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Showing papers on "Job shop published in 2018"


Journal ArticleDOI
TL;DR: A model is formulated for the flexible job shop scheduling problem, an energy consumption model is proposed to compute the energy consumption for a machine in different states, and a non-dominated sorted genetic algorithm is developed to solve the problem.

140 citations


Journal ArticleDOI
TL;DR: Experimental results demonstrate that the discrete GWO algorithm outperforms other algorithms for the scheduling problems under study, and is compared with other published algorithms in the literature for the two scheduling cases.
Abstract: Grey wolf optimization (GWO) algorithm is a new population-oriented intelligence algorithm, which is originally proposed to solve continuous optimization problems inspired from the social hierarchy and hunting behaviors of grey wolves. It has been proved that GWO can provide competitive results compared with some well-known meta-heuristics. This paper aims to employ the GWO to deal with two combinatorial optimization problems in the manufacturing field: job shop and flexible job shop scheduling cases. The effectiveness of GWO algorithm on the two problems can give an idea about its possible application on solving other scheduling problems. For the discrete characteristics of the scheduling solutions, we developed a kind of discrete GWO algorithm with the objective of minimizing the maximum completion time (makespan). In the proposed algorithm, searching operator is designed based on the crossover operation to maintain the algorithm work directly in a discrete domain. Then an adaptive mutation method is introduced to keep the population diversity and avoid premature convergence. In addition, a variable neighborhood search method is embedded to further enhance the exploration. To evaluate the effectiveness, the discrete GWO algorithm is compared with other published algorithms in the literature for the two scheduling cases. Experimental results demonstrate that our algorithm outperforms other algorithms for the scheduling problems under study.

91 citations


Journal ArticleDOI
TL;DR: An eventdriven RFID-based production data analysis method is proposed to construct the RFID events and judge the process command execution and a near big data approach is used to excavate hidden information and knowledge from the historical production data.
Abstract: Under industry 4.0, internet of things U+0028 IoT U+0029, especially radio frequency identification U+0028 RFID U+0029 technology, has been widely applied in manufacturing environment. This technology can bring convenience to production control and production transparency. Meanwhile, it generates increasing production data that are sometimes discrete, uncorrelated, and hard-to-use. Thus, an efficient analysis method is needed to utilize the invaluable data. This work provides an RFID-based production data analysis method for production control in IoT-enabled smart job-shops. The physical configuration and operation logic of IoT-enabled smart job-shop production are firstly described. Based on that, an RFID-based production data model is built to formalize and correlate the heterogeneous production data. Then, an eventdriven RFID-based production data analysis method is proposed to construct the RFID events and judge the process command execution. Furthermore, a near big data approach is used to excavate hidden information and knowledge from the historical production data. A demonstrative case is studied to verify the feasibility of the proposed model and methods. It is expected that our work will provide a different insight into the RFIDbased production data analysis.

66 citations


Journal ArticleDOI
Xuran Gong1, Qianwang Deng1, Guiliang Gong1, Wei Liu1, Qinghua Ren1 
TL;DR: A memetic algorithm (MA) is designed to solve the proposed MO-FJSPW whose objective is to minimise the maximum completion time, the maximum workload of machines and the total workload of all machines.
Abstract: In existing scheduling models, the flexible job-shop scheduling problem mainly considers machine flexibility. However, human factor is also an important element existing in real production that is ...

65 citations


Journal ArticleDOI
TL;DR: A new immune multi-agent scheduling system (NIMASS) to solve the FJSP with the objective of minimizing the maximal completion time (makespan), and the computational time of NIMASS is superior to that of the centralized meta-heuristic algorithms, especially for the complex FJ SPs.
Abstract: Scheduling for the flexible job shop is very important and challenging in manufacturing field. Multi-agent-based approaches have been used to solve the flexible job shop scheduling problem (FJSP), in order to reduce complexity and cost, increase flexibility, and enhance robustness. However, the quality of solution obtained by the multi-agent approach is always worse than the centralized meta-heuristic algorithms. The immune system is a distributed and complicated information processing system, which can protect body from foreign antigens by immune responses. In this paper, we analyze the similarities between the FJSP and humoral immunity, which is one of the immune responses. Based on the similarities, we develop a new immune multi-agent scheduling system (NIMASS) to solve the FJSP with the objective of minimizing the maximal completion time (makespan). In order to acquire the higher-quality solution of the FJSP, we simulate humoral immunity to establish the architecture of NIMASS and the negotiation strategies of NIMASS, which are proposed for negotiation among agents. NIMASS was tested on different benchmark instances of the FJSP. In comparison with the multi-agent approaches and the centralized heuristic algorithms, the computational results indicate that NIMASS can effectively improve the quality of solution in very short time. And the computational time of NIMASS is superior to that of the centralized meta-heuristic algorithms, especially for the complex FJSPs. These results indicate that NIMASS can be very useful in applications that deal with real-time FJSPs.

