Job Shop

This paper focuses on the study of job-shop problems with transportation, blocking and no-wait (BNWT JSSP). This problem is an extension of the job shop problem. We take into account transport operations of jobs between different machines using a limited number of robots. We also have blocking and no wait constraints. It means that after a completion of an operation, a job waits on its machine, thus blocking it, until the next machine and a robot are available. In the other hand, the job starts its processing on the machine immediately and without any interruption after the robot has accomplished the transportation operation, thus no wait constraints. The aim of our problem is to find a feasible order of machine operations and transport operations. Additionally to the problem description, we will present a method based on graph theory which illustrates blocking situations. As the main contribution, we will formalize this problem with an integer linear programming models and propose ...

High level of Manufacturing Capability (MC) (effectiveness) plays significant role in providing competitive advantage to a firm. However, it is difficult for the firm to evaluate the level of MC, which is given by the consistency of decisions taken in the particular manufacturing environment. For example, in job shop production general purpose facilities and high skilled workforce is essential to obtain the desired level of outputs to satisfy customer expectations. First, Hayes and Wheelwright provided a four stage model for evaluating the level of MC and recently Miltenburg gave a framework that classified these four stages as infant, average, adult and world class. Evaluating MC facilitates the firm to know the present status and also provides a pointer to identify weak decisions for further improvement. This paper presents a case study on evaluating the MC of a firm involved in the production of low volume and high variety products. Job Shop Production System has been used to ful...

Paper analyzes manufacturing capability in job shop environments by evaluating the pattern of decisions using a case study approach. Analytical Hierarchical Process (AHP), a multi criteria decision making (MCDM) method, has been employed which consists of three levels. The overall goal, i.e. manufacturing capability index is placed at level 1 followed by six decision areas at level 2 and thirty three decision criteria at level 3. Structured interview process has been used to collect information about thirty three decision criteria. The data was collected and analyzed for five companies operated in a job shop manufacturing environment. Pattern of decisions that are ideally essential for competitiveness of job shop manufacturing are then compared with the pattern of decisions from the five cases. This approach facilitates the practitioners to indentify the weak decision areas in order to improve the competitiveness in job shop manufacturing.

In this research the impact of job mix selection in each production shift in a job shop production environment is examined. This is a critical question within photolithography workstations in semiconductor manufacturing systems. For this purpose, a recently developed Simulation Optimization (SO) method named Evolutionary Learning Based Simulation Optimization (ELBSO) is implemented to solve a set of designed Stochastic Job Shop Scheduling problems captured from a real semiconductor manufacturing data set. Experiment results indicate that the best performance in each shift occurs when machines are flexible in terms of processing different job operations, and the selected jobs for a certain shift have as equal as possible due dates.

CITATION: Nyanga, L., et al. 2016. Agent based job scheduling for a vehicle engine reconditioning machine shop. In Competitive Manufacturing, International Conference on Competitive Manufacturing (COMA '16), 27-29 January 2016, Stellenbosch, Stellenbosch University, South Africa.

A mixed shop is a manufacturing infrastructure designed to process a mixture of a set of flowshop jobs and a set of open-shop jobs. Mixed shops are in general much more complex to schedule than flow-shops and open-shops, and have been studied since the 1980's. We consider the three machine proportionate mixed shop problem denoted as M 3 | prpt | C max , in which each job has equal processing times on all three machines. Koulamas and Kyparisis [European Journal of Operational Research, 243:70-74,2015] showed that the problem is solvable in polynomial time in some very special cases; for the non-solvable case, they proposed a 5/3-approximation algorithm. In this paper, we present an improved 4/3-approximation algorithm and show that this ratio of 4/3 is asymptotically tight; when the largest job is a flow-shop job, we present a fully polynomialtime approximation scheme (FPTAS). On the negative side, while the F 3 | prpt | C max problem is polynomial-time solvable, we show an interesting hardness result that adding one open-shop job to the job set makes the problem NP-hard if this open-shop job is larger than any flow-shop job. We are able to design an FPTAS for this special case too.

The performance of a flexible manufacturing system (FMS) has a great impact on a manufacturing firm success and the analysis of FMS justifies the high investment cost behind it. As the competition in the global market is growing, manufacturing companies compete not only on product cost and quality but also in time to market. FMS as a highly automated system typically comprised of a set of processing workstations allowing in an integrated manner to react rapidly and economically to production-oriented aspects of an enterprise in order to cope significant changes in its operating environment. This paper analysed the performance of a FMS through manufacturing process modelling and simulation. The modelling of a manufacturing system enables to grasp quickly how the current system is working and to evaluate the proposed process changes before actual decision making, on the other hand, the simulation of the system supports the idea of virtual manufacturing and gives prompt response to the decision maker. Moreover, integration of system dynamic analysis with reliability estimation methods enable engineers to find the most unreliable places in a production process and supports the decision making for reliability improvement of a production system. In this paper elaboration of the graphic simulation model of FMS operation has been described, FMS model parameters have been estimated, and criteria have been defined based on requirements regarding system reliability.

