In recent days, Genetic algorithms are one of widely used approach for scheduling of flexible manufacturing system.Chen, H.et al8, presented a new genetic algorithm to solve the flexible job-shop scheduling problem with makespan criterion. The representation of solutions for the problem by chromosomes consists of two parts. The first part defines the routing policy and the second part the sequence of the operations on each machine. Genetic operators are introduced and used in the reproduction process of the algorithm. Numerical experiments show that our algorithm can find out high-quality schedules. Yang 9 presented a genetic algorithm (GA)-based discrete dynamic programming approach. Zhang H.P, Gen M 10, proposed a new multistage operation-based representation of GA (moGA) approach to solve flexible job shop problem (FJSP). The proposed algorithm is designed for optimal the 3 objectives including the makespan , total workloads of all machines , and maximum of workloads for all machines . By using some numerical example of related works, they demonstrate the efficiency of moGA. The optimal result is better than the other related approaches .Jie Gao, Linyan Sun, Mitsuo Gen11,developed a new approach hybridizing genetic algorithm with variable neighborhood descent to exploit the “global search ability” of genetic algorithm and “the local search ability” of variable neighborhood descent for solving multiobjective flexible job shop scheduling problem.Chen et al12, used genetic algorithm with new chromosome representation to solve FJSP.

Guohui Zhang, Yang Shi, Liang Gao 13, the two traditional algorithms of GA and TS are combined to solve the FJSP. TS are executed for local search according to different probability. And the tabu list length is variable according to different solutions. The experiments prove that the proposed algorithm could be used to

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solve the FJSP effectively. Kacem et al. 14,15 proposed a genetic algorithm controlled by the assigned model, which is generated by the approach of localization, for the single-objective and multi-objective FJSSP. In the work of Jerald,J et al 16, simultaneous scheduling of parts and AGVs is done for a particular type of FMS environment by using a non-traditional optimization technique called the adaptive genetic algorithm (AGA). Taghavifard,M.Tet al17,presented a genetic algorithm-based technique to schedule machines and

