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A smart inventory management system with medication demand dependencies in a hospital supply chain: A multi-agent reinforcement learning approach Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-04-18 Esha Saha, Pradeep Rathore
In light of the intense need for quality health and well-being, the healthcare industry must improve its operations by strengthening its service delivery and reducing overall costs. A smart inventory management system for managing medicines in a hospital supply chain (HSC) is one of the best solutions as its advantages ensure availability of affordable medicines as well as inventory cost reduction
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A Bayesian decision network–based pre-disaster mitigation model for earthquake-induced cascading events to balance costs and benefits on a limited budget Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-04-16 Wenjing Gu, Jiangnan Qiu, Jilei Hu, Xiaowei Tang
Cascading disasters induced by earthquakes amplify the severity of the initial impact on environment. Although decisionmakers may face uncertainty, an effective mitigation strategy is critical in environmental management. We propose an earthquake-induced cascading disaster mitigation–Bayesian decision network (ECDM-BDN) model to assess pre-disaster mitigating strategies under limited budgets from the
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Revenue-Sharing contract with government Subsidy: A case of the Indian sugar supply chain Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-04-16 Ranjeet Rajput, Sri Vanamalla Venkataraman
In this research, we study the role of government subsidy in the sugar supply chain in India. Government regulations of sugarcane and sugar prices have been some of the factors leading to distress among farmers and firms. Hence, government aid becomes an integral part of the sugar supply chain Firms having private information about the production cost of sugar has been identified as the primary cause
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A robust minimum cost consensus model based on social networks considering conflict constraints Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-04-16 Zelin Wang, Shaojian Qu, Zhisheng Peng, Zhenhua Dai, Yingying Zhou, Ying Ji
In social network group decision-making, the conflicts among decision-makers (DMs) could lead to significant uncertainty regarding the unit adjustment costs, and excessive intra-group conflicts might impact the subsequent implementation of the decision-making solutions. To address these issues, we propose a robust minimum-cost consensus model based on social networks considering conflict constraints
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Weather risk hedging mechanism for contract farming supply chain with weather-dependent yield Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-04-16 Jiawen Li, Shengzhong Huang, Hongyong Fu, Bin Dan
The stability of contract farming is challenged by the impact of disastrous weather on crop yield. Managing weather risks has become an unavoidable reality for participants in contract farming. In this regard, this paper focuses on investigating weather risk hedging mechanisms for contract farming supply chain in a Newsvendor setting. As a benchmark, we first consider the agricultural production investment
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A sequential three-way risk sorting model with the cautionary principle under probabilistic linguistic environment Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-04-16 Yiqiang Mu, Dun Liu, Ke Liu
Risk assessment plays a crucial role in managing risks associated with systems, products and services. In practical risk assessment, the cautionary principle is widely adopted by individuals to prevent accidents, and the degree of caution varies with the risk level of potential hazardous activities (PHAs). Therefore, it is necessary to explore a sequential risk assessment model integrating the cautionary
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A blockchain-enabled and event-driven tracking framework for SMEs to improve cooperation transparency in manufacturing supply chain Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-04-16 Jiajun Liu, Pingyu Jiang, Jie Zhang
Recently, improving supply chain transparency has become a challenge faced by many Small-Medium Enterprises (SMEs) in manufacturing supply chain. Implementing blockchain-based traceability of cooperation process is an effective and reliable method for distributed SMEs to enhance supply chain transparency. In this context, this paper proposes a general blockchain-based and event-driven tracking (BET)
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Promoting electric vehicle adoption in ride-hailing platforms: To control or subsidize? Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-04-16 Qi Zhang, Yang Liu
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Dynamic scheduling mechanism for intelligent workshop with deep reinforcement learning method based on multi-agent system architecture Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-04-15 Wenbin Gu, Siqi Liu, Zhenyang Guo, Minghai Yuan, Fengque Pei
With the development and changes of industry and market demand, the personalized customization production mode with small batch and multiple batches has gradually become a new production mode. This makes production environment become more complex and dynamic. However, traditional production workshops cannot effectively adapt to this environment. Combining with new technologies, transforming traditional
