A multilevel machine and vehicle scheduling in a flexible manufacturing system

The paper presents a multilevel decision model for simultaneous machine and vehicle scheduling in a flexible manufacturing system. The system is composed of various machine types and a set of automated guided vehicles that permit each part to move between any pair of machines. The upper level of the decision model involves machine loading and part routing for which a bicriterion integer formulation is presented with the objective of balancing machine workloads and intermachine flows of parts. The lower level involves simultaneous scheduling of machines and vehicles for which a period-by-period heuristic is proposed based on a family of complex dispatching rules. The scheduling objective is to meet all part type requirements in a minimum time. Computational examples are included to illustrate the approach proposed.     

Evaluation of entropy-based dispatching in flexible manufacturing systems

Traditionally, part dispatching has been done using static rules, rules that fail to take advantage of the dynamic nature of today’s manufacturing systems. In modern manufacturing systems, machines carry multiple tools so parts have the option of being machined at more than one machine. This flexibility, termed routing flexibility in the literature, opens up new possibilities for shop floor planners for the scheduling and dispatching of parts.     

Exact analysis of a two-workstation one-buffer flow line with parallel unreliable machines

This paper examines a model of a serial flow line with two workstations and an intermediate buffer. Each workstation consists of multiple unreliable parallel machines which are not necessarily identical, viz., the processing times, failure times and repair times of the parallel machines at each workstation are assumed to be exponentially distributed with non-identical mean rates. The system under consideration is solved via exact Markovian analysis. More specifically, a recursive algorithm that generates the transition matrix for any value of the intermediate buffer capacity is developed and all possible transition equations are derived and solved analytically. Once the transition equations are solved the performance measures of the model under consideration can be easily evaluated. This model may be used as a decomposition block for solving larger flow lines with parallel unreliable machines at each workstation.     

A Genetic Algorithm for the Allocation of Buffer Storage Capacities in a Production Line with Unreliable Machines

In this paper, we consider a flow-line manufacturing system organized as a series of workstations separated by finite buffers. The failure and repair times of machines are supposed to be exponentially distributed. The production rate of each machine is deterministic, and different machines may have different production rates. The buffer allocation problem consists in determining the buffer capacities with respect to a given optimality criterion, which depends on the average production rate of the line, the buffer acquisition and installation cost, and the inventory cost. For this problem we propose a genetic algorithm where the tentative solutions are evaluated with an approximate method based on the Markov-model aggregation approach.     

Scheduling job families on non-identical parallel machines with time constraints

This paper studies the scheduling of lots (jobs) of different product types (job family) on parallel machines, where not all machines are able to process all job families (non-identical machines). A special time constraint, associated to each job family, should be satisfied for a machine to remain qualified for processing a job family. This constraint imposes that the time between the executions of two consecutive jobs of the same family on a qualified machine must not exceed the time threshold of the family. Otherwise, the machine becomes disqualified. This problem comes from semiconductor manufacturing, when Advanced Process Control constraints are considered in scheduling problems. To solve this problem, two Mixed Integer Linear Programming models are proposed that use different types of variables. Numerical experiments show that the second model is much more effective, and that there is a trade-off between optimizing the scheduling objective and maximizing the number of machines that remain qualified for the job families. Two heuristics are also presented and studied in the numerical experiments.     

Tabu search algorithms for an industrial multi-product and multi-objective assembly line balancing problem,with reduction of the task dispersion

This paper presents a real-world industrial application of the multi-product and multi-objective assembly line balancing problem, for a company involved in the production of four models of a white goods product. The problem solved is a GALBP-2, with 10 workstations and multiple objectives (to maximize the production rate in order to deal with an increase of the demand forecasted, to reach an equal cycle time of all the models and an equal workload of the different workstations, and finally, to minimize the dispersion of worker tasks on each one of the different models—the common tasks of the different models at the same workstation). The paper presents an integrated approach based on four heuristics cited in the literature and: (1) an improvement procedure based on tabu search, with the objective of minimizing the cycle time; and, subsequently, (2) a second tabu search in order to increase the uniformity of the tasks performed at each workstation (the common tasks at the same workstation).     

A quantitative approach for scheduling activities to reduce set-up in multiple machine lines

The importance of ensuring short set-up times in manufacturing has been well-documented in the literature over the past years. However, this body of work largely addresses situations involving a single machine with no specific worker-related issues. In practice, there exist multiple machines or workstations that form a machine line, and that need set-up operations to be performed by multiple workers. The existing literature does not provide adequate methodologies for set-up reduction in such cases. This paper describes a quantitative modeling and algorithmic approach for scheduling activities or tasks in order to minimize the set-up time in such situations, also taking into account relevant secondary objectives such as balancing the workload amongst the workers, concentrating slack toward the end of the set-up process, and minimizing the movement costs of the workers performing the different set-up tasks. Three real-life examples are used to demonstrate the efficacy of the proposed approach.     

