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

Li, Dongni; Yan, Wei; Xiao, Guangxue; Tang, Jiafu

doi:10.1016/j.cor.2012.11.024

Cited by 32 publications

(21 citation statements)

References 49 publications

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“…Parts cannot be added to, or removed from a batch once transporting of the batch has started. 8. Each batch has and only has one destination cell.…”

Section: A Assumptionsmentioning

confidence: 99%

“…Thus, it is not unusual to transport parts between two or more cells. Such movement caused by exceptional parts and exceptional machines is called intercell move or intercell transfer [1][2][3][4][5][6][7][8] .…”

Section: Introductionmentioning

confidence: 99%

“…Based on the problem model presented by Tang et al, Elmi et al proposed an integer linear programming model and developed a simulated annealing based approach to solve the problem in which parts are allowed to visit machines more than once in the non-consecutive manner [7] . Li et al developed a pheromone-based distributed negotiation approach [8] in which intercell transfers and flexible processing routes are considered simultaneously and dynamic parts arrivals are allowed. Although many efforts have been made about the intercell scheduling, an implicit assumption in the above researches is that the intercell transfer capacity is sufficient so that the parts can be delivered to the next cell (if any) as soon as they completed the operations in the current cell.…”

Section: Introductionmentioning

confidence: 99%

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An intercell scheduling approach considering transportation capacity

Zheng

et al. 2014

2014 IEEE International Conference on Automation Science and Engineering (CASE)

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Intercell scheduling disrupts the cellular manufacturing philosophy of creating independent cells, but is essential for enterprises to reduce costs. Since intercell scheduling is in nature the coordination of intercell production and intercell transportation, the intercell scheduling problem is considered with transportation constraints in this paper. Hyper-heuristics are known for their computational efficiency but are lack in effectiveness since the candidate heuristic rules are usually manually set in advance. In this paper, a hybrid evolution-based hyper-heuristic algorithm is developed for the addressed intercell scheduling problem considering transportation capability. In order to improve the effectiveness of hyper-heuristics, genetic programming is introduced to generate new heuristic rules automatically based on the information of machines or vehicles, thus expanding the set of the candidate rules, and then, a rule selection genetic algorithm is developed to select appropriate rules from the obtained rule set, for the machines and vehicles, respectively. Finally, the scheduling solutions are generated according to the selected rules. The contribution of this work lies in a) intercell transportation is considered in the intercell scheduling problem, and b) heuristic generation is adopted in advance of the heuristic selection, constructing a more effective hyper-heuristic with both computation efficiency and optimization performance.978-1-4799-5283-0/14/$31.00 ©2014 IEEE

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“…Parts cannot be added to, or removed from a batch once transporting of the batch has started. 8. Each batch has and only has one destination cell.…”

Section: A Assumptionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An intercell scheduling approach considering transportation capacity

Zheng

et al. 2014

2014 IEEE International Conference on Automation Science and Engineering (CASE)

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“…The initial parameter values for implementation of GA are as follows: probability of crossover (0.75-0.95), probability of mutation (0.005-0.01), population (80-120) and stopping criterion equal to 100 iterations. In addition, the initial parameter values for implementation of ACO are as follows: Number of ant , rate of evaporation (0-1), q (0-1), (0-3), (1)(2)(3)(4)(5)(6)(7)(8)(9)(10), and stopping criterion equal to 100 iterations. It is noticeable from the literature that the parameters used in GA and ACO have robust e ect on both result time and result quality.…”

Section: Numerical Examplesmentioning

confidence: 99%

“…The key bene ts of CMS described in the literature are: less production cost, less mate-rial handling cost, decline in throughput time and Work-In-Process (WIP), and also simple production control [1]. With the improvement of manufacturing technology and coping with the challenges of customer's demands from mass production to medium product diversity and medium or small production volumes with shorter product life cycles, CMSs received more courtesy from investigators and manufacturers [2][3][4][5][6]. Cell Part Scheduling problem (CPS) involves the allocation of manufacturing resources over time to perform the processes on a collection of parts in cells, which is a decision-making process that plays a signi cant role in CMS [7].…”

Section: Introductionmentioning

confidence: 99%

A flexible cell scheduling problem with automated guided vehicles and robots under energy-conscious policy

2017

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Abstract. A exible Cell Scheduling Problem (CSP) under Time-Of-Use (TOU)electricity tari s is developed in this study. To apply a kind of energy-conscious policy, overconsumption cost of on-peak period electricity, limitations on total energy consumption by all facilities, setup time available on each cell, part defect (pert) percentage, and the total number of Automated Guided Vehicles (AGV) were considered. Additionally, an Ant Colony Optimization (ACO) algorithm was employed to nd a near-optimum solution to the proposed Mixed Integer Linear Programming (MILP) model with the objective of minimizing the total cost of CSP model. Since no benchmark is available in the literature, a lower bound was implemented as well to validate the result achieved. Moreover, to improve the quality of the results obtained by meta-heuristic algorithms, two hybrid algorithms (HGA and HACO) were proposed to solve the model. For parameter tuning of algorithms, Taguchi experimental design method was applied. Then, numerical examples were presented to prove the application of the proposed methodology. Our results were compared with the lower bound, con rming consequently that HACO is capable of nding better and near-optimum solutions.

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Applying Gaia and AUML for the development of multiagent‐based control software for flexible manufacturing systems: addressing methodological and implementation issues

2015

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SUMMARYIn this article, we present the development of a simple multiagent-based system for the control of a flexible manufacturing system. We followed the stages of a methodology specially conceived for the development of agent-based system, which is an integration of the classical methodology for agent-oriented analysis and design Gaia, and AUML (Agent-Unified Modeling Language). We adopted as study case the CIMUBB Laboratory at the University of Bio-Bio, which has a flexible manufacturing system including three flexible manufacturing cells interconnected by a conveyor belt. In the analysis stage, we identified roles involved, and we design models representing roles and protocols. In the design stage, we applied Gaia agent, services, and acquaintance models from Gaia, and we complemented with AUML as the adopted methodology suggests.With the developed models, we constructed a fully functional system where each agent was built as an independent process tree. Agents communicate by passing messages through the Ethernet network with socket interfaces. Various tests executed in our laboratory scale manufacturing system show the effectiveness of our implementation.

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

Cited by 32 publications

References 49 publications

An intercell scheduling approach considering transportation capacity

An intercell scheduling approach considering transportation capacity

A flexible cell scheduling problem with automated guided vehicles and robots under energy-conscious policy

Applying Gaia and AUML for the development of multiagent‐based control software for flexible manufacturing systems: addressing methodological and implementation issues

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