Template-Type: ReDIF-Article 1.0 Author-Name: Sang-u Song Author-X-Name-First: Sang-u Author-X-Name-Last: Song Author-Name: Jun-Hee Han Author-X-Name-First: Jun-Hee Author-X-Name-Last: Han Author-Name: Yoonjea Jeong Author-X-Name-First: Yoonjea Author-X-Name-Last: Jeong Title: Container vehicle scheduling problem with port congestion by using a knowledge-based greedy heuristic Abstract: This paper addresses a container vehicle scheduling problem within the context of congestion at port terminals and formulates a mathematical model aimed at minimising vehicle usage time by efficiently organising the vehicle schedules. The model incorporates congestion information within terminals. A binary integer programming model is employed to propose the scheduling scheme as well as realistic constraints. Given the complexity of the proposed optimisation model, we develop a knowledge-based greedy heuristic to solve it within reasonable timeframes based on three properties derived from the optimal solution. This heuristic significantly improves vehicle utilisation by considering realistic constraints and port congestion. The performance of the heuristic is verified through comparative studies between the mathematical model and the heuristic. Overall, this paper enhances port logistics efficiency by integrating realistic constraints and time-dependent congestion into container vehicle scheduling, effectively addressing the demands of transportation companies and smart port operations. [Submitted: 5 September 2024; Accepted: 18 December 2024] Journal: European J. of Industrial Engineering Pages: 54-88 Issue: 1 Volume: 21 Year: 2026 Keywords: port terminal; port congestion; container truck; binary integer program; knowledge-based greedy heuristic; vehicle scheduling problem; VSP. File-URL: http://www.inderscience.com/link.php?id=151154 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:eujine:v:21:y:2026:i:1:p:54-88 Template-Type: ReDIF-Article 1.0 Author-Name: Mahnaz Naghsh-Nilchi Author-X-Name-First: Mahnaz Author-X-Name-Last: Naghsh-Nilchi Author-Name: Morteza Rasti-Barzoki Author-X-Name-First: Morteza Author-X-Name-Last: Rasti-Barzoki Title: Evaluation of the challenges of blockchain implementation for supply chain management of the Iranian lighting industry Abstract: Blockchain technology has the potential to transform supply chain operations; yet, it poses various challenges for companies, particularly in the lighting sector. This study assesses the limitations of blockchain implementation in supply chain management, categorising challenges into six areas: social, organisational, economic, technical, regulatory, and security. Utilising DEMATEL and OPA methods, the research identifies critical factors such as workforce training, data security, and appropriate platform selection as essential for successful technology integration. Findings highlight the importance of personnel skills development and robust data management for building stakeholder trust. The insights gained are applicable not only to the lighting industry but also to other sectors and countries, depending on local regulatory frameworks and technological capabilities. Ultimately, this research provides strategies for improving efficiency and competitiveness in supply chains, promoting a sustainable approach to technology adoption within the context of Industry 4.0. [Submitted: 1 October 2023; Accepted: 21 January 2025] Journal: European J. of Industrial Engineering Pages: 89-110 Issue: 1 Volume: 21 Year: 2026 Keywords: blockchain; supply chain management; decision making; DEMATEL; ordinal priority approach; challenges. File-URL: http://www.inderscience.com/link.php?id=151157 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:eujine:v:21:y:2026:i:1:p:89-110 Template-Type: ReDIF-Article 1.0 Author-Name: Jae-Dong Kim Author-X-Name-First: Jae-Dong Author-X-Name-Last: Kim Author-Name: Ji-Hoon Yu Author-X-Name-First: Ji-Hoon Author-X-Name-Last: Yu Author-Name: Jung-Ho Choi Author-X-Name-First: Jung-Ho Author-X-Name-Last: Choi Author-Name: Hyoung-Ho Doh Author-X-Name-First: Hyoung-Ho Author-X-Name-Last: Doh Title: Machine learning-based hybrid preprocessing techniques for UAV spare parts demand forecasting Abstract: Recently, there is been a surge in interest in unmanned aircraft as strategic tools for Defense Readiness Condition (DEFCON). In accordance with this worldwide trend, the Korean military has developed unmanned aerial vehicles (UAV) in an effort to improve DEFCON. To ensure the proper operation of these vehicles, it is important to accurately forecast the demand for spare parts for equipment maintenance and procurement. In order to forecast the demand for spare parts, the Korean military has relied on a variety of time series techniques employing information from the equipment maintenance information system. However, alternative demand forecasting models must be investigated to improve accuracy. This study proposes a demand