Article
Engineering, Industrial
Christos Koulamas, George J. Kyparisis
Summary: The study focuses on the no-wait flow shop scheduling problem with a rejection option and presents polynomial-time algorithms to minimize different objective functions efficiently.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2021)
Article
Management
Czeslaw Smutnicki, Jaroslaw Pempera, Grzegorz Bocewicz, Zbigniew Banaszak
Summary: This paper investigates the problem of cyclic scheduling in a manufacturing system, considering the flow of jobs with identical technological routes, no-wait constraints, and missing operations. The problem is decomposed into two sub-problems, and alternative methods are provided for finding the minimal cycle time and optimal processing order of jobs. A metaheuristic approach is used to solve the latter sub-problem. Experimental examination demonstrates the efficiency and quality of the proposed algorithm.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Computer Science, Interdisciplinary Applications
Mario Tonizza Pereira, Marcelo Seido Nagano
Summary: In today's complex business environment, the optimisation of operational processes is crucial for the survival of organizations. The coordination and integration of manufacturing and logistics activities are essential for improving service levels and operational performance. This article proposes and evaluates new heuristic methods for scheduling production and distribution operations, aiming to minimize delivery times and increase service levels. The experimental results demonstrate the potential of these methods to solve transportation-related problems.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Engineering, Industrial
Hugo Hissashi Miyata, Marcelo Seido Nagano
Summary: Nowadays, distributed scheduling problem is a reality in many companies. Over the last years, an increasingly attention has been given to the distributed flow shop scheduling problem and the addition of constraints to the problem. This article introduces a new distributed no-wait flow shop scheduling problem using a mix of mixed-integer linear programming and heuristic algorithms. Studies show that the proposed algorithm performs well in the trade-off between efficiency and effectiveness.
JOURNAL OF MANUFACTURING SYSTEMS
(2021)
Article
Computer Science, Interdisciplinary Applications
Zeynep Adak, Mahmure Ovul Arioglu, Serol Bulkan
Summary: The paper addresses the scheduling problem of multiprocessor open shop, which is strongly NP-hard. By introducing a novel efficient solution representation and ant colony optimization model, an effective algorithm is proposed and outperforms the current state-of-the-art algorithm on 100 benchmark instances.
JOURNAL OF COMBINATORIAL OPTIMIZATION
(2022)
Article
Mathematics
Ting-Chun Lo, Bertrand M. T. Lin
Summary: This paper addresses a variant of the relocation problem, concerning the scheduling of jobs in a two-machine flow shop under resource constraints to minimize makespan. The NP-hardness of two special cases is proven, and two heuristic algorithms and ant colony optimization algorithms are designed to generate feasible schedules. Computational experiments are conducted to evaluate the performances of these algorithms.
Article
Engineering, Industrial
Jinsheng Gao, Xiaomin Zhu, Kaiyuan Bai, Runtong Zhang
Summary: This paper addresses the no-wait job shop scheduling problem with due date and subcontracting cost constraints, introducing two mathmatical models to find solutions for the problem.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2022)
Article
Multidisciplinary Sciences
Jun Li, Xinxin Guo, Qiwen Zhang
Summary: In order to solve the problems of single evolutionary approach, decreasing diversity, inhomogeneity, and meaningfulness in the destruction process when solving the no-wait flow-shop scheduling problem, a multi-strategy discrete teaching-learning-based optimization algorithm (MSDTLBO) is introduced. The algorithm is redefined from the student's point of view, considering the differences between individuals and using basic integer sequence encoding. To address the issue of falling into local optimum and reducing search accuracy, the population is divided into three groups with different teaching strategies based on their learning ability. An iterative greedy algorithm of destruction-reconstruction is used to improve the destruction-and-reconstruction process with symmetry, and a knowledge base is utilized to control the number of meaningless artifacts to be destroyed and dynamically change the artifact-selection method. Experimental results show the algorithm's effectiveness in solving the no-wait flow-shop scheduling problem (NWFSP) compared to other algorithms.