54 citations


Journal ArticleDOI
28 Oct 2018
TL;DR: An improved whale optimization algorithm (IWOA) is presented to solve the energy-efficient scheduling problem with the objective of minimizing the sum of the energy consumption cost and the completion-time cost.
Abstract: Under the current environmental pressure, many manufacturing enterprises are urged or forced to adopt effective energy-saving measures. However, environmental metrics, such as energy consumption and CO2 emission, are seldom considered in the traditional production scheduling problems. Recently, the energy-related scheduling problem has been paid increasingly more attention by researchers. In this paper, an energy-efficient job shop scheduling problem (EJSP) is investigated with the objective of minimizing the sum of the energy consumption cost and the completion-time cost. As the classical JSP is well known as a non-deterministic polynomial-time hard (NP-hard) problem, an improved whale optimization algorithm (IWOA) is presented to solve the energy-efficient scheduling problem. The improvement is performed using dispatching rules (DR), a nonlinear convergence factor (NCF), and a mutation operation (MO). The DR is used to enhance the initial solution quality and overcome the drawbacks of the random population. The NCF is adopted to balance the abilities of exploration and exploitation of the algorithm. The MO is employed to reduce the possibility of falling into local optimum to avoid the premature convergence. To validate the effectiveness of the proposed algorithm, extensive simulations have been performed in the experiment section. The computational data demonstrate the promising advantages of the proposed IWOA for the energy-efficient job shop scheduling problem.

54 citations


Journal ArticleDOI
TL;DR: The results show the capability of the proposed integrated framework to obtain a constructive trade-off between scenario-based cost deviations, and heat and power imbalances considering the attitude of Decision Makers (DMs) toward risk.

46 citations


Journal ArticleDOI
TL;DR: This research presents the resolution of the FJSP multi-objective, using a hierarchical approach that divides the problem into two sub-problems, being the Particle Swarm Optimization (PSO) responsible for resolving the routing sub-problem, and Random Restart Hill Climbing (RRHC) for theresolution of scheduling sub- problem.

44 citations


Journal ArticleDOI
TL;DR: The efficiency of this approach is explained by the flexible selection of the promising parts of the search space by the clustering operator after the genetic algorithm process, and by applying the intensification technique of the tabu search allowing to restart the search from a set of elite solutions to attain new dominant scheduling solutions.
Abstract: The flexible job shop scheduling problem (FJSP) is a generalization of the classical job shop scheduling problem that allows to process operations on one machine out of a set of alternative machines. The FJSP is an NP-hard problem consisting of two sub-problems, which are the assignment and the scheduling problems. In this paper, we propose how to solve the FJSP by hybrid metaheuristics-based clustered holonic multiagent model. First, a neighborhood-based genetic algorithm (NGA) is applied by a scheduler agent for a global exploration of the search space. Second, a local search technique is used by a set of cluster agents to guide the research in promising regions of the search space and to improve the quality of the NGA final population. The efficiency of our approach is explained by the flexible selection of the promising parts of the search space by the clustering operator after the genetic algorithm process, and by applying the intensification technique of the tabu search allowing to restart the search from a set of elite solutions to attain new dominant scheduling solutions. Computational results are presented using four sets of well-known benchmark literature instances. New upper bounds are found, showing the effectiveness of the presented approach.

43 citations


Journal ArticleDOI
TL;DR: Experimental results confirm the validity of the improved energy-efficient scheduling approach in a flexible job shop environment and the effectiveness of the solution.
Abstract: Nowadays, manufacturing industry is under increasing pressure to save energy and reduce emissions, and thereby enhancing the energy efficiency of the machining system (MS) through operational methods on the system-level has attracted more attention. Energy-efficient scheduling (ES) has proved to be a typical measure suitable for all shop types, and an energy-efficient mechanism that a machine can be switched off and back on if it waits for a new job for a relatively long period is another proven effective energy-saving measure. Furthermore, their combination has been fully investigated in a single machine, flow shop and job shop, and the improvement in energy efficiency is significant compared with only applying ES for MS. However, whether such two energy-saving measures can be integrated in a flexible job shop environment is a gap in the existing study. To address this, a scheduling method applying an energy-efficient mechanism is proposed for a flexible job shop environment and the corresponding mathematical model, namely the energy-efficient flexible job shop scheduling (EFJSS) model, considering total production energy consumption (EC) and makespan is formulated. Besides, transportation as well as its impact on EC is taken into account in this model for practical application. Furthermore, a solution approach based on the non-dominated sorting genetic algorithm-II (NSGA-II) is adopted, which can avoid the interference of subjective factors and help select a suitable machine for each operation and undertake rational operation sequencing simultaneously. Moreover, experimental results confirm the validity of the improved energy-efficient scheduling approach in a flexible job shop environment and the effectiveness of the solution.