Scheduling orders for multiple product types to minimize total weighted completion time

We consider the problem of scheduling orders for multiple different product types in an environment with m dedicated machines in parallel. The objective is to minimize the total weighted completion time. Each product type is produced by one and only one of the m dedicated machines; that is, each machine is dedicated to a specific product type. Each order has a weight and may also have a release date. Each order asks for certain amounts of various different product types. The different products for an order can be produced concurrently. Preemptions are not allowed. Even when all orders are available at time 0, the problem has been shown to be strongly NP-hard for any fixed number (?2) of machines. This paper focuses on the design and analysis of efficient heuristics for the case without release dates. Occasionally, however, we extend our results to the case with release dates. The heuristics considered include some that have already been proposed in the literature as well as several new ones. They include various static and dynamic priority rules as well as two more sophisticated LP-based algorithms. We analyze the performance bounds of the priority rules and of the algorithms and present also an in-depth comparative analysis of the various rules and algorithms. The conclusions from this empirical analysis provide insights into the trade-offs with regard to solution quality, speed, and memory space.     

Machine utilizations achieved using balanced FMS production ratios in a simulated setting

Stecke [21] has developed mathematical programming approaches for determining, from a set of part type requirements, the production ratios (part types to be produced next, and their proportions) which maximize overall machine utilizations by balancing machine workloads in a flexible manufacturing system (FMS). These mathematical programming (MP) approaches are aggregate in the sense that they do not take into account such things as contention for transportation resources, travel time for work-in-process, contention for machines, finite buffer space, and dispatching rules. In the current study, the sensitivity of machine utilizations to these aggregations is investigated through simulation modeling. For the situation examined, it is found that achieved machine utilizations are a strong function of some of the factors ignored in the MP methodology, ranging from 9.1% to 22.9% less than those theoretically attainable under the mathematical programming assumptions. The 9.1% degradation results from modeling with nonzero work-in-process travel times (i.e. 2 minutes per transfer) and using only central work-in-process buffers. Resource levels (e.g. the number of automated guided vehicles; the amount of work-in-process; the number of slack buffers) needed to limit the degradation to 9.1% correspond to FMS operating conditions which are feasible in practice.     

Recent developments in Dual Resource Constrained (DRC) system research

Real world manufacturing systems are usually constrained by both machine and human resources. Human operators are often the constraining resource and transfer between workstations to process jobs when required. This kind of system is known as a Dual Resource Constrained (DRC) system and presents additional technical challenges which must be considered during planning and scheduling. These technical challenges can be categorised into the five main dimensions of job release mechanisms, job dispatching, worker flexibility, worker assignment and transfer costs. This paper aims to provide an overview of recent developments in DRC research concerned with each of these areas and also discusses some possible approaches to solving the resource scheduling problem in a DRC system. The focus is on materials published after 1995 and up to 2009. Previous reviews on DRC systems are commented on and followed by a review of recent works associated with each of the five dimensions of DRC system research. Advancements made and new methodologies proposed are discussed and future research directions are identified.     

Control Policy for a Manufacturing System with Random Yield and Rework

We develop a production policy that controls work-in-process (WIP) levels and satisfies demand in a multistage manufacturing system with significant uncertainty in yield, rework, and demand. The problem addressed in this paper is more general than those in the literature in three aspects: (i) multiple products are processed at multiple workstations, and the capacity of each workstation is limited and shared by multiple operations; (ii) the behavior of a production policy is investigated over an infinite-time horizon, and thus the system stability can be evaluated; (iii) the representation of yield and rework uncertainty is generalized. Generalizing both the system structure and the nature of uncertainty requires a new mathematical development in the theory of infinite-horizon stochastic dynamic programming. The theoretical contributions of this paper are the existence proofs of the optimal stationary control for a stochastic dynamic programming problem and the finite covariances of WIP and production levels under the general expression of uncertainty. We develop a simple and explicit sufficient condition that guarantees the existence of both the optimal stationary control and the system stability. We describe how a production policy can be constructed for the manufacturing system based on the propositions derived.     

Dynamic models of production with multiple operations and general processing times

Traditional integer programming model formulations for job-shops and flow-shops do not easily account for characteristics common to high-technology manufacturing such as high-volume semiconductor manufacturing. These characteristics are: (1) products (wafers) are processed by the same machine type more than once during the operation sequence, (2) many lots of similar type are run, and (3) there can be multiple machines of the same type. In this paper, we present two new integer programming formulations which easily account for these characteristics. The approach is based on restricting the allowed domain of events for the start of lot processing. The first model restricts production starts to the beginning of a planning time period. The second model uses a special time grid at each operation with width equal to the processing time, and allows starts to be scheduled at the grid points. In an example problem replicating a high-volume wafer fabrication process, it is shown that it is computationally practical to obtain solutions for the restricted start models where it is not computationally possible for the traditional integer programming model formulations.     

