Showing papers on "Flow shop scheduling published in 2017"
TL;DR: This paper develops a chromosome that can describe a feasible schedule such that meta-heuristics can be applied and innovatively adopts an improved nondominated sorting genetic algorithm to solve the optimization problem for the first time.
Abstract: With the interaction of discrete-event and continuous processes, it is challenging to schedule crude oil operations in a refinery. This paper studies the optimization problem of finding a detailed schedule to realize a given refining schedule. This is a multiobjective optimization problem with a combinatorial nature. Since the original problem cannot be directly solved by using heuristics and meta-heuristics, the problem is transformed into an assignment problem of charging tanks and distillers. Based on such a transformation, by analyzing the properties of the problem, this paper develops a chromosome that can describe a feasible schedule such that meta-heuristics can be applied. Then, it innovatively adopts an improved nondominated sorting genetic algorithm to solve the problem for the first time. An industrial case study is used to test the proposed solution method. The results show that the method makes a significant performance improvement and is applicable to real-life refinery scheduling problems.
229 citations
TL;DR: This work investigates an energy-efficient PFSP with sequence-dependent setup and controllable transportation time from a real-world manufacturing enterprise and proposes a hybrid multi-objective backtracking search algorithm (HMOBSA) to solve this problem.
215 citations
TL;DR: A competitive memetic algorithm (CMA) is proposed to solve the multi-objective distributed permutation flow-shop scheduling problem (MODPFSP) with the makespan and total tardiness criteria.
Abstract: In this paper, a competitive memetic algorithm (CMA) is proposed to solve the multi-objective distributed permutation flow-shop scheduling problem (MODPFSP) with the makespan and total tardiness criteria. Two populations corresponding to two different objectives are employed in the CMA. Some objective-specific operators are designed for each population, and a special interaction mechanism between two populations is designed. Moreover, a competition mechanism is proposed to adaptively adjust the selection rates of the operators, and some knowledge-based local search operators are developed to enhance the exploitation ability of the CMA. In addition, the influence of the parameters on the performance of the CMA is investigated by using the Taguchi method of design-of-experiment. Finally, extensive computational tests and comparisons are carried out to demonstrate the effectiveness of the CMA in solving the MODPFSP.
179 citations
TL;DR: Reinforcement learning with a Q-factor algorithm is used to enhance performance of the scheduling method proposed for dynamic job shop scheduling (DJSS) problem which considers random job arrivals and machine breakdowns.
176 citations
19 Mar 2017
TL;DR: Simulation results show that task offloading scheduling is more critical when the available radio and computational resources in MEC systems are relatively balanced, and it is shown that the proposed algorithm achieves near-optimal execution delay along with a substantial device energy saving.
Abstract: Mobile-edge computing (MEC) has emerged as a prominent technique to provide mobile services with high computation requirement, by migrating the computation- intensive tasks from the mobile devices to the nearby MEC servers. To reduce the execution latency and device energy consumption, in this paper, we jointly optimize task offloading scheduling and transmit power allocation for MEC systems with multiple independent tasks. A low-complexity sub-optimal algorithm is proposed to minimize the weighted sum of the execution delay and device energy consumption based on alternating minimization. Specifically, given the transmit power allocation, the optimal task offloading scheduling, i.e., to determine the order of offloading, is obtained with the help of flow shop scheduling theory. Besides, the optimal transmit power allocation with a given task offloading scheduling decision will be determined using convex optimization techniques. Simulation results show that task offloading scheduling is more critical when the available radio and computational resources in MEC systems are relatively balanced. In addition, it is shown that the proposed algorithm achieves near-optimal execution delay along with a substantial device energy saving.
176 citations
TL;DR: A multi-objective optimization model is developed with three objective functions: minimizing total completion time, maximizing the total availability of the system, and minimizing total energy cost of both production and maintenance operations in the FJSP.
173 citations
TL;DR: A multi-objective genetic algorithm based on a simplex lattice design is proposed to solve this mixed-integer programming model effectively and demonstrates the effectiveness of the proposed model and method for the low-carbon job shop scheduling problem.
129 citations
TL;DR: The current task scheduling mainly concerns the availability of machining resources, rather than the potential errors after scheduling, so to minimise such errors in advance, a big data model is presented.