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This thesis investigate a complex real world job shop scheduling / rescheduling problem, in which the presence of uncertainties and the occurrence of disruptions are tackled to produce efficient and reliable solutions. New orders arrive every day in the shop floor and they have to be integrated in the existent schedule. Match-up algorithms are introduced to collect the idle time on machines and accommodate these newly arriving orders. Their aim is to obtain new schedules with good performance which are at the same time highly stable, meaning that they resemble as closely as possible the initial schedule. Subsequently, a novel approach that combines these algorithms with a fuzzy robust scheduling system is proposed. The goal is to associate an effective repairing mechanism with the production of initial robust schedules that are able to facilitate the accommodation of future disruptions. Statistical analyses reveal that match-up algorithms are effective repairing strategies for managing complex disruptions, in which high quality stable schedules are delivered. Moreover, their combination with fuzzy robust scheduling has a positive effect on responding to these disruptions leading to even more reliable solutions in a real world dynamic and uncertain shop floor.

This paper investigates the problem of inserting new rush orders into a current schedule of a real world job shop floor. Effective rescheduling methods must achieve reasonable levels of performance, measured according to a certain cost function, while preserving the stability of the shop floor, i.e. introducing as few changes as possible to the current schedule. This paper proposes new and effective match-up strategies which modify only a part of the schedule in order to accommodate the arriving jobs. The proposed strategies are compared with other rescheduling methods such as "right shift" and "insertion in the end", which are optimal with respect to stability but poor with respect to performance, and with "total rescheduling" which is optimal with respect to performance but poor with respect to stability. Our results and statistical analysis reveal that the match-up strategies are comparable to the "right shift" and "insertion in the end" with respect to stability and as good as "total rescheduling" with respect to performance.

1. Abstract The present work studies the task programming problem in the garment industry and uses the Job Shop method to solve the problem. A representation is proposed for the numerical solution of the problem based on a heuristic optimization methods known as Simulated Annealing. Although the algorithm is capable of identifying the ideal solution in some cases, at times

La metaheurística sistema hormiga consiste en la analogía entre el procedimiento que utilizan las hormigas reales para la búsqueda de alimentos, encontrando la ruta más corta, y los problemas de optimización combinatoria para encontrar la mejor solución. Entre estos problemas se encuentra el de asignación multidimensional (mAP), el cual es un problema NP-difícil para m > 2. En la actualidad no se ha desarrollado trabajo alguno sobre el 4AP (mAP con m=4), tampoco existe publicación sobre la aplicación del sistema hormiga para el mAP. En este trabajo se desarrolló un software que hace uso de la metaheurística sistema hormiga para encontrar la mejor solución o aproximada al 4AP, con problemas generados aleatoriamente. Se implementó una metodología híbrida entre la metodología de Investigación de Operaciones descrita por Taha (1991) y la ingeniería de software (1990). El número de asignaciones tomadas en cuenta para verificar qué tan buenas son las soluciones arrojadas por el software, varía desde n=2 hasta n=25, obteniéndose soluciones exactas para 2 £ n £ 6, ya que al compararse dichas soluciones con las dadas por XPRESS (software de programación lineal que se usa para resolver modelos matemáticos) se observa la igualdad en los resultados. Para n _ 7, XPRESS (la versión utilizada) no ofrece respuesta alguna, pero el software desarrollado arroja buenos resultados en un tiempo computacional razonable, considerando el número de asignaciones que se procesan en cada n.

This paper presents a differential time-domain comparator formed by two voltage controlled delay lines, one per input terminal, and a binary phase detector for comparison solving. The propagation delay through the respective lines can be adjusted with a set of digitally-controlled inversion-mode varactors. These varactors provide tuning capabilities to the comparator; feature which can be exploited for offset calibration. This is demonstrated with the implementation of a differential 10bit SAR-ADC. The design, fabricated in a 0.18µm CMOS process, includes an automatic mechanism for adjusting the capacitance of the varactors in order to calibrate the offset of the whole converter. Correct functionality was measured in all samples.

This paper presents an approach for solving a job shop problem with sequencedependent setup times where the objective is to minimize maximum lateness and makespan. This approach consists of using the "Shifting Bottleneck Procedure", designed for the job shop problem with the makespan objective, while introducing two major modifications: (1) the use of a new dispatching rule that takes into account the setup times and the due dates; (2) explicit use of the due dates, to optimize both objectives, when solving each one-machine problem generated by this procedure. Encouraging results are obtained through an extensive computational experiment using a set of benchmark instances.

