Flexible job shop scheduling problem with parallel batch processing machines: MIP and CP approaches

Ham, Andy; Cakici, Eray

doi:10.1016/j.cie.2016.11.001

Cited by 60 publications

(16 citation statements)

References 26 publications

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“…Zhang and Wang [48] proposed a CP model to minimize the makespan by incorporating SDST, part sharing, and disruptions such as machine breakdown, material unavailability, and rush orders. Ham and Cakici [44] applied a CP approach, and demonstrated its superiority with parallel batch-processing machines, compared with another MILP approach. Novas [49] described a CP model that addressed lot splitting for determining the number of sublots or parts in a sublot, as well as the scheduling of production tasks for assigning operations on the sublots.…”

Section: Methodsmentioning

confidence: 99%

A Review of Prediction and Optimization for Sequence-Driven Scheduling in Job Shop Flexible Manufacturing Systems

Meilanitasari

Shin

2021

Processes

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This article reviews the state of the art of prediction and optimization for sequence-driven scheduling in job shop flexible manufacturing systems (JS-FMSs). The objectives of the article are to (1) analyze the literature related to algorithms for sequencing and scheduling, considering domain, method, objective, sequence type, and uncertainty; and to (2) examine current challenges and future directions to promote the feasibility and usability of the relevant research. Current challenges are summarized as follows: less consideration of uncertainty factors causes a gap between the reality and the derived schedules; the use of stationary dispatching rules is limited to reflect the dynamics and flexibility; production-level scheduling is restricted to increase responsiveness owing to product-level uncertainty; and optimization is more focused, while prediction is used mostly for verification and validation, although prediction-then-optimization is the standard stream in data analytics. In future research, the degree of uncertainty should be quantified and modeled explicitly; both holistic and granular algorithms should be considered; product sequences should be incorporated; and sequence learning should be applied to implement the prediction-then-optimization stream. This would enable us to derive data-learned prediction and optimization models that output accurate and precise schedules; foresee individual product locations; and respond rapidly to dynamic and frequent changes in JS-FMSs.

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Section: Methodsmentioning

confidence: 99%

A Review of Prediction and Optimization for Sequence-Driven Scheduling in Job Shop Flexible Manufacturing Systems

Meilanitasari

Shin

2021

Processes

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“…Ham and Cakici [14] studied an FJSP with parallel batch processing machines (PBM). They developed a mixed integer programming (MIP) model and a constraint programming (CP) model to reduce computational time.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Constraints (13) determine earliness duration for each job in each order on each machine. Constraints (14) and (15) define the and as binary variables. Constraints (16), (17), and 18 are non-negativity constraints.…”

Section: Decision Variablesmentioning

confidence: 99%

Bir Uygulama: Bir Beyaz Eşya Üretim Firmasının Boyahane Bölümünde Paralel Makineler için Sıraya Bağlı Kurulum Sürelerini içeren bir Model

Kasimoğlu

Demir

Yaz

et al. 2021

Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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Excessive buffer inventory may disrupt production and constitute one of the main problems. One of the ways of coping with the inventory problems in the mass production lines is to achieve and implement a detailed production schedule. In this study, a company with a mass production line in the die house station of a white goods sector is in consideration. A mixed-integer programming model with sequence-dependent setup times has been developed to solve the excessive work in process problems for the dye house station. The developed model has been applied to the company to test the model by using real data and the problem has been solved by using the General Algebraic Modeling System (GAMS) CPLEX 24,1 solver. The optimal solution is obtained in 10 hours and 3 minutes. In the solution, the total earliness and tardiness time for 30 jobs is 4299 minutes.

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“…For batch scheduling problems, as the one presented in this paper, the new constraint programming solvers as the CP Optimizer available in IBM ILOG Optimization Suite (Laborie, 2009), presents some advantages compared with classical mixed-integer optimization approach, as indicated in the studies presented by Ham and Cakici (2016), Ku and Beck (2016) or in Ham et al (2017). Among other facts regarding the CP approach, the authors highlight the ability to write a more natural formulation of the problem, the flexibility and scalability of the code, and the high level description of the problem close to the engineering one where the search algorithms are automatically embedded in the CP solver.…”

Section: State-of-the-artmentioning

confidence: 99%

Optimal batch scheduling of a multiproduct dairy process using a combined optimization/constraint programming approach

Escobet¹,

Puig²,

Quevedo³

et al. 2019

Computers & Chemical Engineering

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This paper presents the optimal batch scheduling of a multi-product dairy process using an approach that combines optimization and constraint programming techniques. A suitable model describing the subprocesses and production rules is developed allowing to obtain scheduling constraints relating the production process and the machines available together with their relative efficiencies. After the scheduling problem has been formulated, the batch scheduling of a real powder milk/yogurt process is obtained in an optimal manner using the proposed approach with the objective of meeting customers' deadlines considering the efficiencies/costs of available alternative machines. Results using real consumer orders on some representative scenarios corresponding to the dairy production plant used as a case study are provided. This application shows a formulation closer to the engineering problem description thanks to the constraint-based language that facilitates the adaptation of the optimization objectives and constraints to real applications.

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Flexible job shop scheduling problem with parallel batch processing machines: MIP and CP approaches

Cited by 60 publications

References 26 publications

A Review of Prediction and Optimization for Sequence-Driven Scheduling in Job Shop Flexible Manufacturing Systems

A Review of Prediction and Optimization for Sequence-Driven Scheduling in Job Shop Flexible Manufacturing Systems

Bir Uygulama: Bir Beyaz Eşya Üretim Firmasının Boyahane Bölümünde Paralel Makineler için Sıraya Bağlı Kurulum Sürelerini içeren bir Model

Optimal batch scheduling of a multiproduct dairy process using a combined optimization/constraint programming approach

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