Multi-purpose Machines Research Articles

Эффективность многоцелевых лесных машин и их комплектов в вероятностных условиях эксплуатации

Эффективность многоцелевых лесных машин и их комплектов в вероятностных условиях эксплуатации

Self-adjusting Process Monitoring System in Series Production

Modern monitoring systems in machine tools are able to detect process errors promptly. Still, the application of monitoring systems is restricted by the complexity of parameterization for save monitoring. In most cases, only specially trained personnel can handle this job especially for multi-purpose machines. The aim of the research project “Proceed” is to figure out in which extent a self-parameterization and autonomous optimization of monitoring systems in industrial series production can be realized. Therefore, a self-adjusting and self-tuning process monitoring system for series production has been developed. This system is based on multi-criteria sensor signal evaluation and is able to assess its monitoring quality quantitatively. For this purpose, the complete process chain of parameterization has been automated. For series production it is assumed, that the first process is not defective. So, process sensitive features are identified by a correlation analysis with a reference signal. The reference signal is selected through an analysis of the process state by an expert system. To assess the monitoring quality resulting from automatic parameterization, normed specific values were used. These values describe the monitoring quality with the help of the distance between a feature and its threshold normed to signal amplitude and noise. A second indicator is the reaction of the monitoring system to a synthetic error added to signal a sequence. Thus it is possible to estimate monitoring quality corresponding to automatic parameterization. The validation is carried out by a comparison between the result of the assessment and the reaction ability of the monitoring system to real process errors from milling, drilling and turning processes.

Online scheduling of two job types on a set of multipurpose machines

We study an online-list scheduling problem on a set of m multipurpose machines for which the objective is to minimize the makespan. It is assumed that there are two different job types, and each job type can be processed on a unique subset of machines. We provide an online algorithm with a competitive ratio of 1+k(m−1)/(k(m−s)+s2) where M1={M1,…,Mk} is the set of machines eligible to process jobs of type 1 and where M2={Ms+1,…,Mm} is the set of machines eligible to process jobs of type 2 with k≥s. By analyzing the competitive ratio function, we show that the worst competitive ratio is obtained for an inclusive processing set structure in which the number of machines (m) is even, the first job type can be processed on any of the m machines and the second job type can be processed only on a subset of m/2 machines. Moreover, we provide a lower bound as a function of the processing set structure.

Electron accelerators: History, applications, and perspectives

This paper will present an outlook on sources of radiation, focusing on electron accelerators. We will review advances that were important for the development of particle accelerators, concentrating on those that led to modern electron accelerators. Electron accelerators are multipurpose machines that deliver beams with energies spanning five orders of magnitude, and are used in applications that range from fundamental studies of particle interactions to cross-linking polymer chains in industrial plants. Each accelerator type presents specific characteristics that make it more suitable for certain applications. Our work will focus on radiation sources for medical applications, dominated by electron linacs (linear accelerators), and those used for research, field where electron rings dominate. We will outline the main technological advances that occurred in the past decades, which made possible the construction of machines fit for clinical environments. Their compactness, efficiency and reliability have been key to their acceptance in clinical applications. This outline will include advances that allowed for the construction of brighter synchrotron light sources, where the relevant beam characteristics are good optical quality and high beam current. The development of insertion devices will also be discussed, as well the development of Free Electron Lasers (FEL). We conclude the review with an outline of the new developments of electron accelerators and the expectations for Energy Recovery Linacs.

An asymptotically optimal online algorithm to minimize the total completion time on two multipurpose machines with unit processing times

In the majority of works on online scheduling on multipurpose machines the objective is to minimize the makespan. We, in contrast, consider the objective of minimizing the total completion time. For this purpose, we analyze an online-list scheduling problem of n jobs with unit processing times on a set of two machines working in parallel. Each job belongs to one of two sets of job types. Jobs belonging to the first set can be processed on either of the two machines while jobs belonging to the second set can only be processed on the second machine. We present an online algorithm with a competitive ratio of ρLB+O(1n), where ρLB is a lower bound on the competitive ratio of any online algorithm and is equal to 1+(−α+4α3−α2+2α−12α2+1)2 where α=13+16(116−678)1/3+(58+378)1/33(2)2/3≈1.918. This result implies that our online algorithm is asymptotically optimal.

On the optimality of the TLS algorithm for solving the online-list scheduling problem with two job types on a set of multipurpose machines

In this paper we study the optimality of the TLS algorithm for solving the online scheduling problem of minimizing the makespan on a set of m multipurpose machines, where there are two different job types and each job type can only be processed on a unique subset of machines. The literature shows that the TLS algorithm is optimal for the special cases where either m=2 or where all processing times are restricted to unity. We show that the TLS algorithm is optimal also for the special cases where the job processing times are either job type or machine set dependent. For both cases, the optimality of the TLS algorithm is proven by showing that its competitive ratio matches the lower bound for any processing set and processing time parameters.

