Performance analysis and optimization of cyclic production lines

In this paper, a model of a cyclic production line is introduced and analyzed. Using Taylor series expansion, we present an approximation method for the analysis of the average steady-state throughput of cyclic production lines with unreliable machines, finite buffers and a fixed number of pallets circulating in the line. Using the approximation method, we suggest an analytic method to calculate the optimal number of pallets that maximizes the line throughput. Also, we show an absolute optimal number of pallets exists which is independent of the machine parameters.     

A tabu search approach for buffer allocation in production lines with unreliable machines

The optimal allocation of buffers is an important research issue in designing production lines. In this study, a tabu search (TS) algorithm is proposed to find near-optimal buffer allocation plans for a serial production line with unreliable machines. The main objective is to maximize the production rate, i.e. throughput, of the line. The efficiency of the proposed method is also tested to solve buffer allocation problems with the objective of total buffer size minimization. To estimate the throughput of the line with a given specific buffer allocation, an analytical decomposition approximation method is used. The performance of the tabu search algorithm is demonstrated on existing benchmark problems. The results obtained by the TS algorithm are clearly encouraging, as the TS algorithm is much better than the other algorithms for all considered benchmark problems.     

Preventive maintenance of manufacturing systems under environmental constraints

The paper describes a new preventive maintenance approach for manufacturing systems under environment constraints. The manufacturing system under consideration consists of a machine M₁ that produces a single product in a Just-in-Time context. To satisfy a constant demand d, the system called upon another machine M₂ (the subcontractor), comprising the so-called environment, which produces at a certain rate the same type of product as M₁. Both machines are subjected to random failures. Whereas machine M₂ is uncontrollable from the maintenance point of view, an age-limit policy is used for preventive maintenance of machine M₁. It is proved that the best age to perform preventive maintenance depends on the history of machine M₁ and the state of M₂. A numerical example is used to illustrate the proposed approach.     

Automatic Petri Net Simulation Model Generation for a Continuous Flow Transfer Line with Unreliable Machines

A production line is studied with several machines in series. The machines are separated with finite buffers. They can break down and have to be repaired. Production is assumed to be continuous, i.e. no individual products can be identified. Analytical models for the performance evaluation of such a system are scarce. Analytical results for the throughput can be obtained only under very restrictive assumptions. The performance study of longer transfer lines requires either approximation or simulation. A Petri net model is developed for an n‐machine ‐buffer system. The rules to design the Petri net transitions are given. The automatic generation of a discrete‐event simulation model from the Petri net model is explained. Finally, some potential applications of the Petri net are illustrated. These applications include the simulation of transfer lines with complex stochastic behaviour, the optimization of buffer size and allocation and the evaluation of approximation models for long transfer lines. Using the simulation model described in this paper, quality and reliability engineers can simulate the performance of a given continuous flow transfer line and evaluate the effect of performance‐increasing measures such as the insertion of extra buffer space or the use of more reliable machines. Copyright © 2004 John Wiley & Sons, Ltd.     

Buffers and backup machines in automatic transfer lines

Buffer storage of the work-in-process inventory, decouples successive stages of automatic transfer production lines, assuring partial operability under machine failure. Certain special features such as secondary (standby) machines, special maintenance and diagnostic systems can lengtben the uptime or shorten the downtime of individual stages. When special features for K stages and spaces for M buffers are available, it is of great interest to system designers to know which stages should have the special features and where the buffer spaces should be inserted to maximize the line output rate. This paper addresses itself to such design problems. A bivariate dynamic programming procedure is developed which provides a layout for the buffers and an allocation of special features, maximizing the line output rate as defined by Buzacott (1967, 1968). The Buzacott formula is based on a heuristic argument which provides, in general, an upper bound on the true system output rate.     

Development of joint maintenance and production strategies in a subcontracting environment

This article, inspired by an industrial problem, develops efficient maintenance and just-in-time production policies in a subcontracting environment according to two orientations. The first invokes subcontracting with the objective of satisfying a constant customer demand knowing that our production system, composed of a machine M ₁, cannot satisfy the totality of demand. Subcontracting is represented by a machine M ₂ which has a constant failure rate, while three maintenance policies for M ₁ are tested and evaluated. The second orientation takes the perspective of our production system as a supplier which is obliged to allocate part of its production capacity to subcontracting so as to satisfy a constant demand. We consider a production system made up of two machines, both of which produce a single type of product, are subject to breakdowns and can carry out subcontracting tasks. The objective of this part of the article is to prove the efficiency of the so-called integrated maintenance policy, which combines production and maintenance decisions in a subcontracting environment.     

