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Psycho-Clonal algorithm based approach to solve continuous flow shop scheduling problem

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Abstract

This research presents a novel approach to solve m-machine no-wait flow shop problem. A continuous flow shop problem with total flow time as criterion is considered. This paper extends the artificial immune system (AIS) approach by proposing a new methodology termed as Psycho-Clonal algorithm. Proposed algorithm enjoys the flavor of AIS and Maslow's need hierarchy theory to evolve a meta heuristic. Numerical simulation with small and large number of jobs with respect to error percentage is reported. The results obtained are compared with the other existing approaches. Numerical simulation has revealed that results obtained using proposed algorithm have significant improvement over others.

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... Pierre and Legault [3] proposed a Genetic Algorithm to generate low-cost feasible computer network topologies. In a similar attempt, this paper approaches the underlying problem by addressing it from a recently introduced Psychoclonal Algorithm [21,22] and proposes an improvement to it utilizing the expectancy of Vroom's valence expectancy theory [23] as threshold to move over various need levels. ...
... Moreover, recent research has been directed towards incorporating motivational aspects to the search algorithms [21,22,35]. Motivation causes goal-directed behavior to a candidate solution. ...
... Performance ¼ Ability  Motivation Therefore, the performance level would be high if both these crucial both the parts have higher values. In order to provide a robust base for the search in order to overcome local halts and other disruptions the Psychoclonal algorithm [21] uses Maslow's need hierarchy theory to move on from one need level to another. ...
Article
In the prevailing era of network and communication technology, the problem pertaining to the determination of the most economic way to interconnect nodes while satisfying some reliability and quality of service constraints has been agnized as one of the most intricate and challenging problem for the modern day researchers and practitioners belonging to Communication and Networking community. Motivated by the improved performance of the concepts like proliferation, affinity maturation, receptor editing, etc., over the more prevalent generalized crossover and mutation; and by the application and effectiveness of Maslow’s need hierarchy in combinatorial optimization as well the more logical motivational concepts provided by Vroom’s valence expectancy theory, authors have proposed and investigated their applications to the topological design of distributed packet switched networks. The extensive computations over the problems of varying complexities and dimensions prove the superiority of the proposed methodology. It has been observed that the proposed Vroom Inspired Psychoclonal Algorithm (VIPA) outperforms the traditional well established random search algorithms (i.e. Genetic Algorithm, Simulated Annealing and Artificial Immune Systems) in the context of underlying problem; the performance being significantly improved as the problem complexity increases.
... Aldowaisan and Allahverdi (2004b) proposed six different versions of an insertion heuristic for the problem and showed that their heuristics perform much better than the GA algorithm of Chen, Neppalli, and Aljaber (1996) . Moreover, Kumar, Prakash, Shankar, and Tiwari (2006) proposed a psycho-clonal algorithm, which performs its search through somatic mutation, receptor editing, and balancing the exploitation of the best solutions with the exploration of the search space. The authors showed that their algorithm performs better than the best heuristic of Aldowaisan and Allahverdi (2004b) . ...
... Fm/no-wait/C max Riyanto and Santosa (2015) Hybridization of ACO algorithm with local search Akrout et al. (2013) A hybrid algorithm, which combines GRASP with DE algorithm Davendra, Zelinka, Bialic-Davendra, Senkerik, and Jasek (2013) A discrete self-organizing migrating algorithm Wang (2014) Fast ILS algorithm Wang, Li, and Wang (2008) ILS algorithm Macchiaroli et al. (1999) TS algorithm Gonzalez, Torres, and Moreno (1995) Hybrid GA Gao et al. (2011a,b) A discrete harmony search algorithm Engin and Günaydin (2011) An algorithm based on adaptive learning approach Laha and Chakraborty (2009) A constructive heuristic Zhu, Li, and Wang (2011b) Hybrid GA Framinan and Nagano (2008) A heuristic Pan, Wang, Tasgetiren, and Zhao (2008d) Hybrid PSO A composite heuristic Li, Wang, and Wu (2008) A composite heuristic Lin and Ying (2016) A heuristic consisting of three phases Fm/no-wait, ST si /C max Brown et al. (2004) A polynomial heuristic Samarghandi and Elmekkawy (2012b) Hybrid GA and PSO algorithm Fm/no-wait, ST sd /C max Zhuang et al. (2014) A hybrid greedy algorithm Samarghandi and Elmekkawy (2014) PSO algorithm A hybrid evolutionary cluster search metaheuristic Zhu et al. (2013a) An iterated algorithm Zhu et al. (2013b) A adaptive intelligent method Xu et al. (2012) An IG algorithm FFm/no-wait/C max Hecker et al. (2014) Modified GA algorithm A hunting search metaheuristic FFm/no-wait, ST sd /C max Jolai et al. (2012) Hybridized algorithm of ICA and SA Ramezani et al. (2015) Hybridized algorithm of VNS , SA , and invasive weed optimization Fm/no-wait/ C j Tasgetiren, Pan, Suganthan, and Liang (2007) Discrete DE algorithm Kumar et al. (2006) A psycho-clonal algorithm Chang, Gong, and Ma (2007) GA algorithm Filho, Nagano, and Lorena (2007) A hybrid evolutionary algorithm Yang, Li, Zhu, and Wang (2008) Hybrid GA and VNS algorithm Czogalla and Fink (2008 ) PSO algorithm Zhu, Li, and Wang (2008a) Hybrid GA algorithm Zhu, Li, and Wang (2008b) A hybrid heuristic Zhu, Li, and Wang (2011a) An evolutionary algorithm Sapkal and Laha (2013) A constructive heuristic Gao, Suganthan, and Chua (2013b) A migrating birds optimization algorithm Sapkal (2011, 2014) A constructive heuristic A penalty-shift insertion algorithm Chaudhry et al. (2014) GA algorithm Fm/no-wait/F l (C max , C j ) Allahverdi and Aldowaisan (2002) An insertion heuristic Tan (2011) PSO algorithm Fm/no-wait/ #( w j C j , w j T j ) ...
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Scheduling involving no-wait in process plays an important role in industries such as plastic, chemical, and pharmaceutical. Moreover, many scheduling problems in other industries, including surgery-scheduling problem, aircraft landing problem, and train scheduling problem, can also be modelled with no-wait constraint. The research interest in scheduling problems with no-wait in process began in 1970s and the interest has been increasing since then. Hall and Sriskandarajah (Operations Research 44, 510-525, 1996) presented an excellent review of the literature, covering about 130 papers, on scheduling problems with no-wait in process since 1970s until mid-1993. This paper is the second survey paper providing analysis and an extensive review of more than 300 papers that appeared since the mid-1993 to the beginning of 2016. This survey paper classifies scheduling problems based on shop environments as flowshop, job shop, or open shop. It further classifies the problems based on the performance measure considered along with some other factors. Throughout the survey paper, many independently investigated problems are determined, less studied problems are identified, and suggestions for future research directions are proposed.
