Multi-criteria optimisation-based dynamic scheduling for controlling FMS

This study deals with controlling flexible manufacturing systems (FMS) operating in volatile production environments. Most studies that address this issue use some sort of adaptive scheduling that enables the FMS to cope with the randomness and variability efficiently. The methods presented in the literature are usually based on heuristics and use simple dispatching rules. They do not consider changing the decision criteria dynamically as the system conditions change. In contrast to previous studies, the present study focuses on developing a control mechanism for dynamic scheduling that is based on incremental optimisation. This means that each time a scheduling decision is made, the local optimisation problem is solved such that the next jobs to be processed on machines are selected. The objective function (dominant decision criterion) for this optimisation problem is selected dynamically based on production order requirements, actual shop-floor status and system priorities. The proposed multi-criteria optimisation-based dynamic scheduling methodology was evaluated and compared with some known scheduling rules/policies. The results obtained demonstrate the superiority of the suggested methodology as well as its capability to cope with a multi-criteria environment.     

A fuzzy logic modelling of dynamic scheduling in FMS

This paper is concerned with scheduling in flexible manufacturing systems (FMSs) using a fuzzy logic (FL) approach. Four fuzzy input variables: machine allocated processing time, machine priority, due date priority and setup time priority are defined. The job priority is the output fuzzy variable, showing the priority status of a job to be selected for next operation on a machine. The model will first select the machines and then assign operations based on a multi-criteria scheduling scheme. The performance of the approach is compared against established methods reported in the literature. The performance measures considered average machine utilisation, meeting due dates, setup times, work in process and mean flow times. The test results demonstrate the superiority of the fuzzy logic approach in most performance measures.     

Auction-based distributed scheduling in a dynamic job shop environment

Developments in computing and communication technology coupled with the inability to address real-time issues in scheduling algorithms based on central control, has led to an interest in solving the problem of a distributed decision-making environment. This paper presents a new job shop formulation that schedules jobs using auctions for distributing control. A theoretical basis is presented for problem decomposition, bid construction, and bid evaluation for the auction using standard mathematical programming tools. Numerical results show that the auction-based approach outperforms the distributed dispatching approaches and can be used to create schedules for a wide range of scheduling objectives.     

Mathematical modelling and heuristic approaches to operation scheduling problems in an FMS environment

This paper addresses an operation scheduling problem with the objective of minimizing total tardiness in a flexible manufacturing system with setup time consideration. The addressed problem is first described as a 0?1 integer programming model, and is then solved optimally. Subsequently, a heuristic is proposed to solve the problem in an acceptable running time. The heuristic begins on a schedule generator called ESCH to obtain an initial solution; then two procedures are designed to improve the solution quality. One is a sequence-improving procedure (SIP) for determining a better performance schedule from a certain routing plan; the other is a routing-exchanging procedure (REP) for selecting a good routing plan. Both procedures are achieved by simulated annealing. Computational experiments show that the proposed simulated annealing based heuristic performs well with respect to solution accuracy and efficiency.     

Workshop scheduling using practical (inaccurate) data Part 2: An investigation of the robustness of scheduling rules in a dynamic and stochastic environment

In research on generating a predictive schedule, the scheduling problem is often viewed as a deterministic problem. However, the real-life job shop environment is stochastic in that information on job attributes and shop floor status is not precisely known in advance. In this situation, in order to increase the effectiveness of a predictive schedule in practice, the focus should be on creating a robust schedule. The purpose of this paper is to investigate the robustness of a number of scheduling rules in a dynamic and stochastic environment using the rolling time horizon approach. A cost-based performance measure is used to evaluate the scheduling rules. The simulation results, under various conditions in a balanced and unbalanced shop, are presented and the effects of the rescheduling interval and operational factors including shop load conditions and a bottleneck on the robustness of the schedule are studied. From the results the key factors that influence the robustness of a scheduling system are identified and, consequently, general guidelines for creating robust schedules are proposed.     

Two-stage stochastic master production scheduling under demand uncertainty in a rolling planning environment

This paper proposes a scenario-based two-stage stochastic programming model with recourse for master production scheduling under demand uncertainty. We integrate the model into a hierarchical production planning and control system that is common in industrial practice. To reduce the problem of the disaggregation of the master production schedule, we use a relatively low aggregation level (compared to other work on stochastic programming for production planning). Consequently, we must consider many more scenarios to model demand uncertainty. Additionally, we modify standard modelling approaches for stochastic programming because they lead to the occurrence of many infeasible problems due to rolling planning horizons and interdependencies between master production scheduling and successive planning levels. To evaluate the performance of the proposed models, we generate a customer order arrival process, execute production planning in a rolling horizon environment and simulate the realisation of the planning results. In our experiments, the tardiness of customer orders can be nearly eliminated by the use of the proposed stochastic programming model at the cost of increasing inventory levels and using additional capacity.     