Joint optimization of preventive maintenance and inventory control in a production line using simulation

This paper proposes an integrated method for preventive maintenance and inventory control of a production line, composed of n machines (n?≥?1) without intermediate buffers. The machines are subject to failures and an age-dependent preventive maintenance policy is used. Approximate analytical results are proposed for the one machine case. Simulation software is used to model and simulate the behaviour of the production line of n machines under various maintenance and inventory control strategies. A methodology combining the simulation and genetic algorithms is proposed jointly to optimize maintenance and inventory control policies. Results are compared with the analytical solutions.     

Throughput centered prioritization of machines in transfer lines

In an environment of scarce resources and complex production systems, prioritizing is key to confront the challenge of managing physical assets. In the literature, there exist a number of techniques to prioritize maintenance decisions that consider safety, technical and business perspectives. However, the effect of risk mitigating elements—such as intermediate buffers in production lines—on prioritization has not yet been investigated in depth. In this line, the work proposes a user-friendly graphical technique called the system efficiency influence diagram (SEID). Asset managers may use SEID to identify machines that have a greater impact on the system throughput, and thus set prioritized maintenance policies and/or redesign of buffers capacities. The tool provides insight to the analyst as it decomposes the influence of a given machine on the system throughput as a product of two elements: (1) system influence efficiency factor and (2) machine unavailability factor. We illustrate its applicability using three case studies: a four-machine transfer line, a vehicle assembly line, and an open-pit mining conveyor system. The results confirm that the machines with greater unavailability factors are not necessarily the most important for the efficiency of the production line, as it is the case when no intermediate buffers exist. As a decision aid tool, SEID emphasizes the need to move from a maintenance vision focused on machine availability, to a systems engineering perspective.     

Improved preventive maintenance in the framework of forecasting problem under subcontractor constraint

The paper presents a new improved preventive maintenance strategy for a forecasting problem of production and maintenance optimisation under subcontractor constraint. In order to satisfy the customer, the manufacturing system consists of a principal machine M₁ and called upon subcontractor machine M_s. Knowing that both machines are subjected to random failures, failure rate of main machine increases with time and according to production rates. An improved preventive maintenance strategy is used for control of the machine M₁, whereas subcontractor machine M_s is uncontrollable from preventive maintenance point of view. An analytic formulation of problem has been proposed in order to determine the economical production plans for M₁ and M_s. An improved maintenance strategy (IMS) is developed in order to minimise the total production loss, when the subcontractor machine is unavailable. It consists of determining the best time to perform preventive maintenance actions taking into account production rates, history of M₁ and the state of subcontractor machine M_s. Numerical results and sensitivity analysis are presented to highlight the performance measure and the usefulness of the IMS.     

Control point policy: part 1–efficiency within make-to-order environments

The recognition of the desire for punctual delivery of products has lead to the use of the service level as a common performance criterion for measuring the proportion of products that meet due dates specified by the customer. To successfully increase the service level, a manufacturing system may respond more quickly to orders by reducing the levels of in-process inventory in the system and hence decrease throughput times. This paper examines the use of the recently developed Control Point Policy (CPP) in improving service levels in re-entrant, ‘make-to-order’ manufacturing systems and compares its effectiveness with that of the Critical Ratio scheduling rule. Simulation studies have been undertaken to provide insight into how and when to apply the CPP policy within such environments with results indicating that, in cases requiring small storage areas between machines, the CPP results in better service level performance.     

CONWIP ASSEMBLY WITH DETERMINISTIC PROCESSING AND RANDOM OUTAGES

We develop structural results and an approximation for the throughput of an assembly system fed by multi-station fabrication lines where releases are governed by the CONWIP protocol and all machines have deterministic processing times but are subject to random outages. This formulation is motivated by a printed circuit board manufacturing process.

We demonstrate that while throughput of such systems is nondecreasing in machine speed, there are cases where throughput declines when mean time between failures (MTBF) increases or mean time to repair (MTTR) decreases. Using the concept of "deterministic steady state," which describes the behavior of the system in die absence of failures, we derive a simple, closed-form approximation for throughput. Comparisons with simulation show that this approximation is robust over a wide range of conditions. Finally, we observe that throughput tends to be higher when the bottleneck is located in fabrication rather than assembly.     

