Mathematical formulation and hybrid meta-heuristic solution approaches for dynamic single row facility layout problem

Şahin, Ramazan; Niroomand, Sadegh; Durmaz, Esra Duygu; Molla‐Alizadeh‐Zavardehi, Saber

doi:10.1007/s10479-020-03704-7

Cited by 18 publications

(18 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The initial attempt to apply the Hidden Markov Model to chord recognition was one of the early scholars to use the model in the field of music classification [ 8 ]. They trained the model using the expectation maximum (EM) algorithm and treating chord labels as hidden values in the EM framework.…”

Section: Related Workmentioning

confidence: 99%

A Study of Piano-Assisted Automated Accompaniment System Based on Heuristic Dynamic Planning

Lin

Zhao

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

In this paper, a piano-assisted automated accompaniment system is designed and applied to a practical process using a heuristic dynamic planning approach. In this paper, we aim at the generation of piano vocal weaves in accompaniment from the perspective of assisting pop song writing, build an accompaniment piano generation tool through a set of systematic algorithm design and programming, and realize the generation of recognizable and numerous weaving styles within a controlled range under the same system. The mainstream music detection neural network approaches usually convert the problem into a similar way as image classification or sequence labelling and then use models such as convolutional neural networks or recurrent neural networks to solve the problem; however, the existing neural network approaches ignore the music relative loudness estimation subtask and ignore the inherent temporality of music data when solving the music detection task. However, the existing music generation neural network methods have not yet solved the problems of discrete integrability brought by piano roll representation music data and the still-limited control domain and variety of instruments generated in the controllable music generation task. To solve these two problems, this paper proposes a controlled music generation neural network model for multi-instrument polyphonic music. The effectiveness of the proposed model is verified by conducting several sets of experiments on the collected MIDICN data set, and the experimental results show that the model achieves better performance in the aspects of negative log-likelihood value, perplexity, musicality measure, domain similarity analysis, and manual evaluation.

show abstract

Section: Related Workmentioning

confidence: 99%

A Study of Piano-Assisted Automated Accompaniment System Based on Heuristic Dynamic Planning

Lin

Zhao

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…A mixed-integer linear programming model and solution approaches for the DSRFLP were proposed by Şahin et al [2]. They used CPLEX to solve the linear model.…”

Section: Related Workmentioning

confidence: 99%

“…However, the solver failed to produce a provably optimal solution for instances of size greater than 10. To find high quality solutions for larger DSRFLP instances, Şahin et al [2] proposed two metaheuristic-based approaches: a genetic algorithm (GA) and a simulated annealing (SA) technique. Their GA is strengthened by integrating a restart strategy and applying the concept of acceptance probability.…”

Section: Related Workmentioning

confidence: 99%

“…A dynamic version of the SRFLP, called the dynamic single-row facility layout problem (DSRFLP) was introduced by Şahin et al [2]. In their problem formulation, the rearrangement cost for a facility occurs when the center coordinate of this facility in one planning period is different from that in the preceding or succeeding periods.…”

Section: Introductionmentioning

confidence: 99%

“…φ tp t (i)p t (j) represents the material flow between facilities p t (i) and p t (j) during period t, ψ p t (i)p t (j) is the cost of transferring a unit of material per distance unit between facilities p t (i) and p t (j), r ts is the rearrangement cost of facility s at the beginning of period t, and I(p t−1 , p t ) is the set of facilities whose location during period t differs from that during period t − 1 (in Figure 1, I(p 1 , p 2 ) = {3, 4, 5} and I(p 2 , p 3 ) = {2, 4}). Equation (2) determines the distance between facilities, and Equation (3) gives the cost of material flow per distance unit between facilities. Average deviation of the objective function from the reference value c tq Sum of flow costs between the first q facilities and the remaining n − q facilities during period t G(u, t, x) Change in rearrangement cost incurred by placing facility u at location x during period t x ts (l)…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Variable Neighborhood Search Approach for the Dynamic Single Row Facility Layout Problem

2022

View full text Add to dashboard Cite

The dynamic single row facility layout problem (DSRFLP) is defined as the problem of arranging facilities along a straight line during a multi-period planning horizon with the objective of minimizing the sum of the material handling and rearrangement costs. The material handling cost is the sum of the products of the flow costs and center-to-center distances between facilities. In this paper, we focus on metaheuristic algorithms for this problem. The main contributions of the paper are three-fold. First, a variable neighborhood search (VNS) algorithm for the DSRFLP is proposed. The main version of VNS uses an innovative strategy to start the search from a solution obtained by constructing an instance of the single row facility layout problem (SRFLP) from a given instance of the DSRFLP and applying a heuristic algorithm for the former problem. Second, a fast local search (LS) procedure is developed. The innovations of this procedure are two-fold: (i) the fast insertion and swap neighborhood exploration techniques are adapted for the case of the dynamic version of the SRFLP; and (ii) to reduce the computational time, the swap operation is restricted on pairs of facilities of equal lengths. Provided the number of planning periods is a constant, the neighborhood exploration procedures for n facilities have only O(n2) time complexity. The superiority of these procedures over traditional LS techniques is also shown by performing numerical tests. Third, computational experiments on DSRFLP instances with up to 200 facilities and three or five planning periods are carried out to validate the effectiveness of the VNS approach. The proposed VNS heuristic is compared with the simulated annealing (SA) method which is the state of the art algorithm for the DSRFLP. Experiments show that VNS outperforms SA by a significant margin.

show abstract

A Holistic Framework for Factory Planning Using Reinforcement Learning

Klar¹,

Mertes²,

Glatt³

et al. 2023

Proceedings of the 3rd Conference on Physical Modeling for Virtual Manufacturing Systems and Processes

View full text Add to dashboard Cite

The generation of an optimized factory layout is a central element of the factory planning process. The generated factory layout predefines multiple characteristics of the future factory, such as the operational costs and proper resource allocations. However, manual layout planning is often time and resource-consuming and involves creative processes. In order to reduce the manual planning effort, automated, computer-aided planning approaches can support the factory planner to deal with this complexity by generating valuable solutions in the early phase of factory layout planning. Novel approaches have introduced Reinforcement Learning based planning schemes to generate optimized factory layouts. However, the existing research mainly focuses on the technical feasibility and does not highlight how a Reinforcement Learning based planning approach can be integrated into the factory planning process. Furthermore, it is unclear which information is required for its application. This paper addresses this research gap by presenting a holistic framework for Reinforcement Learning based factory layout planning that can be applied at the initial planning (greenfield planning) stages as well as in the restructuring (brownfield planning) of a factory layout. The framework consists of five steps: the initialization of the layout planning problem, the initialization of the algorithm, the execution of multiple training sets, the evaluation of the training results, and a final manual planning step for a selected layout variant. Each step consists of multiple sub-steps that are interlinked by an information flow. The framework describes the necessary and optional information for each sub-step and further provides guidance for future developments.

show abstract

Mathematical formulation and hybrid meta-heuristic solution approaches for dynamic single row facility layout problem

Cited by 18 publications

References 39 publications

A Study of Piano-Assisted Automated Accompaniment System Based on Heuristic Dynamic Planning

A Study of Piano-Assisted Automated Accompaniment System Based on Heuristic Dynamic Planning

A Variable Neighborhood Search Approach for the Dynamic Single Row Facility Layout Problem

A Holistic Framework for Factory Planning Using Reinforcement Learning

Contact Info

Product

Resources

About