... Metaheuristics for the no-wait flow shop scheduling problem to minimize makespan as well as total flow time criterion include simulated annealing (Fink and Voß, 2003), genetic algorithm (Chen et al, 1996;Aldowaisan and Allahverdi, 2003), hybrid genetic algorithm and variable neighbourhood search algorithm (Jarboui et al, 2011), tabu search (Grabowski and Pempera, 2005), particle swarm optimization (Liu et al, 2007;Pan et al, 2008a, b), hybrid tabu search and particle swam optimization (Samarghandi and ElMekkawy, 2012), ant colony algorithm (Shyu et al, 2004), and artificial immune system (Kumar et al, 2006). ...
... Hence, a total of 1250 problems are considered in the first experiment and 2000 problems in the second experiment. The processing time probability distribution for a problem instance follows a discrete uniform probability distribution, U(1, 99), a common practice in the literature (Rajendran and Chaudhuri, 1990;Chen et al, 1996;Kumar et al, 2006). To investigate the relative performance of the heuristics in many situations that deal with a high variability of processing times, we consider another experiment with the processing time probability distributions generated as U(1, 200) and U(100, 200). ...
Article
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This paper proposes a penalty-shift-insertion (PSI)-based algorithm for the no-wait flow shop scheduling problem to minimize total flow time. In the first phase, a penalty-based heuristic, derived from Vogel’s approximation method used for the classic transportation problem is used to generate an initial schedule. In the second phase, a known solution is improved using a forward shift heuristic. Then the third phase improves this solution using a job-pair and a single-job insertion heuristic. Results of the computational experiments with a large number of randomly generated problem instances show that the proposed PSI algorithm is relatively more effective and efficient in minimizing total flow time in a no-wait flow shop than the state-of-the-art procedures. Statistical significance of better results obtained by the proposed algorithm is also reported.
... to diverse learning tasks such as Speech Recognition, Game Playing, Medical Diagnosis, Financial Forecasting and Industrial Control (Mitchell, 1997). There have been several applications of ANN in scheduling (Agarwal et. al., 2006; Alagoz et. al., 2003; Cheung, 1994; Dagli et. al., 1995; Fisher et. al., 1963; Foo et. al., 1988; Jain et. al., 1996, Kumar et. al., 2005; Laha et. al., 2007; Lin et. al., 2004; Luangpaiboon et. al., 2004; Rabelo et. al., 1989, Yu et. al., 2001). A comprehensive survey of ANN architectures used in scheduling (Cheung, 1994). These are basically searching networks (hopfield networks), probabilistic networks (boltzmann machine), error correcting networks (multi layer percept ...
... d to diverse learning tasks such as Speech Recognition, Game Playing, Medical Diagnosis, Financial Forecasting and Industrial Control (Mitchell, 1997). There have been several applications of ANN in scheduling (Agarwal et. al., 2006; Alagoz et. al., 2003; Cheung, 1994; Dagli et. al., 1995; Fisher et. al., 1963; Foo et. al., 1988; Jain et. al., 1996, Kumar et. al., 2005 Laha et. al., 2007; Lin et. al., 2004; Luangpaiboon et. al., 2004; Rabelo et. al., 1989, Yu et. al., 2001). A comprehensive survey of ANN architectures used in scheduling (Cheung, 1994). These are basically searching networks (hopfield networks), probabilistic networks (boltzmann machine), error correcting networks (multi layer percepti ...
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Job Scheduling is a decision making process where next operation is selected to a partial schedule from set of competing operations with objective of minimizing performance measure. A complete schedule is consequence of best selected decisions. However, there exists an inherent degree of uncertainty in such problems. Here, we develop Rough Fuzzy Multi Layer Perception Neural Network Scheduler for Job Scheduling Problem. Genetic Algorithms generate optimal schedules to known benchmark problem. In each optimal solution, every individually selected operation of job is treated as decision which contains knowledge. Each decision is function of job characteristics divided into classes using domain knowledge. The Scheduler enhances classification strength and captures predictive knowledge regarding assignment of operation's position in sequence. The trained network successfully replicates the performance of Genetic Algorithms. The better performance of Scheduler on test problems demonstrates the utility of method. The scalability of Scheduler on large problem sets gives satisfactory results.
... Shukla et al. (2013) propose a special psychoclonal algorithm for the design of computer networks. Psycoclonal algorithms (Kumar et al., 2006) are bio-inspired algorithms that leverage the idea of an artificial immune system to evolve a meta-heuristic to solve complex problems. Other common techniques such as simulated annealing (SA) have also been proposed in the con-text of designing industrial switched networks. ...
Preprint
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During the concept design of complex networked systems, concept developers have to assure that the choice of hardware modules and the topology of the target platform will provide adequate resources to support the needs of the application. For example, future-generation aerospace systems need to consider multiple requirements, with many trade-offs, foreseeing rapid technological change and a long time span for realization and service. For that purpose, we introduce NetGAP, an automated 3-phase approach to synthesize network topologies and support the exploration and concept design of networked systems with multiple requirements including dependability, security, and performance. NetGAP represents the possible interconnections between hardware modules using a graph grammar and uses a Monte Carlo Tree Search optimization to generate candidate topologies from the grammar while aiming to satisfy the requirements. We apply the proposed approach to the synthetic version of a realistic avionics application use case and show the merits of the solution to support the early-stage exploration of alternative candidate topologies. The method is shown to vividly characterize the topology-related trade-offs between requirements stemming from security, fault tolerance, timeliness, and the "cost" of adding new modules or links. Finally, we discuss the flexibility of using the approach when changes in the application and its requirements occur.
... There are two types of antigens: self and non-self. Non-self antigens are disease-causing elements, whereas self antigens are harmless to the body [19]. There are two major groups of immune cells: B-cells and Tcells which help in recognizing an almost limitless range of antigenic patterns. ...
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In this study a method that uses artificial immune system (AIS) algorithm has been used to extract rules from medical dataset. The thyroid data set obtained from Irvine California University was used for application part. The accuracy ratio was found as 93.95. This result is one of the best results when it is compared with the previous studies.
... İki tip antijen vardır; kendinden olan ve yabancı. Yabancı antijenler hastalık yapıcı elementlerken, kendinden olan antijenler vücuda zararsızdır [17]. Bhücreleri ve T-hücreleri olarak adlandırılan iki ana bağışıklık hücresi grubu vardır. ...
