Dynamic programming algorithms and Lagrangian lower bounds for a discrete lot streaming problem in a two-machine flow shop

Alfieri, Arianna; Zhou, Shuyu; Scatamacchia, Rosario; Velde, S.L. van de

doi:10.1007/s10288-020-00449-8

Cited by 6 publications

(5 citation statements)

References 36 publications

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“…Each work must flow through each of the previous stages according to the same sequence, the performance of the system is given by the performance of each work at each stage [20], cases presented in industries such as glass, steel, plastic, and pharmaceutical. Its main feature is that the job is not allowed to move to the next stage until the process is completed, which leads to the machine timeout [21].…”

Section: Problem/type Of Production Process Studiedmentioning

confidence: 99%

“…Consistent: If the lot size is the same for each pair of machines, with this type of batches work [17,21,34,38,41,42,[48][49][50][51]57,59,61,64,[67][68][69]75].…”

Section: Types Of Sublots or Jobsmentioning

confidence: 99%

“…HA: Heuristic algorithm [62,63,68] HGA: Hybrid genetic algorithm [46,47,54] SA: Simulated annealing [40,57,66] DABC: Discrete artificial bee colony [37,44] DPA: Dynamic programming algorithms [21,55] DSOMA: Discrete self-organizing migrating algorithm [31,52] GA: Genetic algorithm [61,64] IMMBO: Improved migrating birds optimization [18,60] DEA: Differential Evolution Algorithm/PSO: Particle Swarm Optimization [29] GLASS-POTTS/JOHNSON'S [48] ABC: Artificial bee colony [72] DACS: Distributed ant colony system [76] DIWO: Discrete invasive weed optimization [19] DLHS: Local-best harmony search with dynamic sub-harmony memories [35] DPSO: Discrete particle swarm optimization [53] EDA: Estimation of distribution algorithm [30] EMMBO: Effective modified migrating birds optimization (EMBO) [65] GAJS: Genetic algorithm-based job splitting approach [73] GEA: Greedy constructive algorithm [74] HDABC: Hybrid discrete artificial bee colony [24] HDHS: Hybrid discrete harmony search [56] ILS: Iterated local search [33] INSGA-II: Improved Non-dominated Sorting Genetic Algorithm II [27] MA: Memetic algorithm [77] MABC: Modified artificial bee colony [69] MHA: Metaheuristic algorithm [45] MOMBO: Multi-Objective Migrating Birds Optimization [25] NEMO: Novel evolutionary multi-o...…”

Section: Algorithm Papersmentioning

confidence: 99%

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

Salazar-Moya

García

2021

Designs

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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.

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Section: Problem/type Of Production Process Studiedmentioning

confidence: 99%

“…Consistent: If the lot size is the same for each pair of machines, with this type of batches work [17,21,34,38,41,42,[48][49][50][51]57,59,61,64,[67][68][69]75].…”

Section: Types Of Sublots or Jobsmentioning

confidence: 99%

Section: Algorithm Papersmentioning

confidence: 99%

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

Salazar-Moya

García

2021

Designs

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“…The optimal solution with variable sublots in the three-machine flow shop was proposed by Trietsch & Baker (1993). Alfieri et al (2012) and Alfieri et al (2021) proposed exact, and heuristic solution approaches based on dynamic programming for a single-job problem to minimize the makespan and total flow time, respectively, in a two-machine flow shop with attached setup times. Vickson & Alfredson (1992) considered the concept of lot streaming for scheduling multiple job lots in flow shops.…”

Section: Literature Reviewmentioning

confidence: 99%

Scheduling with lot streaming in a two-machine re-entrant flow shop

Çetinkaya

Duman²

2021

Oper. Res. Eng. Sci. Theor. Appl.

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Lot streaming is splitting a job-lot of identical items into several sublots (portions of a lot) that can be moved to the next machines upon completion so that operations on successive machines can be overlapped; hence, the overall performance of a multi-stage manufacturing environment can be improved. In this study, we consider a scheduling problem with lot streaming in a two-machine re-entrant flow shop in which each job-lot is processed first on Machine 1, then goes to Machine 2 for its second operation before it returns to the primary machine (either Machine 1 or Machine 2) for the third operation. For the two cases of the primary machine, both single-job and multi-job cases are studied independently. Optimal and near-optimal solution procedures are developed. Our objective is to minimize the makespan, which is the maximum completion time of the sublots and job lots in the single-job and multi-job cases, respectively. We prove that the single-job problem is optimally solved in polynomial-time regardless of whether the third operation is performed on Machine 1 or Machine 2. The multi-job problem is also optimally solvable in polynomial time when the third operation is performed on Machine 2. However, we prove that the multi-job problem is NP-hard when the third operation is performed on Machine 1. A global lower bound on the makespan and a simple heuristic algorithm are developed. Our computational experiment results reveal that our proposed heuristic algorithm provides optimal or near-optimal solutions in a very short time.

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“…Thus, our problem is closely related to scheduling problems with job splitting (which are focused on splitting jobs into sublots so that the sublots can be processed simultaneously on parallel, uniform, or unrelated machines) and lot streaming (which refers to splitting production lots into sublots to accelerate the flow of the products through a multiple‐stage production system). Interested readers may refer to the literatures about the latest research on scheduling with job splitting (Avgerinos et al, 2023; Correa et al, 2015; Logendran & Subur, 2004; Serafini, 1996) and lot streaming (Alfieri et al, 2021; Chang & Chiu, 2005; Cheng et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

A branch‐and‐price algorithm for identical parallel machine scheduling with multiple milestones

Zhong,

Cui,

Jiang

2023

Naval Research Logistics

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This article considers an identical parallel‐machine task scheduling problem motivated by operations management of online services. A task with an integer processing time can be split into sub‐tasks with integer processing times. Each task has multiple integer milestones and at each milestone a nonnegative penalty will occur. The penalty value of a task at a milestone is a convex nonincreasing function of the completed amount by this milestone. Our objective is to determine a feasible schedule for all the tasks on given identical parallel machines, such that the sum of all tasks' total penalty at all milestones is minimized. We prove the NP‐hardness of this problem in the ordinary sense and develop a branch‐and‐price algorithm. Computational experiments utilizing data from an online service operations survey show that this algorithm is singularly efficient and promising.

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Dynamic programming algorithms and Lagrangian lower bounds for a discrete lot streaming problem in a two-machine flow shop

Cited by 6 publications

References 36 publications

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

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

Scheduling with lot streaming in a two-machine re-entrant flow shop

A branch‐and‐price algorithm for identical parallel machine scheduling with multiple milestones

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