On the selectivity of order acceptance procedures in batch process industries

Job and resource structures in batch process industries are generally very complex, which renders the assessment of what workload can be completed during a specific period very difficult. Order acceptance procedures have a considerable impact on the mix of jobs that need to be scheduled, by refusing specific jobs from the total demand. In this paper, we investigate whether jobs with specific characteristics are systematically rejected by an aggregate acceptance procedure and a detailed acceptance procedure. We find out that, while both procedures are selective in the kind of jobs they accept when job mix variety is high, the detailed acceptance procedure underestimates the consequences on the total makespan of significantly changing the job mix

Order acceptance under uncertainty in batch process industries

XIV+140hlm.;24c

Bootstrapping to solve the limited data problem in production control : an application in batch process industries

Batch process industries are characterized by complex precedence relationships among operations, which makes the estimation of an acceptable workload very difficult. Previous research indicated that a regression-based model that uses aggregate job set characteristics may be used to support order acceptance decisions. Applications of such models in real life assume that suffcient historical data on job sets and the corresponding makespans are available. In practice, however, historical data may be very limited and may not be suffcient to produce accurate regression estimates. This paper shows that such a lack of data significantly impacts the performance of regression-based order acceptance procedures. To resolve this problem, we devised a method that uses the bootstrap principle. A simulation study shows that performance improvements are obtained when using the parameters estimated from the bootstrapped data set, demonstrating that this bootstrapping procedure can indeed solve the limited data problem in production control

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

Lot Streaming in Different Types of Production Processes: A PRISMA Systematic Review

At present, any industry that wanted to be considered a vanguard must be willing to improve itself, developing innovative techniques to generate a competitive advantage against its direct competitors. Hence, many methods are employed to optimize production processes, such as Lot Streaming, which consists of partitioning the productive lots into overlapping small batches to reduce the overall operating times known as Makespan, reducing the delivery time to the final customer. This work proposes carrying out a systematic review following the PRISMA methodology to the existing literature in indexed databases that demonstrates the application of Lot Streaming in the different production systems, giving the scientific community a strong consultation tool, useful to validate the different important elements in the definition of the Makespan reduction objectives and their applicability in the industry. Two hundred papers were identified on the subject of this study. After applying a group of eligibility criteria, 63 articles were analyzed, concluding that Lot Streaming can be applied in different types of industrial processes, always keeping the main objective of reducing Makespan, becoming an excellent improvement tool, thanks to the use of different optimization algorithms, attached to the reality of each industry.This work was supported by the Universidad Tecnica de Ambato (UTA) and their Research and Development Department (DIDE) under project CONIN-P-256-2019, and SENESCYT by grants “Convocatoria Abierta 2011” and “Convocatoria Abierta 2013”

Does Order Negotiation Improve The Job-Shop Workload Control?

Work flows in a job-shop are determined not only by the release load and the time between release factors, but also by the number of accepted orders. There has been extensive research on workload and input-output control aiming at improving the performance of manufacturing operations in job-shops. This paper explores the idea of controlling the workload since the acceptance/rejection of orders stage. A new acceptance/rejection rule is proposed, and tests are conducted to study the sensitivity of job-shop performance to different order acceptance parameters, like the tolerance of the workload limit and the due date extension acceptance. It also evaluates the effect of the negotiation phase of the proposed acceptance rule on the job-shop performance using a simulation model of a generic random job-shop. The extensive simulation experiments allow us to conclude that having a negotiation phase prior to rejection improves almost all workload performance measures. We also conclude that different tolerances of the workload limit affect slightly the performance of the job-shop.job shop, order negotiation, workload control

Global supply chains of high value low volume products

Imperial Users onl

A machine learning enhanced multi-start heuristic to efficiently solve a serial-batch scheduling problem

Serial-batch scheduling problems are widespread in several industries (e.g., the metal processing industry or industrial 3D printing) and consist of two subproblems that must be solved simultaneously: the grouping of jobs into batches and the sequencing of the created batches. This problem’s NP-hard nature prevents optimally solving large-scale problems; therefore, heuristic solution methods are a common choice to effectively tackle the problem. One of the best-performing heuristics in the literature is the ATCS–BATCS(β) heuristic which has three control parameters. To achieve a good solution quality, most appropriate parameters must be determined a priori or within a multi-start approach. As multi-start approaches performing (full) grid searches on the parameters lack efficiency, we propose a machine learning enhanced grid search. To that, Artificial Neural Networks are used to predict the performance of the heuristic given a specific problem instance and specific heuristic parameters. Based on these predictions, we perform a grid search on a smaller set of most promising heuristic parameters. The comparison to the ATCS–BATCS(β) heuristics shows that our approach reaches a very competitive mean solution quality that is only 2.5% lower and that it is computationally much more efficient: computation times can be reduced by 89.2% on average

Evaluation of the quantiles and superquantiles of the makespan in interval valued activity networks

This paper deals with the evaluation of quantile-based risk measures for the makespan in scheduling problems represented as temporal networks with uncer tainties on the activity durations. More specifically, for each activity only the interval for its possible duration values is known in advance to both the sched uler and the risk analyst. Given a feasible schedule, we calculate the quantiles and the superquantiles of the makespan which are of interest as risk indicators in various applications. To this aim we propose and test a set of novel algorithms to determine rapid and accurate numerical estimations based on the calculation of theoretically proven lower and upper bounds. An extensive experimental campaign compu tationally shows the validity of the proposed methods, and allows to highlight their performances through the comparison with respect to the state-of-the-art algorithms