forecasting model that implements machine learning techniques to enhance the accuracy of spare parts demand forecasting, which is central to the military field. UAV spare consumption data were used to develop a classification model for predicting future demand. [Submitted: 17 November 2023; Accepted: 19 October 2024] Journal: European J. of Industrial Engineering Pages: 33-53 Issue: 1 Volume: 21 Year: 2026 Keywords: unmanned aerial vehicles; UAV; time series; machine learning; deep learning; demand forecasting; pre-processing. File-URL: http://www.inderscience.com/link.php?id=151158 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:eujine:v:21:y:2026:i:1:p:33-53 Template-Type: ReDIF-Article 1.0 Author-Name: Ayoub Tighazoui Author-X-Name-First: Ayoub Author-X-Name-Last: Tighazoui Author-Name: Michael Schlecht Author-X-Name-First: Michael Author-X-Name-Last: Schlecht Author-Name: Roland De Guio Author-X-Name-First: Roland De Author-X-Name-Last: Guio Author-Name: Bertrand Rose Author-X-Name-First: Bertrand Author-X-Name-Last: Rose Title: Scheduling wagons to optimise the remanufacturing time of a train Abstract: This study first investigated the problem of scheduling the wagons in facilities for maintenance with the objective of minimising the time required to remanufacture the train. Accordingly, a mixed integer linear programming (MILP) formulation was implemented with the Makespan as an objective. The MILP model can only be applied to industrial cases that involve few operations. Therefore, to cope with these cases, we show that the problem is an extension of a flexible job shop (system) scheduling problem and propose a heuristic method based on job insertion and Johnson rules to solve a large set of instances. This study then determines the optimal quantity of resources to be dedicated to each facility as a function of the Makespan threshold value. Thus, an optimisation method based on an evolutionary algorithm is designed. This metaheuristic provides the minimum quantity of resources to be dedicated to each facility as a function of the Makespan threshold value. [Submitted: 29 January 2024; Accepted: 4 September 2024] Journal: European J. of Industrial Engineering Pages: 1-32 Issue: 1 Volume: 21 Year: 2026 Keywords: scheduling; rolling stock; railway; makespan; mixed integer linear programming; MILP. File-URL: http://www.inderscience.com/link.php?id=151159 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:eujine:v:21:y:2026:i:1:p:1-32 Template-Type: ReDIF-Article 1.0 Author-Name: J.S. Nimmy Author-X-Name-First: J.S. Author-X-Name-Last: Nimmy Author-Name: Justin Sunny Author-X-Name-First: Justin Author-X-Name-Last: Sunny Author-Name: Dony S. Kurian Author-X-Name-First: Dony S. Author-X-Name-Last: Kurian Author-Name: V. Madhusudanan Pillai Author-X-Name-First: V. Madhusudanan Author-X-Name-Last: Pillai Title: Choosing from the menu of beer game adaptations: a general framework based on a narrative review Abstract: Beer distribution game is a popular simulation game developed in the sphere of supply chain management. Due to its wide acceptance, academicians and researchers have begun developing similar games, and this practice continues. Till this time, these developments have been neither compiled nor analysed in the literature. Moreover, a framework is not yet available to systematically select these adaptations for practical applications. This paper aims to explore the incredible saga of beer distribution game through an evolutionary lens with a narrative review. Findings reveal that the beer game adaptations are more or less similar in their structure and settings, with some differences in the appearance and purpose of development. It can be inferred that the evolution of beer game versions is heavily influenced by the developments in technology. Finally, this work proposes a general framework for the academicians and researchers to select appropriate beer game adaptations from the existing ones. [Submitted: 23 September 2023; Accepted: 10 March 2025] Journal: European J. of Industrial Engineering Pages: 111-136 Issue: 1 Volume: 21 Year: 2026 Keywords: beer distribution game; beer game; operation simulation; role-play game; supply chain management; system dynamics. File-URL: http://www.inderscience.com/link.php?id=151161 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:eujine:v:21:y:2026:i:1:p:111-136 Template-Type: ReDIF-Article 1.0 Author-Name: Mrudul Y. Jani Author-X-Name-First: Mrudul Y. Author-X-Name-Last: Jani Author-Name: Manish R. Betheja Author-X-Name-First: Manish R. Author-X-Name-Last: Betheja Author-Name: Urmila Chaudhari Author-X-Name-First: Urmila Author-X-Name-Last: Chaudhari Title: Optimal allocation strategies in two-warehouse systems: managing expiry dates, trade credit, price-advertisement dependent demand, and shortages Abstract: Trade credit, a widely adopted business practice, allows deferred payments and supporting cash flow. In competitive