An operational measure of routing flexibility in a multi-stage multi-product production system

The high degree of variety in customer demands causes mass production to become outdated and flexible production to be favored. Routing flexibility can be found in systems that implement general-purpose machines, alternative or identical machines, redundant machine tools, or the versatility of material handling systems. It is recognized that routing flexibility can be treated as a tool for enhancing system performance, such as lead time and inventory reduction. However, its implementation entails a huge cost of installation of flexible machines, automated tool changers and fixtures, and machine operators possessing multiple skills. Therefore, system managers must determine the appropriate level of routing flexibility for a specific system configuration in order to balance benefits and costs incurred. This paper presents a background to and a rational for a routing flexibility measure for a multi-stage flow shop. Instead of merely counting the number of available routes, this measure takes into account the loading balance between machines. Therefore, a manufacturing system with overloaded machines will have less routing flexibility as compared with one that is not overloaded, when both systems have the same number of available routes. An example for demonstrating the applicability of the proposed measure is also illustrated.     

Algorithms for flexible flow shop problems with unrelated parallel machines, setup times, and dual criteria

In textile industries, production facilities are established as multi-stage production flow shop facilities, where a production stage may be made up of parallel machines. This known as a flexible or hybrid flow shop environment. This paper considers the problem of scheduling n independent jobs in such an environment. In addition, we also consider the general case in which parallel machines at each stage may be unrelated. Each job is processed in ordered operations on a machine at each stage. Its release date and due date are given. The preemption of jobs is not permitted. We consider both sequence- and machine-dependent setup times. The problem is to determine a schedule that minimizes a convex combination of makespan and the number of tardy jobs. A 0–1 mixed integer program of the problem is formulated. Since this problem is NP-hard in the strong sense, we develop heuristic algorithms to solve it approximately. Firstly, several basic dispatching rules and well-known constructive heuristics for flow shop makespan scheduling problems are generalized to the problem under consideration. We sketch how, from a job sequence, a complete schedule for the flexible flow shop problem with unrelated parallel machines can be constructed. To improve the solutions, polynomial heuristic improvement methods based on shift moves of jobs are applied. Then, genetic algorithms are suggested. We discuss the components of these algorithms and test their parameters. The performance of the heuristics is compared relative to each other on a set of test problems with up to 50 jobs and 20 stages.     

No-wait flowshop with separate setup times to minimize maximum lateness

This paper considers the m-machine no-wait flowshop problem with the objective of minimizing the maximum lateness where setup times are considered as separate from processing times and treated as sequence-independent. A dominance relation is developed for the case of three machines and several heuristics and four new effective and efficient genetic algorithms are proposed. The genetic algorithms make use of advanced concepts like steady-state and elitist generational scheme as well as new fast-selection operators. Extensive experimentation is conducted to evaluate the performance of the dominance rule, the proposed heuristics, and the genetic algorithms. The computational and statistical analyses by means of experimental designs show that the genetic algorithms provide better results than the existing literature under the same conditions. Additionally, the proposed dominance rule shows great potential for instances where the processing and setup times are tightly distributed.     

Due-date scheduling on parallel machines with job splitting and sequence-dependent major/minor setup times

This paper addresses job scheduling problems with parallel machines. To satisfy customers better in a manufacturing company, meeting due dates has been an important performance metric. Besides the numerous other factors affecting due date satisfaction, the splitting of a job through parallel machines can contribute to the reduction of production lead time, resulting in less job tardiness against their due dates. Thus, this paper presents heuristic algorithms for minimizing total tardiness of jobs to meet their due dates in a manufacturing shop with identically functioning machines. The algorithms take into account job splitting and sequence-dependent major/minor setup times. The performance of the proposed heuristics is compared with that of past three algorithms in the literature.     

Influence of elastic effects on the performance of slot-entry journal bearings

Modern high-performance machines require bearings to operate under stringent conditions. For bearings operating under heavy loads, the bearing deformations can no longer be neglected as they are comparable to the order of magnitude of the fluid film thickness. This paper describes the performance of slot-entry hydrostatic/hybrid journal bearings by considering bearing shell flexibility in the analysis. The relevant governing equations have been solved by the finite element method. Slot-entry journal bearings of two separate configurations have been studied over a wide range of bearing operating and geometric parameters. Elastic effects are found to significantly affect the static and dynamic performance characteristics of the bearing studied. The study indicates that, for given operating conditions, to get optimum performance of a bearing proper selection of the bearing flexibility parameter ( ), the concentric design pressure ratio ( ) and the type of bearing configuration (symmetric/asymmetric) are essential.     