The job shop scheduling problem is considered, and an algorithm based on the global equilibrium search method is proposed for its solution. Computational experiments using well-known benchmark problems are presented. Several new upper bounds for these problems are obtained.

Nous présentons dans ce papier les conditions pour lesquelles un système artificiel constitué d'entités mécatroniques peut être lui-même qualifié de mécatronique. Nous montrons que c'est le cas lorsque l'intelligence conférée aux dites entités leur permet de faire fonctionner ce système sans l'intervention d'un centre de pilotage de niveau hiérarchique supérieur. Le principe général de l'affectation de tâches est basé sur un mécanisme de sélection de la meilleure réponse à un appel d'offres diffusé auprès de toutes les entités pour chaque tâche à effectuer. L'approche holonique nous fournit un support conceptuel pour proposer un système générique de pilotage auto-organisé et isoarchique, adapté aux systèmes mécatroniques complexes et fondé sur l'existence d'un pilotage autonome associé au sein de chaque entité mécatronique. Pour conclure, nous présentons un éventail de systèmes que nous pouvons considérer de par leur système de pilotage comme étant des systèmes mécatroniques complexes… ABSTRACT. In this paper, we present the conditions for which an artificial system constituted by mechatronic entities can be it self considered as mechatronic. We show that it is the case when the intelligence conferred to the entities allows them to operate this system without the intervention of a control centre on a higher hierarchical level. The general principle of the tasks assignment is based on the selection of the best response to call for proposal launched all the entities for each task to be made. The holonic approach provides a conceptual support to propose a generic system of self organized and isoarchic control. This control system is adapted to the complex mechatronic systems and based on the existence of an autonomous control associated to each mechatronic entity. To conclude, we present some complex mechatronic systems that have such control system. MOTS-CLÉS : pilotage auto-organisé, système mécatronique complexe, entité de pilotage autonome, holon, architecture PROSA.

Technological developments along with the emergence of Industry 4.0 allow for new approaches to solve industrial problems, such as the Job-shop Scheduling Problem (JSP). In this sense, embedding Multi-Agent Systems (MAS) into Cyber-Physical Systems (CPS) is a highly promising approach to handle complex and dynamic JSPs. This paper proposes a data exchange framework in order to deal with the JSP considering the state-of-the-art technology regarding MAS, CPS and industrial standards. The proposed framework has self-configuring features to deal with disturbances in the production line. This is possible through the development of an intelligent system based on the use of agents and the Internet of Things (IoT) to achieve real-time data exchange and decision making in the job-shop. The performance of the proposed framework is tested in a simulation study based on a real industrial case. The results substantiate gains in flexibility, scalability and efficiency through the data exchange be...

A mixed shop is a manufacturing infrastructure designed to process a mixture of a set of flowshop jobs and a set of open-shop jobs. Mixed shops are in general much more complex to schedule than flow-shops and open-shops, and have been studied since the 1980's. We consider the three machine proportionate mixed shop problem denoted as M 3 | prpt | C max , in which each job has equal processing times on all three machines. Koulamas and Kyparisis [European Journal of Operational Research, 243:70-74,2015] showed that the problem is solvable in polynomial time in some very special cases; for the non-solvable case, they proposed a 5/3-approximation algorithm. In this paper, we present an improved 4/3-approximation algorithm and show that this ratio of 4/3 is asymptotically tight; when the largest job is a flow-shop job, we present a fully polynomialtime approximation scheme (FPTAS). On the negative side, while the F 3 | prpt | C max problem is polynomial-time solvable, we show an interesting hardness result that adding one open-shop job to the job set makes the problem NP-hard if this open-shop job is larger than any flow-shop job. We are able to design an FPTAS for this special case too.

The establishment and sustainability of the small-scale manufacturing sector is a great contributor to any nation's economic and technological development. A job shop is a typical small-scale manufacturing system that produces simple equipment and machine parts as well as technical services in developing nations. This type of manufacturing system requires versatile production machines for its activities. This requires enormous capital that most interested investors could not afford, and a majority of the commercial banks are not willing to provide financial assistance. This study develops a model using the theory of internal funding by employing the little initial capital in phasing- in the acquisition of the needed machinery, construction of factory space in modular forms, and adequate consideration for recurrent expenditure (i.e. working capital). The initial capital is periodically augmented from the ploughed-back earnings for the gradual acquisition of other machinery until ...

In the real-world manufacturing system, various uncertain events can occur and disrupt the normal production activities. This paper addresses the multi-objective job shop scheduling problem with random machine breakdowns. As the key of our approach, the robustness of a schedule is considered jointly with the makespan and is defined as expected makespan delay, for which a meta-model is designed by using a data-driven response surface method. Correspondingly, a multi-objective evolutionary algorithm (MOEA) is proposed based on the meta-model to solve the multi-objective optimization problem. Extensive experiments based on the job shop benchmark problems are conducted. The results demonstrate that the Pareto solution sets of the MOEA are much better in both convergence and diversity than those of the algorithms based on the existing slack-based surrogate measures. The MOEA is also compared with the algorithm based on Monte Carlo approximation, showing that their Pareto solution sets are close to each other while the MOEA is much more computationally efficient.