117 citations
TL;DR: Methodical analysis of task scheduling in cloud and grid computing is presented based on swarm intelligence and bio-inspired techniques and will enable the readers to decide suitable approach for suggesting better schemes for scheduling user’s application.
Abstract: Heterogeneous distributed computing systems are the emerging for executing scientific and computationally intensive applications. Cloud computing in this context describes a paradigm to deliver the resource-like computing and storage on-demand basis using pay-per-use model. These resources are managed by data centers and dynamically provisioned to the users based on their availability, demand and quality parameters required to be satisfied. The task scheduling onto the distributed and virtual resources is a main concern which can affect the performance of the system. In the literature, a lot of work has been done by considering cost and makespan as the affecting parameters for scheduling the dependent tasks. Prior work has discussed the various challenges affecting the performance of dependent task scheduling but did not consider storage cost, failure rate-related challenges. This paper accomplishes a review of using meta-heuristics techniques for scheduling tasks in cloud computing. We presented the taxonomy and comparative review on these algorithms. Methodical analysis of task scheduling in cloud and grid computing is presented based on swarm intelligence and bio-inspired techniques. This work will enable the readers to decide suitable approach for suggesting better schemes for scheduling user’s application. Future research issues have also been suggested in this research work.
114 citations
TL;DR: A backtracking search hyper-heuristic (BS-HH) algorithm is proposed to solve the distributed assembly permutation flow-shop scheduling problem and the computational results indicate the superiority of the proposed BS-HH scheme over the state-of-the-art algorithms.
Abstract: Distributed assembly permutation flow-shop scheduling problem (DAPFSP) is recognized as an important class of problems in modern supply chains and manufacturing systems. In this paper, a backtracking search hyper-heuristic (BS-HH) algorithm is proposed to solve the DAPFSP. In the BS-HH scheme, ten simple and effective heuristic rules are designed to construct a set of low-level heuristics (LLHs), and the backtracking search algorithm is employed as the high-level strategy to manipulate the LLHs to operate on the solution space. Additionally, an efficient solution encoding and decoding scheme is proposed to generate a feasible schedule. The effectiveness of the BS-HH is evaluated on two typical benchmark sets and the computational results indicate the superiority of the proposed BS-HH scheme over the state-of-the-art algorithms.
101 citations
TL;DR: In this article, a dynamic game theory based two-layer scheduling method was developed to reduce makespan, the total workload of machines and energy consumption to achieve real-time multi-objective flexible job shop scheduling.
TL;DR: A hybrid artificial bee colony algorithm (HABC) based on Tabu search (TS) has been developed to solve the model, and a cluster grouping roulette method is proposed to better initialize the population.
TL;DR: This review is a contribution towards the rationalization of the developments in the field, organizing them in terms of the objective functions in the different variants of the problem.
Abstract: Fil: Rossit, Daniel Alejandro. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Bahia Blanca. Instituto de Matematica Bahia Blanca. Universidad Nacional del Sur. Departamento de Matematica. Instituto de Matematica Bahia Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingenieria; Argentina
TL;DR: This paper addresses the Distributed Permutation Flowshop Scheduling Problem (DPFSP) with an artificial chemical reaction metaheuristic which objective is to minimize the maximum completion time and proves the efficiency of the proposed algorithm in comparison with some powerful algorithms.
TL;DR: A genetic-based algorithm as a meta-heuristic method to address static task scheduling for processors in heterogeneous computing systems and improves the performance of genetic algorithm through significant changes in its genetic functions and introduction of new operators that guarantee sample variety and consistent coverage of the whole space.
TL;DR: A mathematical model along with some constructive and improvement heuristics to solve the parallel blocking flow shop problem (PBFSP) and thus minimize the maximum completion time among lines.