Provident decision making by considering dynamic and fuzzy environment for FMS evaluation

When evaluating complexity, cost and risk increase, it is difficult to make a proper decision. In such situations it is necessary to develop a model which simulates a decision maker's mind and consider both a dynamic and a fuzzy environment. In this study future oriented indices are presented which enable us to consider the effect of future changes in the index value during the decision making process. These future oriented indices are named provident indices. Also in this study to effectively integrate these multiple criteria into the decision making process, based on the analysis of the decision situation in such assessments, a suitable concept is selected. This method is based on the preference ranking organisation method for enrichment evaluations (PROMETHEE) which brings together flexibility and simplicity for the user and is therefore chosen for the enhancement towards the evaluation of fuzzy data on preferences, scores and weights. The model developed to investigate these impacts cannot perfectly reproduce all the events of the real system, but it can consider a fair number of elements of variability, which should be identified and analysed by considering present conditions and prediction about criteria values in future periods. Such a model may provide solutions with a high degree of robustness and reliability, comparable with those obtained by direct experimentation, but with a low computational cost. The uniqueness of this paper lies in two important areas: first, the incorporation of variability fuzzy and provident measures in the performance of alternatives into the decision making process; and second, is in the application of fuzzy PROMETHEE that provides the decision maker with effective alternative choices by identifying significant differences among alternatives and appropriate choices through considered future periods, and presents graphic aids for better interpretation of results. A comprehensive numerical example of a flexible manufacturing system (FMS) is provided to illustrate the results of the analysis. In a real-world manufacturing environment, the dynamics of an FMS and its stochastic characteristics require a specific approach for evaluation. This paper specifically focuses on FMSs due to the complexities involved in their proper evaluation that include factors such as high operational and managerial expertise in system implementation phases, high costs and risks. Due to these, evaluation, justification, and implementation of an FMS have been areas of major concern and importance for practitioners and researchers. In this case, various strategic, economic and operational criteria that envelop quantitative, qualitative, tangible, and intangible factors are considered.     

Fuzzy inference-based multiple criteria FMS scheduling

This paper proposes a fuzzy inference-based scheduling decision for flexible manufacturing systems (FMS) with multiple objectives. The objectives have different and dynamic preference levels. It is inferred that the changes in the production environment may be sensed by environmental variables. The detected changes are input in a fuzzy inference mechanism, which outputs the current preference levels of all objectives. A multiple criteria scheduling decision is then made, using the partitioned combination of the preference levels. An example of application is presented. Simulation results show very good performance for the proposed system.     

Dynamic scheduling of FMS using a real-time genetic algorithm

The paper presents a genetic algorithm capable of generating optimised production plans in flexible manufacturing systems. The ability of the system to generate alternative plans following part-flow changes and unforeseen situations is particularly stressed (dynamic scheduling). Two contrasting objectives represented by the reduction of machine idle-times, thanks to dynamic scheduling computation and the reduction of the makespan, are taken into account by the proposed system. The key-point is the real-time response obtained by an optimised evolutionary strategy capable of minimising the number of genetic operations needed to reach the optimal schedule in complex manufacturing systems.     

Real-time production control of an FMS in a produce-to-order environment

This paper deals with real-time control of an FMS, operating in a produce-to-order environment, with machines subject to failure. A two-level production control system (PCS) is developed and examined. The proposed PCS incorporates three principles: no need for pre-planning procedures; separation between due-date and operational considerations; and full exploitation of process flexibility. In an extensive series of simulation experiments it was demonstrated that the proposed PCS leads to good results in both criteria: maximum throughput and minimum tardiness of orders. A comparison study between this PCS and one which is not based on separation between due-date and operational considerations, indicated the advantage of the former.     

Warehouse capacity in a stochastic environment

In this study we examine the elements that affect the required capacity of a warehouse in a stochastic environment. The required capacity is measured in terms of its deviation from the nominal capacity requirement (NCR). A simulation model was developed to measure the relationship between warehouse capacity and the various pertinent parameters. From a set of simulated experiments, it is seen that the deviation in the required capacity is mainly affected by the number of items stored, the ordering quantity and the average issues of items per day.     

Flexible job-shop scheduling/rescheduling in dynamic environment: a hybrid MAS/ACO approach

In real-world manufacturing, disruptions are often encountered during the execution of a predetermined schedule, leading to the degradation of its optimality and feasibility. This study presents a hybrid approach for flexible job-shop scheduling/rescheduling problems under dynamic environment. The approach, coined as ‘HMA’ is a combination of multi-agent system (MAS) negotiation and ant colony optimisation (ACO). A fully distributed MAS structure has been constructed to support the solution-finding process by negotiation among the agents. The features of ACO are introduced into the negotiation mechanism in order to improve the performance of the schedule. Experimental studies have been carried out to evaluate the performance of the approach for scheduling and rescheduling under different types of disruptions. Different rescheduling policies are compared and discussed. The results have shown that the proposed approach is a competitive method for flexible job-shop scheduling/rescheduling for both schedule optimality and computation efficiency.     