A stochastic model of an unreliable kanban production system

We analyze a stochastic model of a production line withk stations (machines) in series. There are finitecapacity buffers between the machines and at the end of the line. The movement of the workpieces through the line is demand-driven, i.e. we deal with a pull (kanban) production system. Processing times are assumed to be deterministic and constant. There are two sources of randomness in the model: Demand for workpieces from outside is stochastic, and the machines may break down (and then be repaired) with a given probability. A demand from outside is lost if the final buffer is empty. This system is described by a discrete-time Markov chain. The steadystate distribution is given for k=1. This is the basis of a decomposition algorithm which approximates the throughput of the line and the percentage of satisfied demand for arbitraryk. A comparison with simulation results shows that this algorithm is very accurate.     

Joint optimization of maintenance,buffers and machines in manufacturing lines

This article considers a series manufacturing line composed of several machines separated by intermediate buffers of finite capacity. The goal is to find the optimal number of preventive maintenance actions performed on each machine, the optimal selection of machines and the optimal buffer allocation plan that minimize the total system cost, while providing the desired system throughput level. The mean times between failures of all machines are assumed to increase when applying periodic preventive maintenance. To estimate the production line throughput, a decomposition method is used. The decision variables in the formulated optimal design problem are buffer levels, types of machines and times between preventive maintenance actions. Three heuristic approaches are developed to solve the formulated combinatorial optimization problem. The first heuristic consists of a genetic algorithm, the second is based on the nonlinear threshold accepting metaheuristic and the third is an ant colony system. The proposed heuristics are compared and their efficiency is shown through several numerical examples. It is found that the nonlinear threshold accepting algorithm outperforms the genetic algorithm and ant colony system, while the genetic algorithm provides better results than the ant colony system for longer manufacturing lines.     

Performance analysis for serial production lines with Bernoulli Machines and Real-time WIP-based Machine switch-on/off control

In recent years, achieving high energy efficiency has become one of the primary goals in manufacturing, along with maintaining high productivity and quality. In many manufacturing systems, it is sometimes possible to temporarily switch off a machine to reserve energy, and switch it back on when a certain condition is met. Indeed, production control-based shop floor continuous improvement is recognised as one of the most cost-effective ways to achieve energy-efficient production. In this paper, we study serial production lines with Bernoulli machines and finite capacity buffers and assume that some of the machines in the line can be switched on and off during the production process according to a state-based feedback control policy. Mathematical models for the system under consideration are derived and analytical methods are developed for calculating the system performance measures during transients. Specifically, exact Markovian analysis is used for two- and three-machine lines in which the switch-on/off operations of only one machine is considered. For longer lines, the switch-on/off operations of multiple machines are considered and an aggregation-based approximation approach is applied to evaluate the system performance measures. Numerical experiments show that the method developed can be used to efficiently calculate the system’s performance with high accuracy.     

Forecasting and maintenance problem under subcontracting constraint with transportation delay

In this paper, a forecasting production/maintenance optimization problem has been proposed with a random demand and single machine M₁ on a finite horizon. The function rate of the machine M₁ is depending on the production rate for each period of the forecasting horizon. In order to satisfy the customer, a subcontracting assures the rest of the production through machine M₂ with transportation delay. An analytic formulation of the problem has been proposed using a sequential computation of the optimal production plan for which an optimal preventive maintenance policy has been calculated based on minimal repair. Firstly, we find, the optimal production plans of principal and subcontracting machines, which minimises the total production and inventory cost for the cases without and with returned products under service level and subcontracting transportation delay. Secondly, we determine a joint effective maintenance policy with the optimal production plan, which integrates the various constraints for the production rates, the transportation delay and the returned production deadline. Numerical results are presented to highlight the application of the developed approach and sensitivity analysis shows the robustness of the model.     

A combinatorial approach to a class of parallel-machine,continuous-time scheduling problems

The paper analyzes a manufacturing system with N non-identical, parallel machines continuously producing one product type in response to its demand. Inventory and backlog costs are incurred when tracking the demand results in inventory surpluses and shortages respectively. In addition, the production cost of a machine is incurred when the machine is not idle. The objective is to determine machine production rates so that the inventory, backlog, and production costs are minimized. For problems with demand defined as an arbitrary function of time, numerical methods are suggested to approximate an optimal solution. The complexity of the approximation methods is polynomial, while finding an exact optimal solution requires exponential time. In a case when production is to cope with a special form of a single-mode, K-level piece-wise constant demand, we prove, with the aid of the maximum principle, that the exact optimal solution can be found as a combination of analytical and combinatorial tools in O(KN ²( max {K,2N})²) time.     