... Computational results demonstrated that the ACO algorithm performed impressively in solving the scheduling problem of interest. Kumar, Prakash, Shankar, and Tiwari (2006) presented a psycho-clonal algorithm, which outperformed both the GA of Chen et al. (1996) and the six constructive heuristics of Aldowaisan and Allahverdi (2004). Tasgetiren, Pan, Suganthan, and Liang (2007) pre sented a discrete differential evolution (DDE) algorithm that was hybridized with the variable neighborhood descent (VND) algorithm to solve the problem. ...
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This paper studies the no-wait flowshop scheduling problem with total flow time criterion, which is NP-complete in the strong sense. A self-adaptive ruin-and-recreate (SR&R) algorithm is proposed to solve this complex problem. The performance of the proposed SR&R algorithm is compared with that of the best available heuristics and the SR&R algorithm without the self-adaptive mechanism by application to a set of classic benchmark instances that were presented by Taillard. Computational results show that the proposed SR&R algorithm improves upon the best known solutions in more than half benchmark instances, and provides the best known solutions for the remaining unimproved instances in a reasonable computational time. The contribution of this work is to provide an easy-to-use approach to solve effectively and efficiently this practical but complex scheduling problem.
... In this context, ―non-self‖ factors are dangerous and disease-causing cells. On the other hand, ―self‖ factors are known as the harmless cells within the body [9]. Within the biological immune system, there are two major groups of immune cells named as B-cells and T-cells and these cells help the system in recognizing an almost limitless range of antigenic patterns [10]. ...
Conference Paper
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The clonal selection algorithm is one of the well-known artificial immunity approaches and it is based on the clonal selection principle, which is used to express basic functions and features of an adaptive immune response given to an antigenic stimulus. According to the clonal selection principle, only specific cells recognizing the existent antigens are selected to proliferate against any potential future antigen presence. At this point, in order to improve effectiveness of the current immunity system, the selected cells are also put to an affinity maturation process and their affinities to specific antigens are improved for providing better protection. In this study, a headache disease diagnosis system has been designed and developed by using the clonal selection algorithm. The obtained results point a successful artificial immunity system that is a capable of diagnosing a headache disease according to the evaluated symptom sets.
... Metaheuristics have been applied to solve flow shop scheduling problems, namely the early works of Osman and Potts (1989) and Taillard (1990). For the no-wait flow shop scheduling problems, several metaheuristics have been studied, namely, genetic algorithm (Aldowaisan & Allahverdi, 2003;Chen, Neppalli, & Aljaber, 1996), simulated annealing (Fink & Voß, 2003), tabu search (Grabowski & Pempera, 2005), particle swarm optimization (Liu, Wang, & Jin, 2007;Pan, Tasgetiren, & Liang, 2008;Pan, Wang, Tasgetiren, & Zhao, 2008), artificial immune system (Kumar, Prakash, Shankar, & Tiwari, 2006), hybrid tabu search and particle swarm optimization (Samarghandi & ElMekkawy, 2012), ant colony optimization algorithm (Shyu, Lin, & Yin, 2004), hybrid genetic algorithm and variable neighborhood search algorithm (Jarboui, Eddaly, & Siarry, 2011), and differential evolution (Qian, Wang, Hub, Huang, & Wang, 2009). Constructive heuristics are frequently used for generating good starting solutions for the metaheuristics. ...
... With these properties, AIS has been successfully been applied to tackle problems in optimization, clustering, pattern recognition, anomaly detection, computer security, machine learning, scheduling, robotics, and control (Hart and Timmis, 2008). In the domain of scheduling, AIS-based algorithms have been developed for flow shop scheduling (Kumar, et al., 2006), job shop scheduling (Chandrasekaran et al., 2006), flexible job shop scheduling (Bagheri et al., 2010;), resource constraint project scheduling (Peteghem and Vanhoucke, 2009), multiprocessor scheduling (Wojtyla et al., 2006). ...
Conference Paper
Process scheduling is a classical problem in the field of production planning and control; in particular, effective job shop scheduling remains an essential component in today's highly dynamic and agile production environment. This paper presents unified framework for solving generic job shop scheduling problems based on the formulation of a job shop into three main classes of problem, namely, static, semi-dynamic and dynamic scheduling problems. Algorithms based on artificial immune systems, an engineering analogy of the human immune system, are developed to solve the respective classes of job shop scheduling problems. A high level decision support model is presented for the effective deployment of the scheduling strategies whereby a unified approach to solving real job shop problems is achieved.
... AIS is successfully used to solve different kinds of complicated optimization problems comprising traveling salesman problem (de Castro and Von Zuben 2002), machine loading problem (Chan et al. 2005), flow shop scheduling (Kumar et al. 2006), economic load dispatch (Panigrahi et al. 2007) and supply chain network design (Tiwari et al. 2010). ...
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A green supply chain with a well-designed network can strongly influence the performance of supply chain and environment. The designed network should lead the supply chain to efficient and effective management to meet the efficient profit, sustainable effects on environment and customer needs. The proposed mathematical model in this paper identifies locations of productions and shipment quantity by exploiting the trade-off between costs, and emissions for a dual channel supply chain network. Due to considering different prices and customers zones for channels, determining the prices and strategic decision variables to meet the maximum profit for the proposed green supply chain is contemplated. In this paper, the transportation mode as a tactical decision has been considered that can affect the cost and emissions. Lead time and lost sales are considered in the modeling to reach more reality. The developed mathematical model is a mixed integer non-linear programming which is solved by GAMS. Due to NP-hard nature of the proposed model and long run time for large-size problems by GAMS, artificial immune system algorithm based on CLONALG, genetic and memetic algorithms are applied. Taguchi technique is used for parameter tuning of all meta-heuristic algorithms. Results demonstrate the strength of CLONALG rather than the other methods.
... . Actualmente, el empleo de técnicas de inteligencia artificial, en los procesos de secuenciación y programación de la producción en ambientes Job Shop no ha sido muy difundido (Santana, 2004); aunque existen algunas metodologías basadas en: Algoritmos genéticos (Akhilesh 2006, Monch 2006, Guo 2006, partículas inteligentes (Van 2006, Fatih 2006, Lian 2006, colonia de hormigas (Huang 2006) y sistemas inmunes (Zandieh 2006) entre otras. En esta área de la ingeniería, han permitido solucionar algunos de sus principales problemas como: Repartición de recursos, ineficiente asignación de máquinas, inadecuada ordenación y secuencia de los I lotes en cada una de las J máquinas, incumplimiento de plazos de entrega, inapropiada estimación de la demanda, difícil manejo de las ordenes de compra, mal control de inventarios, frecuentes acciones de empuje de trabajos, desequilibrio en la capacidad de los centros de trabajo CT e insatisfacción de las condiciones de calidad. ...
