Article
Engineering, Industrial
Gur Mosheiov, Assaf Sarig
Summary: This article studies a common due-date assignment problem on two parallel uniform machines. The objective is to minimize three cost components: total earliness-tardiness cost, cost of the common due-date, and total rejection cost. The problem can be reduced to a non-standard linear assignment problem and the optimal solution can be obtained in polynomial time. The extension to different scenarios and special cases is also discussed.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Lucija Ulaga, Marko Durasevic, Domagoj Jakobovic
Summary: This article discusses the importance of making timely scheduling decisions in real-world situations. It explores the possibility of using efficient but simple iterative local search methods to solve scheduling problems. The study finds that simple methods can achieve better results compared to complex metaheuristic algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Xinrui Xu, Guangqiang Yin, Chunyu Wang
Summary: This study focuses on multitasking scheduling problems with batch distribution and due date assignment. The goal is to identify the optimal primary job sequence, job due dates, and batch production strategy in order to minimize total cost. Efficient algorithms are devised and numerical experiments are conducted to evaluate the impact of multitasking on scheduling cost or value.
COMPLEX & INTELLIGENT SYSTEMS
(2021)
Article
Management
Dvir Shabtay, Gur Mosheiov, Daniel Oron
Summary: Traditional scheduling models assume predefined due dates, but recent models focus on coordinating due date assignment and job scheduling. We analyze a single machine scheduling problem where a common due date is assigned to minimize an objective function. We provide solvability results and algorithms for different scenarios.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Operations Research & Management Science
Baruch Mor, Gur Mosheiov
Summary: In this note, two classical scheduling and due-date assignment models are extended to a two-machine flowshop. All the problems studied are shown to have polynomial time solutions.
ANNALS OF OPERATIONS RESEARCH
(2021)
Article
Operations Research & Management Science
Jun Pei, Qingru Song, Baoyu Liao, Xinbao Liu, Panos M. Pardalos
Summary: This paper introduces a novel algorithm to solve a serial-batching scheduling problem with specific conditions, which outperforms other algorithms in solution quality and running time according to computational experiments.
ANNALS OF OPERATIONS RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Pengju Zhang, Chaofan Zhang, Bingxi Liu, Yihong Wu
Summary: This paper proposes a novel visual localization method that leverages both local and global descriptors to improve accuracy and robustness. By using a parallel search framework to obtain nearest neighbor candidates of 2D query image points, the proposed method outperforms traditional approaches in challenging benchmarks.
PATTERN RECOGNITION
(2022)
Article
Engineering, Multidisciplinary
Shan-Shan Lin
Summary: This paper discusses single machine scheduling problems with position-dependent weights and the learning and deterioration effects of jobs processing times, aiming to minimize the weighted sum of earliness-tardiness under different due-window assignments. It shows that these problems can be solved in polynomial time.
JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION
(2022)
Article
Mathematics, Interdisciplinary Applications
Li-Yan Wang, Mengqi Liu, Ji-Bo Wang, Yuan-Yuan Lu, Wei-Wei Liu
Summary: This paper studies the single-machine scheduling problem by considering due-date assignment and group technology. It proposes algorithms for different due-date assignment methods to minimize the weighted sum of lateness and due-date assignment cost.
Article
Computer Science, Artificial Intelligence
Oguzhan Ahmet Arik, Marco Schutten, Engin Topan
Summary: This paper investigates an unrelated parallel machine scheduling problem with a restrictive common due date and proposes construction-based heuristics and local search algorithms to minimize earliness/tardiness costs. By optimizing start times of machines and job assignment patterns, it achieves a balanced workload per machine and outperforms metaheuristics in solution quality.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Engineering, Industrial
Yusheng Wang, Ada Che, Jianguang Feng
Summary: This paper investigates the energy-efficient scheduling problem on unrelated parallel machines in the labour-intensive textile industry. A mixed-integer linear programming (MILP) model is established, and an iterative heuristic embedded with a variable neighbourhood search procedure is developed. The improved model is significantly faster and the proposed heuristic yields excellent solutions for large-scale instances with tight makespan restrictions.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Engineering, Industrial
Qing Yue, Shenghai Zhou, Haiyan Yan
Summary: This article addresses a single-machine scheduling problem with stochastic processing times and due-date assignment. The decision maker has only knowledge about the mean and support of the processing times. The objective is to minimize the total expected individually weighted costs of earliness, tardiness, and due-date assignment by jointly determining a scheduling policy and a set of due dates. The authors propose an approximated problem by establishing upper and lower bounds with the robust optimization approach and using a linear function to approximate the objective function. They also present a branch-and-bound algorithm for finding an optimal solution. Finally, computational experiments are conducted to evaluate the performance of problem approximation and two developed heuristic algorithms.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Engineering, Industrial
Benedikt Zipfel, Janis Neufeld, Udo Buscher
Summary: This paper studies the customer order scheduling problem in the context of additive manufacturing. It proposes a mixed-integer programming model that integrates different materials and sequence-dependent setup times. Additionally, a metaheuristic based on an iterated local search is proposed. The efficiency of the proposed heuristic approach is evaluated using comprehensive test data, focusing on minimizing the total weighted tardiness of orders, and the importance of the considered order-related objective is demonstrated through qualitative analysis.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Automation & Control Systems
Zuocheng Li, Lixin Tang, Jin-Kao Hao
Summary: The paper focuses on the task assignment problem and proposes a probability learning-based local search algorithm. Extensive computational experiments show that the algorithm achieves good search performance on different problem instances. The learning techniques of the algorithm are also applicable to other real-life optimization problems with grouping features.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Yuji Zou, Jin-Kao Hao, Qinghua Wu
Summary: This article presents an effective heuristic algorithm for the traveling salesman problem with job-times. The algorithm uses a breakout local search method to find high-quality local optimal solutions and incorporates a perturbation procedure to escape local optimum traps. Computational results show that the algorithm outperforms previous methods on benchmark instances.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Oguzhan Ahmet Arik
Summary: This paper proposes a hybrid solution algorithm that combines the best components of iterated greedy algorithm with artificial bee colony algorithm for permutation flow shop scheduling problems, leading to better solutions compared to variants of iterated greedy algorithms.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Gulcin Canbulut, Erkan Kose, Oguzhan Ahmet Arik
Summary: The study focuses on analyzing the revenue matrix of revenue sharing contracts to explore the profit functions of supply chain members at different wholesale prices and revenue sharing rates, as well as determining the best strategies in two-person non-constant sum games.
