A genetic algorithm based heuristic for scheduling of virtual manufacturing cells (VMCs)

Kesen, Saadettin Erhan; Das, Sanchoy K.; Güngör, Zülal

doi:10.1016/j.cor.2009.10.006

Cited by 50 publications

(13 citation statements)

References 25 publications

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“…Gou et al developed a Lagrange relaxation based approach to solve the problem in job shop cells in CMS [25]. Kesen et al proposed a genetic algorithm based heuristic for scheduling of virtual manufacturing cells [26].…”

Section: Et Al Considered Permutation and Non-permutationmentioning

confidence: 99%

Dynamic parts scheduling in multiple job shop cells considering intercell moves and flexible routes

Yan

Xiao

et al. 2013

Computers & Operations Research

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Section: Et Al Considered Permutation and Non-permutationmentioning

confidence: 99%

Dynamic parts scheduling in multiple job shop cells considering intercell moves and flexible routes

Yan

Xiao

et al. 2013

Computers & Operations Research

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“…Kesen et al presented a multi-objective mixed integer programming formulation [16] and a genetic algorithm (GA) based heuristic approach [17] for job scheduling in virtual manufacturing cells to minimize the summation of weighted makespan and weighted total traveling distance. Banaszak et al [18] proposed a heuristic method for process planning and scheduling in virtual manufacturing, which is finding a feasible schedule that satisfies the constraints imposed by the work-order duration, the price, and the time-constrained resource availability.…”

Section: Literature Reviewmentioning

confidence: 99%

A TQCS-based service selection and scheduling strategy in cloud manufacturing

Cao

Wang

Kang

et al. 2015

Int J Adv Manuf Technol

153

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Globalization, servitization, and customization in the marketplace are changing the way manufacturing enterprises do their business. Cloud manufacturing (CMfg) offers a possibility to perform large-scale manufacturing collaboration. However, CMfg systems are immature in many aspects. Service selection and scheduling is a key issue for practical implementation of CMfg. In this paper, a service selection and scheduling model is established, with criteria TQCS (time, quality, cost, and service) being considered. A fuzzy decision-making theory is adopted to transform TQCS values into relative superiority degrees. This is different from the traditional linear weighted method in most previous research, which results in large values of non-standardization error. The four relative superiority degrees are then combined linearly into an overall objective, in which the weight coefficients are calculated through analytic hierarchy process (AHP). Afterwards, ant colony optimization (ACO) is repurposed for the established service selection and scheduling model. Meanwhile, a selection mechanism is added to ACO (now ACOS) to enhance its validity. The simulation results demonstrate the practicality of the proposed model and the effectiveness of ACOS compared with other widely used algorithms.

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“…Wemmerlov and Hyer [11] mentioned that even though the researchers widely focus on CF problem, the cell design would be incomplete if not related to the operational issues like scheduling of the system. Taking into account the above points, a few research works have included scheduling, however in different manner in the CF problems and are: Arkat et al [12] addressed the CF problem with the objectives of minimum transportation cost and minimum makespan; Mak et al [13] considered objectives of minimizing total material travel distance and minimizing the sum of tardiness of all the products in a virtual cellular manufacturing system; Kesen et al [14] proposed scheduling methodology and machine assignment with alternate machine routing for a virtual manufacturing environment with a temporary part grouping and considered a weightbased approach for the objectives of minimizing makespan and total travel distance; Jeon and Leep [15], Egilmez and Süer [16] discussed a two-phase procedure for part-family formation in phase I and scheduling aspects and machine cell formation in phase II. However, Arkat et al [12] and Mak et al [13] have not considered machine duplications, which, if included, can minimize inter-cell moves and minimize makespan under alternate machine routing flexibilities.…”

Section: Introductionmentioning

confidence: 99%

“…However, Arkat et al [12] and Mak et al [13] have not considered machine duplications, which, if included, can minimize inter-cell moves and minimize makespan under alternate machine routing flexibilities. The weight-based multi-objective approach of Kesen et al [14] is often related to the subjective preferences of the designer to the two criteria, makespan and inter-cell move. The two-phase procedure proposed by Jeon and Leep [15] and Egilmez and Süer [16] is a sequential approach and lacks a concurrent approach of CMS issues.…”

Section: Introductionmentioning

confidence: 99%

An improved design for cellular manufacturing system associating scheduling decisions

Subhaa

Jawahar²,

Ponnambalam

2019

Sādhanā

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This paper presents a model for the design of Cellular Manufacturing System (CMS) to evolve simultaneously structural design decisions of Cell Formation (CF) and operational issue decisions of optimal schedule. This integrated decision approach is important for designing a better performing cell. The model allows machine duplication and incorporates cross-flow for scheduling flexibility. The cross-flow is the term introduced to mean the inter-cell movement of parts from parent cell to identical machines in other cells though machines are available in the parent cell. This cross-flow facilitates routing flexibility and paves way for reduced schedule length thereby optimizing resources leading to minimized operational cost. A non-linear integer mathematical programming model is formulated with the objective function of minimizing operating cost which is the sum of Machine Utility Cost (MUC) and inter-cell costs. The MUC is a new cost parameter based on machine utility and it integrates CF, scheduling, and machine duplication decisions. The proposed model belongs to the class of NP-hard problems. A hybrid heuristic (HH) that has ''Simulated Annealing Algorithm (SAA) embedded with Genetic Algorithm (GA)'' is proposed. A comparison with the mathematical solution reveals that the proposed HH is capable of providing solutions closer to optimal in a computationally efficient manner. The model is validated by studying the effect of integrated decisions, machine duplications, and association of scheduling and cross-flow. The model validation reveals that the proposed CMS model evolves CF, scheduling, and machine duplication decisions with minimum operating cost. Thus, it can be inferred that the proposed model gives optimal integrated decisions for designing an effectively and efficiently performing cells and thus evolves improved CMS design decisions.

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A genetic algorithm based heuristic for scheduling of virtual manufacturing cells (VMCs)

Cited by 50 publications

References 25 publications

Dynamic parts scheduling in multiple job shop cells considering intercell moves and flexible routes

Dynamic parts scheduling in multiple job shop cells considering intercell moves and flexible routes

A TQCS-based service selection and scheduling strategy in cloud manufacturing

An improved design for cellular manufacturing system associating scheduling decisions

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