Solving a Dynamic Cell Formation Problem with Machine Cost and Alternative Process Plan by Memetic Algorithms

Tavakkoli–Moghaddam, Reza; Safaei, Nima; Babakhani, Masoud

doi:10.1007/11571155_18

Cited by 22 publications

(8 citation statements)

References 22 publications

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“…Therefore, they applied simulated annealing algorithm to solve the problems involved. Tavakkoli-Moghaddam et al (2005b) employed triangular fuzzy numbers to estimate uncertain demands of each part type. For this purpose, a fuzzy nonlinear mixed integer programming method was developed with the aim of minimizing constant machine, intercellular WIP transferring, and reconfiguration costs.…”

Section: Dynamic Product Demands In Designing Cellular Manufacturing mentioning

confidence: 99%

Evolution of clustering techniques in designing cellular manufacturing systems: A state-of-art review

Delgoshaei¹,

Delgoshaei²,

Ali³

2019

10.5267/j.ijiec

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This paper presents a review of clustering and mathematical programming methods and their impacts on cell forming (CF) and scheduling problems. In-depth analysis is carried out by reviewing 105 dominant research papers from 1972 to 2017 available in the literature. Advantages, limitations and drawbacks of 11 clustering methods in addition to 8 meta-heuristics are also discussed. The domains of studied methods include cell forming, material transferring, voids, exceptional elements, bottleneck machines and uncertain product demands. Since most of the studied models are NP-hard, in each section of this research, a deep research on heuristics and metaheuristics beside the exact methods are provided. Outcomes of this work could determine some existing gaps in the knowledge base and provide directives for objectives of this research as well as future research which would help in clarifying many related questions in cellular manufacturing systems (CMS).

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Section: Dynamic Product Demands In Designing Cellular Manufacturing mentioning

confidence: 99%

Evolution of clustering techniques in designing cellular manufacturing systems: A state-of-art review

Delgoshaei¹,

Delgoshaei²,

Ali³

2019

10.5267/j.ijiec

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“…To validate feasibility of proposed mathematical model and efficiency of the solution approach, a small numerical example (P1) is conducted and exact optimal Pareto frontier is illustrated in Table (8) using the well-known ε-constraint method. Fig.…”

Section: Validation Of Correctness Proposed Approach and Modelmentioning

confidence: 99%

“…Eqs. (8,9) ensure that parts are processed according to plan and to required processes. The time capacity of planning periods is controlled by constraints in Eq.…”

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confidence: 99%

A bi-objective model in sustainable dynamic cell formation problem with skill-based worker assignment

Niakan

Baboli

Moyaux

et al. 2016

Journal of Manufacturing Systems

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The most recent revolution in industry (Industrial Revolution 4.0) requires increased flexibility, agility and efficiency in the use of production equipment. The Dynamic Cellular Manufacturing System (DCMS) is one of the best production systems to meet such requirements. In addition, the increasing importance of environmental and social issues, along with recent laws, is forcing manufacturers and managers to take account of sustainability when designing and configuring manufacturing systems. This paper proposes a new bi-objective mathematical model of the Dynamic Cell Formation Problem (DCFP), in which both the worker's assignment and environmental and social criteria are considered. The first objective in this model is to minimize both production and labor costs, and total waste (e.g., energy, chemical material, raw material, CO2 emissions, etc.). Social criteria are represented as constraints. Due to the NP-hardness of this problem, we propose a new resolution approach called NSGA II-MOSA, that merges an efficient hybrid meta-heuristic based on the Non-dominated Sorting Genetic Algorithm (NSGA-II), with Multi-Objective Simulated Annealing (MOSA). Randomlygenerated test problems demonstrate the performance of our algorithm. Literature reviewThis section gives an overview of the most prominent research on DCMS. Due to the large number of investigations in this area, we focus mainly on recent studies. First, Rheault et al. [3] introduced the concept of a dynamic environment in CFP. Schaller et al. [4] integrated CFP with inventory aspects, then showed the performance of their model on multiple heuristics and evaluated several alternative lower bounding methods. Chen and Cao [5] proposed a mathematical model for multi-period Cellular Manufacturing Systems (Dynamic CMS) minimizing the total cost, which includes: inter-cell material handling, inventory holding and the setting up of cells. They also developed a Tabu Search (TS) method to obtain good solutions and show the efficiency of their model. Next, these authors [6] generated a robust system configuration by integrating cell formation and part allocation. They also proposed a twostage TS to find the optimal or near optimal solutions. Tavakkoli-Moghaddam et al. [7] presented a nonlinear integer model of DCMS with machine capacity limitation, machine replication, inter-cell movements and production in batches. They used constant and variable costs as well as reconfiguration and inter-cell movement costs to formulate their objective function. Some of these authors [8] applied a Memetic Algorithm (MA) to solve their DCMS model. Defersha and Chen [9] formulated a comprehensive model containing dynamic cell configuration, alternative routings, lot splitting, sequence of operations and workload balancing. They also considered machine adjacency and cell size capacity as constraints. Moreover, Defersha and Chen [10] also proposed a two-phase GA-based heuristic to solve DCFP with alternative routings. Safaei et al. [11],[12] presented a DCMS mathematical model with unc...

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“…Seifoddini [12] considered uncertainty in form of probabilistic demands for a CFP, but under one period planning horizon. Tavakkoli-Moghaddam et al [13] extended their previous model and considered trapezoid instead of triangular fuzzy numbers to show the demand uncertainty. They also modified the proposed mathematical model to a mixed-integer nonlinear programming (MINLP) model with fuzzy parameters [14].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Demand Consideration In A Multi-objective Dynamic Cell Formation Problem Using A Robust Scatter Search

Amoozad-Khalili¹,

Ranjbar-Bourani²,

Al-e-hashem³

2010

J. Math. Computer Sci.

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This paper presents a multi-objective cell formation problem considering alternative process routes and machine utilization with fuzzy demand. Two conflicting objectives include the total cell load variation and sum of the other costs consisting machine cost, inter-cell material handling cost, parts purchasing, operation, maintenance, and reconfiguration of machines cost are to be minimized simultaneously. Moreover, we consider demand in fuzzy condition, because it is more realistic to take into account the inexact and uncertain nature of demand. Due to the complexity of this problem, we develop a scatter search algorithm. Also by using the Taguchi as a robust parameter design method, we tune the effective factors of the developed algorithm on two sizes of benchmark problems that are generated randomly. NSGAII and Scatter Search evaluated and the related results confirm the efficiency and the effectiveness of our proposed Scatter Search provides good output according to some quality measures, especially for large-sized problems.

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Solving a Dynamic Cell Formation Problem with Machine Cost and Alternative Process Plan by Memetic Algorithms

Cited by 22 publications

References 22 publications

Evolution of clustering techniques in designing cellular manufacturing systems: A state-of-art review

Evolution of clustering techniques in designing cellular manufacturing systems: A state-of-art review

A bi-objective model in sustainable dynamic cell formation problem with skill-based worker assignment

Fuzzy Demand Consideration In A Multi-objective Dynamic Cell Formation Problem Using A Robust Scatter Search

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