An exploratory study of protective inventory in a re-entrant line with protective capacity

While the effect of protective inventory on the performance of simple lines has received considerable attention, the same cannot be said for re-entrant lines. This paper attempts to meet that deficiency. This paper examines two different but related issues. First, the theory of constraints (TOC) evaporating cloud method is employed to show the traditional dilemma of increasing work-in-process (WIP) to fully utilise resources versus decreasing WIP inventory to reduce cycle time. The assumptions and implications of three different management philosophies (traditional, JIT/lean, and TOC) are explored in addressing this dilemma with respect to the use of both protective inventory and protective capacity. Second, given an unbalanced re-entrant line with fixed capacity, simulation is used to explore the effectiveness of using protective inventory by changing the level of WIP on two dependent variables: cycle time and throughput. Two sources of variation are simulated: processing time and breakdowns (machine failures). At a given WIP level, it was found that the amount of protective capacity at non-bottlenecks changed with increases in variability. Therefore the level of WIP inventory (with its protective inventory) and the level of protective capacity needed to protect against variability play a critical role in determining cycle time and throughput of the re-entrant line. While this is an exploratory study, comments on protective inventory and protective capacity are provided based on the three different management philosophies.     

Joint optimization of a flow-shop group scheduling with sequence dependent set-up times and skilled workforce assignment

Flow-shop sequence-dependent group scheduling (FSDGS) problem has been extensively investigated in the literature also due to many manufacturers who implemented the concept of group technology to reduce set-up costs, lead times, work-in-process inventory costs, and material handling costs. On the other hand, skilled workforce assignment (SWA) to machines of a given shop floor may represent a key issue for enhancing the performance of a manufacturing system. As the body of literature addressing the group scheduling problems ignored up to now the effect of human factor on the performance of serial manufacturing systems, the present paper moves in that direction. In particular, an M-machine flow-shop group scheduling problem with sequence-dependent set-up times integrated with the worker allocation issue has been studied with reference to the makespan minimization objective. First, a Mixed Integer Linear Programming model of the proposed problem is reported. Then, a well-known benchmark arisen from the literature is adopted to carry out an extensive comparison campaign among three properly developed metaheuristics based on a genetic algorithm framework. Once the best procedure among those tested is selected, it is compared with an effective optimization procedure recently proposed in the field of FSDGS problems, being this latter properly adapted to run the SWA issue. Finally, a further analysis dealing with the trade-off between manpower cost and makespan improvement is proposed.     

Scheduling on a proportionate flowshop to minimise total late work

We study a scheduling problem to minimise total late work, i.e. each job is penalised according to the duration of its parts scheduled after its due-date. The machine setting is an m-machine proportionate flow shop. Two versions of the problem are studied: (i) the case that total late work refers to the last operation of the job (i.e. the operation performed on the last machine of the flow shop); (ii) the case that total late work refers to all the operations (on all machines). Both versions are known to be NP-hard. We prove a crucial property of an optimal schedule, and consequently introduce efficient pseudo-polynomial dynamic programming algorithms for the two versions. The dynamic programming algorithms are tested numerically and proved to perform well on large size instances.     

Plant-level maintenance decision support system for throughput improvement

In the current global scenario of extreme competition, factors such as productivity, availability, quality and cost of operations play a vital role in the success of a company. A critical component relating to all of the above is maintenance. The conventional maintenance decision support systems have primarily focused on maximising the gains of a single machine system. However, a real life application usually consists of multiple machines, and the operational level decisions are more complex. In this paper, an on line plant-level maintenance decision support system (PMDSS) is developed by combining the short term and long term decision making process to improve the overall system performance while continuously attempting to maximise immediate profits in the short term. The PMDSS works towards two basic aims: (1) unplanned downtime reduction by predicting the remaining useful life of the machines, and (2) efficient utilisation of the finite maintenance and production resources through identifying the throughput-critical machines. The benefits of this approach are presented by considering an industrial case study of an automotive assembly line. The results obtained using this PMDSS approach shows a big throughput improvement as compared to the conventional maintenance policies.