Article
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Grupo de Innovación y Desarrollo Tecnológico Resumen El objetivo del presente trabajo 77 , es disminuir el tiempo de proceso (Makespan) y aumentar el tiempo de utilización de las máquinas, disminuyendo el tiempo de ocio (idle), en ambientes Job Shop, mediante el diseño de una metodología basada en agentes inteligentes y minería de datos. Este trabajo se desarrolla en dos fases: En la primera, se aborda la identificación y definición de una metodología predictiva para los procesos de secuenciación en ambientes Job Shop. En la segunda etapa, se demuestra la efectividad de este sistema, en los procesos tradicionales de programación de la producción. La investigación propuesta se desarrollo en una empresa del sector metalmecánico, donde por medio de la combinación de agentes inteligentes y minería de datos se mejora la programación de un pedido, logrando un disminuir considerablemente su respectivo tiempo total de proceso y tiempo total de ocio.
... Grabowski and Pempera (2005) investigated various local search techniques for a no-wait flowshop problem to minimize makespan. Kumar et al. (2006) proposed a Psycho-Clonal algorithmbased technique to solve a no-wait flowshop scheduling problem to minimize total flow times. Wang (2007) considered general, no-wait, and no-idle flowshop scheduling problems with deteriorating jobs. ...
Article
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Flow-shop problems, as a typical manufacturing challenge, have become an interesting area of research. The primary concern is that the solution space is huge and, therefore, the set of feasible solutions cannot be enumerated one by one. In this paper, we present an efficient solution strategy based on a genetic algorithm (GA) to minimize the makespan, total waiting time and total tardiness in a flow shop consisting of n jobs and m machines. The primary objective is to minimize the job waiting time before performing the related operations. This is a major concern for some industries such as food and chemical for planning and production scheduling. In these industries, there is a probability of the decay and deterioration of the products prior to accomplishment of operations in workstation, due to the increase in the waiting time. We develop a model for a flowshop scheduling problem, which uses the planner-specified weights for handling a multi-objective optimization problem. These weights represent the priority of planning objectives given by managers. The results of the proposed GA and classic GA are analyzed by the analysis of variance (ANOVA) method and the results are discussed.
... Engin and Döyen [14] propose a computational method based on clonal selection principle and affinity maturation mechanism of the immune response to solve hybrid flowshop prolem. Kumar, Prakash, Shankar and Tiwari [15] used AIS to tackle a continuous flowshop problem with total flow times as the principal criterion. Chan, Swarnkar and Tiwari [16] and Zandieh, Fatemi Ghomi and Moattar Husseini [17] proposed approaches based on immune network interactions and genetic reproduction to address, respectively, flexible manufacturing system assignment and scheduling problems and hybrid flowshops scheduling with sequence dependant setup times. ...
Conference Paper
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This paper investigates permutation flowshop problem with preventive maintenance (PM). The objective functions are to minimize the total completion time for the production part and the total earliness/tardiness for PM part. The resolution consists of two steps: the one consists on scheduling production jobs using an artificial immune algorithm (AIA); the second one consists on deploying PM operations, taking the production schedule as a mandatory constraint of resources unavailability in the resolution of the problem. Furthermore, we use the principles of vaccination and receptor editing in order to strengthen search ability. The efficiency of the proposed AIAs with respect to minimization of makespan for the production part and performance loss after PM insertion, is compared to some referred in the related scheduling literature metaheuristics. Simulation results on both standard PFSP problems and non- standard integrated PFSP with PM problems show the superiority of our proposed algorithms.
... Es así, como se han desarrollado, diferentes metodologías, de secuenciación de máquinas, basadas en: Sistemas inteligentes, algoritmos genéticos [6,7,8], partículas inteligentes [9,10,11], colonia de hormigas [12] y sistemas inmunes [13] entre otras. ...
Article
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RESUMEN El objetivo del presente trabajo 1 , es disminuir el tiempo de proceso (Makespan) y aumentar el tiempo de utilización de las máquinas, disminuyendo el tiempo de ocio (idle), en ambientes Job Shop, mediante el diseño de una metodología basada en Sistemas Expertos y Agentes Inteligentes. Este trabajo se desarrolla en dos fases: En la primera, se aborda la identificación y definición de una metodología para los procesos de secuenciación en ambientes Job Shop. En la segunda etapa, se demuestra la efectividad de este Sistema Informático, en los procesos tradicionales de programación de la producción. La investigación propuesta se desarrolla en una empresa del sector metalmecánico, donde por medio de la combinación de Sistemas Expertos y Agentes Inteligentes se mejora la programación de un pedido, logrando disminuir considerablemente su tiempo total de proceso y en consecuencia su tiempo total de ocio. Igualmente, en este documento se analiza el comportamiento de otras variables como el porcentaje de utilización de las máquinas o centros de trabajo.
... Metaheuristics with the makespan criterion include simulated annealing and genetic algorithm [3], tabu search [4], and particle swarm optimization [5,6,7]. Also, metaheuristics with the total flow time criterion comprise genetic algorithm [8], simulated annealing [9], artificial immune system [10], and particle swarm optimization [6]. On the other hand, constructive heuristics for the makespan minimization problems have been studied by Reddi and Ramamoorthy [11], Wismer [12], Bonney and Gundry [13], King and Spachis [14], Rajendran [15], Gangadharan and Rajendran [16], Framinan and Nagano [17], and Laha and Chakraborty [18]. ...
... The problem of the assignment of times to a set of jobs for processing through a series of machines has long received the attention of researchers (Campbell et al., 1970;Dannenbring, 1977;Hall and Sriskandarajah, 1996;Kumar et al., 2006;Laha and Chakraborty, 2008;Li et al., 2008;Taillard, 1993). The practical importance of such problems is great, as scheduling plays a significant role in successful production planning and control. ...
Article
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This paper presents an efficient simulated annealing algorithm for minimising the total flow time in n-job, m-machine permutation flow shop scheduling problems. Empirical results demonstrate that the proposed approach is competitive with the best three methods in the flow shop literature. Statistical tests of significance are used to validate the improvement in solution quality.