An intelligent decision‐support system was designed for assessing schedulability prior to assigning parts for scheduling. This was designed as part of a larger system for intelligent, real‐time control of a manufacturing system operation, where one of the system objectives was just‐in‐time delivery of production output. The manufacturing system was a conventional cellular manufacturing system where machines were assumed occasionally to fail. All necessary requirements for the processing of a job should actually or anticipatively be satisfied before a job is scheduled. The decision‐support system attempts to satisfy these requirements. The system thus helps the decision maker to make the right decision when system and customer constraints are violated. Illustrates the operation of the system through an example.

Previous studies suggest that despite their intuitive appeal, cellular manufacturing systems are only effective in a batch production environment under a limited set of conditions. However, these studies have not taken into account the potential for productivity improvements unique to shops organized around product and work groups. Improvements that yield processing time reductions and rapid movement down the learning curve may offset the limited flexibility that typically compromises the performance of cellular shops. This paper demonstrates that if a cellular shop can yield even-marginally greater reductions in processing times than a job shop, it can outperform a job shop under conditions previously thought to be unfavorable to a cellular shop. Learning curve theory is used to show that this can be achieved at modest levels of output and also under conditions not typically associated with cellular shop configurations.

Past research has shown that the performance of manufacturing cells can be improved if family oriented scheduling heuristics are used or if jobs are split into smaller transfer batches for processing and material handling purposes. The research has also shown that large amounts of lot splitting results in increased setup frequency due to the reduction in transfer batch size. This can offset any gains in performance. This study examines the combined effect of lot splitting in a manufacturing cell that utilizes family based scheduling heuristics. The results show that family based scheduling is an effective means of reducing the negative impact of lot splitting on flow time. Lot splitting however has little benefit for due date performance even when scheduling rules that reduce setup frequency are used.

In this research the impact of job mix selection in each production shift in a job shop production environment is examined. This is a critical question within photolithography workstations in semiconductor manufacturing systems. For this purpose, a recently developed Simulation Optimization (SO) method named Evolutionary Learning Based Simulation Optimization (ELBSO) is implemented to solve a set of designed Stochastic Job Shop Scheduling problems captured from a real semiconductor manufacturing data set. Experiment results indicate that the best performance in each shift occurs when machines are flexible in terms of processing different job operations, and the selected jobs for a certain shift have as equal as possible due dates.

Practical Constraint Satisfaction Problems (CSPs) such as design of integrated circuits or scheduling generally entail large search spaces with hundreds or even thousands of variables, each with hundreds or thousands of possible values. Often, only a very tiny fraction of all these possible assignments participates in a satisfactory solution. This article discusses techniques that aim at reducing the effective size of the search space to be explored in order to find a satisfactory solution by judiciously selecting the order in which variables are instantiated and the sequence in which possible values are tried for each variable. In the CSP literature, these techniques are commonly referred to as variable and value ordering heuristics. Our investigation is conducted in the job shop scheduling domain. We show that, in contrast with problems studied earlier in the CSP literature, generic variable and value heuristics do not perform well in this domain. This is attributed to the difficulty of these heuristics to properly account for the tightness of constraints and/or the connectivity of the constraint graphs induced by job shop scheduling CSPs. A new probabilistic framework is introduced that better captures these key aspects of the job shop scheduling search space. Empirical results show that variable and value ordering heuristics derived within this probabilistic framework often yield significant improvements in search efficiency and significant reductions in the search time required to obtain a satisfactory solution.

Constraint Satisfaction Problem (CSP) is a discrete combinatorial problem and hence search algorithms belong to the main constraint satisfaction techniques. There exist local search techniques exploring complete but inconsistent assignments and depth-first search techniques extending partial consistent assignments towards complete assignments. For most problems it is impossible to explore complete search space so incomplete techniques are used. In this paper, we survey incomplete depth-first search techniques, in particular, generic incomplete search techniques and discrepancy-based search techniques.

Determining the optimal sequence of jobs in job shop scheduling for small and medium enterprises, affect the machine productivity, earliness and tardiness costs of delivery. The deterministic variant of the problem is well-known to be NP-Hard. If random elements are introduced into the problem, the level of complexity goes higher. Hence, many priority rules have been developed to tackle stochastic job shop scheduling problem. However, to devise a better solution approach, simulation-optimization approach might be used. In this study, a mathematical model was developed for job shop scheduling with random process times and possible machine breakdowns. Then, a simulation-optimization model was applied to choose among a list of priority rules using Rockwell Arena 14. Finally, a numerical example was used to evaluate the quality of the model. Results showed that the rule Longest Processing Time (LPT) yields the lowest total earliness and tardiness costs. However, the total costs of the f...

