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A genetic algorithm enhanced with neighborhood structure for general flexible job shop scheduling with parallel batch processing machine

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 265卷

作者： Zhang, Hucheng Lv, Shengping Xin, Dequan Jin, Hong South China Agr Univ Sch Engn Guangzhou 510642 Peoples R China

In this study, a general flexible job shop scheduling problem with parallel batch processing machine (GFJSP_PBPM) is presented. GFJSP_PBPM allows multiple jobs, whether mandatory or flexible, to be simultaneously processed on the same machine, challenging the conventional constraints of the flexible job shop scheduling problem where a single machine can only undertake one job at any given time. The motivation for this problem arises from real-world scenarios encountered in electronic product performance testing and mold manufacturing workshops. Firstly, the problem of GFJSP_PBPM is defined, and an optimization model is established with the objective of minimizing the makespan using mixed-integer programming. Subsequently, a genetic algorithm enhanced with neighborhood search (GANS) is developed to efficiently tackle the problem at different scales. To evaluate its performance, benchmark instances are created for testing and comparative analysis. Through testing on these instances and a real-world engineering case, the feasibility and superiority of the proposed GANS are demonstrated.

关键词： parallel batch processing General flexible job shop scheduling Genetic algorithm Neighborhood structure

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Auction-based approach to the job-shop problem with parallel batch processing and a machine availability constraint

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ENGINEERING OPTIMIZATION 2023年第1期55卷 71-88页

作者： Zeng, Chengkuan Liu, Zixuan Tang, Jiafu Fan, Zhi-Ping Yan, Chongjun Northeastern Univ Sch Business Adm Dept Informat Management & Decis Sci Shenyang Peoples R China Dongbei Univ Finance & Econ DUFE Coll Management Sci & Engn Dalian Peoples R China

In military manufacturing enterprises, exceptionally high product performance standards are generally met by using heat treatment followed by machining. These procedures are typically parallel batch-processing (BP) scheduling problems and job-shop problems, respectively. A mixed-integer nonlinear programming model is created to describe the problem with a machine availability constraint, which is divided into two stages: BP and machine processing (MP). Furthermore, an auction-based approach is developed in which jobs are categorized into batches during the BP stage and resources are allocated to operating machines during the MP stage. A local search operator is then applied to optimize the obtained feasible solutions. Benchmark instances are enlarged to adapt to the proposed problem. The approach is tested and compared to existing algorithms, and statistical analysis is performed using SPSS Statistics. The results show that the auction-based approach is effective and stable, and has absolute advantages in solving large-scale instances.

关键词： Job-shop scheduling parallel batch processing auction-based approach machine availability constraint local search

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An Efficient Algorithmto Minimize Makespan on parallel batch processing Machines 4

An Efficient Algorithmto Minimize Makespan on Parallel Batch...

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4th International Conference on Computational Intelligence and Networks (CINE)

作者： Chakravorty, Arindam Saha, Shirshasis Laha, Dipak Das, Abhirup St Thomas Coll Engn & Technol Dept Informat Technol Kolkata India Infosys Ltd Bengaluru India Jadavpur Univ Mech Engn Dept Kolkata India

ISBN: (纸本)9781728156880

This paper proposes an efficient optimization approach to minimize makespan on in identical parallel batch processing machines where the ready times and sizes of n jobs are arbitrary. The proposed algorithm initially employs a deterministic batch algorithm (DBA) for formation of the batches in order to minimize the makespan. Then, the batches formed by the DBA are further improved by a modified simulated annealing (MSA). We show through computational experimentation, that the proposed MSA outperforms three state-of-the-art methods for non-sparse problems, while the proposed DBA alone outperforms the compared algorithms for sparse problems.

