A Genetic Algorithm and Tabu Search for Multi Objective Flexible Job Shop Scheduling Problems

Zhang, Guo Hui; Gao, Liang; Shi, Yang

doi:10.1109/ccie.2010.71

Cited by 18 publications

(4 citation statements)

References 9 publications

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“…Jain et al [9] found the Tabu search (TS) algorithms, in general, to be very effective for finding solutions of JSSP. This is mainly attributed to its powerful memory function [48] in coordination with neighborhood structures and flexible move evaluation strategies [5]. This is in disparity with what limited capability PDRs can do due to their myopic nature.…”

Section: Optimization Modulementioning

confidence: 80%

Learning Dispatching Rules for Scheduling: A Synergistic View Comprising Decision Trees, Tabu Search and Simulation

Shahzad

Mebarki

2016

Computers

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Abstract:A promising approach for an effective shop scheduling that synergizes the benefits of the combinatorial optimization, supervised learning and discrete-event simulation is presented. Though dispatching rules are in widely used by shop scheduling practitioners, only ordinary performance rules are known; hence, dynamic generation of dispatching rules is desired to make them more effective in changing shop conditions. Meta-heuristics are able to perform quite well and carry more knowledge of the problem domain, however at the cost of prohibitive computational effort in real-time. The primary purpose of this research lies in an offline extraction of this domain knowledge using decision trees to generate simple if-then rules that subsequently act as dispatching rules for scheduling in an online manner. We used similarity index to identify parametric and structural similarity in problem instances in order to implicitly support the learning algorithm for effective rule generation and quality index for relative ranking of the dispatching decisions. Maximum lateness is used as the scheduling objective in a job shop scheduling environment.

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Section: Optimization Modulementioning

confidence: 80%

Learning Dispatching Rules for Scheduling: A Synergistic View Comprising Decision Trees, Tabu Search and Simulation

Shahzad

Mebarki

2016

Computers

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“…The work reported here is one such attempt using clever ways of combining the search techniques GA and TS along with intelligent subtechniques. There have been a small number of systems that use the combination of GA and TS in providing a solution to JSSP (Meeran and Morshed 2007;Tamilselvan and Balasubramanie 2009;González et al 2009;Chiu et al 2007;Zhang et al 2010). Tamilselvan and Balasubramanie (2009)) have used GA as the base search mechanism and TS to improve their search.…”

Section: Literature Reviewmentioning

confidence: 98%

A hybrid genetic tabu search algorithm for solving job shop scheduling problems: a case study

Morshed¹

2011

J Intell Manuf

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In recent decades many attempts have been made at the solution of Job Shop Scheduling Problem using a varied range of tools and techniques such as Branch and Bound at one end of the spectrum and Heuristics at the other end. However, the literature reviews suggest that none of these techniques are sufficient on their own to solve this stubborn NP-hard problem. Hence, it is postulated that a suitable solution method will have to exploit the key features of several strategies. We present here one such solution method incorporating Genetic Algorithm and Tabu Search. The rationale behind using such a hybrid method as in the case of other systems which use GA and TS is to combine the diversified global search and intensified local search capabilities of GA and TS respectively. The hybrid model proposed here surpasses most similar systems in solving many more traditional benchmark problems and real-life problems. This, the system achieves by the combined impact of several small but important features such as powerful chromosome representation, effective genetic operators, restricted neighbourhood strategies and efficient search strategies along with innovative initial solutions. These features combined with the hybrid strategy employed enabled the system to solve several benchmark problems optimally, which has been discussed elsewhere in Meeran and Morshed (8th Asia Pacific industrial engineering and management science conference, Kaohsiung, Taiwan, 2007). In this paper we bring out the system's practical usage aspect and demonstrate that the system is equally capable of solving real life Job Shop problems.

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“…Among them, production scheduling is the focus of enterprise flexible production management, which will directly affect the perfect allocation of production resources in the workshop, the reasonable and efficient operation of the production line, and the cost and economic benefits of the entire enterprise. [3] . Studies have shown that reasonable tool management can reduce production costs by 10% to 30% and increase production efficiency by 10% to 40% [4][5][6] .…”

Section: Introductionmentioning

confidence: 99%

Research on scheduling of multiple equipment in flexible production line system based on tool data acquisition

Qiu,

Wu,

Chen

et al. 2024

Third International Conference on Testing Technology and Automation Engineering (TTAE 2023)

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The available cutting time of the tool directly affects the machining efficiency and quality of the workpiece, and research and analysis should be carried out to improve the monitoring accuracy of the available cutting time of the tool. Therefore, this paper studies the improvement methods for the above problems, and proposes a production scheduling decisionmaking method and system for multiple CNC machining centers that includes the constraints of available cutting time of tools. The visual interface is used to realize the production scheduling management of multiple CNC machining centers. Verify the feasibility of the development system. The test results show that the system developed in this paper can optimize the production scheduling scheme through the allocation of workpiece processing tasks and the selection of processing equipment under the constraint of the available cutting time of the tool, which can effectively improve the utilization efficiency of tools and processing equipment, and improve the quality of the workshop while ensuring the processing quality.

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A Genetic Algorithm and Tabu Search for Multi Objective Flexible Job Shop Scheduling Problems

Cited by 18 publications

References 9 publications

Learning Dispatching Rules for Scheduling: A Synergistic View Comprising Decision Trees, Tabu Search and Simulation

Learning Dispatching Rules for Scheduling: A Synergistic View Comprising Decision Trees, Tabu Search and Simulation

A hybrid genetic tabu search algorithm for solving job shop scheduling problems: a case study

Research on scheduling of multiple equipment in flexible production line system based on tool data acquisition

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