Minimizing total earliness and tardiness in a nowait flow shop

Purpose Hybrid flow shop with multiprocessor task (HFSMT) has received considerable attention in recent years. The purpose of this paper is to consider an HFSMT scheduling under the environment of a common time window. The window size and location are considered to be given parameters. The research deals with the criterion of total penalty cost minimization incurred by earliness and tardiness of jobs. In this research, a new memetic algorithm in which a global search algorithm is accompanied with the local search mechanism is developed to solve the HFSMT with jobs having a common time window. The operating parameters of memetic algorithm have an important role on the quality of solution. In this paper, a full factorial experimental design is used to determining the best parameters of memetic algorithm for each problem type. Memetic algorithm is tested using HFSMT problems. Design/methodology/approach First, hybrid flow shop scheduling system and hybrid flow shop scheduling with multiprocessor task are defined. The applications of the hybrid flow shop system are explained. Also the background of hybrid flow shop with multiprocessor is given in the introduction. The features of the proposed memetic algorithm are described in Section 2. The experiment results are presented in Section 3. Findings Computational experiments show that the proposed new memetic algorithm is an effective and efficient approach for solving the HFSMT under the environment of a common time window. Originality/value There is only one study about HFSMT scheduling with time window. This is the first study which added the windows to the jobs in HFSMT problems.

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USING GENETIC ALGORITHMS TO SOLVE FUZZY FLOW SHOP SCHEDULING PROBLEMS BASED ON POSSIBILITY AND NECESSITY MEASURES

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems ◽

10.1142/s0218488508005340 ◽

2008 ◽

Vol 16 (03) ◽

pp. 409-433 ◽

Cited By ~ 6

Author(s):

PENG-JEN LAI ◽

HSIEN-CHUNG WU

Keyword(s):

Flow Shop ◽

Flow Shop Scheduling ◽

Fuzzy Sets Theory ◽

Scheduling Problems ◽

Due Dates ◽

Objective Functions ◽

Earliness And Tardiness ◽

Processing Times ◽

Fuzzy Processing Times ◽

Sets Theory

The scheduling problems with fuzzy processing times and fuzzy due dates are investigated in this paper. The concepts of earliness and tardiness are interpreted by using the concepts of possibility and necessity measures that were developed in fuzzy sets theory. Many types of objective function will be taken into account through the different combinations of possibility and necessity measures. The purpose of this paper is to obtain the optimal schedules based on these objective functions. The genetic algorithm will be invoked to tackle these objective functions. Four numerical examples are also provided and solved by using the commercial software MATLAB.

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A Crossbreed Discrete Artificial Bee Colony for Permutation Flow Shop Scheduling Problem to Minimize Total Earliness and Tardiness

International Journal of Intelligent Engineering and Systems ◽

10.22266/ijies2022.0228.40 ◽

2022 ◽

Vol 15 (1) ◽

Keyword(s):

Artificial Bee Colony ◽

Flow Shop ◽

Flow Shop Scheduling ◽

Scheduling Problem ◽

Shop Scheduling ◽

Earliness And Tardiness ◽

Bee Colony ◽

Permutation Flow Shop Scheduling ◽

Permutation Flow Shop

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A Hybrid Meta-Heuristic to Solve a Multi-Criteria HFS Problem

Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance ◽

10.4018/978-1-4666-2086-5.ch009 ◽

2013 ◽

pp. 252-274 ◽

Cited By ~ 1

Author(s):

Fatima Ghedjati ◽

Safa Khalouli

Keyword(s):

Flow Shop ◽

Choquet Integral ◽

Flow Shop Scheduling ◽

Hybrid Flow Shop ◽

Ant Colony Optimization Algorithm ◽

Shop Scheduling ◽

Earliness And Tardiness ◽

Hybrid Flow Shop Scheduling ◽

Feasible Solutions ◽

The One

In this chapter the authors address a hybrid flow shop scheduling problem considering the minimization of the makespan in addition to the sum of earliness and tardiness penalties. This problem is proven to be NP-hard, and consequently the development of heuristic and meta-heuristic approaches to solve it is well justified. So, to deal with this problem, the authors propose a method which consists on the one hand, on using a meta-heuristic based on ant colony optimization algorithm to generate feasible solutions and, on the other hand, on using an aggregation multi-criteria method based on fuzzy logic to assist the decision-maker to express his preferences according to the considered objective functions. The aggregation method uses the Choquet integral. This latter allows to take into account the interactions between the different criteria. Experiments based on randomly generated instances were conducted to test the effectiveness of the approach.

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