Decomposition methods for scheduling semiconductor testing facilities

We present decomposition procedures for scheduling semiconductor testing facilities. These facilities are characterized by the presence of different types of work centers, some of which have sequence-dependent setup times and some parallel identical machines. We exploit the structure of the routings in semiconductor testing to develop tailored decomposition procedures that decompose the shop into a number of work centers that are scheduled using specialized procedures. Extensive computational experiments show that these procedures significantly outperform existing methods in reasonable CPU times. These results indicate that decomposition methods can be successfully applied to complex scheduling problems of the type addressed in this paper, as well as the classical job shop problems addressed in previous research.     

Single-machine scheduling with past-sequence-dependent setup times and effects of deterioration and learning

The paper deals with some single-machine scheduling problems with setup time considerations where the processing time of a job is given as a function of its starting times and position in a sequence. The setup times are proportional to the length of the already processed jobs, i.e., the setup times are past-sequence-dependent (p-s-d). We consider the following objective functions: the makespan, the total completion time, the sum of the δth ( $ \delta \geqslant 0 $ ) power of job completion times, the total weighted completion time, the maximum lateness and the number of tardy jobs. We show that the makespan minimization problem, the total completion time minimization problem, and the sum of the δth power of job completion times minimization problem can be solved in polynomial time, respectively. We also show that the total weighted completion time minimization problem, the maximum lateness minimization problem and the number of tardy jobs minimization problem can be solved in polynomial time under certain conditions.     

Flow time analyses of a simulated flexible job shop by considering jockeying

It has been essential to include flexibility in manufacturing policy making since variability in demand and products are considerably increasing. However, it is important to know and to monitor the proper level and type of flexibility that is required to obtain full benefits from it. This paper analyses the effects of flexibility on flow time performance of a simulated job shop. For that purpose, several scenarios are developed under four flexibility levels with two different machine selection rule and three types of dispatching rules. Furthermore, effect of jockeying as a queuing policy on the flow time performance is also investigated through simulation modeling. Results indicated that full flexibility is a preferable state for most of the cases. However, in some cases, chain configurations perform similar results since it combines the benefits of pooling and specialization. In addition, it is observed that a queue control mechanism like jockeying is an effective way to improve performance even though it may increase complexity of controlling policy.     

Scheduling with past-sequence-dependent setup times and learning effects on a single machine

In this paper, we study the single-machine scheduling problems with learning effect and setup time considerations. The setup times are proportional to the length of the already-processed jobs, i.e., the setup times are past-sequence-dependent (p-s-d). The objective functions are to minimize the sum of the quadratic job completion times, the total waiting time, the total weighted completion time, the maximum lateness, the total absolute differences in waiting times, and the sum of earliness penalties subject to no tardy jobs, respectively. We show that the sum of the quadratic job completion times minimization problem, the total waiting time minimization problem, the total absolute differences in waiting times minimization problem, and the sum of earliness penalties minimization problem subject to no tardy jobs can be solved in polynomial time, respectively. We also show that the total weighted completion time minimization problem and the maximum lateness minimization problem can be solved in polynomial time under some special cases.     

The optimal configuration and workload allocation problem in flexible manufacturing systems

In this article we consider the problem of determining the minimum cost configuration (number of machines and pallets) for a flexible manufacturing system with the constraint of meeting a prespecified throughput, while simultaneously allocating the total workload among the machines (or groups of machines). Our procedure allows consideration of upper and lower bounds on the workload at each machine group. These bounds arise as a consequence of precedence constraints among the various operations and/or limitations on the number or combinations of operations that can be assigned to a machine because of constraints on tool slots or the space required to store assembly components. Earlier work on problems of this nature assumes that the workload allocation is given. For the single-machine-type problem we develop an efficient implicit enumeration procedure that uses fathoming rules to eliminate dominated configurations, and we present computational results. We discuss how this procedure can be used as a building block in solving the problem with multiple machine types.     

Deteriorating jobs and learning effects on a single-machine scheduling with past-sequence-dependent setup times

In this paper, we consider the single-machine setup times scheduling with the effects of learning and deterioration. By the effects of learning and deterioration, we mean that the actual processing time of a job depends not only on the processing times of the jobs already processed but also on its scheduled position. The setup times are proportional to the length of the already processed jobs, i.e., the setup times are past-sequence-dependent (p-s-d). We show that the problems to minimize the makespan, the total completion time, and the sum of the $\mathit{\delta}$ th ( ${\mathit{\delta}} \geq 0$ ) power of job completion times are polynomially solvable. We also show that the total weighted completion time minimization problem, the maximum lateness minimization problem, and the number of tardy jobs minimization problem can be solved in polynomial time under certain conditions.     

