The Relationship between Maximum Completion Time and Total Completion Time in Flowshop Production

We review the literature about shop scheduling problems in manufacturing systems, revealing the concepts and methodologies that most impact the usage of scheduling theory in manufacturing environments. We focus our attention on the job shop and flow shop problems and their variants. We emphasize the interactions between the scheduling functions and manufacturing paradigms such as Industry 4.0, Computer Integrated Manufacturing, Computer‐Aided Process Planning, Advanced Planning and Scheduling, and Integrated Process Planning and Scheduling. We describe the main components and characteristics of the scheduling ecosystem, and we discuss how the scheduling interacts with the components that make it up and how it is affected by them. The metadata collected from the digital libraries on which the review was based (ScienceDirect, Scopus, and Elsevier) made it possible to characterize the historical evolution of the main concepts of the scheduling ecosystem in terms of scientific publications and research trends in the period 2000–2020.

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“…(2014), Pugazhenthi and Anthony Xavior (2014), Rahmani and Heydari (2014), Li et al. (2015), Vallada et al. (2015), Lei (2015b), Yang et al.…”

Section: An Overview Of Shop Scheduling Problems Characteristicsmentioning

confidence: 99%

Shop scheduling in manufacturing environments: a review

Márquez

Ribeiro

2022

Int Trans Operational Res

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“…Hopp and Spearman [1] list the following manufacturing objectives: high throughput, low inventory, high utilisation, short cycle times, and high product variety. Li et al [36] cites utilisation and work-in-process as the two main managerial concerns in manufacturing systems. Finally, throughput and lateness are identified by several authors (e.g., [37,38]) as the main performance indicators in manufacturing.…”

Section: Shop Floor Performance Measuresmentioning

confidence: 99%

“…As a result, throughput is inversely proportional to makespan. Note that the utilisation U(S) can be defined as (see, e.g., [36]):…”

Section: Shop Floor Performance Measuresmentioning

confidence: 99%

Linking Scheduling Criteria to Shop Floor Performance in Permutation Flowshops

Framiñán

Leisten

2019

Algorithms

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The goal of manufacturing scheduling is to allocate a set of jobs to the machines in the shop so these jobs are processed according to a given criterion (or set of criteria). Such criteria are based on properties of the jobs to be scheduled (e.g., their completion times, due dates); so it is not clear how these (short-term) criteria impact on (long-term) shop floor performance measures. In this paper, we analyse the connection between the usual scheduling criteria employed as objectives in flowshop scheduling (e.g., makespan or idle time), and customary shop floor performance measures (e.g., work-in-process and throughput). Two of these linkages can be theoretically predicted (i.e., makespan and throughput as well as completion time and average cycle time), and the other such relationships should be discovered on a numerical/empirical basis. In order to do so, we set up an experimental analysis consisting in finding optimal (or good) schedules under several scheduling criteria, and then computing how these schedules perform in terms of the different shop floor performance measures for several instance sizes and for different structures of processing times. Results indicate that makespan only performs well with respect to throughput, and that one formulation of idle times obtains nearly as good results as makespan, while outperforming it in terms of average cycle time and work in process. Similarly, minimisation of completion time seems to be quite balanced in terms of shop floor performance, although it does not aim exactly at work-in-process minimisation, as some literature suggests. Finally, the experiments show that some of the existing scheduling criteria are poorly related to the shop floor performance measures under consideration. These results may help to better understand the impact of scheduling on flowshop performance, so scheduling research may be more geared towards shop floor performance, which is sometimes suggested as a cause for the lack of applicability of some scheduling models in manufacturing.

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“…Yang and Posner 4,5 established a value-added model by using two heuristic algorithms to minimize the total WIP-level production cost. Li et al 6 indicated that the minimization of WIP inventories might lead to the minimization of production line utilization and proposed a combined objective to improve the flow shop production scheduling. A dynamic system was proposed by Deif and ElMaraghy 7 based on mix-leveling policies, aiming to control accumulated WIP level and to manage the capacity scalability, production costs and WIP-level performance.…”

Section: Introductionmentioning

confidence: 99%

A location prediction method for work-in-process based on frequent trajectory patterns

Cai

Guo

Lü

2017

Proceedings of the Institution of Mechanical Engineers, Part B:

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In the data-rich manufacturing environment, the production process of work-in-process is described and presented by trajectories with manufacturing significance. However, advanced approaches for work-in-process trajectory data analytics and prediction are comparatively inadequate. However, the location prediction of moving objects has drawn great attention in the manufacturing field. Yet most approaches for predicting future locations of objects are originally applied in geography domain. When applied to manufacturing shop floor, the prediction results lack manufacturing significance. This article focuses on predicting the next locations of work-in-process in the workshop. First, a data model is introduced to map the geographic trajectories into the logical space, in order to convert the manufacturing information into logical features. Based on the data model, a prediction method is proposed to predict the next locations using frequent trajectory patterns. A series of experiments are performed to examine the prediction method. The experiment results illustrate the impacts of the user-defined factors and prove that the proposed method is effective and efficient.

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The Relationship between Maximum Completion Time and Total Completion Time in Flowshop Production

Cited by 8 publications

References 17 publications

Shop scheduling in manufacturing environments: a review

Shop scheduling in manufacturing environments: a review

Linking Scheduling Criteria to Shop Floor Performance in Permutation Flowshops

A location prediction method for work-in-process based on frequent trajectory patterns

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