38 citations


Journal ArticleDOI
TL;DR: A new rescheduling method is proposed to find a reschedule with minimum makespan and with less global energy consumption when resolving dynamic flexible job-shop scheduling problem under machine breakdowns.

Journal ArticleDOI
TL;DR: Four MIP formulations without time-indexed variables are developed based on two different transformation approaches of parallel tracks and two different types of decision variables leading to job-shop scheduling problems with or without routing flexibility.
Abstract: The problem of scheduling a set of trains traveling through a given railway network consisting of single tracks, sidings and stations is considered. For every train a fixed route and travel times, an earliest departure time at the origin and a desired arrival time at the destination are given. A feasible schedule has to be determined which minimizes total tardiness of all trains at their destinations. This train scheduling problem is modeled as a job-shop scheduling problem with blocking constraints, where jobs represent trains and machines constitute tracks or track sections. Four MIP formulations without time-indexed variables are developed based on two different transformation approaches of parallel tracks and two different types of decision variables leading to job-shop scheduling problems with or without routing flexibility. A computational study is made on hard instances with up to 20 jobs and 11 machines to compare the MIP models in terms of total tardiness values, formulation size and computation time.

Book ChapterDOI
04 Apr 2018
TL;DR: The results show that co-evolving the two rules together can lead to much more promising results than evolving the sequencing rule only, and a new GPHH algorithm with Cooperative Co-evolution is proposed.
Abstract: Flexible Job Shop Scheduling (FJSS) problem has many real-world applications such as manufacturing and cloud computing, and thus is an important area of study. In real world, the environment is often dynamic, and unpredicted job orders can arrive in real time. Dynamic FJSS consists of challenges of both dynamic optimisation and the FJSS problem. In Dynamic FJSS, two kinds of decisions (so-called routing and sequencing decisions) are to be made in real time. Dispatching rules have been demonstrated to be effective for dynamic scheduling due to their low computational complexity and ability to make real-time decisions. However, it is time consuming and strenuous to design effective dispatching rules manually due to the complex interactions between job shop attributes. Genetic Programming Hyper-heuristic (GPHH) has shown success in automatically designing dispatching rules which are much better than the manually designed ones. Previous works only focused on standard job shop scheduling with only the sequencing decisions. For FJSS, the routing rule is set arbitrarily by intuition. In this paper, we explore the possibility of evolving both routing and sequencing rules together and propose a new GPHH algorithm with Cooperative Co-evolution. Our results show that co-evolving the two rules together can lead to much more promising results than evolving the sequencing rule only.

Journal ArticleDOI
TL;DR: A grey wolf optimization algorithm with double-searching mode (DMGWO) is proposed with the objective of minimizing the total cost of energy-consumption and tardiness and extensive simulations demonstrate the effectiveness of the proposed DMGWO algorithm for solving the energy-efficient job shop scheduling problem.
Abstract: Workshop scheduling has mainly focused on the performances involving the production efficiency, such as times and quality, etc. In recent years, environmental metrics have attracted the attention of many researchers. In this study, an energy-efficient job shop scheduling problem is considered, and a grey wolf optimization algorithm with double-searching mode (DMGWO) is proposed with the objective of minimizing the total cost of energy-consumption and tardiness. Firstly, the algorithm starts with a discrete encoding mechanism, and then a heuristic algorithm and the random rule are employed to implement the population initialization. Secondly, a new framework with double-searching mode is developed for the GWO algorithm. In the proposed DMGWO algorithm, besides of the searching mode of the original GWO, a random seeking mode is added to enhance the global search ability. Furthermore, an adaptive selection operator of the two searching modes is also presented to coordinate the exploration and exploitation. In each searching mode, a discrete updating method of individuals is designed by considering the discrete characteristics of the scheduling solution, which can make the algorithm directly work in a discrete domain. In order to further improve the solution quality, a local search strategy is embedded into the algorithm. Finally, extensive simulations demonstrate the effectiveness of the proposed DMGWO algorithm for solving the energy-efficient job shop scheduling problem based on 43 benchmarks.

