Article
Computer Science, Information Systems
Ming Li, Dechang Pi, Shuo Qin
Summary: This paper tackles the reliability constrained multi-objective workflow scheduling problem in cloud environment and proposes a knowledge-based multi-objective estimation of distribution algorithm (KMOEDA). The algorithm includes a global search strategy and a reliability-aware local search strategy, and introduces an elite enhancement strategy to improve elite non-dominated solutions in an external archive.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Xuewen Xia, Huixian Qiu, Xing Xu, Yinglong Zhang
Summary: In this paper, a multi-objective genetic algorithm (MOGA) is proposed and applied to optimize workflow scheduling problems under the cloud computing environment. An initialization scheduling sequence scheme is introduced to enhance search efficiency, and the longest common subsequence (LCS) is integrated into the genetic algorithm (GA) to achieve a balance between exploration and exploitation. Experimental results demonstrate that the proposed GALCS algorithm outperforms ordinary GA and other state-of-the-art algorithms in finding a better Pareto front.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Interdisciplinary Applications
Yisong Yuan, Sudong Ye, Lin Lin, Mitsuo Gen
Summary: The paper focuses on studying an effective robust project scheduling method for prefabricated building (PB) construction projects. The proposed hybrid cooperative co-evolution algorithm (HCOEA) aims to reduce the impact of uncertain execution time on the overall project. Results show that the HCOEA outperforms existing state-of-the-art methods in terms of project scheduling efficiency and reliability.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Mohammad A. Abido, Ashraf Elazouni
Summary: The study proposed a modified Multi-Objective Evolutionary Programming (MOEP) algorithm to solve scheduling problems of multi-mode activities, outperforming the benchmarked algorithms of SPEA-II and NSGA-II in terms of diversity and quality of the Pareto optimal set.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Review
Computer Science, Artificial Intelligence
Wenxiang Wang, Kangshun Li, Hassan Jalil, Hui Wang
Summary: This paper proposes a mixed-variable multi-objective evolutionary algorithm based on estimation of distribution algorithm (MVMO-EDA) to address challenges in optimizing mixed-variable multi-objective problems, with improvements in generating offspring, coding for discrete variables, and diversity maintenance strategies leading to competitive performance.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Interdisciplinary Applications
Salvador Botello-Aceves, Arturo Hernandez-Aguirre, S. Ivvan Valdez
Summary: This paper introduces a novel Multi-Objective Evolutionary Algorithm (MOEA) that incorporates the Improvement Direction Mapping (IDM) methods into the reproduction operator of an Estimation of Distribution Algorithm (EDA), aiming to improve the efficiency of the search process by directing the search probability distribution towards promising regions. The experimental results demonstrate the statistical evidence of the importance of the orientation of the search probability distribution in improving the convergence to the Pareto front.
MATHEMATICS AND COMPUTERS IN SIMULATION
(2023)
Article
Computer Science, Artificial Intelligence
Yali Wang, Steffen Limmer, Markus Olhofer, Michael Emmerich, Thomas Baeck
Summary: The newly proposed AP-DI-MOEA algorithm can automatically generate preference regions and achieve better solutions within them, especially compared to other MOEA algorithms under the same budget.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Information Systems
Han Song, Guangmin Jia, Wuliang Peng
Summary: This paper proposes a novel bi-objective reactive project scheduling problem under the condition of resource uncertainty, and presents a regret-based biased random sampling heuristic algorithm to solve it. Additionally, the advantages and disadvantages of priority rules are compared and the combinations of parameter values and schedule generating schemes (SGS) are investigated.
Article
Construction & Building Technology
Dina A. Saad, Mohamed Masoud, Hesham Osman
Summary: A new lean-driven Pull-Batch-based Repetitive Scheduling (PBRS) technique was developed to reduce WIP, time, and cost, utilizing batch- and pull-production lean concepts. It has a built-in multi-objective optimization algorithm and was proven superior in performance through a real case study.