关键词： parallel batch processing unequal ready time non-identical job size makespan simulated annealing optimization

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Auction-based approach with improved disjunctive graph model for job shop scheduling problem with parallel batch processing

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年第0期110卷 104735-104735页

作者： Zeng, Chengkuan Qi, Guiqing Liu, Zixuan Tang, Jiafu Fan, Zhi-Ping Yan, Chongjun Northeastern Univ Sch Business Adm Dept Informat Management & Decis Sci Shenyang 110167 Peoples R China Northeastern Univ Sch Business Adm Dept Org & Management Shenyang 110167 Peoples R China Dongbei Univ Finance & Econ DUFE Coll Management Sci & Engn Dalian 116025 Peoples R China

The job-shop scheduling problem (JSSP) is encountered in several industries, including the military where heat treatment is applied prior to the machining process in production. This study aims to minimize the overall make-span of a JSSP with parallel batch processing. The problem is formulated as a mixed-integer linear programming model. Feasible solutions are derived from an auction-based approach for forming batches, allocating operation machines, and scheduling. An improved disjunctive graph model is further developed to search for better solutions. We conduct numerical experiments to test a set of benchmark instances. A comparison of the results with those obtained applying other existing algorithms and CPLEX demonstrates the effectiveness and stability of the proposed auction-based approach and improved graph model. Furthermore, a statistical analysis using IBM SPSS shows that the proposed auction-based approach has an absolute advantage in solving medium-scale and large-scale instances of JSSP with batch processing.

关键词： Job shop scheduling parallel batch processing Auction-based approach Improved disjunctive graph model

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

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2022年第1期298卷 1-24页

作者： Fowler, John W. Moench, Lars Arizona State Univ Dept Supply Chain Management Tempe AZ 85287 USA Univ Hagen Dept Math & Comp Sci Hagen Germany

Multiple jobs are processed simultaneously on a given batch processing machine in parallel batching. The resulting batch is called a p-batch. batching can lead to reduced production costs, but depending how the jobs are grouped into a batch can lead to better or worse delivery times of products. Scheduling jobs on batch processing machines requires grouping decisions in addition to the conventional assignment and sequencing decisions. parallel batching is important in such diverse areas such as semiconductor manufacturing, aircraft manufacturing, shoe manufacturing, and healthcare. This paper surveys the literature on parallel batching and will focus primarily on deterministic scheduling. We provide a taxonomy of parallel batching problems, distinguishing the compatible case where all jobs can be used to form a batch from the incompatible families setting where only jobs from the same family can be used to form a batch. Makespan, flow time-, and due date-related measures are considered. We discuss scheduling approaches for single machines, parallel machines, and other environments such as flow shops and job shops. In addition to the discussion of archived and current papers, we discuss also recent trends in scheduling jobs on machines with parallel batch processing. Finally, we provide a discussion of future research directions for p-batch scheduling. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://***/licenses/by-nc-nd/4.0/ )

关键词： Scheduling parallel batch processing Survey Semiconductor Manufacturing Future Research Directions

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Exact methods for the Oven Scheduling Problem

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CONSTRAINTS 2023年第2期28卷 320-361页

作者： Lackner, Marie-Louise Mrkvicka, Christoph Musliu, Nysret Walkiewicz, Daniel Winter, Felix TU Wien DBAI Christian Doppler Lab Artificial Intelligence & Op Favoritenstr 9 A-1040 Vienna Austria MCP GmbH Canovagasse 7 A-1010 Vienna Austria

The Oven Scheduling Problem (OSP) is a new parallel batch scheduling problem that arises in the area of electronic component manufacturing. Jobs need to be scheduled to one of several ovens and may be processed simultaneously in one batch if they have compatible requirements. The scheduling of jobs must respect several constraints concerning eligibility and availability of ovens, release dates of jobs, setup times between batches as well as oven capacities. Running the ovens is highly energy-intensive and thus the main objective, besides finishing jobs on time, is to minimize the cumulative batch processing time across all ovens. This objective distinguishes the OSP from other batch processing problems which typically minimize objectives related to makespan, tardiness or lateness. We propose to solve this NP-hard scheduling problem using exact techniques and present two different modelling approaches, one based on batch positions and another on representative jobs for batches. These models are formulated as constraint programming (CP) and integer linear programming (ILP) models and implemented both in the solver-independent modeling language MiniZinc and using interval variables in CP Optimizer. An extensive experimental evaluation of our solution methods is performed on a diverse set of problem instances. We evaluate the performance of several state-of-the-art solvers on the different models and on three variants of the objective function that reflect different real-life scenarios. We show that our models can find feasible solutions for instances of realistic size, many of those being provably optimal or nearly optimal solutions.