Scheduling problem with multi-purpose parallel machines

We consider a multi-purpose identical parallel machine scheduling problem, in which jobs should be executed on some machine belonging to a given subset of the set of machines. The problem is PMPM|rj;pj=1|∑wjUj, where there are n independent unit-time jobs, time window constraints, m identical parallel multi-purpose machines, and the objective is the minimization of the total weighted number of tardy jobs. The best previous complexity for this problem is O(n2m(n+logm)), employing network flow techniques. We develop an algorithm that handles successive nesting of on-time jobs more efficiently, with O(n3) overall time complexity.

Maximizing the configuration robustness for parallel multi-purpose machines under setup cost constraints

This paper focuses on the configuration of a parallel multi-purpose machines workshop. An admissible configuration must be chosen in order to ensure that a load-balanced production plan meeting the demand exists. Moreover, the demand is strongly subject to uncertainties. That is the reason why the configuration must exhibit robustness properties: the load-balancing performance must be guaranteed with regard to a given range of uncertainties. A branch-and-bound approach has been developed and implemented to determine a cost-constrained configuration that maximizes a robustness level. Computational results are reported for both academic and industrial-scale instances. More than 80% of the academic instances are solved to optimality by the proposed method. Moreover, this method appears to be a good heuristic for industrial-scale instances.

A sensitivity analysis to assess the completion time deviation for multi-purpose machines facing demand uncertainty

This paper addresses multi-purpose machine configuration in an uncertain context through sensitivity analysis. The so-called configuration is the machine’s ability to process products, and the uncertain context is modelled as a demand variation affecting the forecast demand. Given a configuration, this work aims at assessing the completion time deviation when the workshop demand is subject to perturbation. Such quantitative information can be used in a robustness approach for selecting the most appropriate configuration. To do so, the configuration impact on the completion time value that can be reached by solving the attached scheduling problem is first investigated. Then, the completion time deviation is written as a piecewise linear function of the magnitude of demand variation. The proposed approach, which is based on the solution of a set of linear programs, is illustrated through a detailed example. It is shown to be polynomial, and fast enough for addressing real-world instances. Finally, how to compare two configurations on the basis of completion time deviation in an uncertain context is demonstrated.

Scheduling Flexible Manufacturing Systems with Petri Nets Based on the Cell Enumeration Method

In the field of modern manufacturing, flexible manufacturing systems (FMS) is very important because it can scheduleand optimize multipurpose machines to produce multiple types of products. When applying the FMS technology, Petri Net is used to model the machines, parts and the whole manufacturing progress. The core concern of FMS is to make sure that the manufacturing system can transfer from the original state to the final state, which is called reachabilty. Therefore, reachability analysis is one of the most important problems of FMS. When Petri Net is acyclic, the reachability analysis can be performed by finding a integer solution to a set of linear equation, named fundamental equation, which is known to be NP-complete. In this paper, a novel approach for finding the integer solution is applied by adopting a revised version of the cell enumeration method for an arrangement of hyperplanes in discrete geometry to identify firing count vector solution(s) to the fundamental equation on a bounded integer set with a complexity bound of O((nu)n¡m),where n is the number of nodes, m is the number of arcs and u is the upper bound of the number of firings for all individual arcs.

Online scheduling of two job types on a set of multipurpose machines with unit processing times

We study a problem of scheduling a set of n jobs with unit processing times on a set of m multipurpose machines in which the objective is to minimize the makespan. It is assumed that there are two different job types, where each job type can be processed on a unique subset of machines. We provide an optimal offline algorithm to solve the problem in constant time and an online algorithm with a competitive ratio that equals the lower bound. We show that the worst competitive ratio is obtained for an inclusive job-machine structure in which the first job type can be processed on any of the m machines while the second job type can be processed only on a subset of m/2 machines. Moreover, we show that our online algorithm is 1-competitive if the machines are not flexible, i.e., each machine can process only a single job type.

Scheduling unit length jobs on parallel machines with lookahead information

This paper studies two closely related online-list scheduling problems of a set of n jobs with unit processing times on a set of m multipurpose machines. It is assumed that there are k different job types, where each job type can be processed on a unique subset of machines. In the classical definition of online-list scheduling, the scheduler has all the information about the next job to be scheduled in the list while there is uncertainty about all the other jobs in the list not yet scheduled. We extend this classical definition to include lookahead abilities, i.e., at each decision point, in addition to the information about the next job in the list, the scheduler has all the information about the next h jobs beyond the current one in the list. We show that for the problem of minimizing the makespan there exists an optimal (1-competitive) algorithm for the online problem when there are two job types. That is, the online algorithm gives the same minimal makespan as the optimal offline algorithm for any instance of the problem. Furthermore, we show that for more than two job types no such online algorithm exists. We also develop several dynamic programming algorithms to solve a stochastic version of the problem, where the probability distribution of the job types is known and the objective is to minimize the expected makespan.