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Relief supply prepositioning strategies via option contract reserve fleet vehicles Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-04-15 Xihui Wang, Ziyou Wu, Jianfang Shao
In preparing for disasters, relief supplies can be prepositioned as tangible forms in warehouses or as intangible forms, such as production capacity. However, the prepositioning of relief supplies in the absence of a sufficient number of vehicles may delay the delivery time and reduce relief operation performance. Budget pressure may prevent local disaster relief authorities from maintaining a large
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Automated machine learning driven model for predicting platform supply vessel freight market Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-04-15 Fabian Kjeldsberg, Ziaul Haque Munim
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Digital system for dynamic container loading with neural network-based memory exploiting hybrid genetic algorithm for carbon reduction Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-04-13 Chen-Fu Chien, Yu-Bin Lan, Kanchana Sethanan, Chia-Ching Peng
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Metaheuristic-driven extended exergy accounting for sustainable closed-loop food supply chain management Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-04-12 Mohammad Shokouhifar, Reihaneh Naderi, Alireza Goli, Parapat Gultom, Mohsen Shafiei Nikabadi, Gerhard-Wilhelm Weber
In this research, an exergetic mathematical model is proposed for closed-loop food supply chain network design considering economic, environmental, and social aspects. The studded closed-loop food supply chain consists of six echelons, four echelons for forwarding the products (suppliers, factory, distribution centers, and customers), and two echelons for returning the products (collecting centers
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Organic production competitiveness: A bi-level model integrating government policy, sustainability objectives, and blockchain transparency Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-04-12 Yashar Manteghi, Jamal Arkat, Anwar Mahmoodi
The government subsidizes organic production to reduce the cost of production and encourage consumers to buy it. However, some organic producers do not adhere to organic production standards, which erodes customer confidence in these products. Companies use blockchain technology to display the entire production process to customers and increase transparency. This paper presents a bi-level model designed
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Air traffic controller scheduling Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-04-12 Jia Guo, Jonathan F. Bard
This paper investigates the air traffic controller scheduling problem in which each employee must be assigned up to 10 shifts over a two-week planning horizon. Additional assignments include a 30-minute lunch break and several 15-minute rest breaks within each shift. The goal is to select the minimum number of employees that balances a weighted combination of demand undercoverage, overcoverage and
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Dynamic selection of risk response strategies with resource allocation for construction project portfolios Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-04-12 Libiao Bai, Qi Xie, Jiachen Lin, Shiyi Liu, Chenshuo Wang, Lin Wang
To promote the dynamic risk management of construction project portfolios (CPPs) and improve the effect of risk response, a simulation–optimization model is proposed. The model integrates System Dynamics and optimization to dynamically select risk response strategies (RRSs) and facilitate more refined resource allocation. Specifically, a System Dynamics sub-model simulates the risk level by capturing
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Digital twin model with machine learning and optimization for resilient production–distribution systems under disruptions Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-04-10 Roberto Rosario Corsini, Antonio Costa, Sergio Fichera, Jose M. Framinan
Inspired by a real-life problem in the semiconductor industry, we introduce a novel digital twin model for a company subject to the adverse effects of unpredictable disruptions. Specifically, this company manufactures a product using a raw material provided by an external supplier, whose lead times may abruptly change due to disruptive events. The Smoothing Order-Up-To rule is adopted by the company
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Probabilistic movement primitives based multi-task learning framework Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-04-10 Chengfei Yue, Tian Gao, Lang Lu, Tao Lin, Yunhua Wu
With the increasing complexity of industrial production and manufacturing tasks, industrial robots are expected to learn intricate operations from simple actions easily and quickly with adaption to dynamic environment. In this paper, a task-parameterized multi-task learning framework is proposed to facilitate rapid learning of operational skills for industrial robots. In this framework, a conditional
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An intelligent quality prediction and autonomous decision system for natural products manufacturing processes Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-04-10 Qilong Xue, Yang Yu, Shixin Cen, Yukang Cheng, Xinlong Liu, Guijun Li, Qinglong Gao, Shan Gao, Zheng Li
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Control charting methods for monitoring high dimensional data streams: A conceptual classification scheme Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-04-10 Zahra Jalilibal, Mohammad Hassan Ahmadi Karavigh, Mohammad Reza Maleki, Amirhossein Amiri