Abstract: New Constructive heuristic for both the PBFSP and the DBFSP.Combination of IGA and ILS methods with two types of VNS.A MILP model solved for small-sized instances.The proposed methods are very effective. We consider the NP-hard problem of scheduling n jobs in F identical parallel flow shops, each consisting of a series of m machines, and doing so with a blocking constraint. The applied criterion is to minimize the makespan, i.e., the maximum completion time of all the jobs in F flow shops (lines). The Parallel Flow Shop Scheduling Problem (PFSP) is conceptually similar to another problem known in the literature as the Distributed Permutation Flow Shop Scheduling Problem (DPFSP), which allows modeling the scheduling process in companies with more than one factory, each factory with a flow shop configuration. Therefore, the proposed methods can solve the scheduling problem under the blocking constraint in both situations, which, to the best of our knowledge, has not been studied previously. In this paper, we propose a mathematical model along with some constructive and improvement heuristics to solve the parallel blocking flow shop problem (PBFSP) and thus minimize the maximum completion time among lines. The proposed constructive procedures use two approaches that are totally different from those proposed in the literature. These methods are used as initial solution procedures of an iterated local search (ILS) and an iterated greedy algorithm (IGA), both of which are combined with a variable neighborhood search (VNS). The proposed constructive procedure and the improved methods take into account the characteristics of the problem. The computational evaluation demonstrates that both of them -especially the IGA- perform considerably better than those algorithms adapted from the DPFSP literature.
TL;DR: The experimental results indicate that the proposed HGA approach for solving the distributed and flexible job-shop scheduling problem (DFJSP) is considerably robust, outperforming previous algorithms after 50 runs.
Abstract: In contrast to traditional job-shop scheduling problems, various complex constraints must be considered in distributed manufacturing environments; therefore, developing a novel scheduling solution is necessary. This paper proposes a hybrid genetic algorithm (HGA) for solving the distributed and flexible job-shop scheduling problem (DFJSP). Compared with previous studies on HGAs, the HGA approach proposed in this study uses the Taguchi method to optimize the parameters of a genetic algorithm (GA). Furthermore, a novel encoding mechanism is proposed to solve invalid job assignments, where a GA is employed to solve complex flexible job-shop scheduling problems (FJSPs). In addition, various crossover and mutation operators are adopted for increasing the probability of finding the optimal solution and diversity of chromosomes and for refining a makespan solution. To evaluate the performance of the proposed approach, three classic DFJSP benchmarks and three virtual DFJSPs were adapted from classical FJSP benchmarks. The experimental results indicate that the proposed approach is considerably robust, outperforming previous algorithms after 50 runs.
TL;DR: A simplified model for task scheduling system in cloud computing based on game theory as a mathematical tool is established and the task scheduling algorithm considering the reliability of the balanced task is proposed.
TL;DR: A new packet scheduling scheme named LOES is proposed, which first combines the priority-based packets scheduling scheme with local optimization, and shows that LOES outperforms these previous scheduling schemes.
Abstract: With the widespread applications of Internet of Things (IoT), the emergency response performance for large-scale network packets is facing serious challenge, especially for renewable distributed energy resources monitoring in a smart grid. Therefore, how to improve the real-time performance of the emergency data packets has been a critical issue. Traditional packet scheduling schemes and topology optimization strategies are not suitable for a large-scale IoT-based smart grid. To address this problem, this paper proposes a new packet scheduling scheme named LOES, which first combines the priority-based packet scheduling scheme with local optimization. We exchange local geographic information to reduce the hop counts and distance between distributed source nodes and sink nodes. Each destination node determines the packet scheduling sequence according to the received emergency information. Finally, we compare LOES with first come first serve, multilevel scheme, and dynamic multilevel priority packet scheduling scheme using packet loss rate, packet waiting time, and average packet end-to-end delay as metrics. The simulation results show that LOES outperforms these previous scheduling schemes.
TL;DR: A dynamic optimization model for flexible job shop scheduling based on game theory is put forward to provide a new real‐time scheduling strategy and method to achieve the real‐ time data‐driven optimization decision.
Abstract: With the rapid advancement and widespread application of information and sensor technologies in manufacturing shop floor, the typical challenges that cloud manufacturing is facing are the lack of real-time, accurate, and value-added manufacturing information, the efficient shop floor scheduling strategy, and the method based on the real-time data. To achieve the real-time data-driven optimization decision, a dynamic optimization model for flexible job shop scheduling based on game theory is put forward to provide a new real-time scheduling strategy and method. Contrast to the traditional scheduling strategy, each machine is an active entity that will request the processing tasks. Then, the processing tasks will be assigned to the optimal machines according to their real-time status by using game theory. The key technologies such as game theory mathematical model construction, Nash equilibrium solution, and optimization strategy for process tasks are designed and developed to implement the dynamic optimization model. A case study is presented to demonstrate the efficiency of the proposed strategy and method, and real-time scheduling for four kinds of exceptions is also discussed.