Article
Computer Science, Artificial Intelligence
Oguzhan Ahmet Arik
Summary: This paper investigates permutation flow shop scheduling problems under the effects of position-dependent learning and linear deterioration. A hybrid solution algorithm named TS(POP) is proposed to address the problem, with experimental results showing its outperformance in solution quality compared to other existing algorithms.
Article
Mathematics, Interdisciplinary Applications
Oguzhan Ahmet Arik
Summary: This paper presents a mixed-integer programming model for a single machine earliness/tardiness scheduling problem aiming to minimize total earliness/tardiness duration. The uncertainty of parameters such as processing times and due date is coded with grey numbers, and an effective heuristic method is proposed using expected processing times. The research contributes to the literature by utilizing grey theory and numbers in machine scheduling problems.
GREY SYSTEMS-THEORY AND APPLICATION
(2021)
Article
Computer Science, Artificial Intelligence
Oguzhan Ahmet Arik, Marco Schutten, Engin Topan
Summary: This paper investigates an unrelated parallel machine scheduling problem with a restrictive common due date and proposes construction-based heuristics and local search algorithms to minimize earliness/tardiness costs. By optimizing start times of machines and job assignment patterns, it achieves a balanced workload per machine and outperforms metaheuristics in solution quality.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Operations Research & Management Science
Oguzhan Ahmet Arik
Summary: Additive manufacturing (AM) is a promising general-purpose technology mainly used for producing small batch customized products. While most research focuses on reducing costs and increasing speed and availability of AM machines, there is limited investigation into scheduling problems related to AM machines.
OPERATIONAL RESEARCH
(2022)
Article
Computer Science, Information Systems
Oguzhan Ahmet Arik
Summary: Metaheuristic algorithms are solution approaches to optimization problems that involve exploring and exploiting solutions in the solution space. This study proposes a fuzzy rule-based acceptance criterion for metaheuristics, which fuzzies the inputs and uses a fuzzy inference system to make acceptance decisions. Experimental results show that the proposed criterion leads to fewer acceptance solutions compared to the probabilistic approach, but improves the overall performance of the metaheuristics.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Transportation Science & Technology
Gulcin Canbulut, Erkan Kose, Oguzhan Ahmet Arik
Summary: The study discusses the tramway selection problem of a company in the public transport sector in Turkey, determining evaluation criteria with expert opinions and solving with AHP and GRA methods, providing a scientific approach to solving complex real-life problems.
Article
Computer Science, Artificial Intelligence
Oguzhan Ahmet Arik, Mehmet Duran Toksari
Summary: This study investigates parallel machine scheduling problems with the objective of minimizing total completion times under the effects of learning and deterioration. The authors proposed a genetic algorithm as a solution, which proved to yield good solutions in short execution times and outperform existing metaheuristic algorithms for the problem.
INTERNATIONAL JOURNAL OF APPLIED METAHEURISTIC COMPUTING
(2021)
Article
Management
Oguzhan Ahmet Arik
Summary: This paper presents a promising memetic algorithm for an unrelated parallel machine scheduling problem by using a simple dispatching rule. The experimental study shows that the proposed algorithm outperforms other alternatives in terms of solution quality.
JOURNAL OF MODELLING IN MANAGEMENT
(2023)
Article
Operations Research & Management Science
Oguzhan Ahmet Arik
Summary: This paper addresses the problem of common due date assignment for single machine weighted earliness/tardiness scheduling, where jobs have different weights for earliness and tardiness. The objective is to minimize the cost of weighted earliness/tardiness and assignment of common due date. A polynomial-time algorithm exists for the case where all jobs have the same earliness/tardiness weight. Researchers have also revealed some properties for the problem when the common due date is an input. This paper proposes a heuristic algorithm based on the revealed properties to find better solutions than a commercial solver in a reasonable time.
Article
Multidisciplinary Sciences
Oguzhan Ahmet Arik
Summary: This paper investigates permutation flow shop scheduling problems using a genetic algorithm, demonstrating the effectiveness of the proposed algorithm in optimizing makespan through instance analysis and parameter sensitivity analysis.
GAZI UNIVERSITY JOURNAL OF SCIENCE
(2022)
Article
Environmental Sciences
Hayriye Eren, Oguzhan Ahmet Arik, Halit Yetisir
FRESENIUS ENVIRONMENTAL BULLETIN
(2020)
Article
Transportation Science & Technology
Oguzhan Ahmet Arik, Erkan Kose, Gulcin Canbulut
PROMET-TRAFFIC & TRANSPORTATION
(2020)
Article
Computer Science, Artificial Intelligence
Oguzhan Ahmet Arik
EVOLUTIONARY INTELLIGENCE
(2020)