A mixed shop is a manufacturing infrastructure designed to process a mixture of a set of flowshop jobs and a set of open-shop jobs. Mixed shops are in general much more complex to schedule than flow-shops and open-shops, and have been studied since the 1980's. We consider the three machine proportionate mixed shop problem denoted as M 3 | prpt | C max , in which each job has equal processing times on all three machines. Koulamas and Kyparisis [European Journal of Operational Research, 243:70-74,2015] showed that the problem is solvable in polynomial time in some very special cases; for the non-solvable case, they proposed a 5/3-approximation algorithm. In this paper, we present an improved 4/3-approximation algorithm and show that this ratio of 4/3 is asymptotically tight; when the largest job is a flow-shop job, we present a fully polynomialtime approximation scheme (FPTAS). On the negative side, while the F 3 | prpt | C max problem is polynomial-time solvable, we show an interesting hardness result that adding one open-shop job to the job set makes the problem NP-hard if this open-shop job is larger than any flow-shop job. We are able to design an FPTAS for this special case too.

We consider a job-shop scheduling problem with release dates and due dates, with the objective of minimizing the total weighted tardiness. A genetic algorithm is combined with an iterated local search that uses a neighborhood based on a disjunctive graph model. In this paper, we show that the efficiency of genetic algorithms does no longer depend on the schedule builder when an iterated local search is used. Computational experiments carried out on instances of the literature show the efficiency of the proposed algorithm over the existing methods.

Globalization has demanded innovative manufacturing and continuous improvement in order to stay competitive. This need has compelled the manufacturing world to devise strategies for producing cost-efficient parts without compromising quality. The Toyota Production System was at the beginning of such initiatives. It was successful in addressing cost through elimination of non-value-added time and quality by monitoring and controlling the productions of defective parts. Lean thinking originated from the Toyota Production System and inherited its concepts and methodology. In contrast to the Toyota Production System, the implementation of lean has been proposed in almost every domain of life. In the manufacturing domain it is a common misconception that lean is suitable for mass production only. This research has been built upon the belief that lean is for everything and has challenged this stereotype by implementing it within a job shop environment. A manufacturing industry was selected that was rebuilding battlefield tanks. The existing system was suffering delays and missing delivery targets due to uncertain and costly production. The proposed and existing systems were modeled and simulated using Arena 10.0 software. This work was successful in reducing the manufacturing-led time, work in process inventory and average cycle times with a reduction in cost and space utilization. Cost benefit analysis was performed showing that the proposed system would be beneficial after 1500 parts. We are further expanding our proposed approach towards the tool manufacturing shop in order to study the impact of lean and its suitability for scheduling in job shops.

A linguistic-based meta-heuristic modeling and solution approach for solving the¯exible job shop scheduling problem (FJSSP) is presented in this study. FJSSP is an extension of the classical jobshop scheduling problem. The problem de®nition is to assign each operation to a machine out of a set of capable machines (the routing problem) and to order the operations on the machines (the sequencing problem), such that prede®ned performance measures are optimized. In this research, the scope of the problem is widened by taking into account the alternative process plans for each part (process plan selection problem). Probabilistic selection of alternative process plans and machines are also considered. The FJSSP is presented as a grammar and the productions in the grammar are de®ned as controls (Baykasog AElu, 2002). Using these controls and Gif¯er and Thompson's (1960) priority rule-based heuristic along with the multiple objective tabu search algorithm of Baykasog AElu et al. (1999) FJSSP is solved. This novel approach simpli®es the modeling process of the FJSSP and enables usage of existing job shop scheduling algorithms for its fast solution. Instead of scheduling job shops with in¯exible algorithms that cannot take into account the¯exibility which is available in the job shop, the present algorithm is developed which can take into account the¯exibility during scheduling. Such an approach will considerably increase the responsiveness of the job shops.

The job-shop is a classical problem in manufacturing, arising daily in factories and workshops. From an AI perspective, the job-shop is a constraint satisfaction problem (CSP), and many specific techniques have been developed to solve it efficiently. In this context, one may believe that generic search and CSP methods (which typically are better understood and easier to develop, codify and maintain than specific approaches) are not appropriated for this problem. In this paper, we contradict this belief. We show that generic search and CSP algorithms and heuristics can be successfully applied to job-shop problem instances that have been considered challenging by the job-shop community. In particular, we use forward checking with support-based heuristics, a combination of a generic CSP algorithm with generic heuristics. We improve this combination replacing the depth-first search strategy of forward checking by a discrepancy-based schema, a generic search strategy recently developed. Our approach obtains similar results to specific approaches in terms of the number of solved problems, with reasonable requirements in computational resources.