... Kumar et al.(2006) Tsukiyama and Fukuda (1997) 경로문제 Iceko et al.(2003), Keko et al.(2004) Wang et al.(2005, Ma et al.(2006) Endo et al.(1998, Sun et al.(2004) Dong et al.(2005, Wang et al.(2006) Toma et al. (2001 공급사슬 Ding et al.(2003) 최적설계 Chun et al.(1997), Tazawa et al.(1996) 비선형계획법 Panigrahi et al.(2006) 워크플로 Fang et al.(2006) 탐지 Gaspar and Collard(2000) Forrest ( Endo et al.(1998), Sun et al.(2004), Toma et al.(2001Toma et al.( , 2003이였다. 이 ...
Article
Artificial immune systems (AIS) are one of natural computing inspired by the natural immune system. The fault detection, the pattern recognition, the system control and the optimization are major application area of artificial immune systems. This paper gives a concept of artificial immune systems and useful techniques as like the clonal selection, the immune network theory and the negative selection. A concise survey on the optimization problem based on artificial immune systems is generated. The overall performance of artificial immune systems for the optimization problem is discussed.
... Currently, the use of hyperheuristics in the processes of production scheduling and programming in Job Shop environments has not been widely spread [4]. However, there are some hyperheuristics based on methodologies such as heuristics [5], algorithms [6][7][8][9][10], genetic algorithms [11][12][13][14], intelligent particles [15][16][17][18][19], ant colonies [20], immune systems [21], and others [22][23][24][25][26][27][28]. ...
Article
Full-text available
El objetivo del presente trabajo es disminuir el tiempo de proceso (Makespan) e incrementar el tiempo de trabajo de las maquinas, diminuyendo el tiempo de ocio en ambientes de job shop, a través del diseño de una hiperheurística basada en colonia de hormigas y algoritmos genéticos. Este trabajo se desarrolla en dos etapas: en la primera se realiza la definición e identificación de una hiperheurística para la secuenciación de procesos en ambientes Job shop. En la segunda etapa, es mostrada la efectividad del sistema en la programación de la producción. En el proyecto de investigación, se seleccionó una empresa del sector metalmecánico, donde por medio de una combinación de colonia de hormigas y algoritmos genéticos, se programa la ruta óptima para un pedido, logrando la optimización o suboptimización de su respectivo tiempo total de proceso en un porcentaje superior al 95%.
... For no-wait flow shop scheduling problems, noteworthy heuristics with the TFT criterion have been studied by Rajendran and Chaudhuri (1990), Bertolissi (2000), Aldowaisan and Allahverdi (2004), and Framinan et al. (2010). On the other hand, metaheuristics on TFT criterion include genetic algorithm (Chen, Neppalli, & Aljaber, 1996), simulated annealing (Fink & Voß, 2003), artificial immune system (Kumar, Prakash, Shankar, & Tiwari, 2006), and particle swarm optimization (Pan, Tasgetiren, & Liang, 2008). Rajendran and Chaudhuri (1990) proposed two constructive heuristics considering two heuristic preference relations as the basis for selecting the initial sequence of jobs. ...
... On the other hand, metaheuristics with the makespan criterion include simulated annealing and genetic algorithm [20], tabu search [21], and particle swarm optimization [22][23][24]. Also, metaheuristics with the total flow time criterion comprise genetic algorithm [25], simulated annealing [26], artificial immune system [27], and particle swarm optimization [23]. ...
Article
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This paper presents an efficient heuristic method to minimize total flow time in no-wait flow shop scheduling. It is based on the assumption that the priority of a job in the initial sequence is given by the sum of its processing times on the bottleneck machines. Empirical results demonstrate the superiority of the proposed method over the best-known heuristics in the literature, while remaining the same complexity order of O(n 2).
... Metaheuristics with the makespan criterion include simulated annealing and genetic algorithm [3], tabu search [4], and particle swarm optimization [5,6,7]. Also, metaheuristics with the total flow time criterion comprise genetic algorithm [8], simulated annealing [9], artificial immune system [10], and particle swarm optimization [6]. On the other hand, constructive heuristics for the makespan minimization problems have been studied by Reddi and Ramamoorthy [11], Wismer [12], Bonney and Gundry [13], King and Spachis [14], Rajendran [15], Gangadharan and Rajendran [16], Framinan and Nagano [17], and Laha and Chakraborty [18]. ...
Article
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Since the no-wait flow shop scheduling problems have been proved to be NP-hard, heuristic procedures are considered as the most suitable ones for their solution, especially for large - sized problems. We present a constructive heuristic for minimizing total flow time criterion in no- wait flow shop scheduling problems. The proposed heuristic is based on the assumption that the priority of a job in the sequence is given by the sum of its processing times on the bottleneck machine(s) for selecting the initial sequence of jobs. The final sequence is based on the principle of job insertion for minimizing the total flow time. The computational results show that the proposed heuristic significantly outperforms the existing heuristics, while not affecting its computational CPU time.
... There are two types of antigens: self and non-self. Non-self antigens are disease-causing elements, whereas self-antigens are harmless to the body [34]. There are two major groups of immune cells: B-cells and T-cells which helps in recognizing an almost limitless range of antigenic patterns. ...
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Although Artificial Neural Network (ANN) usually reaches high classification accuracy, the obtained results sometimes may be incomprehensible. This fact is causing a serious problem in data mining applications. The rules that are derived from ANN are needed to be formed to solve this problem and various methods have been improved to extract these rules. In our previous works Artificial Immune Systems (AIS) algorithm has been used to extract rules from trained ANN and has been applied to various databases (11, 41-43). In this study, association rules have been composed using Apriori algorithm and transactions, which provide these rules, were eliminated. This provides shrinking database. Then ANN has been trained and used Opt-aiNET for composing rule set. It's been observed that this method increased classification accuracy despite decreasing number of rules.
... However, there is still a need to improve the computational effi ciency of the fault diagnosis algorithms. Therefore, the psycho-clonal based evolutionary algorithm (Akhilesh Kumar et al., 2006;Sanjay Kumar Shukla, 2010;Singh et al., 2006;Dashora et al., 2008) is utilized in this chapter. ...
Chapter
Multiple Fault Diagnosis (MFD) is used as an effective way to tackle the problems of a real shop floor environment in order to reduce the total lifetime maintenance costs of the system. The major challanges associated with with MFD are: (i) large number of variables associated with response; (ii) low sample size; and (iii) ill-conditioned system caused due to system architecture. Further, the computational complexity associated with MFD increases exponentially with number of failure; thus, it warrants the application of heuristic techniques or artificial intelligence (AI) based optimization tools to diagnose the exact faults in real time. In this chapter, a methodology based on a Probabilistic Causal Model has been illustrated to resolve graph based multiple fault diagnosis problems. This methodology involves a new nature inspired algorithm know as the psycho-clonal algorithm for fault diagnosis. The proposed methodology collect the faults corresponding to each observed manifestation that can give the best possible result instead of finding all possible combinations of faults. Intensive computational experiments on well-known data sets witness the superiority of the proposed psycho-clonal algorithms existing state-of-art approaches proposed in the literature. Experimental results demonstrate the capability of proposed methodology in diagnosing the the exact fault in the minimum fault isolation time as compared to other approaches.