Intelligent dynamic control of stochastic economic lot scheduling by agent-based reinforcement learning

This paper addresses a stochastic economic lot scheduling problem (SELSP) for a single machine make-to-stock production system in which the demands and the processing times for N types of products are random. The sequence-independent setup times and costs are explicitly considered and may have different values for various types of products. The SELSP is to decide when, what, and how much (the lot size) to produce so that the long-run average total cost, including setup, holding and backorder costs, is minimised. We develop a mathematical model and propose two reinforcement learning (RL) algorithms for real-time decision-making, in which a decision agent is assigned to the machine and improves the accuracy of its action-selection decisions via a ‘learning’ process. Specifically, one is a Q-learning algorithm for a semi-Markov decision process (QLS) and another is a Q-learning algorithm with a learning-improvement heuristic (QLIH) to further improve the performance of QLS. We compare the performance of QLS and QLIH with a benchmarking Brownian policy and the first-come-first-served policy. The numerical results show that QLIH outperforms QLS and both benchmarking policies.     

Performability of FMS based on stochastic process models

We address the design for reliability of complex systems, such as Flexible Manufacturing Systems (FMS), through mathematical modelling of the machine failure, spares inventory, and repair processes. The unique model allows separation of systems by Machine Part Type (MPT); thus, we can model an extremely complicated FMS through many simpler models. Our measure of system effectiveness is based on a performability measure that credits partial availability by the performance of the system in that state. By comparing performability measures, designers and managers can affect the system reliability. Examples demonstrate how to use these developments.     

Robust scheduling in an advanced planning and scheduling environment

A new scheduling problem that appears in an advanced planning and scheduling (APS) environment is discussed. Under the precondition that all materials are available when needed, the problem is formulated as follows: min N i =1 ( D i - E i )/N subject to D i - C i S 0, for all i , where N is the number of customer orders arriving randomly at the shop during a certain period, D i is the estimated due-date for customer order i , E i is the due-date estimation time for customer order i and C i is the completion time for customer order i . Then, D i and C i are endogenous variables and E i is an exogenous variable. The ability to construct a flexible scheduling process that avoids the need to fix ongoing schedules is essential to all APS systems. Therefore, the concept of a due-date buffer is introduced which is expected to enable the production schedules for each customer order to be flexible at the beginning and gradually become fixed as the processing of the order progresses. A simulation-based scheduling algorithm using the concept of a due-date buffer is developed here and subsequently examined through a series of numerical experiments. The obtained computation results show that the performance of due-date buffers is outstanding with respect to complicated production processes having higher utilizations.     

A competitive neural network approach to multi-objective FMS scheduling

The main contribution of this paper is the development of a multi-objective FMS scheduler which is designed to maximally satisfy the desired values of multiple objectives set by the operator. For each production interval, a decision rule for each decision variable is chosen by the FMS scheduler. A competitive neural network is applied to present fast but good decision rules to the operator. A unique feature of the FMS scheduler is that the competitive neural network generates the next decision rules based on the current decision rules, system status and performance measures. A commercial FMS is simulated to prove the effectiveness of the FMS scheduler. The result shows that the FMS scheduler can successfully satisfy multiple objectives.     

Value function approximation via linear programming for FMS scheduling

In this paper, we develop a linear programming framework for computing a quadratic approximation to the value function, which constitutes the off-line computation of a hierarchical FMS scheduling approach previously developed by us. In contrast to previous work, where relatively crude value functions were used, we develop a quadratic approximation that is a prior fit. We consider the multiple part multiple machine discounted cost case and illustrate the approach via a simulation example in the context of an industrial setting.     

Integration of inductive learning and neural networks for multi-objective FMS scheduling

In this paper, we propose an integrated approach of inductive learning and competitive neural networks for developing multi-objective flexible manufacturing system (FMS) schedulers. Simulation and competitive neural networks are applied sequentially to extract a set of classified training data which is used to create a compact set of scheduling rules through inductive learning. The FMS scheduler can assist the operator to make decisions in real time, while satisfying multiple objectives desired by the operator. A simulation-based experiment is performed to evaluate the performance of the resulting scheduler.     

Robust stochastic mine production scheduling

The production scheduling of open pit mines aims to determine the extraction sequence of blocks such that the net present value (NPV) of a mining project is maximized under capacity and access constraints. This sequencing has significant effect on the profitability of the mining venture. However, given that the values of coefficients in the optimization procedure are obtained in a medium of sparse data and unknown future events, implementations based on deterministic models may lead to destructive consequences to the company. In this article, a robust stochastic optimization (RSO) approach is used to deal with mine production scheduling in a manner such that the solution is insensitive to changes in input data. The approach seeks a trade off between optimality and feasibility. The model is demonstrated on a case study. The findings showed that the approach can be used in mine production scheduling problems efficiently.