Lean manufacturing techniques are widely used in all mass production, assembly and other manufacturing industries; it can be also applied to job shop production / batch production. There are various methodologies adopted for optimizing the unit in lean manufacturing. One method is eliminating all forms of waste. In job shop production, the time is wasted through job waiting for further processing, sequencing, ordering, changeover etc. Due to delay in the active schedule generation, the total lead time for manufacturing is increased thereby production cost is increased. The method Muda (waste) is used to eliminate all forms of time wasting in production. By doing so, we can reduce the idle time of the machines which minimizes the total production time. In this paper, active schedule generation is done by using priority dispatching rule algorithm to minimize the lead time and to reduce the idle time of the machines operated in the job shop industries.

In this paper we apply the tabu search (TS) technique to a complex machine scheduling problem. The problem can be considered as a generalization of the job-shop problem with both simple and parallel machines, job batches. setup times and release and due dates. The goal is to obtain feasible schedules by minimizing the makespan C,,,,, if the problem is feasible, or to obtain a "best compromise" schedule if a full solution is not possible. The TS algorithm developed here is distinguished mainly by two aspects. Firstly, the core of the procedure is a tabu thresholding algorithm which uses a sophisticated set of moves aimed at resolving violated constraints. Secondly. the TS algorithm supplements the central tabu thresholding algorithm with two diversification strategies which are dependent on the state of the search. These strategies involve fairly major disruption of the schedules, and force significant diversification of the search. Computational experiments show that our algorithm can find high quality schedules in short running times.

Many heuristics and intelligent methods have been proposed and applied in order to solve the Job Shop Scheduling Problems (JSSP). Several researches have so far been interested in solving the production planning in JSSP and few of them have focused on solving production scheduling with the presence of maintenance tasks. This paper presents a new heuristic method (NHGA) that includes two new techniques. The first, is a Modified Genetic Algorithm (MGA) which is inspired from the different steps of standard Genetic Algorithms (GA). Practically, when the GA is used, usually many steps, such as crossover and mutation, are based on random choices. The idea of MGA technique is to enhance the random character of such choices through guiding the steps of GA in a logical procedure, while following at each generation and each step the most plausible solutions to solve the JSS problem with maintenance periods. Henceforth, the new modifications reported in the MGA take into consideration the initial population, selection, crossover, mutation and the running mechanism of the algorithm. This has been sustained by a second technique called Heuristic Displacement of Genes (HDG) such a technique would take as an objective improving the obtained solutions of JSSP. The technique NHGA has been tested on many benchmarks, and compared with standard GA and other recent methods. The obtained results actually shed light on the efficiency of our new heuristic method.

Maintenance is a strategic function in a company. It aims to ensure the proper functioning of an equipment. It no longer has the sole objective to repair a system but also to anticipate and prevent its failures. Recently, new failure prediction techniques have emerged. They provide information on the real-time condition of equipment and its remaining useful life (RUL). Thus, new methods of decision-making based on the analysis of these recent developments can be considered. Indeed, this paper presents a heuristic method for solving the makespan and the cost of a resumable job shop problem while predicting and preventing failures. It is based on the elimination of duplication that may exist between the different operations at a given point in time, while selecting a single operation and shifting the others at the end of the execution thereof. The best selection of the operation to assign on the machine depends on the effectiveness of the decision rule used for scheduling. The proposed method has been successfully tested on some experimental benchmarks and it allows a satisfactory resolution of relatively important problem in a reasonable time.

O controlo do fluxo de materiais num job-shop envolve várias decisões tais como a aceitação ou rejeição de uma ordem, a definição da data de entrega, a determinação da data de lançamento de um trabalho para o shop floor e a decisão acerca do trabalho que deve ser processado em primeiro lugar. A consideração destas quatro decisões em simultâneo é um tópico de investigação relativamente recente em job-shops. Este estudo examina os efeitos de três regras de aceitação, quatro mecanismos de lançamento dos jobs e duas regras de dispatching, sob vários níveis de "aperto" da data de entrega nas principais medidas de desempenho do job-shop. São utilizadas medidas de avaliação da quantidade de trabalho e das entregas. É efectuada, ainda, um estudo das regras de decisão quando existe a possibilidade de avaria do equipamento. Foram feitas simulações de forma a comparar várias regras de decisão-regras padrão e mecanismos apresentados na literatura-e estudar o impacto nas medidas de desempenho aquando da ocorrência de falhas nas máquinas. Os resultados mostram que considerando-se os quatro tipos de decisões simultaneamente melhora o desempenho do job-shop em medidas como o atraso médio ou o tempo total (médio) gasto no sistema. Os resultados mostram ainda que as falhas nos equipamentos têm diferentes impactos no atraso médio, dependendo da regra de decisão utilizada. RESUMEN Controlar el flujo de materiales en job-shop implica varias decisiones tales como la aceptación o el rechazamiento de una orden entrante, la definición de la fecha debida de la orden, de lanzar y de dispatching del trabajo. Considerar estas cuatro decisiones simultáneamente es un asunto relativamente reciente de la investigación en job-shop. Este estudio examina los efectos de tres reglas de la aceptación, de cuatro mecanismos del lanzamiento y de dos reglas de dispatching bajo varios niveles de la proximidad de la fecha debida en algunas medidas de la carga de trabajo y de funcionamiento de la entrega del job-shop. También se compara el funcionamiento de esas reglas de decisión bajo posibilidad de averías de las máquinas. Los experimentos extensos de la simulación fueron realizados para comparar las varias reglas-las reglas benchmark y las reglas presentadas en estudios anteriores-y para estudiar el impacto de las faltas de la máquina en los criterios del funcionamiento. Los resultados demuestran que eso la consideración de las cuatro decisiones simultáneamente mejora algunas medidas de funcionamiento como la job-shop como la tardiness medio y del tiempo total en el sistema. Averías en las máquinas tienen impacto desigual en la tardiness medio de los trabajos,