... Techniques like hyperheuristics, handle the selection of heuristic methods on a lower level; and depending on the state of the solution, it determines at each step, the heuristic method to be applied. Currently, the use of heuristics in the processes of production scheduling and programming in Job Shop environmental surroundings is not widely spread; though these are hyper heuristics based on methodologies such as heuristics, algorithms, genetic algorithms [3,4], intelligent swarm [5,6] [7], ant colony optimization (ACO) [8,9,10,11,12,13,14], and immune systems [15], among others [16]. But, the use of these techniques does not always lead to a good solution in the processes of production scheduling and programming. ...
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The purpose of this study is to reduce the total process time (Makespan) and to increase the machines working time, in a job shop environment, using a heuristic based on ant colony optimization. This work is developed in two phases: The first stage describes the identification and definition of heuristics for the sequential processes in the job shop. The second stage shows the effectiveness of the system in the traditional programming of production. A good solution, with 99% efficiency is found using this technique. RESUMEN: El objetivo del presente trabajo es disminuir el tiempo total de proceso e incrementar el tiempo de trabajo de las maquinas, en un ambiente Job Shop, por medio de una heurística basada en la optimización de colonia de hormigas. Este trabajo es desarrollado en dos fases. En la primera es descrita la identificación y definición de la heurística para los procesos de secuenciación en ambientes Job shop. En la segunda etapa, es mostrada la efectividad del sistema en la programación tradicional de la producción. A través de esta técnica una buena solución, con el 99% de efectividad, es encontrada PALABRAS CLAVE: Planificación, Heurística, Simulación, Tiempo de Proceso, Tiempo Muerto.
... The results indicate that SA and RTS generate better solutions within 1,000 seconds of CPU time. Kumar et al. [15] propose a psycho-clonal algorithm, which compares favourably with the GA by Chen [11] and the set of heuristics by Aldowaisan and Allahverdi [4]. For the specific case of a two-machine flowshop, Shyu et al. [20] propose an ant colony optimization procedure. ...
Article
In this paper, we address the problem of scheduling jobs in a no-wait flowshop with the objective of minimising the total completion time. This problem is well-known for being nondeterministic polynomial-time hard, and therefore, most contributions to the topic focus on developing algorithms able to obtain good approximate solutions for the problem in a short CPU time. More specifically, there are various constructive heuristics available for the problem [such as the ones by Rajendran and Chaudhuri (Nav Res Logist 37:695–705, 1990); Bertolissi (J Mater Process Technol 107:459–465, 2000), Aldowaisan and Allahverdi (Omega 32:345–352, 2004) and the Chins heuristic by Fink and Voβ (Eur J Operat Res 151:400–414, 2003)], as well as a successful local search procedure (Pilot-1-Chins). We propose a new constructive heuristic based on an analogy with the two-machine problem in order to select the candidate to be appended in the partial schedule. The myopic behaviour of the heuristic is tempered by exploring the neighbourhood of the so-obtained partial schedules. The computational results indicate that the proposed heuristic outperforms existing ones in terms of quality of the solution obtained and equals the performance of the time-consuming Pilot-1-Chins.
... Fink and Voß [17] use several construction heuristics, the pilot method, tabu search, and simulated annealing and provide detailed computational results. Kumar et al. [34] present a so-called "psycho-clonal algorithm" for the CFSP. Pan et al. [45] discuss a discrete particle swarm optimization (DPSO) algorithm for the CFSP (for both makespan and total flow-time criteria). ...
Chapter
Evolutionary algorithms are generally based on populations of solutions which are subject to the application of operators such as recombination, mutation, and selection in order to evolve the population and eventually obtain high-quality solutions. Different, yet often similar evolutionary algorithms are discussed in various research communities from different perspectives. In this work we strive for a better understanding of the performance of different designs within the general framework of evolutionary computation. We examine and compare (discrete) particle swarm optimization with classic genetic algorithms, both with and without hybridization with local search. In particular, we analyze the effect of different selection and reproduction mechanisms on solution quality, population diversity, and convergence behavior, and examine approaches for maintaining population diversity. As application we consider an NP-hard combinatorial optimization problem, namely the no-wait (continuous) flow-shop scheduling problem with flow-time criterion. The computational results support the importance of local search within (hybridized) evolutionary algorithms and show how solution quality depends on a reasonable design of crossover operators, distance functions, population diversity measures, and the control of population diversity.
... The results indicate that SA and RTS generate better solutions within 1,000 seconds of CPU time. Kumar et al. [15] propose a psycho-clonal algorithm, which compares favourably with the GA by Chen [11] and the set of heuristics by Aldowaisan and Allahverdi [4]. For the specific case of a two-machine flowshop, Shyu et al. [20] propose an ant colony optimization procedure. ...
Article
The most effective approximate methods to tackle the problem of scheduling jobs in a permutation flowshop with the bicriteria of makespan and maximum tardiness are based on genetic algorithms (GA). In these methods, the performance of the GA is improved by applying local search to all offsprings belonging to the current population. To do so, the two objectives must be aggregated into a single objective, which is usually accomplished by weighting them into a scalar function. Since the weighting scheme is considered to be critical for the performance of the algorithm, several weighting mechanisms have been suggested in the literature. In this paper, we propose two new weighting schemes and conduct an extensive computational experience under a variety of parameter settings in order to show their effectiveness as compared to existing ones.
... The problem of the assignment of times to a set of jobs for processing through a series of machines has long received the attention of researchers [1][2][3][4][5][6][7][8][9][10][11]. The practical importance of such problems is great, as scheduling plays a significant role in successful production planning and control. ...
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This paper considers the permutation flow shop scheduling problem with the objective of minimizing the makespan. A new hybrid heuristic, based on simulated annealing and an improvement heuristic, is presented. The proposed hybrid heuristic uses simulated annealing in conjunction with the constructive heuristic of Nawaz et al. (Omega 11:91–95, 1983). Computational experiments carried out with the benchmark problems of Taillard (Eur J Oper Res 64:278–285, 1993) show that the proposed method produces solutions that are mostly superior to those obtained with five state-of-the-art approaches. Statistical tests of significance are used to verify the improvement in solution quality.