There are always challenges in various industrial or non-industrial processes in which the product quality/service is described by a large number of quality characteristics. Thus, statistical process monitoring (SPM) techniques for capturing the quality of high-dimensional processes are becoming increasingly important, and various control charts have been developed to monitor different types of quality
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An integrated simulation and optimization tool for short-term mining planning problems with different prioritization among competing plant targets Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-04-10 Aldrin Gustavo Martins, Marcone Jamilson Freitas Souza, Paulo Santos Assis
This work proposes a tool that integrates a hierarchical mixed-integer linear programming (MILP) model with a discrete event simulation (DES) model to simulate an annual mining plan discretized in shift-by-shift periods. The hierarchical MILP model has ten objectives and optimizes the shift schedule according to the available materials in the free faces at each simulation moment. The DES model simulates
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Optimizing variable selection and neighbourhood size in the K-nearest neighbour algorithm Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-04-09 Ka Yuk Carrie Lin
The -nearest neighbour () algorithm is one of the well-known classifiers applied in various research areas. The input requirement includes a set of variables, the choice of the neighbourhood size () and the distance metric which are typically selected on experimenting with the data. The first two are usually decided sequentially in previous studies. This paper proposes a mixed integer linear program
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An adaptive large neighborhood search for the multi-depot dynamic vehicle routing problem with time windows Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-04-08 Sihan Wang, Wei Sun, Min Huang
As part of this study, we examine the multi-depot dynamic vehicle routing problem with time windows (MD-DVRPTW), in which customer requests emerge stochastically throughout the operational horizon. To provide timely and comprehensive service to these customers, a re-optimization framework utilizing an adaptive large neighborhood search (ALNS) has been developed. In our ALNS algorithm, two novel removal
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Further expansion from smart manufacturing system (SMS) to social smart manufacturing system (SSMS) based on industrial internet Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-04-08 Yuguang Bao, Xianyu Zhang, Chengjun Wang, Xinguo Ming
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Robust capabilities design and optimization for a modular block-based organization Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-04-05 Luis San Martin, Jorge Vera
Several organizations perform their tasks by combining different groups of people, equipment, etc., in such a way as to reach a goal. These modules, which we call building blocks (BB), have capabilities and their combination provides an overall capability for the whole organization. How to optimally combine those blocks to achieve capabilities for certain goals is what we address in this work. Therefore
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On solving the 1.5-dimensional cutting stock problem with heterogeneous slitting lines allocation in the steel industry Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-04-04 María Sierra-Paradinas, Óscar Soto-Sánchez, Antonio Alonso-Ayuso, F. Javier Martín-Campo, Micael Gallego
This paper presents a mathematical optimisation model to solve the slitting problem in a Spanish steel manufacturing company. In order to satisfy the demand for steel strip, coils are selected from the warehouse and cut lengthwise on the slitting lines. The main challenge in this problem is to determine a slitting plan, which involves defining a set of cutting patterns for the coils and selecting the
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A combined system based on data preprocessing and optimization algorithm for electricity load forecasting Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-04-04 Lei Gu, Jianzhou Wang, Jingjiang Liu
Creating steady models for predicting electricity load can enhance the equilibrium between power supply and demand, a critical factor in advancing precise distribution management and optimizing economic advantages at a granular level. Electricity load forecasting is a challenging research area, and the accuracy improvement of existing single-point load forecasting models is limited by the randomness
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Multiple-failure mode division and condition-based maintenance decision making for systems with multi-indicator performance degradation Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-04-04 Xiaohong Zhang, Yongfei Zhang, Guannan Shi, Hui Shi, Bin Wu, Shangju Hu
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Parcel delivery network optimization problem considering multiple hubs and consolidation of small-sized parcels Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-04-03 Ohhyun Kweon, Byung-In Kim, Giho Lee, Hyeonu Im, Chang Yun Chung, Ok Kyung Lim
With the growth in delivery demand owing to the expansion of e-commerce and COVID-19, parcel delivery network optimization has become a significant issue for courier companies. This study introduces a real-world parcel delivery network problem in which parcels are transported from origin to destination spokes via one or two hubs. The parcels are sorted in hubs for distribution to spokes. Regular- and