AUTOMATION IN CONSTRUCTION
(2021)
Article
Computer Science, Artificial Intelligence
Yang-Yuan Li, Jian Lin, Zhou-Jing Wang
Summary: This paper presents a multi-objective discrete Jaya algorithm to address the multi-skill resource constrained project scheduling problem, aiming to minimize makespan and total cost simultaneously. The algorithm enhances exploration ability using simple heuristics and efficient encoding and decoding pairs to construct feasible schedules, while also proposing different assignment criteria to increase solution diversity. The performance evaluation on a benchmark dataset indicates the superiority of the proposed MODJaya algorithm in solving the multi-objective MS-RCPSP.
APPLIED INTELLIGENCE
(2022)
Article
Chemistry, Multidisciplinary
Hidetoshi Togo, Kohei Asanuma, Tatsushi Nishi, Ziang Liu
Summary: This paper presents two methods to estimate the weighting factors of the objective function in production scheduling problems. These methods, based on machine learning and inverse optimization, utilize historical data. Experimental results show the effectiveness of these methods.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Artificial Intelligence
Xiaoxia Huang, Kwon Ryong Hong, Jang Su Kim, Il Jong Choe
Summary: This paper discusses a multi-objective mean-variance model and its solution algorithms for project selection considering synergy under uncertain environment. The effect of uncertainty and synergy on project selection is analyzed and new solution algorithms are proposed. Numerical experiments show the performance of the proposed algorithms and a numerical example is given to demonstrate the validity of the model.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Junfeng Tang, Handing Wang, Lin Xiong
Summary: In preference-based multi-objective optimization, knee solutions are the implicit preferred promising solutions. However, finding knee solutions is difficult and computationally expensive. To address this issue, we propose a surrogate-assisted evolutionary multi-objective optimization algorithm that uses surrogate models to replace expensive evaluations. Experimental results show that our proposed algorithm outperforms state-of-the-art knee identification evolutionary algorithms on most test problems within a limited computational budget.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Information Systems
Jae-Hun Jeong, Eunbin Lee, Jong-Hyun Lee, Chang Wook Ahn
Summary: This article proposes a music composition multi-objective optimization algorithm based on dimensionality reduction in decision space, which effectively explores the search space of large-scale multi-objective optimization problems.
Article
Computer Science, Artificial Intelligence
Zhen Quan, Yan Wang, Zhicheng Ji
Summary: This paper proposes a virtual workflow modeling method and a multi-objective virtual workflow scheduling algorithm to achieve static and dynamic multi-objective optimization scheduling for manufacturing process, addressing the optimization problems of processing quality and energy consumption.
APPLIED SOFT COMPUTING
(2022)
Article
Engineering, Industrial
Hiroshi Matsuhisa, Nobuo Matsubayashi
Summary: This study investigates the formation of an alliance between competing manufacturers and a monopolistic platform retailer, and analyzes the impact of the degree of differentiation among manufacturers on the formation of the alliance and the profitability of the retailer.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2024)
Article
Engineering, Industrial
Lingxuan Kong, Ge Zheng, Alexandra Brintrup
Summary: Supply Chain Financing is used to optimize cash flows in supply networks, but recent scandals have shown inefficiencies in risk evaluation. This paper proposes a Federated Learning framework to address order-level risk evaluation.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2024)
Article
Engineering, Industrial
Jing Gu, Xinyu Shi, Junyao Wang, Xun Xu
Summary: The asymmetric market power between a firm and its partners negatively affects the firm's financial performance. Building relationships with suppliers or customers that have matched market power is the best approach. The strength of the buyer-supplier relationship amplifies the negative impact of asymmetric market power, while the level of relationship embeddedness reduces its negative effect. Moreover, firm-specific institutional, industry, and regional economic heterogeneities also influence the financial impact of asymmetric market power.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2024)
Article
Engineering, Industrial
Yu Du, Jun-qing Li
Summary: This study investigates the group scheduling of a distributed flexible job shop problem using the concrete precast process. The proposed solution utilizes three coordinated double deep Q-networks (DQN) as a learn-to-improve reinforcement learning approach. The algorithm shows superiority in minimizing costs and energy consumption.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2024)
Article
Engineering, Industrial
Xiaoyu Yan, Weihua Liu, Ou Tang, Jiahe Hou
