A simple yet effective grouping evolutionary strategy (GES) algorithm for scheduling parallel machines

Kashan, Ali Husseinzadeh; Keshmiry, Marziehsadat; Dahooie, Jalil Heidary; Abbasi-Pooya, Amin

doi:10.1007/s00521-016-2789-3

Cited by 15 publications

(3 citation statements)

References 33 publications

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“…Because the building blocks that should be preserved in an evolutionary or swarm intelligence method should be the groups or the group segments, focusing on items isolated may have little impact during the search. e most well-adapted grouping evolutionary algorithms for grouping problems are the grouping genetic algorithm and the grouping evolution strategy algorithm [53][54][55][56].…”

Section: The Solution Methodsmentioning

confidence: 99%

A Mixed Integer Linear Formulation and a Grouping League Championship Algorithm for a Multiperiod‐Multitrip Order Picking System with Product Replenishment to Minimize Total Tardiness

et al. 2022

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Order picking, which is collecting a set of products from different locations in a warehouse, has repeatedly been described as one of the most laborious and time-consuming internal logistic processes. Each order is issued to pick some products located at given locations in the warehouse. In this paper, we consider an order picking problem, in which a number of orders with different delivery due dates are going to be retrieved by a limited number of order pickers in multiperiods such that the total tardiness is minimized. The aim is to determine a retrieval plan in terms of order batching and order picker multitrip routing as decision variables. Besides, products are arrived and replenished at the predetermined locations at different periods. Therefore, products sitting in those locations should be delivered soon to provide empty rooms for replenishment. A mixed integer linear programming formulation is proposed for this new problem. The model is optimally solved for small-size problems. For larger instances, grouping metaheuristic algorithms are proposed based on particle swarm optimization and the league championship algorithm that use group-based operators to generate reasonable batches of orders. Improvement heuristics are designed as well. The performance of the MILP formulation and metaheuristic algorithms is analyzed for different problem instances whose designs are based on real data gathered from an auto parts warehouse. Results indicate that our algorithms can stably solve large instances of the problem in a reasonable time.

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Section: The Solution Methodsmentioning

confidence: 99%

A Mixed Integer Linear Formulation and a Grouping League Championship Algorithm for a Multiperiod‐Multitrip Order Picking System with Product Replenishment to Minimize Total Tardiness

et al. 2022

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“…In the literature, many research works have been used EC algorithms for JSS but these algorithms mainly focus on optimizing a single objective [67,159]. For example, GAs have been utilized for minimizing makespan while scheduling identical parallel machines [99]. Park et al have also considered using cooperative evolutionary technologies to minimize the TWT in dynamic JSS problems [159].…”

Section: Multi-objective and Many-objective Jssmentioning

confidence: 99%

Many-Objective Genetic Programming for Job-Shop Scheduling

Masood

2022

SIGEVOlution

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This article overviews a recent PhD dissertation representing the first effort at many-objective optimization in job shop scheduling (JSS). The thesis develops genetic programming hyperheuristic (GP-HH) approaches to evolve effective dispatching rules for many conflicting objectives in JSS problems. The aim is to develop GP-HH methods that alleviate issues related to many-objective optimization in JSS problems and evolve new effective dispatching rules capable of enhancing job shops' productivity.

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“…In the same year, Chen et al (2012) proposed a dynamic harmony search algorithm and a hybrid version, that additionally performs a variable neighborhood search based local search. Among the most recently published meta-heuristic algorithms are the grouping evolutionary strategy of Kashan et al (2018) and an improved cuckoo search of Laha and Gupta (2018). Only recently, Della Croce et al (2019) and Della Croce and Scatamacchia (2018) have revisited the famous longest processing time (LPT) rule of Graham (1969).…”

Section: Introductionmentioning

confidence: 99%

A characterization of optimal multiprocessor schedules and new dominance rules

Walter

Lawrinenko

2020

J Comb Optim

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The paper on hand approaches the classical makespan minimization problem on identical parallel machines from a rather theoretical point of view. Using an approach similar to the idea behind inverse optimization, we identify a general structural pattern of optimal multiprocessor schedules. We also show how to derive new dominance rules from the characteristics of optimal solutions. Results of our computational study attest to the efficacy of the new rules. They are particularly useful in limiting the search space when each machine processes only a few jobs on average.

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A simple yet effective grouping evolutionary strategy (GES) algorithm for scheduling parallel machines

Cited by 15 publications

References 33 publications

A Mixed Integer Linear Formulation and a Grouping League Championship Algorithm for a Multiperiod‐Multitrip Order Picking System with Product Replenishment to Minimize Total Tardiness

A Mixed Integer Linear Formulation and a Grouping League Championship Algorithm for a Multiperiod‐Multitrip Order Picking System with Product Replenishment to Minimize Total Tardiness

Many-Objective Genetic Programming for Job-Shop Scheduling

A characterization of optimal multiprocessor schedules and new dominance rules

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