Order scheduling with controllable processing times, common due date and the processing deadline

Yue, Qing; Wan, Guohua

doi:10.1007/s11518-016-5323-3

Cited by 4 publications

(5 citation statements)

References 40 publications

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“…• Type 1: The unloading time is generated uniformly from [1,10]. • Type 2: The unloading time is generated uniformly from [1,20].…”

Section: Test Problemsmentioning

confidence: 99%

“…• Type 1: The unloading time is generated uniformly from [1,10]. • Type 2: The unloading time is generated uniformly from [1,20]. • Type 3: The unloading time is generated uniformly from [1,40].…”

Section: Test Problemsmentioning

confidence: 99%

“…• Type 2: The unloading time is generated uniformly from [1,20]. • Type 3: The unloading time is generated uniformly from [1,40]. The three types indicate the importance of unloading time relative to processing time.…”

Section: Test Problemsmentioning

confidence: 99%

“…Scheduling plays a critical role in the production planning of manufacturing systems and serves as a key determinant of success in today's competitive marketplace [1]. The flexible flow shop scheduling problem (FFSP) is prevalent in diverse industries, including the chemical industry [2], metallurgy [3], textile manufacturing [4], semiconductor production [5], logistics, paper manufacturing, and construction.…”

Section: Introductionmentioning

confidence: 99%

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Effective Two-Phase Heuristic and Lower Bounds for Multi-Stage Flexible Flow Shop Scheduling Problem with Unloading Times

Hidri

2023

Symmetry

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This paper addresses the flexible flow shop scheduling problem with unloading operations, which commonly occurs in modern manufacturing processes like sand casting. Although only a few related works have been proposed in the literature, the significance of this problem motivates the need for efficient algorithms and the exploration of new properties. One interesting property established is the symmetry of the problem, where scheduling from the first stage to the last or vice versa yields the same optimal solution. This property enhances solution quality. Considering the problem’s theoretical complexity as strongly NP-Hard, approximate solutions are preferable, especially for medium and large-scale instances. To address this, a new two-phase heuristic is proposed, consisting of a constructive phase and an improvement phase. This heuristic builds upon an existing efficient heuristic for the parallel machine-scheduling problem and extends it to incorporate unloading times efficiently. The selection of the two-phase heuristic is justified by its ability to generate high-quality schedules at each stage. Moreover, new efficient lower bounds based on estimating minimum idle time in each stage are presented, utilizing the polynomial parallel machine-scheduling problem with flow time minimization in the previous stage. These lower bounds contribute to assessing the performance of the two-phase heuristic over the relative gap performance measure. Extensive experiments are conducted on benchmark test problems, demonstrating the effectiveness of the proposed algorithms. The results indicate an average computation time of 9.92 s and a mean relative gap of only 2.80% for several jobs up to 200 and several stages up to 10.

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“…• Type 1: The unloading time is generated uniformly from [1,10]. • Type 2: The unloading time is generated uniformly from [1,20].…”

Section: Test Problemsmentioning

confidence: 99%

Section: Test Problemsmentioning

confidence: 99%

Section: Test Problemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Effective Two-Phase Heuristic and Lower Bounds for Multi-Stage Flexible Flow Shop Scheduling Problem with Unloading Times

Hidri

2023

Symmetry

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“…Scheduling is a methodology that optimizes the use of resources by ordering a sequence of jobs assigned to each resource. It plays a fundamental role in production planning of manufacturing systems, and it is an important success factor in the modern competitive marketplace (Yue and Wan, 2017). The hybrid flow shop (HFS) is one of the most recognized scheduling problems, and has attracted much attention given its complexity and practical relevance.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Worker Learning on Scheduling Jobs in a Hybrid Flow Shop: A Bi-Objective Approach

Pargar

Zandieh

Kauppila

et al. 2018

J. Syst. Sci. Syst. Eng.

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This paper studies learning effect as a resource utilization technique that can model improvement in worker's ability as a result of repeating similar tasks. By considering learning of workers while performing setup times, a schedule can be determined to place jobs that share similar tools and fixtures next to each other. The purpose of this paper is to schedule a set of jobs in a hybrid flow shop (HFS) environment with learning effect while minimizing two objectives that are in conflict: namely maximum completion time (makespan) and total tardiness. Minimizing makespan is desirable from an internal efficiency viewpoint, but may result in individual jobs being scheduled past their due date, causing customer dissatisfaction and penalty costs. A bi-objective mixed integer programming model is developed, and the complexity of the developed bi-objective model is compared against the bi-criteria one through numerical examples. The effect of worker learning on the structure of assigned jobs to machines and their sequences is analyzed. Two solution methods based on the hybrid water flow like algorithm and non-dominated sorting and ranking concepts are proposed to solve the problem. The quality of the approximated sets of Pareto solutions is evaluated using several performance criteria. The results show that the proposed algorithms with learning effect perform well in reducing setup times and eliminate the need for setups itself through proper scheduling.

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Machine tool reliability: an operational perspective

Purohit

Ringard

Lad

2019

Life Cycle Reliab Saf Eng

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Order scheduling with controllable processing times, common due date and the processing deadline

Cited by 4 publications

References 40 publications

Effective Two-Phase Heuristic and Lower Bounds for Multi-Stage Flexible Flow Shop Scheduling Problem with Unloading Times

Effective Two-Phase Heuristic and Lower Bounds for Multi-Stage Flexible Flow Shop Scheduling Problem with Unloading Times

The Effect of Worker Learning on Scheduling Jobs in a Hybrid Flow Shop: A Bi-Objective Approach

Machine tool reliability: an operational perspective

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