Multi-Objective Optimization of Energy-Efficient Buffer Allocation Problem for Non-Homogeneous Unreliable Production Lines

Alaouchiche, Yasmine; Ouazène, Yassine; Yalaoui, Farouk

doi:10.1109/access.2021.3139954

Cited by 8 publications

(4 citation statements)

References 53 publications

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“…Brundage [47] presents the possibility of saving energy based on a window calculated for on-line production data, which allows turning off a specific machine without negatively affecting throughput. Temporarily switching off a machine and switching it back on under a certain condition in order to save energy is also suggested by [48][49][50]. In our approach, we propose the switching-off strategy for the critical machine (if necessary) and extending the operating time of non-critical machines.…”

Section: Comparative Discussion On Energy-saving Methodsmentioning

confidence: 99%

A Discrete-Time Queueing Model of a Bottleneck with an Energy-Saving Mechanism Based on Setup and Shutdown Times

Kempa,

Paprocka

2024

Symmetry

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Producers are encouraged to reduce their energy consumption of manufacturing systems by applying less-energy-intensive modern technologies and advanced machine tools and operating methods at the system level. In the paper, organizational and analytical solutions are combined to model the sustainable production system. Managers can study the behavior of a production system organized using energy-saving rules by changing key parameters of the input model (arrival intensity, bottleneck service rate, buffer size, setup and shutdown time) to analyze the queue size of the production system and therefore performance. A discrete-time queueing model of a single-bottleneck production line with a finite input buffer capacity is proposed. Jobs occur according to a binomial process and are processed individually, one by one, according to the natural FIFO service discipline, with a general discrete-type cumulative distribution function. The total number of jobs present in the system is bounded by a non-random fixed value N. Every time the system becomes empty, an energy-saving mechanism is started: the processing machine (server) is turned off during a geometrically distributed shutdown time. Similarly, the first job arriving into the empty system initializes a geometrically distributed setup time. Identifying renewal moments in the evolution of the model, a system of difference equations is built for the transient queue-size distribution conditioned by the state of the system at the opening. The solution is obtained explicitly in terms of probability-generating functions. In addition, the Drum-Buffer-Rope concept is proposed to reduce the energy consumption of the production line. The throughput of the production system is maximized by adjusting the time between the order arrivals and the size of the input buffer to the capacity of the bottleneck. Turning off a machine under certain conditions and slowing down non-critical machines are strategies to reduce energy consumption. A detailed illustrating numerical and simulation study of the considered model is attached as well, in which the sensitivity of the queue-size behavior to changes of the key input model parameters is investigated.

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Section: Comparative Discussion On Energy-saving Methodsmentioning

confidence: 99%

A Discrete-Time Queueing Model of a Bottleneck with an Energy-Saving Mechanism Based on Setup and Shutdown Times

Kempa,

Paprocka

2024

Symmetry

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“…Metaheuristics are another category of optimization methods that effectively manage the search process and explore the solution space within reasonable computing times. Examples of metaheuristics include genetic algorithms (GA) [36], simulated annealing (SA) [37], tabu search (TS) [38], and ant colony optimization [39]. Hybridization of metaheuristics with other methods has become a trend to enhance their efficiencies, such as combining TS with Nested Partitions [40], GA with SA [41], PSO and Optimal Computing Budget Allocation (OCBA) [42], and GA with Finite Perturbation analysis (FPA) [43].…”

Section: Literature Reviewmentioning

confidence: 99%

Adaptive Dynamic Jumping Particle Swarm Optimization for Buffer Allocation in Unreliable Production Lines

Kassoul,

Cheikhrouhou,

Zufferey

2023

IEEE Access

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Over the past half-century, the Buffer Allocation Problem (BAP) in production lines has remained a topic of continuous interest and research. In this context, the BAP refers to determining the optimal allocation of buffers along a production line to maximize efficiency and productivity. This paper presents a novel approach to address the BAP in unreliable serial production lines. The objective is to maximize the production rate of the production line, which directly influences its overall performance and profitability. The proposed approach introduces an adaptive simulation-optimization methodology, APSO, that leverages the particle swarm optimization (PSO) algorithm. PSO is a metaheuristic optimization technique inspired by the behavior of bird flocking or fish schooling, where particles explore the solution space to find optimal solutions. The novelty of this approach lies in integrating a jumping strategy into the PSO algorithm's velocity equation. The jumping strategy incorporates logarithmic and exponential functions into the velocity equation of the PSO algorithm, utilizing dynamic parameters. This modification enables the algorithm to quickly converge towards (or very close to) optimal solutions. By incorporating this jumping strategy, the proposed approach enhances the algorithm's exploration-exploitation balance, efficiently navigating complex solution spaces and overcoming local optima. To evaluate the effectiveness of the proposed method, extensive numerical experiments are conducted using various instances of production lines, ranging from 3 to 100 machines. Additionally, benchmark algorithms from the existing literature are employed for comparison purposes. The obtained results from these experiments serve as empirical evidence to demonstrate the efficiency and accuracy of the proposed approach. The results indicate that the proposed adaptive approach outperforms the benchmark algorithms regarding efficiency and solution quality.

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“…The study was critical for small farmers as well as poultry farm owners. One of the latest researches (16) regarding product line allocation has addressed the issue of maintaining the buffer stock. The Buffer allocation program generally deals with the challenge of product line disruption and keeping the optimum buffer minimizing the cost of carrying the inventory.…”

Section: Linear Programmingmentioning

confidence: 99%

A Study on Application of Linear Programming on Product Mix for Profit Maximization and Cost Optimization

Chanda¹,

Pabalkar²,

Gupta³

2022

IJST

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Objectives: This research aims to sneak into the retailer's perception about that customer segment and plan a product mix accordingly. The focus is on small players in small towns not having deep pockets to synergize the product mix decisions effectively. Methods: The data used in this research paper is from Hatchers, a medium-sized enterprise with zero budget for software for product mix decisions. The data was collected through face-to-face interviews with ten representatives and five supervisors in compliance with the existing documents and existing datasheet obtained from the production department, which was slightly updated to make the final output. The data was for one season, i.e., April to March. The data was analyzed to study pre-Linear Programming and post-Linear Programing profits. Findings: This examination distinguishes the current asset usage level and the benefit of every period of one of the apparel producing organizations, utilizing a linear programming procedure. Actual consumption of resources (product wise) was calculated to evaluate profit post applying Linear Programing to see the wastage and cost. There was a 54% increase post LP compared to the product-wise resource utilization. Similarly, the profit using Linear programming was more than double as wastage and costing were minimum, and revenue was high. Novelty: The article focused on the simple basic principle of linear programming for identifying product mix using Excel(Solver). LINGO. The software solutions become costlier for small firms, whereas Excel is more accessible and cost-efficient. There is a gap in existing literature as previous research has not focused on this aspect for small business houses where adapting software solutions is challenging.

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Multi-Objective Optimization of Energy-Efficient Buffer Allocation Problem for Non-Homogeneous Unreliable Production Lines

Cited by 8 publications

References 53 publications

A Discrete-Time Queueing Model of a Bottleneck with an Energy-Saving Mechanism Based on Setup and Shutdown Times

A Discrete-Time Queueing Model of a Bottleneck with an Energy-Saving Mechanism Based on Setup and Shutdown Times

Adaptive Dynamic Jumping Particle Swarm Optimization for Buffer Allocation in Unreliable Production Lines

A Study on Application of Linear Programming on Product Mix for Profit Maximization and Cost Optimization

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