Population-Based Meta-Heuristic Algorithms for Integrated Batch Manufacturing and Delivery Scheduling Problem

Kim, Yong-Jae; Kim, Byung Soo

doi:10.3390/math10214127

Cited by 7 publications

(4 citation statements)

References 28 publications

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“…Other recently related studies can be found in Zhang et al [12], Masadeh et al [13], Zhang et al [14], Kim and Kim [15], Valouxis et al [16], and Wang et al [17] among many others.…”

Section: Notation Terminology P Jmentioning

confidence: 91%

Scheduling of Jobs with Multiple Weights on a Single Machine for Minimizing the Total Weighted Number of Tardy Jobs

2023

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We consider the scheduling of jobs with multiple weights on a single machine for minimizing the total weighted number of tardy jobs. In this setting, each job has m weights (or equivalently, the jobs have m weighting vectors), and thus we have m criteria, each of which is to minimize the total weighted number of tardy jobs under a corresponding weighting vector of the jobs. For this scheduling model, the feasibility problem aims to find a feasible schedule such that each criterion is upper bounded by its threshold value, and the Pareto scheduling problem aims to find all the Pareto-optimal points and for each one a corresponding Pareto-optimal schedule. Although the two problems have not been studied before, it is implied in the literature that both of them are unary NP-hard when m is an arbitrary number. We show in this paper that, in the case where m is a fixed number, the two problems are solvable in pseudo-polynomial time, the feasibility problem admits a dual-fully polynomial-time approximation scheme, and the Pareto-scheduling problem admits a fully polynomial-time approximation scheme.

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“…Other recently related studies can be found in Zhang et al [12], Masadeh et al [13], Zhang et al [14], Kim and Kim [15], Valouxis et al [16], and Wang et al [17] among many others.…”

Section: Notation Terminology P Jmentioning

confidence: 91%

Scheduling of Jobs with Multiple Weights on a Single Machine for Minimizing the Total Weighted Number of Tardy Jobs

2023

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“…In studies regarding electric vehicle (EV) systems, metaheuristics have been implemented to optimize the routing problem of the plugin hybrid electric vehicles (PHEV) [1], fast charging stations [2], distributed photovoltaic systems [3], and so on. In studies regarding manufacturing systems, metaheuristics have been implemented to optimize the coupling between integrated batch machines and direct shipping trucks [4], reducing risk in the spare part inventory system [5], and so on. In studies regarding power systems, metaheuristics have been implemented to optimize the installation and allocation of the distributed generation units [6], radial distribution system [7], reactive power flow in the large-scale power system [8], and so on.…”

Section: Introductionmentioning

confidence: 99%

Extended Stochastic Coati Optimizer

2023

IJIES

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A new metaheuristic can be developed by constructing from scratch, modifying the existing metaheuristics, or hybridizing some metaheuristics. This work presents a new metaheuristic: extended stochastic coati optimizer (ESCO). ESCO is developed by expanding the shortcoming coati optimization algorithm (COA). ESCO expands the number of searches and references used in COA. ESCO also implements a stochastic process for each unit to choose the searches that will perform. It differs from COA, which splits the population into two fixed groups, each performing its strategy. ESCO implements three sequential phases in every iteration. Two options can be chosen in every phase. ESCO has three references in its guided search: the global best unit, a randomly selected unit, and a randomized unit within the search space. In this work, ESCO is challenged to solve 23 classic functions and benchmarked with five shortcoming metaheuristics: guided pelican algorithm (GPA), puzzle optimization algorithm (POA), average subtraction-based optimizer (ASBO), and coati optimization algorithm (COA). The result presents the superiority of ESCO among five shortcoming metaheuristics by outperforming the GPA, POA, GSO, ASBO, and COA in solving 13, 21, 23, 16, and 13 functions, respectively. Through investigation, the multiple search approach is more effective than the single search approach.

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“…With the development of computer technology and the change in social demand, product scheduling in the manufacturing industry is transformed from single machine scheduling to distributed scheduling [3,4]. In the era of big data and artificial intelligence, product scheduling methods based on cloud computing and fog computing are also developing rapidly [5,6].…”

Section: Introductionmentioning

confidence: 99%

A Heuristic Integrated Scheduling Algorithm Based on Improved Dijkstra Algorithm

Zhou,

Xie,

Zhou

et al. 2023

Electronics

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In the process of the integrated scheduling of multi-variety and small-batch complex products, the process structure and attribute characteristics are often ignored, which affects the overall scheduling effect. Aiming at solving this problem, a heuristic integrated scheduling algorithm (HIS-IDA) based on the improved Dijkstra algorithm is proposed. The algorithm takes the processing time of the process itself as the path value of the preceding and the following adjacent processes. Firstly, the improved Dijkstra algorithm prioritized the scheduling of the process sequence with long longitudinal paths and realized the “longitudinal optimization” of the integrated scheduling. Secondly, the layer priority strategy is used to shorten the interval time of process processing and realize the “horizontal optimization” of integrated scheduling. On the basis of “vertical and horizontal optimization”, the idle time of the equipment is further reduced by using the process priority strategy of the leaf node, and the “idle optimization” of the integrated scheduling is realized, so as to optimize the overall effect of the integrated scheduling. The effectiveness and superiority of the algorithm are proved using comparison analysis.

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Population-Based Meta-Heuristic Algorithms for Integrated Batch Manufacturing and Delivery Scheduling Problem

Cited by 7 publications

References 28 publications

Scheduling of Jobs with Multiple Weights on a Single Machine for Minimizing the Total Weighted Number of Tardy Jobs

Scheduling of Jobs with Multiple Weights on a Single Machine for Minimizing the Total Weighted Number of Tardy Jobs

Extended Stochastic Coati Optimizer

A Heuristic Integrated Scheduling Algorithm Based on Improved Dijkstra Algorithm

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