Workshop scheduling using practical (inaccurate) data Part 3: A framework to integrate job releasing,routing and scheduling functions to create a robust predictive schedule

A comprehensive simulation study conducted by the authors investigated the robustness of a predictive scheduling system in a dynamic and stochastic environment. The results revealed that to improve the robustness of a scheduling system, besides using a robust scheduling method with a frequent rescheduling policy, the shop load should be well controlled and kept balanced. Integrating the planning and the scheduling functions has been shown to achieve this objective. This paper discusses the effects of the planning i.e. job releasing and routing and the scheduling functions in creating a robust schedule and a framework to integrate the above functions is proposed. This system consists of a planning module that is concerned with job releasing and routing decisions and a scheduling module that provides the detailed scheduling. A mathematical model using the integer programming technique is use to demonstrate a solution for the planning module. In addition, a heuristic algorithm is used to solve the scheduling problem. It is shown that, in terms of shop load balance level and job delivery time, the proposed system performs better than a benchmark loading strategy on the basis of minimum processing cost.     

Robust scheduling of a two-machine flow shop with uncertain processing times

This paper focuses on manufacturing environments where job processing times are uncertain. In these settings, scheduling decision makers are exposed to the risk that an optimal schedule with respect to a deterministic or stochastic model will perform poorly when evaluated relative to actual processing times. Since the quality of scheduling decisions is frequently judged as if processing times were known a priori, robust scheduling, i.e., determining a schedule whose performance (compared to the associated optimal schedule) is relatively insensitive to the potential realizations of job processing times, provides a reasonable mechanism for hedging against the prevailing processing time uncertainty. In this paper we focus on a two-machine flow shop environment in which the processing times of jobs are uncertain and the performance measure of interest is system makespan. We present a measure of schedule robustness that explicitly considers the risk of poor system performance over all potential realizations of job processing times. We discuss two alternative frameworks for structuring processing time uncertainty. For each case, we define the robust scheduling problem, establish problem complexity, discuss properties of robust schedules, and develop exact and heuristic solution approaches. Computational results indicate that robust schedules provide effective hedges against processing time uncertainty while maintaining excellent expected makespan performance     

A hybrid computer simulation-artificial neural network algorithm for optimisation of dispatching rule selection in stochastic job shop scheduling problems

Industrial systems are constantly subject to random events with inevitable uncertainties in production factors, especially in processing times. Due to this stochastic nature, selecting appropriate dispatching rules has become a major issue in practical problems. However, previous research implies that using one dispatching rule does not necessarily yield an optimal schedule. Therefore, a new algorithm is proposed based on computer simulation and artificial neural networks (ANNs) to select the optimal dispatching rule for each machine from a set of rules in order to minimise the makespan in stochastic job shop scheduling problems (SJSSPs). The algorithm contributes to the previous work on job shop scheduling in three significant ways: (1) to the best of our knowledge it is the first time that an approach based on computer simulation and ANNs is proposed to select dispatching rules; (2) non-identical dispatching rules are considered for machines under stochastic environment; and (3) the algorithm is capable of finding the optimal solution of SJSSPs since it evaluates all possible solutions. The performance of the proposed algorithm is compared with computer simulation methods by replicating comprehensive simulation experiments. Extensive computational results for job shops with five and six machines indicate the superiority of the new algorithm compared to previous studies in the literature.     

A hybrid genetic algorithm and tabu search for a multi-objective dynamic job shop scheduling problem

In most real manufacturing environments, schedules are usually inevitable with the presence of various unexpected disruptions. In this paper, a rescheduling method based on the hybrid genetic algorithm and tabu search is introduced to address the dynamic job shop scheduling problem with random job arrivals and machine breakdowns. Because the real-time events are difficult to express and take into account in the mathematical model, a simulator is proposed to tackle the complexity of the problem. A hybrid policy is selected to deal with the dynamic feature of the problem. Two objectives, which are the schedule efficiency and the schedule stability, are considered simultaneously to improve the robustness and the performance of the schedule system. Numerical experiments have been designed to test and evaluate the performance of the proposed method. This proposed method has been compared with some common dispatching rules and meta-heuristic algorithms that have been widely used in the literature. The experimental results illustrate that the proposed method is very effective in various shop-floor conditions.     

