A research survey: heuristic approaches for solving multi objective flexible job shop problems

Türkyılmaz, Alper; Şenvar, Özlem; Ünal, İrem; Bulkan, Serol

doi:10.1007/s10845-020-01547-4

Cited by 48 publications

(24 citation statements)

References 168 publications

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“…Comparatively to finding a unique optimal solution in single-objective optimization, multi-objective problems present a possibly uncountable set of solutions (Türkyılmaz et al 2020). Providing a small number of representative optimal solutions may be of great help for project managers to make their decision.…”

Section: Application and Results Discussionmentioning

confidence: 99%

“…These techniques can eliminate most of the difficulties of classical methods (Hojjati et al 2018). They allow finding multiple Pareto solutions in one simulation run (Türkyılmaz et al 2020). The most popular optimization algorithms used to solve multi-objective problems are the Non-dominated Storing Genetic Algorithm (NSGA-II) and the Multi-Objective Particle Swarm Optimization method (MOPSO).…”

Section: Introductionmentioning

confidence: 99%

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Multi-objective optimization of stepped spillway and stilling basin dimensions

2021

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In the present paper, an optimization procedure is proposed for stepped spillway design dimensions, which leads to maximum energy dissipation rate and minimum construction cost considering independently the chute cost and stilling basin cost. Three independent objective functions are thus simultaneously satisfied. The procedure involves four main tools: The multi-objective particle swarm optimization method (MOPSO) to find Pareto solutions in one run, the K-means clustering algorithm to reduce the size of the obtained non-dominated solutions, the pseudo-weight vector approach (PWV) to facilitate the decision making and to select some adequate solutions, and finally, CFD simulations to analyze the retained optimal solutions. The suitability of the proposed procedure is tested through an example of application. As results, a set of twenty solutions with different satisfaction levels are found and compared to existing solutions. A multi-objective optimization problem may have many different solutions, the originality of the present work lies in the proposed procedure which explores several possible ones and reduces their number to give help for the decision making. Furthermore, an approximate expression of spillway total cost is also derived as a function of flow energy dissipation rate.

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Section: Application and Results Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization of stepped spillway and stilling basin dimensions

2021

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“…The restriction to a non-preemptive setup is the general case in literature and therefore adopted in the considered problem [19][20][21][22][23]. In the description of the dependencies of a task τ on other tasks (R1), only direct dependencies are provided in the sets T τ .…”

Section: Restrictions and Flexibility In The Scheduling Problemmentioning

confidence: 99%

Model Predictive Control for Flexible Job Shop Scheduling in Industry 4.0

Wenzelburger

Allgöwer

2021

Applied Sciences

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In the context of Industry 4.0, flexible manufacturing systems play an important role. They are designed to provide the possibility to adapt the production process by reacting to changes and enabling customer specific products. The versatility of such manufacturing systems, however, also needs to be exploited by advanced control strategies. To this end, we present a novel scheduling scheme that is able to flexibly react to changes in the manufacturing system by means of Model Predictive Control (MPC). To introduce flexibility from the start, the initial scheduling problem, which is very general and covers a variety of special cases, is formulated in a modular way. This modularity is then preserved during an automatic transformation into a Petri Net formulation, which constitutes the basis for the two presented MPC schemes. We prove that both schemes are guaranteed to complete the production problem in closed loop when reasonable assumptions are fulfilled. The advantages of the presented control framework for flexible manufacturing systems are that it covers a wide variety of scheduling problems, that it is able to exploit the available flexibility of the manufacturing system, and that it allows to prove the completion of the production problem.

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“…As potential applications for our method, we give edge networks where the users have some applications that need tasks to be executed in real time or with less latency, which requires renting some local nodes for this purpose instead of sending them to the cloud. Other potential applications are for the developed multi-objective optimization, which can be used for various combinatory problems, such as surgeries planning in hospitals [ 46 ] and job-shop planning [ 47 ].…”

Section: Conclusion and Future Considerationsmentioning

confidence: 99%

Directionally-Enhanced Binary Multi-Objective Particle Swarm Optimisation for Load Balancing in Software Defined Networks

Albowarab

Zakaria

Abidin

2021

Sensors

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Various aspects of task execution load balancing of Internet of Things (IoTs) networks can be optimised using intelligent algorithms provided by software-defined networking (SDN). These load balancing aspects include makespan, energy consumption, and execution cost. While past studies have evaluated load balancing from one or two aspects, none has explored the possibility of simultaneously optimising all aspects, namely, reliability, energy, cost, and execution time. For the purposes of load balancing, implementing multi-objective optimisation (MOO) based on meta-heuristic searching algorithms requires assurances that the solution space will be thoroughly explored. Optimising load balancing provides not only decision makers with optimised solutions but a rich set of candidate solutions to choose from. Therefore, the purposes of this study were (1) to propose a joint mathematical formulation to solve load balancing challenges in cloud computing and (2) to propose two multi-objective particle swarm optimisation (MP) models; distance angle multi-objective particle swarm optimization (DAMP) and angle multi-objective particle swarm optimization (AMP). Unlike existing models that only use crowding distance as a criterion for solution selection, our MP models probabilistically combine both crowding distance and crowding angle. More specifically, we only selected solutions that had more than a 0.5 probability of higher crowding distance and higher angular distribution. In addition, binary variants of the approaches were generated based on transfer function, and they were denoted by binary DAMP (BDAMP) and binary AMP (BAMP). After using MOO mathematical functions to compare our models, BDAMP and BAMP, with state of the standard models, BMP, BDMP and BPSO, they were tested using the proposed load balancing model. Both tests proved that our DAMP and AMP models were far superior to the state of the art standard models, MP, crowding distance multi-objective particle swarm optimisation (DMP), and PSO. Therefore, this study enables the incorporation of meta-heuristic in the management layer of cloud networks.

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A research survey: heuristic approaches for solving multi objective flexible job shop problems

Cited by 48 publications

References 168 publications

Multi-objective optimization of stepped spillway and stilling basin dimensions

Multi-objective optimization of stepped spillway and stilling basin dimensions

Model Predictive Control for Flexible Job Shop Scheduling in Industry 4.0

Directionally-Enhanced Binary Multi-Objective Particle Swarm Optimisation for Load Balancing in Software Defined Networks

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