm automatically learns good heuristics for a diverse range of optimization problems over graphs. Mosadegh et al [72] proposed novel hyper-simulated annealing (HSA) to tackle the NP-hard problem. They developed new mathematical models to describe a mixed-model sequencing problem with stochastic processing times (MMSPSP).…”

Section: Deep Rl For Solving Np-hard Problemsmentioning

confidence: 99%

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Artificial Intelligence and Its Application in Optimization under Uncertainty

Sadeghi¹,

Amiri²,

Mooseloo³

2022

Artificial Intelligence

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Nowadays, the increase in data acquisition and availability and complexity around optimization make it imperative to jointly use artificial intelligence (AI) and optimization for devising data-driven and intelligent decision support systems (DSS). A DSS can be successful if large amounts of interactive data proceed fast and robustly and extract useful information and knowledge to help decision-making. In this context, the data-driven approach has gained prominence due to its provision of insights for decision-making and easy implementation. The data-driven approach can discover various database patterns without relying on prior knowledge while also handling flexible objectives and multiple scenarios. This chapter reviews recent advances in data-driven optimization, highlighting the promise of data-driven optimization that integrates mathematical programming and machine learning (ML) for decision-making under uncertainty and identifies potential research opportunities. This chapter provides guidelines and implications for researchers, managers, and practitioners in operations research who want to advance their decision-making capabilities under uncertainty concerning data-driven optimization. Then, a comprehensive review and classification of the relevant publications on the data-driven stochastic program, data-driven robust optimization, and data-driven chance-constrained are presented. This chapter also identifies fertile avenues for future research that focus on deep-data-driven optimization, deep data-driven models, as well as online learning-based data-driven optimization. Perspectives on reinforcement learning (RL)-based data-driven optimization and deep RL for solving NP-hard problems are discussed. We investigate the application of data-driven optimization in different case studies to demonstrate improvements in operational performance over conventional optimization methodology. Finally, some managerial implications and some future directions are provided.

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Section: Deep Rl For Solving Np-hard Problemsmentioning

confidence: 99%

Section: Deep Rl For Solving Np-hard Problemsmentioning

confidence: 99%

Artificial Intelligence and Its Application in Optimization under Uncertainty

Sadeghi¹,

Amiri²,

Mooseloo³

2022

Artificial Intelligence

View full text Add to dashboard Cite

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“…Equation (15) represents the total quantity constraint for each car model. Equation (16) shows that the sum of the number of each car model is equal to the total car body number.…”

Section: Idle Time and Overload Timementioning

confidence: 99%

“…An improved integrated smart multi-criterion Nawaz, Enscore, and Ham algorithm was presented to solve the problem. Reference [15] proposed a mixed-model sequencing problem of multi-station assembly line with stochastic processing time. The optimization objective was to minimize the weighted sum of expected total overload and idleness.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Objective Cuckoo Search Algorithm Based on the Record Matrix for a Mixed-Model Assembly Line Car-Sequencing Problem

Wang

Sheng

et al. 2020

IEEE Access

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In the car-sequencing problem of mixed-model assembly lines, a series of cars with different model types will be put into the assembly line in a certain order considering a variety of goals and constraints. In this paper, a multi-objective cuckoo search algorithm based on the record matrix is proposed to solve this problem. In this algorithm, the factors, including the variation of parts usage rates, variation of workstation workload, idle time, overload time, and model switching cost are considered. The record matrix proposed in this paper is utilized to record the characteristic information of the optimal solutions and historical solutions. Meanwhile, two search strategies based on the record matrix are proposed to enhance the ability of local search and global search in the algorithm. The proposed algorithm is verified by a real case. The results show that the proposed model and algorithm have good results, and they have the potential to address other similar problems. INDEX TERMS Car-sequencing problem, mixed-model assembly line, multi-objective cuckoo search algorithm, record matrix.

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“…In general, the use of a mixed-model assembly line enables the production of various versions of a base product with some differences from the base product [10,11]. The mixed-model assembly line has several advantages, such as quick response to customer demands, fewer expenses to produce the new models of a given base model by using the same facilities, and more production flexibility [12,13]. In other words, the mixed-model assembly line is an essential factor for today's manufacturers to overcome the recent challenges associated with operating in the Industry 4.0 environment, particularly regarding the diversity of products, as well as the rapid response to ever-changing customer expectations and demands [14].…”

Section: Introductionmentioning

confidence: 99%

Enabling Mass Customization and Manufacturing Sustainability in Industry 4.0 Context: A Novel Heuristic Algorithm for in-Plant Material Supply Optimization

Fathi

Ghobakhloo

2020

Sustainability

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The fourth industrial revolution and the digital transformation of consumer markets require contemporary manufacturers to rethink and reshape their business models to deal with the ever-changing customer demands and market turbulence. Manufacturers nowadays are inclined toward product differentiation strategies and more customer-focused approaches to stay competitive in the Industry 4.0 environment, and mass customization and product diversification are among the most commonly implemented business models. Under such circumstances, an economical material supply to assembly lines has become a significant concern for manufacturers. Consequently, the present study deals with optimizing the material supply to mixed-model assembly lines that contribute to the overall production cost efficiency, mainly via the reduction of both the material transportation and material holding costs across production lines, while satisfying certain constraints. Given the complexity of the problem, a novel two-stage heuristic algorithm is developed in this study to enable a cost-efficient delivery. To assess the efficiency and effectiveness of the proposed heuristic algorithm, a set of test problems are solved and compared against the best solution found by a commercial solver. The results of the comparison reveal that the suggested heuristic provides reasonable solutions, thus offering immense opportunities for production cost efficiency and manufacturing sustainability under the mass customization philosophy.

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Stochastic mixed-model assembly line sequencing problem: Mathematical modeling and Q-learning based simulated annealing hyper-heuristics

Cited by 57 publications

References 23 publications

Artificial Intelligence and Its Application in Optimization under Uncertainty

Artificial Intelligence and Its Application in Optimization under Uncertainty

A Multi-Objective Cuckoo Search Algorithm Based on the Record Matrix for a Mixed-Model Assembly Line Car-Sequencing Problem

Enabling Mass Customization and Manufacturing Sustainability in Industry 4.0 Context: A Novel Heuristic Algorithm for in-Plant Material Supply Optimization

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