O controlo do fluxo de materiais num job-shop envolve várias decisões tais como a aceitação ou rejeição de uma ordem, a definição da data de entrega, a determinação da data de lançamento de um trabalho para o shop floor e a decisão acerca do trabalho que deve ser processado em primeiro lugar. A consideração destas quatro decisões em simultâneo é um tópico de investigação relativamente recente em job-shops. Este estudo examina os efeitos de três regras de aceitação, quatro mecanismos de lançamento dos jobs e duas regras de dispatching, sob vários níveis de "aperto" da data de entrega nas principais medidas de desempenho do job-shop. São utilizadas medidas de avaliação da quantidade de trabalho e das entregas. É efectuada, ainda, um estudo das regras de decisão quando existe a possibilidade de avaria do equipamento. Foram feitas simulações de forma a comparar várias regras de decisão-regras padrão e mecanismos apresentados na literatura-e estudar o impacto nas medidas de desempenho aquando da ocorrência de falhas nas máquinas. Os resultados mostram que considerando-se os quatro tipos de decisões simultaneamente melhora o desempenho do job-shop em medidas como o atraso médio ou o tempo total (médio) gasto no sistema. Os resultados mostram ainda que as falhas nos equipamentos têm diferentes impactos no atraso médio, dependendo da regra de decisão utilizada. RESUMEN Controlar el flujo de materiales en job-shop implica varias decisiones tales como la aceptación o el rechazamiento de una orden entrante, la definición de la fecha debida de la orden, de lanzar y de dispatching del trabajo. Considerar estas cuatro decisiones simultáneamente es un asunto relativamente reciente de la investigación en job-shop. Este estudio examina los efectos de tres reglas de la aceptación, de cuatro mecanismos del lanzamiento y de dos reglas de dispatching bajo varios niveles de la proximidad de la fecha debida en algunas medidas de la carga de trabajo y de funcionamiento de la entrega del job-shop. También se compara el funcionamiento de esas reglas de decisión bajo posibilidad de averías de las máquinas. Los experimentos extensos de la simulación fueron realizados para comparar las varias reglas-las reglas benchmark y las reglas presentadas en estudios anteriores-y para estudiar el impacto de las faltas de la máquina en los criterios del funcionamiento. Los resultados demuestran que eso la consideración de las cuatro decisiones simultáneamente mejora algunas medidas de funcionamiento como la job-shop como la tardiness medio y del tiempo total en el sistema. Averías en las máquinas tienen impacto desigual en la tardiness medio de los trabajos,

In the shipbuilding industry, since production processes are so complicated that the data collection for decision making cannot be fully automated, most of production planning and controls are based on the information provided only by field workers. Therefore, without sufficient information it is very difficult to manage the whole production process efficiently. Job status is one of the most important information used for evaluating the remaining processing time in production control, specifically, in block assembly shop. Currently, it is checked by a production manager manually and production planning is modified based on that information, which might cause a delay in production control, resulting in performance degradation. Motivated by these remarks, in this paper we propose an efficient algorithm for identifying job status in block assembly shop for shipbuilding. The algorithm is based on the multi-layer perceptron neural network model using two key factors for input parameters. We showed the superiority of the algorithm by using a numerical experiment, based on real data collected from block assembly shop.

In this paper, we focus on exact methods to solve the preemptive Flexible Job-Shop Scheduling Problem with makespan and total completion time minimisation objective functions. Mathematical and constraint programming models enable the resolution of this problem for small instances. However, as an NP-hard problem, the cost of solving grows rapidly when considering larger instances. In this regard, we propose a logic-based Benders decomposition that relies on an efficient branch-and-bound procedure to solve the subproblem representing a pure (non-flexible) preemptive job-shop scheduling problem. Computational experiments are carried out and show the very good performance of our proposals.