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Decision-making analysis of electric vehicle battery recycling under different recycling models and deposit-refund scheme Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-04-03 Wenqi Wu, Ming Zhang, Danlin Jin, Pingping Ma, Wendi Wu, Xueli Zhang
Recycling electric vehicle (EV) batteries has gained great attention in China. However, the firm's initiative will be weakened due to high costs. A deposit-refund scheme has been pilot-implemented in Shenzhen city. This paper tries to explore the role of the deposit-refund scheme in end-of-life EV battery recycling by using the Stackelberg game theory. Specifically, this study builds single-channel
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Regulating the imbalance for the container relocation problem: A deep reinforcement learning approach Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-04-02 Yin Tang, Zengjian Ye, Yongjian Chen, Jie Lu, Shuqiang Huang, Jian Zhang
The main objective of the container relocation problem (CRP) is to retrieve all containers stacked in a container terminal while following the required retrieval sequence and minimizing the efforts of relocating containers. However, the serious imbalance in containers’ duration of stay in the terminal causes highly inconsistent incoming and outgoing sequences of containers as well as a highly irregular
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Dimensions of data sparseness and their effect on supply chain visibility Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-04-01 Isabelle M. van Schilt, Jan H. Kwakkel, Jelte P. Mense, Alexander Verbraeck
Supply chain visibility concerns the ability to track parts, components, or products in transit from supplier to customer. The data that organizations can obtain to establish or improve supply chain visibility is often sparse. This paper presents a classification of the dimensions of data sparseness and quantitatively explores the impact of these dimensions on supply chain visibility. Based on a review
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Layer-wise surface quality improvement in laser powder bed fusion through surface anomaly detection and control Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-04-01 Chenguang Ma, Di Wang, Kai Zhao, Jiali Gao, Heng Wang, Aoming Zhang, Lang Cheng, Yingjie Zhang
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Human error probability evaluation based on reference task using intuitionistic fuzzy theory Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-03-30 Xing Pan, Song Ding, Xianheng Zhao, Wenjin Zhang, Dujun Zuo, Liuwang Sun
Human Reliability Analysis (HRA) is a critical issue for addressing human error in system reliability. There are numerous tasks for which human factors-related data are not available, rendering expert knowledge the only basis for assessing such tasks. However, the knowledge obtained from experts is subject to ambiguity and vagueness, which affects the usability of the assessment results. To overcome
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Optimal design of adaptive EWMA monitoring schemes for the coefficient of variation and performance evaluation with measurement errors Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-03-30 Suying Zhang, Xuelong Hu, Jianjun Wang, Panpan Zhou, Xiaolei Ren
The coefficient of variation (CV) usually describes the relative dispersion in production or service processes and has been widely applied in various fields. Monitoring the CV has received great attention in statistical process monitoring. This paper develops two one-sided adaptive EWMA (AEWMA) CV schemes to enhance the existing CV schemes’ monitoring efficiency. This scheme can effectively defect
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Cognitive intelligence in industrial robots and manufacturing Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-03-30 Avishek Mukherjee, Divya A.B., Sivvani M., Surjya Kanta Pal
The transition from manual to autonomous manufacturing processes, which has been propelled by consecutive industrial revolutions, is concurrently contingent upon advancements in artificial intelligence (AI). AI does have dependency on data, making it incompatible in handling industrial uncertainties. Human cognition, adept at navigating uncertain situations, presents a potential solution. This article
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Scheduling of twin automated stacking cranes based on Deep Reinforcement Learning Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-03-30 Xin Jin, Nan Mi, Wen Song, Qiqiang Li
Effective scheduling of twin automated stacking cranes (ASCs) in automated storage yard is critical to maximize operational efficiency. While Deep Reinforcement Learning (DRL) is promising in solving NP-hard scheduling problems, twin ASCs scheduling is challenging due to its unique properties including sequence-dependent setup and potential ASC interferences. In this paper, we propose a novel DRL method
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Prediction of Covid-19 confirmed cases and deaths using hybrid support vector machine-Taguchi method Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-03-29 Seda Hatice Gökler
With the emergence of the COVID-19 pandemic, experts aim to predict the number of confirmed cases and take measures to reduce death rates in order to be prepared for future pandemics. Determining the variables that affect the confirmed cases and deaths and predicting the number of confirmed cases and deaths is important for taking precautions quickly.