Summary: This study analyzes the market amplification effect and the impact of entrant's overconfidence on a two-sided platform. The results show that overconfident entrants can lead to price increases and benefit both the existing firms and themselves to a certain extent.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2024)
Article
Engineering, Industrial
Illya Kaynov, Marijn van Knippenberg, Vlado Menkovski, Albert van Breemen, Willem van Jaarsveld
Summary: The One-Warehouse Multi-Retailer (OWMR) system is a typical distribution and inventory system. Previous research has focused on heuristic reordering and allocation strategies, which are time-consuming and problem-specific. This paper proposes a Deep Reinforcement Learning (DRL) algorithm for OWMR problems, which infers a multi-discrete action distribution and improves performance with a random rationing policy.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2024)
Article
Engineering, Industrial
Yimeng Sun, Ruozhen Qiu, Minghe Sun
Summary: This study considers a multi-period inventory management problem for a retailer offering limited-time discounts and having a joint service-level requirement under demand uncertainty. It proposes a double-layer iterative approach to solve the problem and maximize total profit while balancing the service level using a posteriori method and an affinely adjustable robust chance-constrained model.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2024)
Article
Engineering, Industrial
Anas Neumann, Adnene Hajji, Monia Rekik, Robert Pellerin
Summary: This paper presents a new mathematical formulation for planning and scheduling activities of Engineer-To-Order (ETO) projects, along with a new ETO strategy to reduce the impacts of design uncertainty. The study proposes a hybrid Layered Genetic Algorithm combined with an adaptive Lamarckian learning process (LLGA) and compares it with the branch-and-cut procedure of CPLEX. The results show good performance of the proposed mathematical model for small and medium-sized instances.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2024)
Article
Engineering, Industrial
Thilini Ranasinghe, Chanaka D. Senanayake, Eric H. Grosse
Summary: Production systems are undergoing transformative changes, necessitating adaptability from human workers. This study developed an analytical model to account for stochastic processing times and learning heterogeneity, revealing insights into system performance.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2024)
Article
Engineering, Industrial
Sunil Tiwari, Pankaj Sharma, Ashish Kumar Jha
Summary: Black Swan events such as the COVID-19 pandemic and the Suez Canal blockage have a significant impact on firms' technology adoption decisions, especially in terms of disruptions and digitalization in the supply chains. This study investigates the influence of institutional forces and environmental contingencies on supply chain digitalization from an institutional and contingency theory perspective. The findings emphasize the importance of organizational readiness and people readiness, including top management involvement and employee training, in facilitating digitalization.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2024)
Article
Engineering, Industrial
Fabio Neves-Moreira, Pedro Amorim
Summary: Omnichannel retailers are using stores as distribution centers to provide faster online order fulfillment services. However, in-store picking operations can impact the offline customer experience. To address this, we propose a Dynamic In-store Picker Routing Problem (diPRP) that minimizes customer encounters while fulfilling online orders. Our solution approach combines mathematical programming and reinforcement learning to find efficient picking policies that reduce customer encounters.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2024)
Article
Engineering, Industrial
Richard Kraude, Ram Narasimhan
Summary: In this study, the relationship between Vertical Integration (VI) and Environmental Performance (EP) is examined, revealing that highly integrated firms produce less waste but engage in fewer environmental initiatives. These findings are crucial for understanding the impact of stakeholder exposure on organizational behavior.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2024)
Review
Engineering, Industrial
Korina Katsaliaki, Sameer Kumar, Vasilis Loulos
Summary: This research conducts a systematic literature review (SLR) and content analysis on Supply Chain Coopetition (SCC) through the PRISMA framework. It examines the theory of coopetition and organizational relationships in intra-firm and inter-firm supply chains, focusing on collaboration between rival manufacturers. The study identifies structures and mechanisms of coopetition, such as buyer-supplier coopetition, supply networks coopetition, and production and distribution/logistics coopetition. It provides a holistic approach to SCC management practices and serves as a guide for future research.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2024)