Journal ArticleDOI
TL;DR: A mathematical model of the cyclic scheduling problem in the production system working according to the so called open shop policy and a few graph models are provided, and an approximation algorithm of tabu search type is proposed.

Journal ArticleDOI
TL;DR: A state-of-the-art review of the different works proposed for the DRCJSP and DRCFJSP is made, where a new classification schema is presented according to six criteria such as the used method, the machine flexibility, the worker flexible, the optimization criteria, the implemented approaches, and the structure of the approach.

Journal ArticleDOI
TL;DR: Results show that the integrated algorithms outperform the intuitive decomposed ones significantly and are proposed to solve the disassembly, reprocessing and reassembly scheduling sub-problems separately.

Journal ArticleDOI
TL;DR: A MAS approach to control the production in job shop manufacturing systems is applied within a simulation study based on a real industrial case and achieves a good performance compared to the standard scheduling approach applied by the considered company.

Journal ArticleDOI
TL;DR: A classification of hybrid shop scheduling problem based on the criterion of machine environment is proposed and the problem is classified into hybridShop scheduling with parallel machines, hybrid shop schedules with multiprocessor task, and other hybrids shop scheduling such as the mixed shop scheduling.
Abstract: Hybrid shop scheduling has gained popularity due to the rapid rise of market demand and development of production technology. It is a combination of more than one classical shop scheduling, such as flow shop scheduling, job shop scheduling, open shop scheduling, parallel machine scheduling, and multiprocessor task scheduling. In this paper, a classification of hybrid shop scheduling problem based on the criterion of machine environment is proposed. The problem is classified into hybrid shop scheduling with parallel machines, hybrid shop scheduling with multiprocessor task, and other hybrid shop scheduling such as the mixed shop scheduling. The citation analysis method is used to give a brief review of hybrid flow shop and job shop with parallel machines. At the same time, for hybrid shop scheduling with multiprocessor task and other hybrid shop scheduling, a detailed overview is given because of its relatively few researches. Finally, some research directions for the hybrid shop scheduling are also discussed.

Journal ArticleDOI
TL;DR: The results show that the proposed algorithm can obtain better flexible job shop scheduling scheme and especially has more significant advantages in solving large-scale problems in comparison with other algorithms.
Abstract: Reasonable scheduling of flexible job shop is key to improve production efficiency and economic benefits; in order to solve the problem in flexible job shop scheduling problem, a novel flexible job shop scheduling method based on improved artificial immune algorithm is proposed. Firstly, a mathematical model of the flexible job shop scheduling is established, and the total shortest processing time is taken as the objective function. Secondly, artificial immune algorithm is used to solve the problem, and particle swarm optimization algorithm is taken as the operator to embed into manual immune algorithm for maintaining the diversity of population and prevent obtaining local optimal solution. Finally, the performance of the algorithm is tested by simulation experiments on standard set. The results show that the proposed algorithm can obtain better flexible job shop scheduling scheme and especially has more significant advantages in solving large-scale problems in comparison with other algorithms.

Proceedings ArticleDOI
07 Mar 2018
TL;DR: The results revealed that the proposed approach is well suited to solve the problem effectively and an attempt has been made to help the administration to decide the changes in the exiting layout by implementing the well-known "cost-benefit-analysis".
Abstract: Today's techno savvy world is evolving with the prompt changes in technology, everyday fluctuation in demand of products as well as the increasing diversity of products, the exiting layout of facilities may invalid frequently. These changes lead to makes the improvement in the existing layout of available facilities on shop floor to cope up with the growing market competition. By keeping this concept in mind, the present study deals with the development of a model to solve facility layout problem evolving job shop production system. This problem is itself a Non-Polynomial hard and considering the various cost factors makes it more difficult to attain the optimum solution. Therefore, a nature inspired algorithm i.e. Genetic Algorithm (GA) is applied to deal with this problem while considering handling and moving cost of facilities as well as setup cost in a job shop type of production system. Moreover, the Variable Neighborhood Search (VNS) method has been integrated with GA to enhance the local search of optimal solution. The results revealed that the proposed approach is well suited to solve the problem effectively. Also, an attempt has been made to help the administration to decide the changes in the exiting layout by implementing the well-known "cost-benefit-analysis".