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Sharing instant delivery UAVs for crowdsensing: A data-driven performance study Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-03-29 Junhui Gao, Yan Pan, Xin Zhang, Qingye Han, Yujiao Hu
In recent years, there has been a significant increase in demand for instant deliveries, such as rapid delivery of takeaway food and medicine. Many logistics companies are planning to realize real-time delivery services through unmanned aerial vehicles (UAVs). However, costs of running such an autonomous delivery system are too expensive. Fortunately, urban management departments (UMD) that are responsible
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Optimizing integrated lot sizing and production scheduling in flexible flow line systems with energy scheme: A two level approach based on reinforcement learning Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-03-28 Mohamed Habib Jabeur, Sonia Mahjoub, Cyril Toublanc, Veronique Cariou
Many production environments are faced with the need to simultaneously determine the planning of lot sizing and the scheduling of production sequences while ensuring cost minimization. This issue becomes even more complex when integrating multiple energy sources with the goal of a low-carbon economy. To address this challenge, this paper proposes an integrated lot sizing and flexible flow line production
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Uncertainty dynamics in energy planning models: An autoregressive and Markov chain modeling approach Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-03-27 Esnil Guevara, Frédéric Babonneau, Tito Homem-de-Mello
This paper deals with the modeling of stochastic processes in long-term multi-stage energy planning problems when limited information is available about the distributions of such processes. Starting from simple estimates of variation intervals for uncertain parameters, such as energy demands and costs, we model the temporal correlation of these parameters through carefully constructed autoregressive
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An optimal “Trade-Online-Deliver-Offline” strategy for recycling unwanted medicines by reutilizing the existent informative pharmaceutical sales network Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-03-27 Yufeng Luo, Zhong Wan
Circular economy and sustainable development resort to creating friendly and profitable environment of recycling unwanted goods in practice. This research aims to design a novel “Trade-Online-Deliver-Offline” (TODO) system to recycle unwanted medicines (UMs) by reutilizing the existent informative pharmaceutical sales network, which is the first dual-channel recycle system with synergies of manufacturer
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Blockchain-based collaborative data analysis framework for distributed medical knowledge extraction Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-03-26 Zhi Li, Ming Li, Aofei Li, Zhiyu Lin
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Who should own the data? The impact of data value creation on data ownership Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-03-26 Chundong Zheng, Yizhen Li, Runliang Dou
With the popularization and application of the Industrial Internet of Things, equipment manufacturers as upstream enterprises can collect in real time the operational data of their downstream enterprise customers’ devices. As the notion of “data as an asset” gains acceptance, enterprises increasingly value the data value chain. In the current market mechanism, ownership of data is not clear, which
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Team orienteering with possible multiple visits: Mathematical model and solution algorithms Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-03-26 Hyun-Bum Jung, Hyeon-Il Kim, Dong-Ho Lee
This study addresses a new team orienteering problem in which each point can be visited possibly multiple times with decreasing scores. The problem is to determine a set of routes from a starting to a finishing point within an allowed travel time limit for the objective of maximizing the total collected score. A mixed integer programming model is developed to represent the problem mathematically. Then
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Disassembly line balancing with hazardous task failures – Model based solution approaches Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-03-25 Eda Goksoy Kalaycilar, Meral Azizoğlu, Sakine Batun
In this study, we consider a disassembly line balancing problem that involves a fixed number of workstations and the possibility of failures in hazardous tasks. We assume that each hazardous task may fail with a pre-specified probability and once it fails, the disassembly line stops, and all tasks assigned to subsequent workstations are interrupted. Our problem is to select the set of tasks for processing
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Task derivation and decomposition in crowdsourced manufacturing by bilevel coordinated optimization of product family planning and manufacturer load balancing Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-03-25 Xuejian Gong, Roger J. Jiao, Nagi Z. Gebraeel
Crowdsourcing in manufacturing enables collaborative product fulfillment through supply contracting, aligned with task derivation and decomposition. Effective coordination of product family planning (PFP) with manufacturing processes is crucial for manufacturer load balancing (MLB). This paper focuses on game-theoretic decisions in crowdsourcing task derivation and decomposition, proposing a bilevel
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Advancing predictive maintenance for gas turbines: An intelligent monitoring approach with ANFIS, LSTM, and reliability analysis Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-03-24 Larbi Brahimi, Nadji Hadroug, Abdelhamid Iratni, Ahmed Hafaifa, Ilhami Colak
Gas turbine malfunctions can significantly impact production and safety. This study proposes an intelligent monitoring system for MS5002C gas turbines using Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Long Short-Term Memory (LSTM) algorithms for real-time anomaly detection and predictive maintenance. Based on extensive historical data (1985–2021), the system predicts component degradation and
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Optimization of chemotherapy regimens using mathematical programming Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-03-24 Konstantin Bräutigam
Cancer is a leading cause of death and a cost burden on healthcare systems worldwide. The mainstay of treatment is chemotherapy which is most often administered empirically. Optimizing the frequency of drug administration would benefit patients by avoiding overtreatment and reducing costs. In this work, the optimization of chemotherapy regimens using mathematical programming techniques is demonstrated