TL;DR: A novel algorithm extending the natural-based Intelligent Water Drops (IWD) algorithm that optimizes the scheduling of workflows on the cloud, which is gaining a lot of attention recently because workflows have emerged as a paradigm to represent complex computing problems.
TL;DR: Two common pattern matching schemes and heuristics are proposed to be combined with the classical genetic algorithm and Computational experiments show that the proposed GA performs better than the random key GA method, especially for large problems.
TL;DR: This paper focuses on the problem of scheduling embarrassingly parallel jobs composed of a set of independent tasks and considers energy consumption during scheduling to determine task placement plan and resource allocation plan for such jobs in a way that minimizes the Job Completion Time (JCT).
Abstract: In cloud computing, with full control of the underlying infrastructures, cloud providers can flexibly place user jobs on suitable physical servers and dynamically allocate computing resources to user jobs in the form of virtual machines. As a cloud provider, scheduling user jobs in a way that minimizes their completion time is important, as this can increase the utilization, productivity, or profit of a cloud. In this paper, we focus on the problem of scheduling embarrassingly parallel jobs composed of a set of independent tasks and consider energy consumption during scheduling. Our goal is to determine task placement plan and resource allocation plan for such jobs in a way that minimizes the Job Completion Time (JCT). We begin with proposing an analytical solution to the problem of optimal resource allocation with pre-determined task placement. In the following, we formulate the problem of scheduling a single job as a Non-linear Mixed Integer Programming problem and present a relaxation with an equivalent Linear Programming problem. We further propose an algorithm named TaPRA and its simplified version TaPRA-fast that solve the single job scheduling problem. Lastly, to address multiple jobs in online scheduling, we propose an online scheduler named OnTaPRA. By comparing with the start-of-the-art algorithms and schedulers via simulations, we demonstrate that TaPRA and TaPRA-fast reduce the JCT by 40-430 percent and the OnTaPRA scheduler reduces the average JCT by 60-280 percent. In addition, TaPRA-fast can be 10 times faster than TaPRA with around 5 percent performance degradation compared to TaPRA, which makes the use of TaPRA-fast very appropriate in practice.
21 Sep 2017
TL;DR: This paper proposes the first “practical” feature selection algorithm for job shop scheduling, and develops a Niching-based search framework for extracting a diverse set of good rules and reduces the complexity of fitness evaluation by using a surrogate model.
Abstract: Automated design of job shop scheduling rules using genetic programming as a hyper-heuristic is an emerging topic that has become more and more popular in recent years. For evolving dispatching rules, feature selection is an important issue for deciding the terminal set of genetic programming. There can be a large number of features, whose importance/relevance varies from one to another. It has been shown that using a promising feature subset can lead to a significant improvement over using all the features. However, the existing feature selection algorithm for job shop scheduling is too slow and inapplicable in practice. In this paper, we propose the first “practical” feature selection algorithm for job shop scheduling. Our contributions are twofold. First, we develop a Niching-based search framework for extracting a diverse set of good rules. Second, we reduce the complexity of fitness evaluation by using a surrogate model. As a result, the proposed feature selection algorithm is very efficient. The experimental studies show that it takes less than 10% of the training time of the standard genetic programming training process, and can obtain much better feature subsets than the entire feature set. Furthermore, it can find better feature subsets than the best-so-far feature subset.
TL;DR: Comparisons with the recently published algorithms demonstrate the high effectiveness and searching ability of the proposed IG algorithms for solving the DNWFSP.