Scheduling hybrid flow shop with sequence-dependent setup times and machines with random breakdowns

Much of the research on operations scheduling problems has either ignored setup times or assumed that setup times on each machine are independent of the job sequence. Furthermore, most scheduling problems which have been discussed in the literature are under the assumption that machines are continuously available. Nevertheless, in most real life industries, a machine can be unavailable for many reasons, such as unanticipated breakdowns, i.e., stochastic unavailability, or due to a scheduled preventive maintenance where the periods of unavailability are known in advance, i.e., deterministic unavailability. This paper deals with the hybrid flow shop scheduling problems in which there are sequence-dependent setup times, commonly known as the SDST, and machines which suffer stochastic breakdown to optimize objectives based on expected makespan. This type of production system is found in industries such as chemical, textile, metallurgical, printed circuit board, and automobile manufacture. With the increase in manufacturing complexity, conventional scheduling techniques for generating a reasonable manufacturing schedule have become ineffective. The genetic algorithm can be used to tackle complex problems and produce a reasonable manufacturing schedule within an acceptable time. This paper describes how we can incorporate simulation into genetic algorithm approach to the scheduling of a SDST hybrid flow shop with machines that suffer stochastic breakdown. An overview of the hybrid flow shops and scheduling under stochastic unavailability of machines are presented. Subsequently, the details of incorporated simulation into genetic algorithm approach are described and implemented. Consequently, the results obtained are analyzed with Taguchi experimental design.     

Unrelated parallel machine scheduling with secondary resource constraints

This paper considers unrelated parallel machine scheduling with secondary resource constraints. There are n jobs, each needing to be processed on one of the fitted machines. A setup that includes detaching one die and attaching another from the fitted die type is incurred if the type of job scheduled is different from the last job on that machine. For each kind of die type, the number of dies available is limited. Due to the mechanical structure of the machines, the processing time of a job depends on the machine on which the job is processed, and some jobs are restricted to be processed only on certain machines. In this paper, a heuristic with a capability relative to a runtime and solution quality is developed to minimise the makespan. The performance of the presented heuristic is evaluated through extensive computational experiments. Computational results show that the presented heuristic outperforms the search method tested. It is expected that this research can be applied in industry where unrelated parallel machines are used to process different components and setups for auxiliary equipments are required.     

Single-machine group scheduling problems with deteriorating jobs

This paper investigates single-machine scheduling problems with deteriorating jobs and the group technology (GT) assumption. By deteriorating jobs and the group technology assumption, we mean that the group setup times and job processing times are both increasing functions of their starting times, i.e., the group setup times and job processing times are both described by a function which is proportional to a linear function of time. The two objectives of scheduling problems are to minimize the makespan and the total weighted completion time, respectively. We show that these problems remain solvable in polynomial time when deterioration and group technology are considered simultaneously.     

A tabu search algorithm for unrelated parallel machine scheduling with sequence- and machine-dependent setups: minimizing total tardiness

This study considers the problem of scheduling independent jobs on unrelated parallel machines with machine- and sequence-dependent setup times for the objective of minimizing the total tardiness, i.e., R _m │S _ijk │∑T _j . Since the parallel machines are unrelated, sequence-dependent setup times must depend on machines. To the best of the authors’ knowledge, the simulated annealing and the iterated greedy algorithms are two existing ones for the new class of scheduling problem with an additional constraint of strict due date constraints for some jobs, i.e., deadlines. In this study, we suggest a tabu search algorithm that incorporates various neighborhood generation methods. A computational experiment was done on the instances generated by the method used in the two previous research articles, and the results show that the tabu search algorithm outperforms the simulated annealing algorithm significantly. In particular, it gave optimal solutions for more than 50 % of small-sized test instances. Also, an additional test was done to compare the performances of the tabu search and the existing iterated greedy algorithms, and the result shows that the tabu search algorithm gives quicker solutions than the iterated greedy algorithm although it gives less quality solutions.     

