Showing papers on "Job shop published in 2017"

A hybrid artificial bee colony algorithm for the job-shop scheduling problem with no-wait constraint

Abstract: This paper studies a hybrid artificial bee colony (ABC) algorithm for finding high quality solutions of the job-shop scheduling problem with no-wait constraint (JSPNW) with the objective of minimizing makespan among all the jobs. JSPNW is an extension of well-known job-shop scheduling problem subject to the constraint that no waiting time is allowed between operations for a given job. ABC algorithm is a swarm intelligence technique based on intelligent foraging behavior of honey bee swarm. The proposed hybrid approach effectively coordinates the various components of ABC algorithm such as solution initialization, selection and determination of a neighboring solution with the local search in such a way that it leads to high quality solutions for the JSPNW. The proposed approach is compared with the two best approaches in the literature on a set of benchmark instances. Computational results show the superiority of the proposed approach over these two best approaches.

Robust multiobjective optimisation for fuzzy job shop problems

Abstract: Graphical abstractDisplay Omitted HighlightsWe solve a fuzzy JSP with triangular fuzzy numbers to model uncertain durations.We take the stance that solution robustness means an overall acceptable performance.We define a new a priori robustness measure and adapt an a posteriori measure.We propose a hybrid approach to optimise the fuzzy makespan and the robustness.We test the algorithm and analyse the correlation between both robustness measures. In this paper we tackle a variant of the job shop scheduling problem with uncertain task durations modelled as fuzzy numbers. Our goal is to simultaneously minimise the schedule's fuzzy makespan and maximise its robustness. To this end, we consider two measures of solution robustness: a predictive one, prior to the schedule execution, and an empirical one, measured at execution. To optimise both the expected makespan and the predictive robustness of the fuzzy schedule we propose a multiobjective evolutionary algorithm combined with a novel dominance-based tabu search method. The resulting hybrid algorithm is then evaluated on existing benchmark instances, showing its good behaviour and the synergy between its components. The experimental results also serve to analyse the goodness of the predictive robustness measure, in terms of its correlation with simulations of the empirical measure.

Pollux: a dynamic hybrid control architecture for flexible job shop systems

Abstract: Nowadays, manufacturing control systems can respond more effectively to exigent market requirements and real-time demands. Indeed, they take advantage of changing their structural and behavioural arrangements to tailor the control solution from a diverse set of feasible configurations. However, the challenge of this approach is to determine efficient mechanisms that dynamically optimise the configuration between different architectures. This paper presents a dynamic hybrid control architecture that integrates a switching mechanism to control changes at both structural and behavioural level. The switching mechanism is based on a genetic algorithm and strives to find the most suitable operating mode of the architecture with regard to optimality and reactivity. The proposed approach was tested in a real flexible job shop to demonstrate the applicability and efficiency of including an optimisation algorithm in the switching process of a dynamic hybrid control architecture.

An effective operations permutation-based discrete harmony search approach for the flexible job shop scheduling problem with makespan criterion

Abstract: The Flexible Job Shop Scheduling Problem (FJSSP) represents a challenging applicative problem for metaheuristic algorithms because it imposes the development of innovative domain-dependent search operators that have to deal both with its combined discrete and permutation nature. Emerging as an effective approach for the resolution of a broad spectrum of hard optimization problems, some few discrete declinations of the Harmony Search (HS) algorithm have been recently proposed for tackling the FJSSP. Recent advances include an investigation of an innovative and promising permutation-based proposal. Accordingly, this paper proposes an Effective Operations Permutation-based Discrete Harmony Search (EOP-DHS) approach for FJSSP with Makespan criterion. The approach adopts an integrated two-part “affectation-sequencing” representation of the solution harmony and a dedicated improvisation operator particularly adapted to the integer-valued and operations permutation-based used coding scheme. Besides, a Modified Intelligent Mutation (MIM) operator is integrated to the adopted framework in order to enhance its overall search ability. Mainly, by balancing maximum machine workload during the overall search process, MIM operator allows essentially maintaining and enhancing the reciprocal equilibrium of diversification and intensification abilities of the proposed EOP-DHS algorithm. Conducted numerical experimentations on 188 benchmarking instances validate the proposal comparatively to a representative set of previously deployed metaheuristic approaches to FJSSP with Makespan criterion. Furthermore, main contribution of the paper is extended with an experimental procedure proving the effectiveness of the adopted permutation-based HS scheme for the resolution of combinatorial optimization problems. Hard benchmarking instances of the classical Job Shop Scheduling Problem (JSSP) are thus considered for exemplification.

