Stochastic customer order scheduling with setup times to minimize expected cycle time

Zhao, Yaping; Xu, Xiaoyun; Li, Haidong; Liu, Yanni

doi:10.1080/00207543.2017.1381348

Cited by 12 publications

(5 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…, 2018a) to further improve the computational performance. Zhao et al. (2018b) further consider the same objective with product-dependent setup time considerations.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Resource-controlled stochastic customer order scheduling via particle swarm optimization with bound information

Zhao

Kong

et al. 2022

IMDS

Self Cite

View full text Add to dashboard Cite

PurposeCycle time reduction is important for order fulling process but often subject to resource constraints. This study considers an unrelated parallel machine environment where orders with random demands arrive dynamically. Processing speeds are controlled by resource allocation and subject to diminishing marginal returns. The objective is to minimize long-run expected order cycle time via order schedule and resource allocation decisions.Design/methodology/approachA stochastic optimization algorithm named CAP is proposed based on particle swarm optimization framework. It takes advantage of derived bound information to improve local search efficiency. Parameter impacts including demand variance, product type number, machine speed and resource coefficient are also analyzed through theoretic studies. The algorithm is evaluated and benchmarked with four well-known algorithms via extensive numerical experiments.FindingsFirst, cycle time can be significantly improved when demand randomness is reduced via better forecasting. Second, achieving processing balance should be of top priority when considering resource allocation. Third, given marginal returns on resource consumption, it is advisable to allocate more resources to resource-sensitive machines.Originality/valueA novel PSO-based optimization algorithm is proposed to jointly optimize order schedule and resource allocation decisions in a dynamic environment with random demands and stochastic arrivals. A general quadratic resource consumption function is adopted to better capture diminishing marginal returns.

show abstract

“…, 2018a) to further improve the computational performance. Zhao et al. (2018b) further consider the same objective with product-dependent setup time considerations.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Simulation optimization methods are adopted in (Xu et al, 2015b;Zhao et al, 2018a) to further improve the computational performance. Zhao et al (2018b) further consider the same objective with product-dependent setup time considerations. Lang et al (2020) extend the study to cellular manufacturing systems with parallel machines.…”

Section: Literature Reviewmentioning

confidence: 99%

Resource-controlled stochastic customer order scheduling via particle swarm optimization with bound information

Zhao

Kong

et al. 2022

IMDS

Self Cite

View full text Add to dashboard Cite

show abstract

“…Zhao et al, considered a system in which production is limited by inventory, and they provided maximum power through analysis and mathematical programming. For a multi-stage processing environment, most research focuses on tasks or a static environment, while random customer orders receive less attention [7]. Chen et al, considered the synchronization of order production production with the dynamic entry into a flowshop problem [8].…”

Section: Cell Production Systemmentioning

confidence: 99%

Cell Production System Design: A Literature Review

Ghahremani-Nahr

Bathaee

Mazloumzadeh

et al. 2021

IJIMES

View full text Add to dashboard Cite

In a cell production system, a number of machines that differ in function are housed in the same cell. The task of these cells is to complete operations on similar parts that are in the same group. Determining the family of machine parts and cells is one of the major design problems of production cells. Cell production system design methods include clustering, graph theory, artificial intelligence, meta-heuristic, simulation, mathematical programming. This article discusses the operation of methods and research in the field of cell production system design. Methodology:To examine these methods, from 187 articles published in this field by authoritative scientific sources, based on the year of publication and the number of restrictions considered and close to reality, which are searched using the keywords of these restrictions and among them articles Various aspects of production and design problems, such as considering machine costs and cell size and process routing, have been selected simultaneously.Findings: Finally, the distribution diagram of the use of these methods and the limitations considered by their researchers, shows the use and efficiency of each of these methods. By examining them, more efficient and efficient design fields of this type of production system can be identified.Originality/Value: In this article, the literature on cell production system from 1972 to 2021 has been reviewed.

show abstract

“…Lengthy setup times can jeopardize the on-time delivery of customer orders, leading to potential losses in customer satisfaction and incurring direct or indirect costs. Effectively incorporating and managing the setup time factor in scheduling decisions can yield substantial benefits ( Kim & Bobrowski, 1997 ; Allahverdi, Gupta & Aldowaisan, 1999 ; Allahverdi et al, 2008 ; van Donk & van Doorne, 2016 ; Zhao et al, 2018 ; Ying et al, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

A bi-criterion sequence-dependent scheduling problem with order deliveries

Xu,

Lin,

et al. 2024

PeerJ Computer Science

View full text Add to dashboard Cite

The manufacturing sector faces unprecedented challenges, including intense competition, a surge in product varieties, heightened customization demands, and shorter product life cycles. These challenges underscore the critical need to optimize manufacturing systems. Among the most enduring and complex challenges within this domain is production scheduling. In practical scenarios, setup time is whenever a machine transitions from processing one product to another. Job scheduling with setup times or associated costs has garnered significant attention in both manufacturing and service environments, prompting extensive research efforts. While previous studies on customer order scheduling primarily focused on orders or jobs to be processed across multiple machines, they often overlooked the crucial factor of setup time. This study addresses a sequence-dependent bi-criterion scheduling problem, incorporating order delivery considerations. The primary objective is to minimize the linear combination of the makespan and the sum of weighted completion times of each order. To tackle this intricate challenge, we propose pertinent dominance rules and a lower bound, which are integral components of a branch-and-bound methodology employed to obtain an exact solution. Additionally, we introduce a heuristic approach tailored to the problem’s unique characteristics, along with three refined variants designed to yield high-quality approximate solutions. Subsequently, these three refined approaches serve as seeds to generate three distinct populations or chromosomes, each independently employed in a genetic algorithm to yield a robust approximate solution. Ultimately, we meticulously assess the efficacy of each proposed algorithm through comprehensive simulation trials.

show abstract

Stochastic customer order scheduling with setup times to minimize expected cycle time

Cited by 12 publications

References 53 publications

Resource-controlled stochastic customer order scheduling via particle swarm optimization with bound information

Resource-controlled stochastic customer order scheduling via particle swarm optimization with bound information

Cell Production System Design: A Literature Review

A bi-criterion sequence-dependent scheduling problem with order deliveries

Contact Info

Product

Resources

About