关键词： Oven Scheduling Problem parallel batch processing Constraint programming Integer linear programming

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A hybrid discrete differential evolution - genetic algorithm approach with a new batch formation mechanism for parallel batch scheduling considering batch delivery

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2024年第1-2期62卷 460-482页

作者： Kucukkoc, Ibrahim Keskin, Gulsen Aydin Karaoglan, Aslan Deniz Karadag, Sevgi Balikesir Univ Dept Ind Engn Cagis Campus TR-10145 Balikesir Turkiye HasTavuk Co Res & Dev Dept Susurluk Balikesir Turkiye

Scheduling is an important decision-making problem in production planning and the resulting decisions have a direct impact on reducing waste, including energy and idle capacity. batch scheduling problems occur in various industries from automotive to food and energy. This paper introduces the parallel p-batch scheduling problem with batch delivery, content-dependent loading/unloading times and energy-aware objective function. The problem has been motivated by a real system used for freezing products in a food processing company. A mixed-integer linear programming model (MILP) has been developed and explained through a numerical example. As it is not practical to solve large-size instances via a mathematical model, the discrete differential evolution algorithm has been improved (iDDE) and hybridised with the genetic algorithm (GA). A release-oriented vector generation procedure and a heuristic batch formation mechanism have been developed to efficiently solve the problem. The performance of the proposed approach (iDDEGA) has been compared with CPLEX, iDDE and GA through a comprehensive computational study. A case study was conducted based on real data collected from the freezing process of the company, which also verified the practical use and advantages of the proposed methodology.

关键词： parallel batch processing batch delivery freezing room hybrid discrete differential evolution genetic algorithm approach batch scheduling mixed-integer linear programming

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CAMIRA: a consolidation-aware migration avoidance job scheduling strategy for virtualized parallel computing clusters

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JOURNAL OF SUPERCOMPUTING 2022年第9期78卷 11921-11948页

作者： Padhy, Satyajit Tsai, Ming-Han Sharma, Shalini Chou, Jerry Natl Tsing Hua Univ Inst Informat Syst & Applicat Hsinchu Taiwan Natl Tsing Hua Univ Dept Comp Sci Hsinchu Taiwan

Server virtualization and consolidation techniques have been widely adapted in the modern large-scale computing systems to reduce energy consumption and increase resource utilization. In these systems, physical servers are turned on/off dynamically according to the workload variation, and the loading from computing tasks are balanced among active servers through virtual machine (VM) migration. However, the downside of this approach is the overhead of VM migration can cause several negative impacts to the system and users, including application performance degradation, service interruption, prolonged job execution time, extra network bandwidth consumption, and risk of failure, etc. The existing works in the literature attempt to reduce VM migration cost for persistent running web servers in a reactive manner. In contrast, we tackle the problem for parallel computing jobs of batch processing systems. Our approach can proactively avoid VM migrations with the co-design of between job scheduling and VM consolidation strategies, and minimize communication overhead of jobs by considering the traffic pattern between the tasks of a job. Our evaluations have used real parallel job workload trace and a synthetically generated workload to show that our approach can notably reduce the number of VM migrations by 35%-50% and communication cost by up to 25% compared to the traditional job scheduling approaches.

关键词： Virtual machine consolidation Migration overhead Job scheduling parallel batch processing

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