Implementation of hybrid Kanban-CONWIP system: a case study

Abstract: Purpose Shop floor control systems are generally major points of discussion in production planning and control literature. The purpose of this paper is to investigate how lean production control principles can be used in a make-to-order (MTO) job shop, where the volume is typically low and there is high variety. This paper examines the procedures involved in implementing a constant work-in-process (CONWIP)/Kanban hybrid system in the shop floor environment and also provides insights and guidelines on the implementation of a hybrid system in a high-variety/low-volume environment. Design/methodology/approach The authors review literature on Kanban, CONWIP, and CONWIP/Kanban hybrid systems to analyze how lean production control principles can be used in a MTO job shop. The second part focuses on the process of implementation. Using a case study of a manufacturer of electromechanical components for valve monitoring and controls, the paper describes how the operation is transformed by for more efficient shop floor control systems. Real experiments are used to compare pre- and post-improvement performance. Findings The study shows that the proposed hybrid Kanban-CONWIP system reduced the cycle time and achieved an increase of 38 percent in inventory turnover. The empirical results from this pilot study provide useful managerial insights for a benchmarking analysis of the actions to be taken into consideration by companies that have similar manufacturing systems. Research limitations/implications The statistic generalization of the results is impossible due to the use of a single case method of study. Originality/value This paper provides insights and guidelines on the implementation of a hybrid system in a high-variety/low-volume environment. The literature on real applications of hybrid CONWIP/Kanban by case study is limited.

Multi-Objective Scheduling Simulation of Flexible Job-Shop Based on Multi-Population Genetic Algorithm

Abstract: In view of the difficulty of obtaining the optimal solution to the multi-objective scheduling of flexible job-shop by the general genetic algorithm, this paper takes into account the shortest processing time and the balanced use of machines, and puts forward the multi-population genetic algorithm based on the multi-objective scheduling of flexible job-shop. The method attempts to minimize the longest make-span of workpieces, the load of each machine, and the total machine load through the overall process scheduling of the job-shop. Research results reveal that the proposed method is highly efficient in seeking the optimal machine allocation chain, and effective in avoiding the complex process of intermediate assignment, making it easier to obtain the said optimal solution. The feasibility and effectiveness of the proposed method are also validated by two instances. Compared with the conventional flexible job-shop scheduling algorithms, the proposed algorithm boasts better population quality, algorithm starting point, and initial expression. Besides, it is far better than other algorithms in terms of the initial solution quality and the convergence rate. Despite the local fluctuations in the early phase of the genetic process, the total machine load and the machine load variance are gradually declining and the curves start to converge after the 50 generation. (Received, processed and accepted by the Chinese Representative Office.)

Novel Complexity Indicator of Manufacturing Process Chains and Its Relations to Indirect Complexity Indicators

Abstract: Manufacturing systems can be considered as a network of machines/workstations, where parts are produced in flow shop or job shop environment, respectively. Such network of machines/workstations can be depicted as a graph, with machines as nodes and material flow between the nodes as links. The aim of this paper is to use sequences of operations and machine network to measure static complexity of manufacturing processes. In this order existing approaches to measure the static complexity of manufacturing systems are analyzed and subsequently compared. For this purpose, analyzed competitive complexity indicators were tested on two different manufacturing layout examples. A subsequent analysis showed relevant potential of the proposed method.

