Order Acceptance and Scheduling: A Taxonomy and Review

Over the past 20 years, the topic of order acceptance has attracted considerable attention from those who study scheduling and those who practice it. In a firm that strives to align its functions so that profit is maximized, the coordination of capacity with demand may require that business sometimes be turned away. In particular, there is a trade-off between the revenue brought in by a particular order, and all of its associated costs of processing. The present study focuses on the body of research that approaches this trade-off by considering two decisions: which orders to accept for processing, and how to schedule them. This paper presents a taxonomy and a review of this literature, catalogs its contributions and suggests opportunities for future research in this area

Dynamic scheduling in a multi-product manufacturing system

To remain competitive in global marketplace, manufacturing companies need to improve their operational practices. One of the methods to increase competitiveness in manufacturing is by implementing proper scheduling system. This is important to enable job orders to be completed on time, minimize waiting time and maximize utilization of equipment and machineries. The dynamics of real manufacturing system are very complex in nature. Schedules developed based on deterministic algorithms are unable to effectively deal with uncertainties in demand and capacity. Significant differences can be found between planned schedules and actual schedule implementation. This study attempted to develop a scheduling system that is able to react quickly and reliably for accommodating changes in product demand and manufacturing capacity. A case study, 6 by 6 job shop scheduling problem was adapted with uncertainty elements added to the data sets. A simulation model was designed and implemented using ARENA simulation package to generate various job shop scheduling scenarios. Their performances were evaluated using scheduling rules, namely, first-in-first-out (FIFO), earliest due date (EDD), and shortest processing time (SPT). An artificial neural network (ANN) model was developed and trained using various scheduling scenarios generated by ARENA simulation. The experimental results suggest that the ANN scheduling model can provided moderately reliable prediction results for limited scenarios when predicting the number completed jobs, maximum flowtime, average machine utilization, and average length of queue. This study has provided better understanding on the effects of changes in demand and capacity on the job shop schedules. Areas for further study includes: (i) Fine tune the proposed ANN scheduling model (ii) Consider more variety of job shop environment (iii) Incorporate an expert system for interpretation of results. The theoretical framework proposed in this study can be used as a basis for further investigation

Hybrid job shop scheduling

We consider the problem of scheduling jobs in a hybrid job shop. We use the term\ud 'hybrid' to indicate that we consider a lot of extensions of the classic job shop, such as transportation times, multiple resources, and setup times. The Shifting Bottleneck procedure can be generalized to deal with those extensions. We test this approach for an assembly shop. In this shop, we study the influence of static and dynamic scheduling, setup times, batch sizes, and the arrival process of the jobs

Job-shop scheduling with approximate methods

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Dynamic set-up rules for hybrid flow shop scheduling with parallel batching machines

An S-stage hybrid (or flexible) flow shop, with sequence-independent uniform set-up times, parallel batching machines with compatible parallel batch families (like in casting or heat treatments in furnaces, chemical or galvanic baths, painting in autoclave, etc.) has been analysed with the purpose of reducing the number of tardy jobs (and the makespan); in Graham’s notation: FPB(m_1, m_2, … , m_S)|p-batch, STsi,b|SUM(Ui). Jobs are sorted dynamically (at each new delivery); batches are closed within sliding (or rolling) time windows and processed in parallel by multiple identical machines. Computation experiments have shown the better performance on benchmarks of the two proposed heuristics based on new formulations of the critical ratio (CRsetup) considering the ratio of allowance set-up and processing time in the scheduling horizon, which improves the weighted modified operation due date rule

Heuristic Algorithms to Minimize Total Weighted Tardiness on the Single Machine and Identical Parallel Machines with Sequence Dependent Setup and Future Ready Time

This study generates heuristic algorithms to minimize the total weighted tardiness on the single machine and identical parallel machines with sequence dependent setup and future ready time. Due to the complexity of the considered problem, we propose two new Apparent Tardiness Cost based (ATC-based) rules. The performances of these two rules are evaluated on the single machine and identical parallel machines. Besides of these two rules, we also propose a look-ahead identical parallel machines heuristic (LAIPM). When a machine becomes idle, it selects a job to process from available jobs and near future jobs. The proposed method, LAIPM, is evaluated with other look-ahead methods on the identical parallel machines

Evolutionary methods for the design of dispatching rules for complex and dynamic scheduling problems

Three methods, based on Evolutionary Algorithms (EAs), to support and automate the design of dispatching rules for complex and dynamic scheduling problems are proposed in this thesis. The first method employs an EA to search for problem instances on which a given dispatching rule performs badly. These instances can then be analysed to reveal weaknesses of the tested rule, thereby providing guidelines for the design of a better rule. The other two methods are hyper-heuristics, which employ an EA directly to generate effective dispatching rules. In particular, one hyper-heuristic is based on a specific type of EA, called Genetic Programming (GP), and generates a single rule from basic job and machine attributes, while the other generates a set of work centre-specific rules by selecting a (potentially) different rule for each work centre from a number of existing rules. Each of the three methods is applied to some complex and dynamic scheduling problem(s), and the resulting dispatching rules are tested against benchmark rules from the literature. In each case, the benchmark rules are shown to be outperformed by a rule (set) that results from the application of the respective method, which demonstrates the effectiveness of the proposed methods

Clips: a capacity and lead time integrated procedure for scheduling.

We propose a general procedure to address real life job shop scheduling problems. The shop typically produces a variety of products, each with its own arrival stream, its own route through the shop and a given customer due date. The procedure first determines the manufacturing lot sizes for each product. The objective is to minimize the expected lead time and therefore we model the production environment as a queueing network. Given these lead times, release dates are set dynamically. This in turn creates a time window for every manufacturing order in which the various operations have to be sequenced. The sequencing logic is based on a Extended Shifting Bottleneck Procedure. These three major decisions are next incorporated into a four phase hierarchical operational implementation scheme. A small numerical example is used to illustrate the methodology. The final objective however is to develop a procedure that is useful for large, real life shops. We therefore report on a real life application.Model; Models; Applications; Product; Scheduling;