Scheduling rules to minimize total tardiness in a parallel machine problem with setup and calendar constraints

Quality control lead times are one of most significant causes of loss of time in the pharmaceutical and cosmetics industries. This is partly due to the organization of laboratories that feature parallel multipurpose machines for chromatographic analyses. The testing process requires long setup times and operators are needed to launch the process. The various controls are non-preemptive and are characterized by a release date, a due date and available routings. These quality processes lead to significant delays, and we therefore evaluate the total tardiness criterion. Previous heuristics were defined for the total tardiness criterion, parallel machines, and setup such as apparent tardiness cost (ATC) and ATC with setups (ATCS). We propose new rules and a simulated annealing procedure in order to minimize total tardiness.

Scheduling on parallel machines considering job-machine dependency constraints

Abstract We consider a multi-purpose machine scheduling problem, where jobs should be executed in some machine belonging to a given subset of the set of machines. The problem is P M P M | r j ; p j = 1 | ∑ w j U j , with n independent unit-time jobs, time window constraints, m identical parallel multi-purpose machines, and the minimization of the total weighted number of tardy jobs. The best previous complexity for this problem is O ( n 2 m ( n + log m ) ) , employing network flow techniques. We develop an algorithm that uses basic concepts of computer science to handle more efficiently successive nesting of on-time jobs, with O ( n 3 ) overall time complexity.

Scheduling with arranged multi-purpose machines

In the present study, we consider a special case of multi-purpose machines (MPMs) in which there is a linear order given for the machines. In addition, for each job Ji(1≤i≤n), a 'first permissible' machine hi (1≤h(i)≤m) is given on which it can be processed. Thus, machines Mhi,…, Mm are capable of processing job Ji while machines M1,…, Mhi−1 cannot process job Ji. Each job Ji requires a time pi and the goal is to minimise the makespan. We prove the NP-hardness of the general problem and present some polynomial sub-problems. Heuristics with an exact algorithm of branch and bound type are also presented with numerical experimentations.

Theoretical and practical problems of thermal dynamics and simulation of the friction and wear of tribocouples

The main postulates of the thermal dynamics and simulation of friction and wear of tribocontacts are considered. Practical problems are considered of the application of multipurpose friction machines to estimation of the friction heat resistance of materials and thermoimpulse friction. The example of braking devices and clutches is used to evaluate the application of the thermal dynamics method and friction and wear simulation for prediction of the working characteristics of tribodevices at the design stage.

A PARTICLE SWARM OPTIMIZATION ALGORITHM ON JOB-SHOP SCHEDULING PROBLEMS WITH MULTI-PURPOSE MACHINES

This paper is a contribution to the research which aims to provide an efficient optimization algorithm for job-shop scheduling problems with multi-purpose machines or MPMJSP. To meet its objective, this paper proposes a new variant of particle swarm optimization algorithm, called GLN-PSOc, which is an extension of the standard particle swarm optimization algorithm that uses multiple social learning topologies in its evolutionary process. GLN-PSOc is a metaheuristic that can be applied to many types of optimization problems, where MPMJSP is one of these types. To apply GLN-PSOc in MPMJSP, a procedure to map the position of particle into the solution of MPMJSP is proposed. Throughout this paper, GLN-PSOc combined with this procedure is named MPMJSP-PSO. The performance of MPMJSP-PSO is evaluated on well-known benchmark instances, and the numerical results show that MPMJSP-PSO performs well in terms of solution quality and that new best known solutions were found in some instances of the test problems.

A generic off-line approach for dealing with uncertainty in production systems optimisation

This paper addresses production systems optimisation in uncertain context. A standard framework for solving such type of problems is depicted in a 3-step approach. The two first steps are addressed in this paper. They consist of off-line characterisation of the problem and calculation of solutions with some desired performance. A generic approach to implement these off-line steps is introduced in this paper. This approach relies on calculation of robust off-line solutions. A generic framework of robustness is defined. Then five standard optimisation problems are derived and related to the so called stability and sensitivity analysis. This generic approach is then applied to a multi-purpose machines problem.

A robustness measure of the configuration of multi-purpose machines

This paper presents new results for assessing the robustness of a configuration for multi-purpose machines. The workshop manager is provided with quantitative and meaningful information on how a configuration behaves when disturbances affect the demand. Thus, configurations can be compared on the basis of temporal performance, and also on robustness. The robustness measure of a configuration is returned by assessing the minimum magnitude of disturbances affecting the forecast demand that may lead to breaking the deadline provided by the decision-maker. First, it is shown how to minimise the completion time for a given demand. The demands such that the deadline can be met for a given configuration are then characterised, and two robustness measures for a configuration are also provided. The theoretical results are illustrated in detail using an application example.

Minimizing setup costs for parallel multi-purpose machines under load-balancing constraint

This article focuses on the minimization of the setup costs of a workshop modeled with parallel multi-purpose machines. Any admissible workshop configuration has to ensure that a load-balanced production plan meeting a given demand exists. This problem is shown to be NP-hard in the strong sense, and is stated as a mixed integer linear program. It is shown that under some hypotheses, it can be stated as a transportation problem and solved in polynomial time. An upper bound and lower bound are proposed, as well as a performance ratio assessment that is reached only when degenerate optimal solutions to the transportation problem exist.