Predictable scheduling of a single machine with breakdowns and sensitive jobs

Production schedules released to the shop floor have two important functions: allocating shop resources to different jobs to optimize some measure of shop performance and serving as a basis for planning external activities such as material procurement, preventive maintenance and delivery of orders to customers. Schedule modification may delay or render infeasible the execution of external activities planned on the basis of the predictive schedule. Thus it is of interest to develop predictive schedules that can absorb disruptions without affecting planned external activities while maintaining high shop performance. We present a predictable scheduling approach, that inserts additional idle time into the schedule to absorb the impacts of breakdowns. The effects of disruptions on planned support activities are measured by the deviations of job completion times in the realized schedule from those in the predictive schedule. We apply our approach to minimizing total tardiness on a single machine with stochastic machine failures. We then extend the procedure to consider the case where job processing times are affected by machine breakdowns, and provide specialized rescheduling heuristics. Extensive computational experiments show that this approach provides high predictability with minor sacrifices in shop performance.     

Dynamic job shop scheduling using variable neighbourhood search

In this paper a scheduling method based on variable neighbourhood search (VNS) is introduced to address a dynamic job shop scheduling problem that considers random job arrivals and machine breakdowns. To deal with the dynamic nature of the problem, an event-driven policy is selected. To enhance the efficiency and effectiveness of the scheduling method, an artificial neural network with a back propagation error learning algorithm is used to update parameters of the VNS at any rescheduling point according to the problem condition. The proposed method is compared with some common dispatching rules that have been widely used in the literature for the dynamic job shop scheduling problem. Results illustrate the high efficiency and effectiveness of the proposed method in a variety of shop floor conditions.     

The model of maintenance planning and production scheduling for maximising robustness

The accuracy of prediction and detection capability have a strong influence over the efficiency of the bottleneck, all equipment and the production system. The function of predictive scheduling is to obtain stable and robust schedules for a shop floor. The first objective is to present an innovative maintenance planning and production scheduling method. The approach consists of four modules: a database to collect information about failure-free times, a prediction module of failure-free times, predictive scheduling and rescheduling module, a module for evaluating the accuracy of prediction and maintenance performance. The second objective is to apply the proposed methods for a job shop scheduling problem. Usually, researchers who are concerned about maintenance scheduling do not take unexpected disturbances into account. They assume that machines are always available for processing tasks during the future-planned production time. Moreover, researches use the criteria that are not effective to deal with the situation of unpredicted failures. In this paper, a method based on probability theory is proposed for maintenance scheduling. For unpredicted failures, a rescheduling method is also proposed. The evaluation module which gives information about the degradation of each performance measure and the stability of a schedule is proposed.     

Probabilistic asymptotic analysis of stochastic online scheduling problems

In the stochastic online scheduling environment, jobs with unknown release times and weights arrive over time. Upon arrival, the information on the weight of the job is revealed but the processing requirement remains unknown until the job is finished. In this paper we consider the objective of minimizing the total weighted completion time. With the assumptions that job weights are bounded, machine capacity is adequate, and processing requirements are bounded and identical and independently distributed across the machines and jobs, we show that any nondelay algorithm is asymptotically optimal for the stochastic online single machine problem, flow shop problem, and uniform parallel machine problem. Our simulation studies of these stochastic online scheduling problems show that two generic nondelay algorithms perform very well as long as the number of jobs is larger than 100.     

A model to examine the effect of loading conditions on scheduling rules in a job shop environment

Although a great deal of research has been carried out in the field of job scheduling this has generally been directed towards examining the benefits of particular rules and presenting improved algorithms. This paper examines how real job shop problems can be modelled and available scheduling rules examined for particular capacity loading conditions. A model of a medium-size production job shop is developed and it is shown that, for their particular shop layout and job mix, the performance and ranking of particular rules with respect to certain criteria, change with shop conditions. The model developed can easily be applied to a wide range of job shop situations and once performance charts have been produced for those scheduling rules available, they can be used to aid the existing scheduling system whether manual or computer based.     

Robust scheduling of a two-stage hybrid flow shop with uncertain interval processing times

This paper studies the makespan minimisation scheduling problem in a two-stage hybrid flow shop. The first stage has one machine and the second stage has m identical parallel machines. Neither the processing time nor probability distribution of the processing time of each job is uncertain. We propose a robust (min–max regret) scheduling model. To solve the robust scheduling problem, which is NP-hard, we first derive some properties of the worst-case scenario for a given schedule. We then propose both exact and heuristic algorithms to solve this problem. In addition, computational experiments are conducted to evaluate the performance of the proposed algorithms.     

