Andrea Matta

Analysis of two-machine lines with multiple failure modes

Abstract This paper presents an analytical method for evaluating the performance of production lines with a finite buffer and two unreliable machines. Unlike in earlier papers, each machine can fail in more than one way. For each failure mode, geometrically distributed times to failure and times to repair are specified. The method evaluates the steady-state probabilities of the states of the system with a computational effort that depends only on the number of failure modes considered and not on the capacity of the buffer. A comparison of performance of the method with those obtained with existing techniques that consider only one failure mode is reported.

A Home Care Scheduling Model For Human Resources

Home care service is recognized to be one of the major solutions to contain costs in health care. However delivering this service is not an easy task because of the large number of actors that participate in the process, of the variety of clinical and organizational decisions, and last but not less important, of the difficulty of synchronizing human and material resources at patients home. In this paper we propose a linear integer scheduling model developed to support human resource short term planning in home care. In particular, the model deals with the problem of deciding (a) which human resource should be used and (b) when to execute the service during the planning horizon, in order to satisfy the care plan for each patient served by the home care providers. The scheduling model has been tested on different real data sets collected from two Italian home care providers. The weekly plans generated by the proposed model have been compared with the real ones. The main result is a significant benefit, in terms of the increase in service quality and in efficiency, obtained by using operations research techniques in the home care short term planning

Performance evaluation of continuous production lines with machines having different processing times and multiple failure modes

This paper presents an approximate analytical method for the performance evaluation of asynchronous production lines with deterministic processing times, multiple failure modes and finite buffer capacity. The discrete flow of parts is approximated by a continuous flow of material. The technique developed here is based on the approximate evaluation of a K machine line by the evaluation of K - 1 two-machine lines: unlike previous work on this subject, the multiple failure approach allows to consider real and virtual failure modes for each building block in which the original line is decomposed. Indeed, to reproduce in each two-machine lines the flow of material observed in the related buffer of the original line, real failures are used to take into account the reliability of the physical machines while virtual failures are introduced to model the different interruptions of flow that can arise inside the line thus preventing machines from working. To assess the accuracy of the proposed method, extensive simulation and numerical experiments have been carried out.

Performance Evaluation of Production Lines with Finite Buffer Capacity Producing Two Different Products

Abstract.This paper presents an approximate analytical method for the performance evaluation of a production line with finite buffer capacity, multiple failure modes and multiple part types. This paper presents a solution to a class of problems where flexible machines take different parts to process from distinct dedicated input buffers and deposit produced parts into distinct dedicated output buffers with finite capacity. This paper considers the case of two part types processed on the line, but the method can be extended to the case of n part types. Also, the solution is developed for deterministic processing times of the machines which are all identical and are assumed to be scaled to unity. The approach however is amenable of extension to the case of inhomogeneous deterministic processing times. The proposed method is based on the approximate evaluation of the performance of the k-machine line by the evaluation of 2(k-1) two-machine lines. An algorithm inspired by the DDX algorithm has been developed and the validation of the method has been carried out by means of testing and comparison with simulation.

A patient stochastic model to support human resource planning in home care

A large number of variables and unpredictable events affect the quality of home care (HC) services. Changes in patient clinical and social conditions and troubles in service organisation are only some examples that can make the management of HC activities quite difficult. The estimation of patient requirements would support HC providers in human resource planning before the care execution, thus improving the service efficiency. This article proposes a stochastic model to represent the patients care pathway; on the basis of historical data of an HC structure, the model provides predictions on the major variables of interest: how many patients are followed up in the course of time and, for each of them, the duration of care and the amount of required visits. The predicted variables of interest provide information about the future workload of each operator. This becomes a useful support tool for human resource planning in the medium and short terms. Numerical results prove the applicability of the proposed stochastic model in practice.

Performance evaluation of linear and non-linear multi-product multi-stage lines with unreliable machines and finite homogeneous buffers

More and more machines are increasing their flexibility so as to be able to produce different products with the same tooling. In this context, it is necessary to have fast and accurate methods to evaluate the system performance. This paper presents an approximate analytical method for the performance evaluation of production systems in which Z different types of products are produced. The proposed method is based on the decomposition of the complex system into a set of simpler building blocks, each one formed by two machines and Z homogeneous buffers. The set of building blocks models the whole behavior of the original system. The performance of each building block is evaluated by using the aggregation technique applied to two-machine lines in which the behavior of several products is modeled by an aggregate product, thus simplifying the complexity of the analysis. The numerical results reported in this paper prove the method provides accurate results.

Analysis of assembly systems controlled with kanbans

This paper deals with the problem of evaluating the performance of assembly systems managed with kanbans. In particular we analyze kanban systems functioning with different control policies depending on how kanbans are released in assembly stages. Simultaneous and independent releases of kanbans are considered in the analysis as alternative control policies to be used in assembly systems. Queuing network techniques are used to calculate the major system performance measures such as throughput of the system, percentage of satisfied orders, average level of finished products and average time delay of unsatisfied orders. Simultaneous and independent kanban assembly systems are compared in some numerical cases. The differences, in terms of performance, between the two alternative kanban control policies are evaluated so that the proper control policy can be selected in the design phase of the system. Simulation results are also reported to validate the approximation of the analytical method used to calculate the performance of assembly kanban systems.

An integrated approach for the configuration of automated manufacturing systems

Abstract The paper proposes a new integrated approach for supporting firms in their decisions of dimensioning automated production systems. The problem is closely related to the performance evaluation of the system since discriminating indicators are necessary to rank different alternatives. Traditionally, analytical methods and simulation have been used to evaluate the production system performance, with minor emphasis on the relationships between the tools and their use. Given the complexity of the problem, it is not possible to use only analytical methods that cannot enter deeply in problem details; at the same time the space of potential system configurations is too large to be evaluated by means of detailed tools such as simulation. In the proposed methodology, the problem is decomposed hierarchically into different sub-problems; each one has a different level of detail and a specific performance evaluation tool is used. At each level of the analysis, each system configuration is compared, by means of statistical tests, with the other alternatives with the purpose of discarding unprofitable solutions.

Simulation optimization with mathematical programming representation of discrete event systems

Optimization-via-simulation consists in applying iteratively two detached models until an optimality condition is reached: a simulation model for predicting the system performance, and a model for generating potential optimal solutions. Mathematical programming representation has been recently used to describe the behavior of discrete event systems as well as their formal properties. This paper proposes explicit mathematical programming representations for jointly simulating and optimizing discrete event systems. The main advantage of such models is the rapidity of searching for the optimal solution, given to the explicit knowledge of objective function and constraints. Three types of formulations are proposed for solving the buffer allocation problem in flow lines with finite buffer capacities: an exact mixed integer linear model, an approximate LP model and a stochastic programming model. Numerical analysis shows that the computational time required to solve resource allocation problems can be significantly reduced by using the proposed formulations.

Mathematical programming formulations for approximate simulation of multistage production systems

Mathematical programming representation has been recently used to describe the behavior of discrete event systems as well as their formal properties. This new way of representing discrete event systems paves the way to the creation of simpler mathematical programming models that reduce the complexity of the system analysis. The paper proposes an approximate representation for a class of production systems characterized by several stages, limited buffer capacities and stochastic production times. The approximation exploits the concept of a time buffer, modeled as a constraint that put into a temporal relationship the completion times of two customers in a sample path. The main advantage of the proposed formulation is that it preserves its linearity even when used for optimization and, for such a reason, it can be adopted in simulation–optimization problems to reduce the initial solution space. The approximate formulation is applied to relevant problems such as buffer capacity allocation in manufacturing systems and control parameters setting in pull systems.