A fast simulated annealing method for batching precedence-constrained customer orders in a warehouse

Matusiak, Marek; Koster, René de; Kroon, Leo; Saarinen, Jyrki

doi:10.1016/j.ejor.2013.06.001

Cited by 114 publications

(68 citation statements)

References 29 publications

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“…In a reassignment phase of a clustering algorithm (Sağlam et al 2006), reversion to a relaxed mixed-integer solution is embedded within a heuristic process. In other search processes, the chain of events in transforming one solution to another may technically very often revert to the best solution stored in memory, particularly when the probability of acceptance of inferior solutions decreases with search time and the search is designed to revert to the best solution (Cordeau and Maischberger 2012;Matusiak et al 2014). However, this is not the standard case in some s-metaheuristics such as tabu search.…”

Section: Introductionmentioning

confidence: 89%

Search reversion within s-metaheuristics: impacts illustrated with a forest planning problem

Bettinger¹,

Demirci²,

Boston³

2015

Silva Fenn.

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Search reversion within s-metaheuristics: impacts illustrated with a forest planning problemBettinger P., Demirci M., Boston K. (2015). Search reversion within s-metaheuristics: impacts illustrated with a forest planning problem. Silva Fennica vol. 49 no. 2 article id 1232. 20 p. Highlights• The interruption of the sequence of events used to explore a solution space and develop a forest plan, and the re-initiation of the search process from a high-quality, known starting point (reversion) seems necessary for some s-metaheuristics.• When using a s-metaheuristic, higher quality forest plans may be developed when the reversion interval is around six iterations of the model. AbstractThe use of a reversion technique during the search process of s-metaheuristics has received little attention with respect to forest management and planning problems. Reversion involves the interruption of the sequence of events that are used to explore the solution space and the re-initiation of the search process from a high-quality, known starting point. We explored four reversion rates when applied to three different types of s-metaheuristics that have previously shown promise for the forest planning problem explored, threshold accepting, tabu search, and the raindrop method. For two of the s-metaheuristics, we also explored three types of decision choices, a change to the harvest timing of a single management unit (1-opt move), the swapping of two management unit's harvest timing (2-opt moves), and the swapping of three management unit's harvest timing (3-opt moves). One hundred independent forest plans were developed for each of the metaheuristic / reversion rate combinations, all beginning with randomly-generated feasible starting solutions. We found that (a) reversion does improve the quality of the solutions generated, and (b) the rate of reversion is an important factor that can affect solution quality.

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Section: Introductionmentioning

confidence: 89%

Search reversion within s-metaheuristics: impacts illustrated with a forest planning problem

Bettinger¹,

Demirci²,

Boston³

2015

Silva Fenn.

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“…As for instance, we can mention the On-line OBP by Henn (2012), the Order Batching and Sequencing Problem by Henn and Schmid (2013), the combined Precedence Constrained Picker Routing and Order Batching Problem by Matusiak et al (2014), the Integrated Batching and Batch Sequencing Problem by Hong et al (2012) and, the Integrated Order Batching and Vehicle Routing Problem with Time Windows by Schmid et al (2013). For an interested reader, we refer to Gu et al (2007Gu et al ( ,2010 and Baker and Canessa (2009) for a review of several warehousing issues and, Gong and de Koster (2013) for a comparative discussion of stochastic warehouse models.…”

Section: Literature Surveymentioning

confidence: 99%

MILP formulations and an Iterated Local Search Algorithm with Tabu Thresholding for the Order Batching Problem

Öncan

2015

European Journal of Operational Research

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“…Roshani and Nezami presented a new MIP mathematical formulation with the objective of minimizing the total number of workers and the number of multi‐manned workstations as the first and second priorities, and a SA algorithm was proposed to efficiently solve the problem within a reasonable computational time. Matusiak et al . introduced a joint order‐batching and picker routing method to solve combined precedence‐constrained routing and order‐batching problem.…”

Section: Introductionmentioning

confidence: 99%

Optimization of sound absorption property for polyurethane foam using adaptive simulated annealing algorithm

Zhu

Chen

Zhu

et al. 2018

J of Applied Polymer Sci

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Polyurethane foam is one of the sound absorbing materials because of its advantage in lightweight, sound absorption and low cost. Therefore, it is important to optimize the formulation for better sound absorption performance. In this study, experimental optimization was carried out with a response surface methodology to investigate the effects of different variables including catalyst triethanolamine, catalyst A33, and additive polyethylene fiber. The mathematical model was developed for correlating experimental data. The model parameters were optimized by adaptive simulated annealing algorithm. The maximum acoustic property of the foams was found to be 0.68 by adding 3.2 g triethanolamine, 1.0 g A33, and 0.1 g polyethylene fiber. Then, the polyurethane foam was synthesized according to the optimization results. The sound absorption coefficient of it is within the allowable error of the optimization result.

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A fast simulated annealing method for batching precedence-constrained customer orders in a warehouse

Cited by 114 publications

References 29 publications

Search reversion within s-metaheuristics: impacts illustrated with a forest planning problem

Search reversion within s-metaheuristics: impacts illustrated with a forest planning problem

MILP formulations and an Iterated Local Search Algorithm with Tabu Thresholding for the Order Batching Problem

Optimization of sound absorption property for polyurethane foam using adaptive simulated annealing algorithm

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