The topic of this paper is the cyclic job shop problem which aims at minimizing the cycle time under precedence and resource constraints. Based on graph theory, we propose a new branch and bound enumeration procedure to solve this problem. We compare the new procedure with other exact methods and present numerical test results.

In this paper, we focus on exact methods to solve the preemptive Flexible Job-Shop Scheduling Problem with makespan and total completion time minimisation objective functions. Mathematical and constraint programming models enable the resolution of this problem for small instances. However, as an NP-hard problem, the cost of solving grows rapidly when considering larger instances. In this regard, we propose a logic-based Benders decomposition that relies on an efficient branch-and-bound procedure to solve the subproblem representing a pure (non-flexible) preemptive job-shop scheduling problem. Computational experiments are carried out and show the very good performance of our proposals.

Dynamic job shop scheduling (DJSS) problems are combinatorial optimisation problems that have been extensively studied in the literature due to their difficulty and their applicability to real-world manufacturing systems, e.g., car manufacturing systems. In a DJSS problem instance, jobs arrive on the shop floor to be processed on specific sequences of machines on the shop floor and unforeseen events such as dynamic job arrivals and machine breakdown occur that affect the properties of the shop floor. Many researchers have proposed genetic programming based hyper-heuristic (GP-HH) approaches to evolve high quality dispatching rules for DJSS problems with dynamic job arrivals, outperforming good man-made rules for the problems. However, no GP-HH approaches have been proposed for DJSS problems with dynamic job arrivals and machine breakdowns, and it is not known how well GP generalises over both DJSS problem instances with no machine breakdown to problem instances with machine breakdown. Therefore, this paper investigates the generality of GP for DJSS problem with dynamic job arrivals and machine breakdowns. To do this, a machine breakdown specific DJSS dataset is proposed, and an analysis procedure is used to observe the differences in the structures of the GP rules when evolved under different machine breakdown scenarios. The results show that performance and the distributions of the terminals for the evolved rules is sensitive to the frequency of machine breakdowns in the training instances used to evolve the rules.

Abstract— Reactive repair of a disrupted schedule is a better alternative to total rescheduling, as the latter is a time consuming process and also results in shop floor nervousness. The schedule repair heuristics reported in the literature generally address ...

The HSE technique is used for evaluating the health, safety and enviromental risks. This is a systematic method which is utilized for protection of capital, manpower and environmental towards the risks of unpredictable events in construction projects. In this paper, HSE method is utilized for evaluating construction projects in Mashhad. For this purpose, health, safety and environmental risks are identified. Then a performance function is proposed based on assumed risks. Weight of identified factors and evaluation the efficiency of proposed function are performed by SWOT matrix and Monte Carlo simulaton technique, respectively. To do this, by prapering, distribution and purchasing health, safety and environment checklists between HSE experts, project managers and supervisors of workshops, their comments are recieved. By running SWOT matrix method and Monte Carlo simulation, health, safety and environmental risks of Mashhad construction projects are classified based on proposed function. Results show that the highest and the lowest risks in construction projects in Mashhad are caused by welding safety related processes and the risks of HSE planning, respectively.

The development of "green" machine tools will require novel approaches for design, production and operation for energy savings and reduced environmental impact. We describe here work on three projects: i. influence of process parameters on power consumption of end-milling using force and process time models with experimental verification. Process parameters are chosen to minimize process time since power consumed by a machine tool is essentially independent of the load and energy per unit manufactured decreases with process time; ii. KERS (kinetic energy recovery system) for machine design and modeling the integration of a recovery system into a machine tool to calculate the amount of energy that could be recovered, and whether the environmental benefits are significant; and iii. evaluation of interoperability solutions, such as MTConnect, as tools enabling a standardized "plug-and-play" platform to integrate sensors with a unified monitoring scheme to achieve improved energy performance.

In this paper we discuss the problem of defining the manufacturing route taken by parts in terms of their technological features. We look closely at the use of the new computer aided process planning (CAPP) systems, and at the need to define groups of operations which will facilitate the generation of the route sheet. Generating different alternatives must be made easier to avoid calculating a range of incoherent possibilities. Our aim was to define groups of precedence between machine operations, which would allow us to reduce the alternatives and therefore, calculate and study the truly plausible routes while ignoring those which, despite being mathematically computable, would not be technologically coherent. In order to determine these groups of precedence, we evaluated a range of parts, which are produced by metal removal using applied group technology. For each one of the parts we generated the possible manufacturing alternatives, and applying graph theory, we determined which operations generally preceded others. The results obtained allowed us to reduce considerably the computational possibilities and thus save time in generating the route sheet.