Dynamic scheduling of parallel heat treatment furnaces: A case study at a manufacturing system

Baykasoğlu, Adil; Özsoydan, Fehmi Burçin

doi:10.1016/j.jmsy.2017.12.005

Cited by 44 publications

(19 citation statements)

References 26 publications

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“…The scheduling problems for single/multiple parallel and serial batch processing machines have also been studied, and various approaches have been developed [24,26,[30][31][32][33]. Based on their previous studies of dynamic scheduling problem [34,35], a case study focusing on online and dynamic scheduling of parallel heat treatment furnaces at a real manufacturing company was recently presented by Baykasoğlu and Ozsoydan [36] where a multi-start and constructive search algorithm was proposed to minimize the maximum completion time of the schedule. Concerning the latest industrial revolution (Industry 4.0), a novel general framework of assembly system was introduced by Bortolini et al [37] and an innovative multi-objective optimization model as well as the key enabling technologies were introduced for the assembly line balancing problem [38,39].…”

Section: Related Workmentioning

confidence: 99%

A dynamic order acceptance and scheduling approach for additive manufacturing on-demand production

Qiang

Zhang

Wang

et al. 2019

Int J Adv Manuf Technol

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Additive manufacturing (AM), also known as 3D printing, has been called a disruptive technology as it enables the direct production of physical objects from digital designs and allows private and industrial users to design and produce their own goods enhancing the idea of the rise of the "prosumer". It has been predicted that, by 2030, a significant number of small and medium enterprises will share industry-specific AM production resources to achieve higher machine utilization. The decision-making on the order acceptance and scheduling (OAS) in AM production, particularly with powder bed fusion (PBF) systems, will play a crucial role in dealing with on-demand production orders. This paper introduces the dynamic OAS problem in on-demand production with PBF systems and aims to provide an approach for manufacturers to make decisions simultaneously on the acceptance and scheduling of dynamic incoming orders to maximize the average profit-per-unit-time during the whole makespan. This problem is strongly NP hard and extremely complicated where multiple interactional subproblems, including bin packing, batch processing, dynamic scheduling, and decision-making, need to be taken into account simultaneously. Therefore, a strategy-based metaheuristic decisionmaking approach is proposed to solve the problem and the performance of different strategy sets is investigated through a comprehensive experimental study. The experimental results indicated that it is practicable to obtain promising profitability with the proposed metaheuristic approach by applying a properly designed decision-making strategy.

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Section: Related Workmentioning

confidence: 99%

A dynamic order acceptance and scheduling approach for additive manufacturing on-demand production

Qiang

Zhang

Wang

et al. 2019

Int J Adv Manuf Technol

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“…Therefore, the problem of daily heat supply scheduling is associated with an insuffi cient amount of research in the fi eld of algorithmization and intellectualization of the managerial decision making process concerning heat supply. In general, the current state of mathematical and algorithmic support for daily heat supply scheduling in the DHS is characterized by the absence of ready-made algorithms that take into account the real thermophysical properties of heating networks and enclosing structures, as well as their operating characteristics [1,2]. These algorithms should refl ect the thermal inertia of heating networks with regard to their thermophysical properties, branching and hydraulic modes.…”

Section: Introductionmentioning

confidence: 99%

Development and experimental verification of the mathematical model of thermal inertia for a branched heat supply system

Batukhtin¹,

Batukhtina²,

Bass³

et al. 2019

J Appl Eng Science

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The article describes a new method for making management decisions on heat supply in district heating systems, based on solving a sequence of recurrence relations of fi rst-order differential equations, enabling to synthesize daily schedules of heat supply in such systems. Using fi rst order differential equations, we implement real-time daily heat supply scheduling, predict the time-temperature dependence for heating water in the supply line, and we form a decision on the thermal energy delivery on the basis of this information. The effectiveness of our method is confi rmed by numerical modeling and comparative analysis of daily heat supply scheduling with the help of advanced intelligent decision making tools. For comparative analysis, we considered daily scheduling using a nonlinear regression model, a generalized regression neural network, a radial basis neural network, and a linear neural network. The effectiveness of our method was estimated on the basis of MAPE (mean absolute percentage error) and Accuracy coeffi cients. The model was recognized as most effective for which the MAPE value was maximum, and the Accuracy value tended to one hundred percent. Experimental studies showed that our proposed model has an advantage over the regression model by 1.68 times and over the neural models by more than 10.2 times when modeling for a hundred heating network sections. Thus, the main purpose of our study was to increase the accuracy of the method of making a managerial heat supply decision based on the experimental verifi cation of a mathematical model of thermal inertia of a branched district heating system.

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“…Some of the related population based metaheuristic algorithms include particle swarm optimization (PSO) algorithms (see [ 9 – 13 ]), ant colony optimization (ACO) algorithm (see [ 14 – 17 ]), and cuckoo search (CS) algorithm (see [ 18 , 19 ]), artificial bee colony (BAC) algorithm (see [ 20 ], and genetic algorithm (GA) [ 21 ]. More so, relevant literatures on UPMSPs can be found in [ 22 – 26 ].…”

Section: Introductionmentioning

confidence: 99%

Symbiotic organisms search algorithm for the unrelated parallel machines scheduling with sequence-dependent setup times

2018

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This paper addresses the problem of makespan minimization on unrelated parallel machines with sequence dependent setup times. The symbiotic organisms search (SOS) algorithm is a new and popular global optimization technique that has received wide acceptance in recent years from researchers in continuous and discrete optimization domains. An improved SOS algorithm is developed to solve the parallel machine scheduling problem. Since the standard SOS algorithm was originally developed to solve continuous optimization problems, a new solution representation and decoding procedure is designed to make the SOS algorithm suitable for the unrelated parallel machine scheduling problem (UPMSP). Similarly, to enhance the solution quality of the SOS algorithm, an iterated local search strategy based on combining variable numbers of insertion and swap moves is incorporated into the SOS algorithm. More so, to further improve the SOS optimization speed and performance, the longest processing time first (LPT) rule is used to design a machine assignment heuristic that assigns processing machines to jobs based on the machine dynamic load-balancing mechanism. Subsequently, the machine assignment scheme is incorporated into SOS algorithms and used to solve the UPMSP. The performances of the proposed methods are evaluated by comparing their solutions with other existing techniques from the literature. A number of statistical tests were also conducted to determine the variations in performance for each of the techniques. The experimental results showed that the SOS with LPT (SOS-LPT) heuristic has the best performance compared to other tested method, which is closely followed by SOS algorithm, indicating that the two proposed algorithms’ solution approaches are reasonable and effective for solving large-scale UPMSPs.

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Dynamic scheduling of parallel heat treatment furnaces: A case study at a manufacturing system

Cited by 44 publications

References 26 publications

A dynamic order acceptance and scheduling approach for additive manufacturing on-demand production

A dynamic order acceptance and scheduling approach for additive manufacturing on-demand production

Development and experimental verification of the mathematical model of thermal inertia for a branched heat supply system

Symbiotic organisms search algorithm for the unrelated parallel machines scheduling with sequence-dependent setup times

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