... No-wait flow shop scheduling problems have been most commonly studied with two optimization (minimization) criteria: total flow time and makespan. No-wait flow shop scheduling with respect to total flow time minimization has been studied by, e.g., Rajendran and Chaudhuri [2], Aldowaisan [3], Aldowaisan and Allahverdi [4,5], Allahverdi and Aldowaisan [6,7], Adiri and Pohoryles [8], Kumar et al. [9], Bertolissi [10], van der Veen and van Dal [11], and Chen et al. [12]. No-wait flow shop with the makespan objective has been investigated by, e.g., Reddi and Ramamoorthy [13], Wismer [14], Bonney and Gundry [15], and King and Spachis [16]. ...
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This paper presents a new constructive heuristic, based on the principle of job insertion, for minimizing makespan in no-wait permutation flow shop scheduling problems. Empirical results demonstrate the superiority of the proposed approach over four of the best-known methods in the literature. Analytical expressions for the total number of partial and complete sequences generated by the algorithms are derived. Statistical tests of significance substantiate the improvement in solution quality.
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Traffic jam is a daily problem in nearly all major cities in the world and continues to increase with population and economic growth of urban areas. Traffic lights, as one of the key components at intersections, play an important role in control of traffic flow. Hence, study and research on phase synchronization and time optimization of the traffic lights could be an important step to avoid creating congestion and rejection queues in a urban network. Here, we describe the application of NSGA-II, a multi-objective evolutionary algorithm, to optimize both vehicle and pedestrian delays in an individual intersection. In this paper, we improve NSGA-II algorithm based on the regression line to find a Pareto-optimal solution or a restrictive set of Pareto-optimal solutions based on our solution approaches to the problem, named PDNSGA (Non-dominated Sorting Genetic Algorithm based on Perpendicular Distance). The high speed of the proposed algorithm and its quick convergence makes it desirable for large scheduling with a large number of phases. It is demonstrated that our proposed algorithm (PDNSGA) gives better outputs than those of Moga, NSGA-II, and WBGA in traffic signal optimization problem, statistically .
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An improved clonal selection algorithm (HSMCSA) is proposed for the parallel machine scheduling problem with setup time, which is a type of NP-hard problems and widely spreads in the real industrial production. A single machine scheduling based encoding method with inserting the dividing point uniformly is proposed to improve the efficiency and performance of the algorithm, especially for the large scale problems. Then, the initialization strategy of combining the single machine scheduling based optimal solutions and random solutions generated by heuristic method is investigated so as to enhance the performance of the initial solutions. Furthermore, the optimization performance of four mutation operators in the clonal selection algorithm are compared and analyzed in details, and the HSMCSA is successfully applied to the parallel machine scheduling problem with setup time. The simulation results indicate that the proposed algorithm exhibits great performance. Compared with the genetic algorithm and the basic clonal selection algorithm, the performance of the solution has been improved by 18.5% and 7.2%, respectively.
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Process scheduling is a classical problem in the field of production planning and control; in particular, effective job shop scheduling remains an essential component in today's highly dynamic and agile production environment. This paper presents unified framework for solving generic job shop scheduling problems based on the formulation of a job shop into three main classes of problem, namely, static, semi-dynamic and dynamic scheduling problems. Algorithms based on artificial immune systems, an engineering analogy of the human immune system, are developed to solve the respective classes of job shop scheduling problems. A high level decision support model is presented for the effective deployment of the scheduling strategies whereby a unified approach to solving real job shop problems is achieved.
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This paper deals with a general continuous or no-wait manufacturing scheduling problem. Due to its applications in advanced manufacturing systems, no-wait scheduling has gained much attention in both practical and academic fields. Due to its NP-hard nature, most of the contributions focus on development of approximation based optimization methods or heuristics for the problem. Several heuristic procedures have been developed to solve this problem. This paper presents a survey of various methodologies developed to solve no-wait flow shop scheduling problem with the objective of minimizing single performance measure
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Keywords Solution of Systems of Linear Inequalities and Linear Programming Problems Complexity of the Fourier–Motzkin Method See also References
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Article
Introduction Variations Exact Algorithms for the Flow Shop Scheduling Problem Heuristic Algorithms for the Flow Shop Scheduling Problem Metaheuristic Algorithms for the Flow Shop Scheduling Problem References
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Conference Paper
For the past two decades simulated annealing has been playing a crucial role in the design of optimization strategies for flow shop scheduling applications. This paper presents an efficient simulated annealing algorithm for minimizing the total flow time in permutation flow shop scheduling problems. Empirical results demonstrate the improvement in solution quality obtained by the proposed approach over state-of-the-art methods in the literature.
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Artificial immune systems (AIS) are relatively young emerging techniques, which explore, derive and apply different biologically inspired immune mechanisms, aimed at computational problem solving. Although several researchers have already attempted to adapt such metaphors to production and service scheduling problems, we are not aware of any literature review reporting the use of AIS for scheduling problems. This review of existing scheduling AIS applications shows that the published studies are related to several types of problems: single machine, hybrid and no wait flow shops, job shops, parallel processors. Task allocation and sequencing problems are also addressed in single or multi-objective optimization. After a first part introducing the main principles of artificial immune systems, we summarize how AIS paradigms are used and adapted in existing works to tackle scheduling problems. A discussion is then presented and, finally, several opened research directions are drawn
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This paper considers the three-machine no-wait flowshop problem with the objective of minimizing total completion time where setup times are considered as separate from processing times and sequence independent. We present optimal solutions for certain cases, and a dominance relation for the general case. We also develop and evaluate five heuristic algorithms for small and large number of jobs. Computational experience for up to 100 jobs shows that the proposed heuristics are quite effective and their performance do not depend on the number of jobs. The computational experience has been conducted for the uniform processing time distributions of U(1, 10) and U(1, 100). The best heuristic gives an overall average error of 0.47% for U(1, 10) and it gives an overall average error of 1.23% for U(1, 100).