Article
Computer Science, Artificial Intelligence
Mohamed Kurdi
Summary: This work proposes a new metaheuristic algorithm called ACONEH for open shop scheduling problem with the goal of improving the exploration capability of ant colony optimization and solving OSSP more effectively. The algorithm utilizes a new heuristic information approach that incorporates randomness, diversity, and improvability. Experimental results show that ACONEH achieves significant improvements in reducing the makespan of OSSP compared to traditional methods.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Ningning Zhu, Fuqing Zhao, Ling Wang, Ruiqing Ding, Tianpeng Xu, Jonrinaldi
Summary: This study proposes a discrete knowledge-guided learning fruit fly optimization algorithm to solve the distributed no-wait flow shop scheduling problem. By introducing a probability knowledge model and a local search strategy, the algorithm performs well in minimizing the total weighted earliness and tardiness.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Management
Michael Morin, Irene Abi-Zeid, Claude-Guy Quimper
Summary: This paper addresses the problem of efficiently optimizing search paths in search and rescue operations, and proposes variant solutions based on ant colony optimization algorithms. The empirical results demonstrate that these variants perform well in solving real-world problems.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Automation & Control Systems
Fuqing Zhao, Zesong Xu, Ling Wang, Ningning Zhu, Tianpeng Xu, J. Jonrinaldi
Summary: This article investigates a distributed assembly no-wait flow-shop scheduling problem (DANWFSP) and proposes a population-based iterated greedy algorithm (PBIGA) to address the problem. The PBIGA is shown to be effective and outperforms state-of-the-art algorithms in terms of minimizing total flowtime. Experimental results on large-scale benchmark instances demonstrate the superiority of the proposed PBIGA.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Interdisciplinary Applications
Mohamed-Naceur Azaiez, Anis Gharbi, Imed Kacem, Yosra Makhlouf, Malek Masmoudi
Summary: This paper studies the operating room scheduling problem and proposes a mixed integer linear programming model. Valid inequalities, lower bounds, and heuristics are also introduced to handle large scale problems. Experimental results on model performance and comparisons among lower bounds and heuristics are reported.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Management
Christos Koulamas, George J. Kyparisis
Summary: This study focuses on two-stage no-wait proportionate flow shops and aims to minimize the variation in service time by introducing the Total Absolute Deviation of mid-processing Points (TADZ) metric. The researchers demonstrate that the TADZ objective in this context can be solved in O(nlogn) time, providing a solution to an open research question. Additionally, they explore the solvability of a generic two-stage no-wait proportionate flow shop scheduling problem and present practical applications of TADZ. Furthermore, a new metric called the sum of all partial schedule lengths (SPSL) is introduced, and its related problem is shown to be solvable. Finally, the option of rejecting a job from the schedule is considered, and the resulting problem is solved using dynamic programming.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Automation & Control Systems
Sujun Zhang, Xingsheng Gu
Summary: This paper presents a discrete whale optimization algorithm (DWOA) for solving the no-wait flow shop scheduling problem (NWFSSP) with the objective of minimizing the makespan. An effective combination of nearest neighbor (NN) and standard deviation heuristics (SDH) is used to obtain initial solutions. Three crossover operators mimicking the humpback whales hunting process are designed, namely two-point crossover (TPX), multiple-point crossover (MPX), and job-based crossover (JBX) operators. A dynamic transform mechanism is also designed to balance the exploration and exploitation ability of DWOA. Furthermore, parallel neighborhood search (PNS) and serial neighborhood search (SNS) are used for local and global search to improve the optimization performance of DWOA. Experimental results on benchmark instances demonstrate the effectiveness of the improved mechanisms and the superior performance of DWOA compared to other algorithms for solving NWFSSP.
MEASUREMENT & CONTROL
(2023)
