A survey of scheduling with parallel batch (p-batch) processing

Fowler, John W.; Mönch, Lars

doi:10.1016/j.ejor.2021.06.012

Cited by 93 publications

(23 citation statements)

References 238 publications

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“…The distribution of this random number follows a uniform distribution. This random number is formalized by using (2). Then, the movement is selected based on the location of this random number related to the thresholds.…”

Section: Proposed Modelmentioning

confidence: 99%

“…On the other hand, the term result can also be translated into many ways, such as sales, profit, served customers, accuracy, and so on. In the production process, optimization is widely used, such as in the flow-shop scheduling [1], batch-shop scheduling [2], assembly line balancing [3], procurement [4], and so on. In transportation and logistics, optimization is implemented in route planning [5], storage management [6], and so on.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Pelican Komodo Algorithm

Kusuma¹,

Dinimaharawati²

2022

IJACSA

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In this work, a new metaheuristic algorithm, namely the hybrid pelican Komodo algorithm (HPKA), has been proposed. This algorithm is developed by hybridizing two shortcoming metaheuristic algorithms: the Pelican Optimization Algorithm (POA) and Komodo Mlipir Algorithm (KMA). Through hybridization, the proposed algorithm is designed to adapt the advantages of both POA and KMA. Several improvisations regarding this proposed algorithm are as follows. First, this proposed algorithm replaces the randomized target with the preferred target in the first phase. Second, four possible movements are selected stochastically in the first phase. Third, in the second phase, the proposed algorithm replaces the agent's current location with the problem space width to control the local problem space. This proposed algorithm is then challenged to tackle theoretical and real-world optimization problems. The result shows that the proposed algorithm is better than grey wolf optimizer (GWO), marine predator algorithm (MPA), KMA, and POA in solving 14, 12, 14, and 18 functions. Meanwhile, the proposed algorithm creates 109%, 46%, 47%, and 1% better total capital gain rather than GWO, MPA, KMA, and POA, respectively in solving the portfolio optimization problem.

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Section: Proposed Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid Pelican Komodo Algorithm

Kusuma¹,

Dinimaharawati²

2022

IJACSA

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“…The difference is that the size of the formulation increases with the number of distinct sizes and processing times of the jobs, but it does not increase with the number of jobs. For the details of deterministic parallel batching scheduling problems, we refer the readers to a recent survey of Fowler and M€ onch (2022).…”

Section: Related Literaturementioning

confidence: 99%

A branch-and-price algorithm for robust parallel batch scheduling problem with uncertain size

Wang

Shao

Yan

2022

IMDS

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PurposeIn intelligent scheduling, parallel batch processing can reasonably allocate production resources and reduce the production cost per unit product. Hence, the research on a parallel batch scheduling problem (PBSP) with uncertain job size is of great significance to realize the flexibility of product production and mass customization of personalized products.Design/methodology/approachThe authors propose a robust formulation in which the job size is defined by budget constrained support. For obtaining the robust solution of the robust PBSP, the authors propose an exact algorithm based on branch-and-price framework, where the pricing subproblem can be reduced to a robust shortest path problem with resource constraints. The robust subproblem is transformed into a deterministic mixed integer programming by duality. A series of deterministic shortest path problems with resource constraints is derived from the programming for which the authors design an efficient label-setting algorithm with a strong dominance rule.FindingsThe authors test the performance of the proposed algorithm on the extension of benchmark instances in literature and compare the infeasible rate of robust and deterministic solutions in simulated scenarios. The authors' results show the efficiency of the authors' algorithm and importance of incorporating uncertainties in the problem.Originality/valueThis work is the first to study the PBSP with uncertain size. To solve this problem, the authors design an efficient exact algorithm based on Dantzig–Wolfe decomposition. This can not only enrich the intelligent manufacturing theory related to parallel batch scheduling but also provide ideas for relevant enterprises to solve problems.

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“…Metaheuristic algorithms have been implemented widely in many optimization problems. In the manufacturing sector, they have been used to solve many scheduling problems, such as in flow-shop [1], job-shop [2], batch-shop [3], and so on. In the transportation sector, they also have been utilized to solve many routing problems, such as in the capacitated vehicle routing problem [4], where each vehicle has a maximum load capacity that cannot be surpassed, and in the pickup delivery problem [5] where the vehicle must tackle two activities: pickup and delivery.…”

Section: Introductionmentioning

confidence: 99%

Guided Pelican Algorithm

2022

IJIES

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This paper presented a new metaheuristic technique, namely the guided pelican algorithm (GPA). GPA has the improvements for a shortcoming algorithm, namely the pelican optimization algorithm (POA), that mimics the behaviour of pelican birds during hunting prey. It improves the original POA in three ways. First, GPA replaces the randomized target with the global best solution as a deterministic target in phase one. Second, GPA replaces the pelican's current location with the search space size in determining the local search space size in phase two. Third, GPA implements multiple candidates in both phases rather than a single candidate as it is used in the original POA. Simulation is implemented to observe GPA's performance in optimizing both theoretical and real-world problems. GPA is compared with four algorithms: marine predator algorithm (MPA), particle swarm optimization (PSO), komodo mlipir algorithm (KMA), and POA. The result shows that GPA outperforms all sparing algorithms in optimizing most benchmark functions. GPA is also implemented to optimize the portfolio problem. The result shows that GPA outperforms all sparing algorithms in optimizing the portfolio problem. It outperforms three sparing algorithms in optimizing the portfolio problem. Its performance is 9%, 11%, and 13%, better than PSO, MPA, and KMA consecutively. Meanwhile, its less than 1% worse than POA.

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A survey of scheduling with parallel batch (p-batch) processing

Cited by 93 publications

References 238 publications

Hybrid Pelican Komodo Algorithm

Hybrid Pelican Komodo Algorithm

A branch-and-price algorithm for robust parallel batch scheduling problem with uncertain size

Guided Pelican Algorithm

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