Automated Guided Vehicle (AGV), simultaneously. Choudhury B.B. et al18,used two different approaches viz.GA and SA on the same set of problems to determine the optimized schedule in an FMS.. They used both these methods to find out the appropriate one for the purpose. The coding of the methods is designed in a manner that yields global optima for the solution. The results show that GA scores over SA in dealing with the FMS scheduling under constraint condition and such behavior of GA against that of SA may be attributed to the fact that, the intricacies of the problem are well taken care of by the coding methods of GA. Pezzella et al. 19 integrate different strategies for generating the initial population,selecting the individuals and reproducing new individuals. Chryssolouris ,Subramaniam 20 used genetic algorithms for dynamic scheduling of manufacturing job shops in the presence of machine breakdown and alternate job routine. Two performance measures were used, namely mean job tardiness and mean job cost. Whenever a dynamic event occurs, genetic algorithms are used to propose an alternative schedule. In addition, the solution of genetic algorithms was compared to several common dispatching rules. The results indicated that the performance of genetic algorithms is significantly superior to that of the common dispatching rules. Leon et al 21 and Jensen 22 used genetic algorithms to generate robust schedules and to evaluate the performance of various robustness measures. Wu et al23,24, compared the performance of genetic algorithms and local search heuristics to generate robust schedules. The results showed the performance of genetic algorithms in generating schedules with much better makespan and stability than local search heuristics. However, Bierwirth ,Mattfeld 25 reported in their experimental results that the capabilities of genetic algorithms vanish with an increasing problem size, and they are not efficient to find a near-optimal solution in a reasonable time. Ho,Tay26 proposes an architecture for learning and evolving of Flexible Job-Shop schedules called LEarnable Genetic Architecture (LEGA). LEGA provides an effective integration between evolution and learning within a random search process. Unlike the canonical evolution algorithm, where random elitist selection and mutational genetics are assumed; through LEGA, the knowledge extracted from previous generation by its schemata learning module is used to influence the diversity and quality of offsprings. In addition, the architecture specifies a population generator module that generates the initial population of schedules and also trains the schemata learning module. Experimental results indicate that an instantiation of LEGA called GENACE outperforms current approaches using canonical EAs in computational time and quality of schedules. Libo Song, Xuejun Xu27presented a hybrid genetic algorithm (GA) combined with chaotic local search to solve the FJSP with MAKESPAN criterion. A small percentage of elitist individuals are introduced into the initial population to fasten GA’s convergence speed, efficient crossover and mutation operators are adopted to avoid infeasible solutions and to hasten the emergency of optimum solution. During the local search process, Logistic chaotic sequence is adopted to explore better neighborhood solutions around the best individual of the current generation. Representative flexible job shop scheduling benchmark problems are solved in order to test the effectiveness and efficiency of the proposed algorithm. Nasr Al-Hinai ,ElMekkawy T.Y.28 proposes hybridized genetic algorithm architecture for the Flexible Job Shop Scheduling Problem (FJSP). The efficiency of the genetic algorithm is enhanced by integrating it with an initial population generation algorithm and a local search method. The usefulness of the proposed methodology is illustrated with the aid of an extensive computational study on 184 benchmark problems with the objective of minimizing the makespan. Results highlight the ability of the proposed algorithm to first obtain optimal or near-optimal solutions, and second to outperform or produce comparable results with these obtained by other best-known approaches in literature. Jahangirian, Conroy 29 develop a learning mechanism which is driven by a genetic algorithm to determine the best multi-criteria scheduling option in an FMS. Fanti et al.30 combine fuzzy logic and a genetic algorithm to solve a multi-criteria scheduling problem. Saravana Sankar S, Ponnanbalam S.G.,Rajendran C.A,31proposed an appropriate scheduling mechanism is designed to generate a nearer-to-optimum schedule using Genetic Algorithm (GA) with two different GA Coding Schemes. Two contradictory objectives of the system were achieved simultaneously by the scheduling mechanism. The results are compared with those obtained by different scheduling rules and conclusions are presented. Erkmen, A.M, et al.32,focused on the development and implementation of a genetically tuned fuzzy scheduler (GTFS) for heterogeneous FMS under uncertainty. The scheduling system takes input from a table and creates an optimum master schedule. The GTFS uses fuzzy rulebase and inferencing where fuzzy sets are generated by a genetic algorithm to tune the optimization. The fuzzy optimization is based on time criticality in deadline and machine need, taking into account machine availability, uniformity, process time and select ability.Luis Rabelo et al. 33, A scheme for the scheduling of Flexible Manufacturing Systems (FMS) has been developed which integrates neural networks, parallel Monte-Carlo simulation, genetic algorithms and machine learning. Modular neural networks are used to generate a small set of attractive plans and schedules from a larger list of such plans and schedules. Parallel Monte-Carlo Simulation predicts the impact of each on the future evolution of the manufacturing system. Genetic algorithms are utilized to combine attractive alternatives into a single “best” decision. Induction mechanisms are used for learning and simplify the decision process in future performances. Sankar, S.S.; Ponnambalam, S.G. 34 Proposed a genetic algorithm based iterative procedure to approximately solve the integrated scheduling problem, which accommodates the simultaneous scheduling of incoming jobs, machines, and vehicle dispatching in a flexible manufacturing system (FMS) having a single device, an automated guided vehicle (AGV). The objective is to find an optimal sequence of incoming parts, which will reduce the waiting times due to blocking and starving of resources and deadheading times, resulting in overall minimization of makespan. The procedure is evaluated through different benchmark problems. Hou, E.S.H.,Li, H.-Y. 35 The authors present a genetic algorithm approach to solving the task scheduling problem in flexible manufacturing systems (FMSs) An FMS is modeled as a collection of m workstations and p automated guided vehicles (AGVs). The FMS completes a task by performing a series of operations through the workstations, and the parts are transported between the workstations by the AGVs. The problem of task scheduling in an FMS can be stated as finding a schedule for the p AGVs among the m workstations such that n tasks can be completed in the shortest time. The genetic algorithm developed uses a reproduction operator and five mutation operators to perform the task scheduling. Computer simulations of the proposed genetic algorithm are also presented. Haipeng Zhang, Mitsuo Gen 36,proposed a novel approach for designing chromosome has been proposed to improve the effectiveness, which called multistage operation-based genetic algorithm (moGA). The objective is to find the optimal resource selection for assignments, operations