Experimental investigation of FMS machine and AGV scheduling rules against the mean flow-time criterion

Although a significant amount of research has been carried out in the scheduling of flexible manufacturing systems (FMSs), it has generally been focused on developing intelligent scheduling systems. Most of these systems use simple scheduling rules as a part of their decision process. While these scheduling rules have been investigated extensively for a job shop environment, there is little guidance in the literature as to their performance in an FMS environment. This paper attempts to investigate the performances of machine and AGV scheduling rules against the mean flow-time criterion. The scheduling rules are tested under a variety of experimental conditions by using an FMS simulation model.     

Robust makespan minimisation in identical parallel machine scheduling problem with interval data

Parallel machine scheduling problems are commonly encountered in a wide variety of manufacturing environments and have been extensively studied. This paper addresses a makespan minimisation scheduling problem on identical parallel machines, in which the specific processing time of each job is uncertain, and its probability distribution is unknown because of limited information. In this case, the deterministic or stochastic scheduling model may be unsuitable. We propose a robust (min–max regret) scheduling model for identifying a robust schedule with minimal maximal deviation from the corresponding optimal schedule across all possible job-processing times (called scenarios). These scenarios are specified as closed intervals. To solve the robust scheduling problem, which is NP-hard, we first prove that a regret-maximising scenario for any schedule belongs to a finite set of extreme point scenarios. We then derive two exact algorithms to optimise this problem using a general iterative relaxation procedure. Moreover, a good initial solution (optimal schedule under a mid-point scenario) for the aforementioned algorithms is discussed. Several heuristics are developed to solve large-scale problems. Finally, computational experiments are conducted to evaluate the performance of the proposed methods.     

Cyclic scheduling heuristics for a re-entrant job shop manufacturing environment

Two efficient cyclic scheduling heuristics for re-entrant job shop environments were developed. Each heuristic generated an efficient and feasible cyclic production schedule for a job shop in which a single product was produced repetitively on a set of machines was to determine an efficient and feasible cyclic schedule which simultaneously minimized flow time and cycle time. The first heuristic considered a repetitive production re-entrant job shop with a predetermined sequence of operations on a single product with known processing times, set-up and material handling times. The second heuristic was a specialization of the first heuristic where the set-up for an operation could commence even while the preceding operation was in progress. These heuristics have been extensively tested and computational results are provided. Also, extensive analysis of worst-case and trade-offs between cycle time and flow time are provided. The results indicate that the proposed heuristics are robust and yield efficient and superior cyclic schedules with modest computational effort.     

An efficient priority rule for scheduling job shops to minimize mean tardiness

The ability to respond quickly to customer demands is a key factor in successfully competing in today's globally competitive markets. Thus, meeting due dates could be the most important goal of scheduling in many manufacturing and service industries. Meeting due dates can naturally be translated into minimizing job tardiness. In this paper we present a priority rule for dynamic job shop scheduling that minimizes mean job tardiness. The rule is developed based on the characteristics of existing dispatching rules. With job due dates set by the generalized total work content rule, the computational results of simulation experiments show that the proposed dispatching rule consistently, and often, significantly outperforms a number of well-known priority rules in the literature. The proposed rule is also robust being the best or near-best rule for reducing the mean flowtime, for all the shop load levels and due date tightness factors tested.     

A multiple-pass heuristic rule for job shop scheduling with due dates

This paper presents an efficient multiple-pass heuristic algorithm for job shop scheduling problems with due dates wherein the objective is to minimize total job tardiness. Algorithm operation is carried out in two phases. In phase 1 a dispatching rule is employed to generate an active or non-delay initial schedule. In phase 2, tasks selected from a predetermined set of promising target operations in the initial schedule are tested to ascertain whether by left-shifting their start times and rearranging some subset of the remaining operations one can reduce total tardiness. Performance evaluation is carried out over a range of shop sizes focusing, first of all, on the quality of the initial schedule produced through five commonly used dispatching rules and, secondly, the schedule improvement achieved with the multiple-pass heuristic. Results indicate that the proposed technique is capable of yielding notable reductions in total tardiness (over initial schedules) for practical size problems and would suggest that the approach presents an efficient scheduling option for this class of complex optimization problems.     