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The clonal selection principle is used to explain the basic features of an adaptive immune response to an antigenic stimulus. It establishes the idea that only those cells that recognize the antigens (Ag's) are selected to proliferate. The selected cells are subject to an affinity maturation process, which improves their affinity to the selective Ag's. This paper proposes a computational implementation of the clonal selection principle that explicitly takes into account the affinity maturation of the immune response. The general algorithm, named CLONALG, is derived primarily to perform machine learning and pattern recognition tasks, and then it is adapted to solve optimization problems, emphasizing multimodal and combinatorial optimization. Two versions of the algorithm are derived, their computational cost per iteration is presented, and a sensitivity analysis in relation to the user-defined parameters is given. CLONALG is also contrasted with evolutionary algorithms. Several benchmark problems are considered to evaluate the performance of CLONALG and it is also compared to a niching method for multimodal function optimization
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The no-wait flow-shop scheduling problem (NWFSSP) with a makespan objective function is considered. As is well known, this problem is NP-hard for three or more machines. Therefore, it is interesting to consider special cases, i.e. special structured processing time matrices, that allow polynomial time solution algorithms. Furthermore, it is well known that the NWFSSP with a makespan objective function can be formulated as a travelling salesman problem (TSP). It is observed that special structured processing time matrices for the NWFSSP lead to special structured distance matrices for which the TSP is polynomially solvable. Using this observation, it is shown that some NWFSSPs with fixed processing times on all except two machines are well solvable while the others are conjectured to be NP-hard. Also, it is shown that NWFSSPs with a mean completion time objective function restricted to semi-ordered processing time matrices are easily solvable.
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The optimization problem of minimizing the completion time in flow-shop sequencing with an environment of no intermediate storage is considered. Application of this problem in computer systems is pointed out and techniques are developed to solve the problem.
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The no-wait flow-shop scheduling problem (NWFSSP) with a makespan objective function is considered. As is well known, this problem is NP-hard for three or more machines. Therefore, it is interesting to consider special cases, i.e. special structured processing time matrices, that allow polynomial time solution algorithms. Furthermore, it is well known that the NWFSSP with a makespan objective function can be formulated as a travelling salesman problem (TSP). It is observed that special structured processing time matrices for the NWFSSP lead to special structured distance matrices for which the TSP is polynomially solvable. Using this observation, it is shown that some NWFSSPs with fixed processing times on all except two machines are well solvable while the others are conjectured to be NP-hard. Also, it is shown that NWFSSPs with a mean completion time objective function restricted to semi-ordered processing time matrices are easily solvable.
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An important class of machine scheduling problems is characterized by a no-wait or blocking production environment, where there is no intermediate buffer between machines. In a no-wait environment, a job must be processed from start to completion, without any interruption either on or between machines. Blocking occurs when a job, having completed processing on a machine, remains on the machine until a downstream machine becomes available for processing. A no-wait or blocking production environment typically arises from characteristics of the processing technology itself, or from the absence of storage capacity between operations of a job. In this review paper, we describe several well-documented applications of no-wait and blocking scheduling models and illustrate some ways in which the increasing use of modern manufacturing methods gives rise to other applications. We review the computational complexity of a wide variety of no-wait and blocking scheduling problems and describe several problems which remain open as to complexity. We study several deterministic flowshop, jobshop, and openshop problems and describe efficient and enumerative algorithms, as well as heuristics and results about their performance. The literature on stochastic no-wait and blocking scheduling problems is also reviewed. Finally, we provide some suggestions for future research directions.
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In this article we present two heuristic algorithms for scheduling in the constrained or continuous flow shop to minimize total flow time of jobs. Two heuristic preference relations are used as the basis for job insertion to build up a schedule by the heuristics. When evaluated over a large number of problems of various sizes, the heuristics are found to be very effective in yielding near-optimal solutions.
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This paper deals with flowshop/sum of completion times scheduling problems, working under a “no-idle” or a “no-wait” constraint, the former prescribes for the machines to work continuously without idle intervals and the latter for the jobs to be processed continuously without waiting times between consecutive machines. Under either of the constraints the problem is unary NP-Complete for two machines. We prove some properties of the optimal schedule for n/2/F, no-idle/σCi. For n/m/P, no-idle/σCi, and n/m/P, no-wait/σCi, with an increasing or decreasing series of dominating machines, we prove theorems that are the basis for polynomial bounded algorithms. All theorems are demonstrated numerically.
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Each of a collection of items are to be produced on two machines (or stages). Each machine can handle only one item at a time and each item must be processed through machine one and then through machine two. The setup time plus work time for each item for each machine is known. A simple decision rule is obtained in this paper for the optimal scheduling of the production so that the total elapsed time is a minimum. A three-machine problem is also discussed and solved for a restricted case.
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An important class of scheduling problems is characterized by a no-wait constraint where the jobs have to be processed continuously without waiting between or on consecutive machines. This constraint of no-waiting arises from the characteristics of the processing technology itself. Considering setup times separate from processing times of the jobs forms another important class of scheduling problems. This is particularly important when the ratio of the setup time to the processing time is non-negligible. This paper addresses a scheduling problem which falls in the combined category of no-wait and separate setup times. The performance measure considered is the total flowtime.This paper addresses the two-machine no-wait flowshop problem where the setup time of a job is separated from its processing time. The performance measure considered is the total flowtime. An elimination criterion is developed and optimal solutions are obtained for two special cases. For the generic case, a heuristic algorithm is provided. Computational experience shows that the algorithm yields good solutions.
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This research develops an approach for applying Genetic Algorithms (GA) to scheduling problems. We generate a GA based heuristic for continuous flow shop problems with total flow time as the criterion. The effects of several crucial factors of GA on the performance of the heuristic for the problem are explored in detail. The computational experience of heuristic provides several observations of the application of GA, and strongly supports that the applications of GA are problem specific. The computational experience also shows that GA can be good techniques for scheduling problems.
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This paper presents several new heuristics for the m-machine no-wait flowshop with total completion time as the criterion. The performance of the proposed heuristics is compared with those of three existing heuristics including a recently developed Genetic Algorithm. Computational experience with small and large number of jobs and processing time distributions of U(1,100) and U(1,10) demonstrates the superiority of the proposed heuristics with respect to error performance. For example, for U(1,100), number of jobs 400, and number of machines 25, the suggested proposed heuristics PH1(p) and PH3(p) yield an average percentage relative error of 0.006% and 0.257%. This is compared with an average percentage relative error of 2.764% for the best performing existing heuristic.
Article
The basic concepts of Genetic Algorithms are described, following which a Genetic Algorithm is developed for finding (approximately) the minimum makespan of the n-job, m-machine permutation flowshop sequencing problem. The performance of the algorithm is then compared with that of a naive Neighbourhood Search technique and with a proven Simulated Annealing algorithm on some carefully designed sets of instances of this problem.
A new heuristic and dominance relations for no-wait flowshops with set-ups
  • Aldowiasan
Aldowiasan, T. (2000). A new heuristic and dominance relations for no-wait flowshops with set-ups. Computers and Operations Research, 28, 563–584.
Total flow time in no-wait flowshops with separated set-up times
  • Aldowiasan
Aldowiasan, T., & Allhverdi, A. (1998). Total flow time in no-wait flowshops with separated set-up times. Computers and Operations Research, 25, 757–765.