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Evaluation of disassembly line layouts using an integrated fermatean fuzzy decision-making methodology: An application for refrigerator disassembly line Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-03-24 Yildiz Kose, Ertugrul Ayyildiz, Emre Cevikcan
The layout focusing on the physical arrangement of workstations is an important consideration when designing a disassembly line. The layout of a line has a significant impact on its efficiency. The new problem addressed within this study is evaluating the main disassembly line layout types with respect to a thorough hierarchy of evaluation criteria classifying quantitative and qualitative. The addressed
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Addressing the challenges of using autonomous robots for last-mile delivery Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-03-24 Ertugrul Ayyildiz, Melike Erdogan
The importance of last-mile delivery (LMD) in today's logistics networks has increased recently due to the rising popularity of e-commerce and home delivery. Various LMD modes, such as drones and autonomous vehicles, have emerged to solve the problems encountered during LMD activities and to reduce human intervention in the process. The purpose of this paper is to determine the challenges that arise
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Machine learning-based identification of cybersecurity threats affecting autonomous vehicle systems Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-03-23 Furkan Onur, Serkan Gönen, Mehmet Ali Barışkan, Cemallettin Kubat, Mustafa Tunay, Ercan Nurcan Yılmaz
With the advancement of humanity, transportation and trade activities have increased, leading to the development process of basic land vehicles as more than physical power became necessary. Hand tools were developed with the invention of the wheel, followed by animal-powered vehicles, and then steam engine technology. After the advancement of electromechanical technologies, today's modern vehicles
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Intelligent optimal preventive replacement maintenance policy for non-repairable systems Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-03-21 Moses Effiong Ekpenyong, Nse Sunday Udoh
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Renewable energy input strategy considering different electricity price regulation policies Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-03-21 Yexia Zhang, Wei Chen, Huan Yang, Hua Wang
This paper examines the impact of the electricity price cap regulation and different policies on renewable energy inputs, electricity prices, demands, and the profits of power firm. Specifically, we compare the impact of the peak-trough electricity price policy ( policy) and the uniform electricity price policy ( policy) based on electricity price cap regulation. The findings are as follows: (1) Regardless
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Green-resilient model for smartphone closed-loop supply chain network design: A novel four-valued refined neutrosophic optimization Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-03-21 Ayesha Saeed, Ming Jian, Muhammad Imran, Gul Freen, Aziz ur Rehman Majid
This study endeavors to integrate green supply chain and resilience proactive strategies within a closed-loop supply chain network. The research employs a three-step methodology. In the first phase, a multi-objective, multi-period, multi-node, mixed integer linear programming model (MILP) is developed. The objective of this study is to maximize resilience while minimizing both greenhouse gas (GHG)
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Quantifying the effects of carbon tax policy on servitization Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-03-20 Mehmet Alegoz
Servitization is an innovative business model in which the use of a product is sold instead of the product itself. Although effect of a carbon tax policy on a traditional selling business model is comprehensively addressed in the existing literature, to the best of our knowledge, there is no study proposing models for servitization with and without carbon tax to quantify the effects of carbon tax policy
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Imperialist competitive algorithm for unrelated parallel machine scheduling with sequence-and-machine-dependent setups and compatibility and workload constraints Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-03-20 Milad Elyasi, Yagmur Selenay Selcuk, O. Örsan Özener, Elvin Coban
In this paper, we present an in-depth analysis of the (UPMSP) in the context of washing machine production at Vestel Electronics. Vestel Electronics is a leading producer of washing machines and holds a noteworthy market position in the European consumer electronics industry. The production process at Vestel Electronics is (MTO) and requires 20 assembly lines to produce 200 different products. The
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Finite time preventive maintenance optimization by using a Semi-Markov process with a degraded state. A case study for diesel engines in mining Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-03-20 Antonio Sánchez-Herguedas, Angel Mena-Nieto, Adolfo Crespo-Márquez, Francisco Rodrigo-Muñoz
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An ensemble method with a hybrid of genetic algorithm and K-prototypes algorithm for mixed data classification Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-03-19 R.J. Kuo, Cian-Ying Wu, Timothy Kuo
Due to challenges posed by mixed data clustering, this study aims to introduce an innovative clustering-based classification algorithm that possesses the advantages of both classification and clustering techniques for mixed data analysis. The proposed algorithm employs the -prototypes algorithm with a genetic algorithm to optimize weights and centroids and utilizes the bagging method to build multiple
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Enhancing value creation of operational management for small to medium manufacturer: A conceptual data-driven analytical system Comput. Ind. Eng. (IF 7.9) Pub Date : 2024-03-18 Samuel Harno, Hing Kai Chan, Min Guo
This paper aims to explore the challenges and opportunities for Small and Medium-sized Manufacturing Enterprises (SMMEs) in implementing data-driven techniques in their operations. SMMEs are often considered to be low and medium–low tech companies, even if they have machinery, as they still rely on traditional processes and manpower and lack any digital technology. Previous research has shown that