Scheduling of no buffer job shop cells with blocking constraints and automated guided vehicles

Zeng, Chengkuan; Tang, Jiafu; Yan, Chongjun

doi:10.1016/j.asoc.2014.08.028

Cited by 35 publications

(14 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Zeng and Tang () and Zeng et al. () proposed an integer linear programming model for blocking JCS with automated guided vehicles and combined an improving timetabling method and local search to solve this problem. Zeng et al.…”

Section: Introductionmentioning

confidence: 99%

A flexible job shop cell scheduling with sequence‐dependent family setup times and intercellular transportation times using conic scalarization method

Deliktaş

Torkul

Üstün

2017

Int Trans Operational Res

View full text Add to dashboard Cite

In this paper, we propose novel mathematical models involving both single‐ and biobjective functions that deal with a flexible job shop scheduling problem in cellular manufacturing environment by taking into consideration exceptional parts, intercellular moves, intercellular transportation times, sequence‐dependent family setup times, and recirculation. The problem has been known as NP‐hard. The proposed models have been tested and solved using Lingo 11.0 with minimization of makespan for the problems involving about 4 cells, 4 part families, 15 parts, and 12 machines. The most suitable model among the proposed single‐objective models is determined using the test results. Then, another objective function as total tardiness is added to this model. The obtained biobjective model is solved using the scalarization methods, the weighted sum method, ɛ‐constraint method, and conic scalarization method (CSM), in order to convert the mathematical model's objectives into a single‐objective function. By utilizing these scalarization methods, the Pareto effective solutions are generated for a specific test problem. The advantages of the CSM are demonstrated by considering the Pareto effective solutions.

show abstract

Section: Introductionmentioning

confidence: 99%

A flexible job shop cell scheduling with sequence‐dependent family setup times and intercellular transportation times using conic scalarization method

Deliktaş

Torkul

Üstün

2017

Int Trans Operational Res

View full text Add to dashboard Cite

show abstract

“…The first example had 6 jobs ( J 1 , …, J 6 ) processing on 6 machines ( M 1 , …, M 6 ), and each job with 2 to 5 operations. The second one with 15 jobs ( J 1 , …, J 15 ) processing on 10 machines ( M 1 , …, M 10 ), and each job with 1 to 5 operations [23, 61]. Tables 3 and 4 show the AGV travel time among L/U point and machines and Tables 5 and 6 demonstrate the processing time of every operation on the machines for both the examples.…”

Section: Computational Results and Discussionmentioning

confidence: 99%

Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization

et al. 2017

View full text Add to dashboard Cite

Flexible manufacturing system (FMS) enhances the firm’s flexibility and responsiveness to the ever-changing customer demand by providing a fast product diversification capability. Performance of an FMS is highly dependent upon the accuracy of scheduling policy for the components of the system, such as automated guided vehicles (AGVs). An AGV as a mobile robot provides remarkable industrial capabilities for material and goods transportation within a manufacturing facility or a warehouse. Allocating AGVs to tasks, while considering the cost and time of operations, defines the AGV scheduling process. Multi-objective scheduling of AGVs, unlike single objective practices, is a complex and combinatorial process. In the main draw of the research, a mathematical model was developed and integrated with evolutionary algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and hybrid GA-PSO) to optimize the task scheduling of AGVs with the objectives of minimizing makespan and number of AGVs while considering the AGVs’ battery charge. Assessment of the numerical examples’ scheduling before and after the optimization proved the applicability of all the three algorithms in decreasing the makespan and AGV numbers. The hybrid GA-PSO produced the optimum result and outperformed the other two algorithms, in which the mean of AGVs operation efficiency was found to be 69.4, 74, and 79.8 percent in PSO, GA, and hybrid GA-PSO, respectively. Evaluation and validation of the model was performed by simulation via Flexsim software.

show abstract

“…The problem consists of 82 test instances with 4 different path layouts and 2 AGVs. Also, the variants of this benchmark problem have been studied: alternative routing of parts (Kumar, Janardhana, and Rao 2011), single AGV-based FMS with limited input/output buffer capacity at machines (Caumond et al 2009;Lacomme, Moukrim, and Tchernev 2005), and machines with blocking (Zeng, Tang, and Yan 2014). Other works consider just-in-time production of complex assemblies under multiple capacity constraints (Anwar and Nagi 1998), conflict-free routing (Nishi, Hiranaka, and Grossmann 2011;Saidi-Mehrabad et al 2015;Umar et al 2015), and single AGV-based FMS in a closed loop (Suri and Desiraju 1997;Jawahar et al 1998).…”

Section: Related Workmentioning

confidence: 98%

A coloured Petri net-based hybrid heuristic search approach to simultaneous scheduling of machines and automated guided vehicles

Baruwa

Piera

2015

International Journal of Production Research

View full text Add to dashboard Cite

To achieve a significant improvement in the overall performance of a flexible manufacturing system, the scheduling process must consider the interdependencies that exist between the machining and transport systems. However, most works have addressed the scheduling problem as two independent decision making problems, assuming sufficient capacity in the transport system. In this paper, we study the simultaneous scheduling (SS) problem of machines and automated guided vehicles using a timed coloured Petri net (TCPN) approach under two performance objectives; makespan and exit time of the last job. The modelling approach allows the evaluation of all the feasible vehicle assignments as opposed to the traditional dispatching rules and demonstrates the benefits of vehicle-controlled assignments over machine-controlled for certain production scenarios. In contrast with the hierarchical decomposition technique of existing approaches, TCPN is capable of describing the dynamics and evaluating the performance of the SS problem in a single model. Based on TCPN modelling, SS is performed using a hybrid heuristic search algorithm to find optimal or near-optimal schedules by searching through the reachability graph of the TCPN with heuristic functions. Large-sized instances are solved in relatively short computation times, which were a priori unsolvable with conventional search algorithms. The algorithm's performance is evaluated on a benchmark of 82 test problems. Experimental results indicate that the proposed algorithm performs better than the conventional ones and compares favourably with other approaches.

show abstract

Scheduling of no buffer job shop cells with blocking constraints and automated guided vehicles

Cited by 35 publications

References 57 publications

A flexible job shop cell scheduling with sequence‐dependent family setup times and intercellular transportation times using conic scalarization method

A flexible job shop cell scheduling with sequence‐dependent family setup times and intercellular transportation times using conic scalarization method

Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization

A coloured Petri net-based hybrid heuristic search approach to simultaneous scheduling of machines and automated guided vehicles

Contact Info

Product

Resources

About