Analysis of dispatching rules in a stochastic dynamic job shop manufacturing system with sequence-dependent setup times

Stochastic dynamic job shop scheduling problem with consideration of sequence-dependent setup times are among the most difficult classes of scheduling problems. This paper assesses the performance of nine dispatching rules in such shop from makespan, mean flow time, maximum flow time, mean tardiness, maximum tardiness, number of tardy jobs, total setups and mean setup time performance measures viewpoint. A discrete event simulation model of a stochastic dynamic job shop manufacturing system is developed for investigation purpose. Nine dispatching rules identified from literature are incorporated in the simulation model. The simulation experiments are conducted under due date tightness factor of 3, shop utilization percentage of 90 % and setup times less than processing times. Results indicate that shortest setup time (SIMSET) rule provides the best performance for mean flow time and number of tardy jobs measures. The job with similar setup and modified earliest due date (JMEDD) rule provides the best performance for makespan, maximum flow time, mean tardiness, maximum tardiness, total setups and mean setup time measures.     

Performance management in flexible manufacturing systems

This article treats several performance management decision problems in flexible manufacturing systems (FMSs). This work differs from a number of other studies in that we allow the processing rates at the machines to be varied, and the system has to meet a given throughput goal per unit time. The managerial decision options modeled here include part routing and allocation of tasks to machines, work-in-progress (WIP) levels, capacity expansions, tool-type selection, the setting of throughput goals, and multiperiod production planning. We discuss and explain the insights and implications, partly nonintuitive, gained from our investigations. Finally, extensive numerical evaluations are included to illustrate the economic and performance impact of the various performance management alternatives. These results demonstrate that substantial economic benefits can be achieved by careful tuning of the FMS operational parameters.     

A genetic algorithm and particle swarm optimization for no-wait flow shop problem with separable setup times and makespan criterion

This paper considers the problem of no-wait flow shop scheduling, in which a number of jobs are available for processing on a number of machines in a flow shop context with the added constraint that there should be no waiting time between consecutive operations of a job. Each operation has a separable setup time, meaning that the setup time of an operation is independent on the previous operations; and the machine can be prepared for a specific operation and remain idle before the operation actually starts. The considered objective function in this paper is the makespan. The problem is proven to be NP-hard. In this paper, two frameworks based on genetic algorithm and particle swarm optimization are developed to deal with the problem. For the case of no-wait flow shop problem without setup times, the developed algorithms are applied to a large number of benchmark problems from the literature. Computational results confirm that the proposed algorithms outperform other methods by improving many of the best-known solutions for the test problems. For the problems with setup time, the algorithms are compared against the famous 2-Opt algorithm. Such comparison reveals the efficiency of the proposed method in solving the problem when separable setup times are considered.     

Two-machine, no-wait job shop problem with separable setup times and single-server constraints

No-wait job shop scheduling problems refer to the set of problems in which a number of jobs are available for processing on a number of machines in a job shop context with the added constraint that there should be no waiting time between consecutive operations of the jobs. In this paper, a two-machine, no-wait job shop problem with separable setup times and a single-server constraint is considered. The considered performance measure is the makespan. This problem is strongly NP-hard. A mathematical model of the problem is developed and a number of propositions are proven for the special cases. Moreover, a genetic algorithm is proposed in this paper to find the optimal (or near-optimal) solutions. In order to evaluate the developed algorithm, a number of small instances are solved to optimality using the developed mathematical model. The proposed algorithm is able to find the optimal solution of all of these cases. For larger instances, the developed algorithm has been compared with the 2-opt algorithm as well as a proposed lower bound. Computational results show the efficiency of the proposed algorithm in generating good quality solutions compared to the developed lower bounds and 2-opt algorithm.     

ACO-based multi-objective scheduling of parallel batch processing machines with advanced process control constraints

This research was motivated by a scheduling problem in the dry strip operations of a semiconductor wafer fabrication facility. The machines were modeled as parallel batch processing machines with incompatible job families and dynamic job arrivals, and constraints on the sequence-dependent setup time and the qual-run requirements of advanced process control. The optimization had multiple objectives, the total weighted tardiness (TWT) and makespan, to consider simultaneously. Since the problem is NP-hard, we used an Ant Colony Optimization (ACO) algorithm to achieve a satisfactory solution in a reasonable computation time. A variety of simulation experiments were run to choose ACO parameter values and to demonstrate the performance of the proposed method. The simulation results showed that the proposed ACO algorithm is superior to the common Apparent Tardiness Cost-Batched Apparent Tardiness Cost rule for minimizing the TWT and makespan. The arrival time distribution and the number of jobs strongly affected the ACO algorithm’s performance.