Journal ArticleDOI
TL;DR: In this research, the problem of scheduling and sequencing of manufacturing system is presented and a new model for flexible job shop problem sequencing problem is proposed.
Abstract: In today highly competitive and globalized markets, an efficient use of production resources is necessary for manufacturing enterprises. In this research, the problem of scheduling and sequencing of manufacturing system is presented. A flexible job shop problem sequencing problem is analyzed in detail. After formulating this problem mathematically, a new model is proposed. This problem is not only theoretically interesting, but also practically relevant. An illustrative example is also conducted to demonstrate the applicability of the proposed model.

Journal ArticleDOI
TL;DR: This study proposes an integrated decision support system that combines simulation modelling and multi-criteria decision making for job shop lot streaming problem and reveals that customer-oriented dispatching rules provide better outcomes in case of high level of bottleneck resource utilization and high fluctuation amongst the customer importance weights.

Journal ArticleDOI
TL;DR: In this article, a discrete firefly algorithm (FA) and a genetic algorithm (GA) were proposed to solve the FJSP with parallel operations (EFJSP) problem.
Abstract: Traditional planning and scheduling techniques still hold important roles in modern smart scheduling systems Realistic features present in modern manufacturing systems need to be incorporated into these techniques Flexible job-shop scheduling problem (FJSP) is one of the most challenging combinatorial optimization problems FJSP is an extension of the classical job shop scheduling problem where an operation can be processed by several different machines In this paper, we consider the FJSP with parallel operations (EFJSP) and we propose and compare a discrete firefly algorithm (FA) and a genetic algorithm (GA) for the problem Several FJSP and EFJSP instances were used to evaluate the performance of the proposed algorithms Comparisons among our methods and state-of-the-art algorithms are also provided The experimental results demonstrate that the FA and GA achieved improvements in terms of efficiency and efficacy Solutions obtained by both algorithms are comparable to those obtained by algorithms with local search In addition, based on our initial experiments, results show that the proposed discrete firefly algorithm is feasible, more effective and efficient than our proposed genetic algorithm for the considered problem

Journal ArticleDOI
TL;DR: In this paper, the authors present a formal and simple approach for production scheduling for small and medium enterprises (SMEs) in case of an event that introduces the introduction of new technologies.
Abstract: One common problem among Small and Medium Enterprises (SME) is to don’t have at disposal formal and simple approaches for production scheduling, especially in case of an event that introduces the r...

Journal ArticleDOI
TL;DR: A mixed integer linear programming (MILP) formulation to find an optimal solution to a small instance of the complex scheduling problem in a make-to-order production finds that optimal solutions cannot be calculated for most real world scenarios due to the complexity.

Journal ArticleDOI
Yixiong Feng1, Qirui Wang1, Yicong Gao1, Jin Cheng1, Jianrong Tan1 
TL;DR: A dynamic scheduling system based on the cyber-physical energy monitoring system is proposed to improve the energy efficiency of the job-shop and a modified genetic algorithm with multi-layer coding is applied to generate an energy-efficient schedule.
Abstract: Nowadays, a large amount of energy is consumed by buildings in the city. Among them, industrial energy consumption accounts for a large proportion due to the high power of manufacturing devices in the job-shop. To achieve higher energy-efficiency, lots of scheduling methods in the job-shop are developed. However, most of the current scheduling method is based on the prior-experimental data and the schedule of the production cycle cannot be adjusted according to energy status of manufacturing devices. Thus a dynamic scheduling system based on the cyber-physical energy monitoring system is proposed to improve the energy efficiency of the job-shop. First, a novel process of “scheduling-monitoring-updating-optimizing” is implemented in the proposed system. In the scheduling model, tool aging condition is considered along with the geometry information of the work-piece to estimate energy consumption of the manufacturing process. At last, a modified genetic algorithm with multi-layer coding is applied to generate an energy-efficient schedule. The proposed system is implemented in a production cycle which contains 36 operations of six work-pieces and ten machine tools to prove its validity.

Journal ArticleDOI
TL;DR: This study takes into consideration a rank-dependent utility approach combined with a multi-attribute utility theory to identify the best dispatching rule for dynamic job shop environments.

Journal ArticleDOI
TL;DR: This work presents a tabu search heuristic for a large class of job shop scheduling problems, where the objective is non-regular in general and minimizes a sum of separable convex cost functions attached to the operation start times and the differences between the start times of arbitrary pairs of operations.

Journal ArticleDOI
TL;DR: Two mixed integer linear programming models MILP1, MILP2 are presented and some properties concerning the optimality of Jackson’s algorithm under availability constraints are introduced, and a branch and bound algorithm incorporating these bounds is proposed to solve the problem.