sequences, and allocation of variable transfer batches, in order to minimize the total makespan, considering the setup time, transportation time, and operations processing time. The plans and schedules are designed considering flexible flows, resources status, capacities of plants, precedence constraints, and workload balance

in Flexible Manufacturing System (FMS). The experimental results of various Advanced Planning and Scheduling (APS) problems have offered to demonstrate the efficiency of moGA by comparing with the previous methods. António Ferrolho, Manuel Crisóstomo 37,propsed that, Genetic algorithms (GA) can provide good solutions for scheduling problems. But, when a GA is applied to scheduling problems various crossovers and mutations operators can be applicable. They examines a new concept of genetic operators for scheduling problems. A software tool called hybrid and flexible genetic algorithm (HybFlexGA) was developed to examine the performance of various crossover and mutation operators by computing simulations of job scheduling problems. Kumar et al38 studied the simple genetic algorithm and proposed a new methodology, constraint-based genetic algorithm (CBGA) to handle a complex variety of variables and constraints in a typical FMS-loading problem. Toachieve this aim, two objective functions were introduced to minimize system unbalance leading to maximizing system utilization and to maximize throughput, leading to maximization of system efficiency. Chen ,Ho39 proposed an approach to solve multi-objective production planning problems (MOPPPs) of FMS using an efficient multi-objective genetic algorithm EMOGA. The proposed approach had four objectives: minimization of total flow time, minimization of machine workload unbalance, minimization of greatest machine workload and minimization of total tool cost.Zhao ,Wu40 presented a genetic algorithm approach to flexible routing scheduling problems. They implemented the concepts of a flexible-routing scheduling problem, which involves routing selection, machine selection, and processing sequence selection.Chan F.T.S et al. 41, proposed a genetic algorithm with dominant genes (GADG) approach to deal with distributed flexible manufacturing system (FMS) scheduling problems subject to machine maintenance constraint. The optimization performance of the proposed GADG will be compared with other existing approaches, such as simple genetic algorithms to demonstrate its reliability. The significance and benefits of considering maintenance in distributed scheduling will also be demonstrated by simulation runs on a sample problem. Jie Gao, Linyan Sun, Mitsuo Gen42, developed a hybrid genetic algorithm (GA) for the flexible job shop scheduling problem (fJSP) with three objectives: min makespan, min maximal machine workload and min total workload.. The GA uses two vectors to represent solutions. Advanced crossover and mutation operators are used to adapt to the special chromosome structure and the characteristics of the problem. In order to strengthen the search ability, individuals of GA are first improved by a variable neighborhood descent (VND), which involves two local search procedures: local search of moving one operation and local search of moving two operations. Moving an operation is to delete the operation, find an assignable time interval for it, and allocate it in the assignable interval. They developed an efficient method to find assignable time intervals for the deleted operations based on the concept of earliest and latest event time. The local optima of moving one operation are further improved by moving two operations simultaneously. Zhang H.P, Gen M 43 proposed a multistage operation-based GA to deal with the flexible job-shop scheduling problem from a point view of dynamic programming. Mesghouni, K., Hammadi, S, Borne, P44, presented an article with an objective to improve performance of the genetic algorithms based approach to jobshop scheduling problems by developing effective genetic operators, such as a parallel representation of the chromosome, on the one hand, and genetic operators associated with this original representation. In this article they deal with the problem of flexible job-shop scheduling which presents two difficulties : the first one is the assignment of each operation to a machine, and the second one is scheduling this set of operations in order to minimize the makespan criterion .Tay, J. C.,Wibowo, D. 45 proposed a new chromosome representation and a design of related parameters to solve the FJSP efficiently. The results of applying the new chromosome representation for solving the 10 jobs x 10 machines FJSP are compared with three other chromosome representations. Empirical experiments show that the proposed chromosome representation obtains better results than the others in both quality and processing time requiredGuohui Zhang, Liang Gao, Yang Shi, .46 proposed an effective genetic algorithm for solving the flexible job-shop scheduling problem (FJSP) to minimize makespan time. In the proposed algorithm, Global Selection (GS) and Local Selection (LS) are designed to generate high-quality initial population in the initialization stage. An improved chromosome representation is used to conveniently represent a solution of the