Scheduling orders on either dedicated or flexible machines in parallel to minimize total weighted completion time

We are interested in the problem of scheduling orders for different product types in a facility with a number of machines in parallel. Each order asks for certain amounts of various different product types which can be produced concurrently. Each product type can be produced on a subset of the machines. Two extreme cases of machine environments are of interest. In the first case, each product type can be produced on one and only one machine which is dedicated to that product type. In the second case, all machines are identical and flexible; each product type can be produced by any one of the machines. Moreover, when a machine in this case switches over from one product type to another, no setup is required. Each order has a release date and a weight. Preemptions are not allowed. The objective is minimizing the total weighted completion time of the orders. Even when all orders are available at time 0, both types of machine environments have been shown to be NP-hard for any fixed number (≥2) of machines. This paper focuses on the design and analysis of approximation algorithms for these two machine environments. We also present empirical comparisons of the various algorithms. The conclusions from the empirical analyses provide insights into the trade-offs with regard to solution quality, speed, and memory space. Electronic Supplementary Material The online version of this article () contains supplementary material, which is available to authorized users. This research is supported by the National Science Foundation through grants DMI-0300156 and DMI-0245603.     

Quality Assurance of a Maker-Packer Model

We study a maker-packer production system in cigarette manufacturing where two different types of detectors are installed on the making and packing machines to maintain the product quality. The making machine detector has a continuously adjustable setting which determines the proportion of defective cigarettes removed. The packing machine detector has only discrete settings. We develop an economic model to derive the optimal settings for these detectors to achieve a specified average outgoing quality. The proposed maker-packer model is useful in other industries.     

Dynamic task assignment for throughput maximization with worksharing

The traditional assembly line balancing problem involves assigning a set of partially precedence constrained tasks to workstations to maximize efficiency. Each task is assigned to a unique workstation. The case is considered where task sequences are known but the workforce is partially cross-trained and some tasks can alternate between workstations. The flexibility afforded by cross-training allows the line balance to improve. Task times are allowed to be random and small buffers are allowed between workstations. Decision rules are developed and tested for various levels of cross-training between adjacent workers. Cross-training is shown to have significant impact on throughput and easy to administer rules are proven to be effective. The number of decision points for deciding to hold or pass a unit of product is also shown to be important.     

Mixed integer programming for scheduling flexible flow lines with limited intermediate buffers

This paper presents new mixed integer programming formulations for scheduling of a flexible flow line with blocking. The flexible flow line consists of several processing stages in series, separated by finite intermediate buffers, where each stage has one or more identical parallel machines. The line produces several different product types and each product must be processed by, at most, one machine in each stage. A product which has completed processing on a machine may remain there and block the machine until a downstream machine becomes available for processing. The objective is to determine a production schedule for all products so as to complete the products in a minimum time. The basic mixed integer programming formulations have been enhanced to model blocking scheduling with alternative processing routes where for each product a set of routes is available for processing. A reentrant flow line where a product visits a set of stages more than once is also considered. Numerical examples are presented to illustrate applications of the various models proposed.     

New heuristics for total tardiness minimization in a flexible flowshop

This paper analyses the total tardiness minimization in a flowshop with multiple processors at each stage. While there is considerable research to minimize the makespan, very little work is reported on minimizing the total tardiness for this problem. This research focuses on heuristic methods that consider this environment as a series of parallel machine problems. New dispatching rules are introduced. One of the proposed rules is able to deal with jobs that will come afterwards and not only the available jobs at the decision time. Dispatching rules are also associated with classical (forward and backward) and new list scheduling algorithms. A special scheduling algorithm able to deal with idle times is proposed. Computational experiments in a set of 4,320 literature instances show that the developed heuristics are competitive and outperforms their classical counterparts.     

Search heuristics for a flowshop scheduling problem in a printed circuit board assembly process

We consider a scheduling problem in a factory producing printed circuit boards (PCBs). The PCB assembly process in this factory can be regarded as a flowshop which has two special characteristics: jobs have sequence dependent setup times and each job consists of a lot (batch) of identical PCBs. Because of the latter characteristic, it is possible to start a job on a following machine before the job is entirely completed on a previous machine, that is, there is time-lag between machines. In this paper, we propose several heuristics, including taboo search (TS) and simulated annealing (SA) methods, for this generalized flowshop scheduling problem with the objective of minimizing mean tardiness. We compare suggested heuristics after series of tests to find appropriate values for parameters needed for the two search algorithms, TS and SA. Results of computational tests on randomly generated test problems are reported.     

A heuristic for scheduling general job shops to minimize maximum lateness

Based on the various priority dispatching rules, three dominant rules were formulated. Heuristic approaches were developed based on these dominant rules and dominant matrix. These heuristics can solve problems where a job may need to be processed by the same machine more than once in its operation sequence and different release times are allowed for different machines. The proposed heuristics appear to be fairly effective procedures, and various numerical results are obtained and compared with the various dispatching rules and with a branch and bound approach.     

Optimal Control of a Stochastic Assembly Production Line

The system under consideration comprises n workstations in parallel and one assembly workstation. The workstations are either reliable or unreliable and the product demand is random. The n different type parts are processed first in the parallel workstations and then are joined in the assembly workstation. By minimizing the expected discounted cost, it is shown that the optimal control policy is of the bang–bang type and can be described by a set of switching manifolds. The structural properties of the optimal policy, such as monotonicity and asymptotic behavior, are investigated. These structural properties are very useful to find the optimal policy in large-size systems. Three numerical examples are given to demonstrate the results.