Priority scheduling policies for repair shops

The use of repair as an alternative to the replacement of products is a growing trend in many industries, especially those employing expensive assets. Repair shop environments are characterized by a greater degree of uncertainty than traditional job or assembly shop environments, and this introduces unique managerial complications. In this study, scheduling policies are examined in the repair shop environment where no end-item spares are available or where the spares stocking decision is deferred until the minimum obtainable flowtimes for enditems are established. Previous studies of scheduling policies have focused on the case where spares are allowed and have not considered all of the sources of variation in the repair environment. The model developed in this study incorporates these sources of uncertainty, as well as other factors likely to influence repair shop performance. The results show that the variability in repair shops is sufficiently higher than in traditional job or assembly shops to warrant different scheduling policies than those previously reported. The choice of scheduling policy to provide minimum flowtimes and RMS tardiness is operating-environment specific, and clear guidelines are presented for the manager in a repair shop environment.     

A discrete time reactive scheduling model for new order insertion in job shop,make-to-order industries

This research presents a new reactive scheduling methodology for job shop, make-to-order industries. An integer linear programming formulation previously developed by the authors to schedule these types of industries is extended to address the problem of inserting new orders in a predetermined schedule, which is important in order-driven industries. A reactive scheduling algorithm is introduced to iteratively update the schedules. Numerical results on realistic examples of job shops of different sizes illustrate the effectiveness of the approach. In each case, different alternatives for inserting a set of new orders in an initial schedule are optimally generated, enabling the user to choose the most convenient one. Solutions are characterised by measures of scheduling efficiency as well as stability measures that assess the impact of rescheduling operations in a previously defined scheduling solution.     

An immune algorithm for scheduling a 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 that 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 (stochastic unavailability), or due to scheduled preventive maintenance where the periods of unavailability are known in advance (deterministic unavailability). This paper deals with hybrid flow shop scheduling problems in which there are sequence-dependent setup times (SDSTs), and machines suffer stochastic breakdowns, to optimise objectives based on the expected makespan. With the increase in manufacturing complexity, conventional scheduling techniques for generating a reasonable manufacturing schedule have become ineffective. An immune algorithm (IA) can be used to tackle complex problems and produce a reasonable manufacturing schedule within an acceptable time. In this research, a computational method based on a clonal selection principle and an affinity maturation mechanism of the immune response is used. This paper describes how we can incorporate simulation into an immune algorithm for the scheduling of a SDST hybrid flow shop with machines that suffer stochastic breakdowns. The results obtained are analysed using a Taguchi experimental design.     

Workshop scheduling using practical (inaccurate) data Part 1: The performance of heuristic scheduling rules in a dynamic job shop environment using a rolling time horizon approach

This paper is a report on a simulation study to investigate the performance of a number of scheduling rules on the basis of a rolling time horizon approach for a dynamic job shop environment. The performance measure considered is an economic objective which includes the main costs involved in a scheduling decision. The first purpose of the study was to find the best scheduling rule and the second to investigate the effects of the rescheduling interval on performance and examine whether there is a policy that can always improve performance. The simulation study, which is part of a larger project on practical workshop scheduling, has been carried out under widely varying conditions in terms of due date tightness, shop load level, and shop load balance level. The results show that a recently developed scheduling rule, SPT-C/R, is the most appropriate scheduling rule in minimizing overall cost and that the relationship between performance and rescheduling interval can be shown.     

A priority scheduling approach for flexible job shops with multiple process plans

This paper considers the job shop scheduling problem with alternative operations and machines, called the flexible job shop scheduling problem. As an extension of previous studies, operation and routing flexibilities are considered at the same time in the form of multiple process plans, i.e. each job can be processed through alternative operations, each of which can be processed on alternative machines. The main decisions are: (a) selecting operation/machine pair; and (b) sequencing the jobs assigned to each machine. Since the problem is highly complicated, we suggest a practical priority scheduling approach in which the two decisions are done at the same time using a combination of operation/machine selection and job sequencing rules. The performance measures used are minimising makespan, total flow time, mean tardiness, the number of tardy jobs, and the maximum tardiness. To compare the performances of various rule combinations, simulation experiments were done on the data for hybrid systems with an advanced reconfigurable manufacturing system and a conventional legacy system, and the results are reported.