FJSP, and different strategies for crossover and mutation operator are adopted. Various benchmark data taken from literature are tested. Computational results prove the proposed genetic algorithm effective and efficient for solving flexible job-shop scheduling problem. Jensen, M.T 47 considered the issue of robust and flexible solutions for job shop scheduling problems. A robustness measure is defined and its properties are investigated. Through experiments, it is shown that using a genetic algorithm it is possible to find robust and flexible schedules with a low makespan. These schedules are demonstrated to perform significantly better in rescheduling after a breakdown than ordinary schedules. The rescheduling performance of the schedules generated by minimizing the robustness measure is compared with the performance of another robust scheduling method taken from literature, and found to outperform this method in many cases. Haipeng Zhang, Mitsuo Gen48, proposed a new multistage operation based representation of GA (moGA) approach to solve fJSP. The proposed algorithm is designed for optimal the 3 objectives including the makespan , total workloads of all machines , and maximum of workloads for all machines . By using some numerical example of related works, they demonstrate the efficiency of moGA. The optimal result is better than the other related approaches. Guohui Zhang, Yang Shi, Liang Gao49, combined the two traditional algorithms of GA and TS to solve the FJSP. TS is executed for local search according to different probability. And the tabu list length is variable according to different solutions. The experiments prove that the proposed algorithm could be used to solve the FJSP effectively.

Research in job shop scheduling has predominantly concentrated on a simplified optimization model. In this model all jobs have identical release dates such that each job can start immediately. Furthermore due-dates are assumed loose and are therefore not considered at all. The objective is to minimize the make span which aims at reducing the completion time of the final job. Very efficient heuristics have been developed for the minimum makespan problem 8. Many algorithms benefit from a schedule representation known as the ”disjunctive graph formulation” 4. Particularly local-search algorithms like Simulated Annealing and Tabu Search have been applied with great success. It turned out that general purpose methods like Gas are only second best choice among the modern heuristics, particularly if applied to large makespan problems 7. For tardiness objectives, several local search approaches have been reported. With respect to the minimization of the total tardiness of jobs, a heuristic exchange neighborhood of asymptotic time complexity is used 8. This neighborhood is engaged in a Simulated Annealing algorithm resulting in an effective but time consuming search. Also Tabu Search has been applied to tardiness problems 4. In order to avoid computationally expensive neighborhood evaluations the authors propose a makespan minimization by treating the due-dates as constraints. The task for Tabu Search is to obtain a feasible schedule or a good compromise schedule in the event that the violation of due-dates cannot be circumvented. Tabu Search is also applied for directly pursuing the mean absolute lateness as objective 1. Efficient neighborhood definitions based on job sequences and binary schedule representations are compared. Unfortunately the approach is limited to single machine problems. Recently, a neighborhood definition originated for makespan problems has been used for minimizing the weighted sum of tardiness. A random perturbation of schedules relying on this neighborhood leads to encouraging results for the particular objective pursued 2. Despite some progress gained by local-search algorithms so far, it is still unknown how to derive efficient neighborhood definitions for tardiness problems. The approaches sketched above lack efficiency because the neighborhoods are either too large or they are reasonably sized but the neighborhood definition is not goal-oriented. This motivates to consider general purpose procedures for tardiness problems. Unlike local-search algorithms, GAs can be easily modified to handle release-dates, due-dates and alternate performance measures 8, 9, 10. Besides of static environments a GA has also been applied to problems where jobs have release-dates that are not known in advance 10. The tunable schedule builder, which is also used in this synopsis, leads to satisfying results for minimizing the mean flow time of jobs.

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