Abstract: The distributed production lines widely exist in modern supply chains and manufacturing systems. This paper aims to address the distributed no-wait flow shop scheduling problem (DNWFSP) with the makespan criterion by using proposed iterated greedy (IG) algorithms. Firstly, several speed-up methods based on the problem properties of DNWFSP are investigated to reduce the evaluation time of neighborhood with O(1) complexity. Secondly, an improved NEH heuristic is proposed to generate a promising initial solution, where the iteration step of the insertion step of NEH is applied to the factory after inserting a new job. Thirdly, four neighborhood structures (i.e. Critical_swap_single, Critical_insert_single, Critical_swap_multi, Critical_insert_multi) based on factory assignment and job sequence adjustment are employed to escape from local optima. Fourthly, four local search methods based on neighborhood moves are proposed to enhance local searching ability, which contains LS_insert_critical_factory1, LS_insert_critical_factory2, LS_swap, and LS_insert. Finally, to organize neighborhood moves and local search methods efficiently, we incorporate them into the framework of variable neighborhood search (VNS), variable neighborhood descent (VND) and random neighborhood structure (RNS). Furthermore, three variants of IG algorithms are presented based on the designed VNS, VND and RNS. The parameters of the proposed IG algorithms are tuned through a design of experiments on randomly generated benchmark instances. The effectiveness of the initialize phase and local search methods is shown by numerical comparison, and the comparisons with the recently published algorithms demonstrate the high effectiveness and searching ability of the proposed IG algorithms for solving the DNWFSP. Ultimately, the best solutions of 720 instances from the well-known benchmark set of Naderi and Ruiz for the DNWFSP are proposed.
TL;DR: In this article, a mixed-integer programming formulation is proposed for the problem, which minimizes not only the manufacturing and remanufacturing costs, but also the energy costs paid for the utilization of machines and the compression of processing times.
TL;DR: A new approximate optimization approach is developed, which is based on the imperialist competitive algorithm hybridized with an efficient neighborhood search, and the effectiveness of the proposed approach is demonstrated through an experimental evaluation.
TL;DR: The results compared to some state-of-the-art metaheuristics demonstrate the effectiveness of the proposed DWWO in solving BFSP with SDST.
Abstract: This paper considers n-job m-machines blocking flow-shop scheduling problem (BFSP) with sequence-dependent setup times (SDST), which has important ramifications in the modern industry. To solve this problem, two efficient heuristics are firstly presented according to the property of the problem. Then, a novel discrete water wave optimization (DWWO) algorithm is proposed. In the proposed DWWO, an initial population with high quality and diversity is constructed based on the presented heuristic and a perturbation procedure. A two-stage propagation is designed to direct the algorithm towards the good solutions. The path relinking technique is employed in refraction phase to help individuals escape from local optima. A variable neighborhood search is developed and embedded in breaking phase to enhance local exploitation capability. A new population updating scheme is applied to accelerate the convergence speed. Moreover, a speedup method is presented to reduce the computational efforts needed for evaluating insertion neighborhood. Finally, extensive numerical tests are carried out, and the results compared to some state-of-the-art metaheuristics demonstrate the effectiveness of the proposed DWWO in solving BFSP with SDST.
TL;DR: Experimental simulations and comparisons demonstrate that the HHS algorithm outperforms the standard HS algorithm and other recently proposed efficient algorithms in terms of solution quality and stability.
TL;DR: A novel multiobjectives discrete artificial bee colony algorithm based decomposition, called MODABC/D, is presented to solve the sequence dependent setup times multiobjective permutation flowshop scheduling problem with the objective to minimize makespan and total flowtime.
Abstract: The multiobjective permutation flow shop scheduling problem with sequence dependent setup times has been an object of investigations for decades. This widely studied problem from the scheduling theory links the sophisticated solution algorithms with the moderate real world applications. This paper presents a novel multiobjective discrete artificial bee colony algorithm based decomposition, called MODABC/D , to solve the sequence dependent setup times multiobjective permutation flowshop scheduling problem with the objective to minimize makespan and total flowtime. First, in order to make the standard artificial bee colony algorithm to solve the scheduling problem, a discrete artificial bee colony algorithm is proposed to solve the problem based on the perturbation operation. Then, a problem-specific solution builder heuristic is used to initialize the population to enhance the quality of the initial solution. Finally, a further local search method are comprised of a single local search procedures based on the insertion neighborhood structures to find the better solution for the nonimproved individual. The performance of the proposed algorithms is tested on the well-known benchmark suite of Taillard. The highly effective performance of the multiobjective discrete artificial bee colony algorithm-based decomposition is compared against the state of art algorithms from the existing literature in terms of both coverage value and hypervolume indicator.