Job shop schedules analysis in the context of industry 4.0

Abstract: Industry 4.0 is announced as a fourth industrial revolution. The next level of evolution will comprehend the wide spread inclusion of machines sensors and big data analytics. Enterprise Resource Planning and Manufacturing Execution Systems will be the information management tools for the revolution. Different forms of optimization will be in the brink of development to answer this revolution needs. In this paper is proposed a model for solving a classical job shop scheduling problem, which is NP-hard in the strong sense. For accomplishing this, a test problem is run to evaluate the difference between the performance of Shifting Bottleneck Heuristic (SBH) and some dispatching rules, such as First Come First Served (FCFS), Earliest due Date (EDD), and Shortest Processing Time (SPT). The evaluation criteria used were the makespan (Cmax) and the total weighted tardiness (TWT). The results did show that the SBH outperforms the dispatching rules, although the computation time turns out to be considerable higher.

Reliability evaluation and component importance measure for manufacturing systems based on failure losses

Abstract: Little work has been done to assess the reliability of a vital system like the manufacturing system. In this article, a novel and effective system reliability evaluation method in terms of failure losses has been proposed for manufacturing systems of job shop type, and then the failure losses based component importance measure (CIM) is used for importance analysis of equipment. The former indicates the present system reliability situation and the latter points the way to reliability improvement efforts. In this scheme, the problem is described and modeled by a dynamic directed network. Consider that the actual processing time of machines is to contribute to failure occurrence, it is used to calculate the failure times and failure losses. The obtained total failure times and failure losses of the system are applied to evaluate its reliability. Techniques to estimate two kinds of failure losses based CIMs are presented. They offer guidelines to realize system reliability growth cost-effectively. A case study of a real job shop is provided as an example to demonstrate the validity of the proposed methods. Comparison to other commonly used methods shows the efficiency of the proposed methods.

Effect of routing flexibility and sequencing rules on performance of stochastic flexible job shop manufacturing system with setup times: Simulation approach:

Abstract: Routing flexibility is a major contributor towards flexibility of a flexible job shop manufacturing system. This article focuses on a simulation-based experimental study on the effect of routing fl...

A hybrid algorithm for flexible job-shop scheduling problem with setup times

Abstract: Job-shop scheduling problem is one of the most important fields in manufacturing optimization where a set of n jobs must be processed on a set of m specified machines. Each job consists of a specific set of operations, which have to be processed according to a given order. The Flexible Job Shop problem (FJSP) is a generalization of the above-mentioned problem, where each operation can be processed by a set of resources and has a processing time depending on the resource used. The FJSP problems cover two difficulties, namely, machine assignment problem and operation sequencing problem. This paper addresses the flexible job-shop scheduling problem with sequence-dependent setup times to minimize two kinds of objectives function: makespan and bi-criteria objective function. For that, we propose a hybrid algorithm based on genetic algorithm (GA) and variable neighbourhood search (VNS) to solve this problem. To evaluate the performance of our algorithm, we compare our results with other methods existing in literature. All the results show the superiority of our algorithm against the available ones in terms of solution quality.

Iterated greedy algorithms to minimize the total family flow time for job-shop scheduling with job families and sequence-dependent set-ups

Abstract: This study addresses a variant of job-shop scheduling in which jobs are grouped into job families, but they are processed individually. The problem can be found in various industrial systems, especially in reprocessing shops of remanufacturing systems. If the reprocessing shop is a job-shop type and has the component-matching requirements, it can be regarded as a job shop with job families since the components of a product constitute a job family. In particular, sequence-dependent set-ups in which set-up time depends on the job just completed and the next job to be processed are also considered. The objective is to minimize the total family flow time, i.e. the maximum among the completion times of the jobs within a job family. A mixed-integer programming model is developed and two iterated greedy algorithms with different local search methods are proposed. Computational experiments were conducted on modified benchmark instances and the results are reported.