markets, retailers leverage trade credit to attract consumers, though it complicates inventory management, particularly for products with longer expiry periods requiring additional storage. Understanding the inverse relationship between price and demand, along with the influence of advertising, this study aims to: 1) examine the impact of supplier-to-retailer and retailer-to-consumer trade credit on inventory and financial outcomes; 2) explore the interplay between pricing, demand, and advertising; 3) develop an algorithm to optimise advertisement frequency alongside traditional optimisation methods; 4) optimise advertisement frequency, selling price, and cycle time to maximise retailer profits; 5) conduct sensitivity analyses on key parameters. For TechTrends case study, the research seeks to establish a comprehensive framework for improving trade credit strategies, optimising inventory management, and supporting sustainable growth for fashionable electronics. [Received: 22 March 2024; Accepted: 11 March 2025] Journal: European J. of Industrial Engineering Pages: 200-255 Issue: 2 Volume: 21 Year: 2026 Keywords: maximum fixed life-span; price-advertisement dependent demand; time varying holding cost for rented warehouse; two-layer trade credit policy; two-warehouse environment; partially backlogged shortages. File-URL: http://www.inderscience.com/link.php?id=151918 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:eujine:v:21:y:2026:i:2:p:200-255 Template-Type: ReDIF-Article 1.0 Author-Name: Zeyu Luo Author-X-Name-First: Zeyu Author-X-Name-Last: Luo Author-Name: Zhaotong Lian Author-X-Name-First: Zhaotong Author-X-Name-Last: Lian Author-Name: Zhixin Yang Author-X-Name-First: Zhixin Author-X-Name-Last: Yang Title: Strategic optimisation of service systems with cold-standby parts using inventory policy Abstract: Ensuring the reliability of terminal servers is a cardinal concern in today's business operations, given the potential for system failures and resultant operational interruptions. One efficient strategy that has emerged is using cold standbys, inactive redundant components that spring into action when the primary system falters. There has been a lot of research on queueing systems and how they work with repairable servers, but there is still a clear need to combine these findings with the best way to order standby parts. This paper pioneers the development and detailed study of an <i>M</i>/<i>M</i>/1 queueing model outfitted with cold standby parts, regulated by an (<i>r</i>, <i>q</i>) ordering policy. Our investigation goes a step further, delving into customer behaviours and how server availability influences their strategic decisions, ultimately affecting the service provider's profit margin. By intertwining the principles of queueing systems with inventory theory, we propose a novel model that offers companies a refined strategy to maximise anticipated profit against the backdrop of potential server breakdowns. [Submitted: 17 April 2024; Accepted: 18 April 2025] Journal: European J. of Industrial Engineering Pages: 256-276 Issue: 2 Volume: 21 Year: 2026 Keywords: queueing; cold standby; inventory; customer strategy; optimisation. File-URL: http://www.inderscience.com/link.php?id=151919 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:eujine:v:21:y:2026:i:2:p:256-276 Template-Type: ReDIF-Article 1.0 Author-Name: Changbin Chen Author-X-Name-First: Changbin Author-X-Name-Last: Chen Author-Name: Qiaobo Xu Author-X-Name-First: Qiaobo Author-X-Name-Last: Xu Author-Name: Zhengtao Wang Author-X-Name-First: Zhengtao Author-X-Name-Last: Wang Author-Name: Yaoxing Xie Author-X-Name-First: Yaoxing Author-X-Name-Last: Xie Title: Research on supply chain collaboration carbon emission reduction strategies embedded in a blockchain under cost-sharing contracts Abstract: We construct a two-level, low-carbon supply chain comprising a manufacturer and retailer. Under the incentive contract model of traditional cost sharing, blockchain technology is embedded, and a game model is established considering the influence of consumers' green trust and low-carbon preference coefficient. Through calculations, this study examines the impact of manufacturers adopting blockchain technology and retailers cooperating with suppliers to reduce carbon emissions through cost sharing on the carbon emission reduction rate and corporate profits. This study finds that if blockchain technology is adopted, retailers' sharing of carbon emission reduction costs can improve supply chain efficiency but reduce retailers' profits. When the costs of blockchain technology are below a certain threshold, its adoption can effectively improve the carbon emission reduction rate, win the green trust of consumers, expand the market for green and low-carbon products, and improve the profits of the supply chain. [Submitted: 8 March 2024; Accepted: 21 October 2024] Journal: European J. of Industrial Engineering Pages: 137-168 Issue: 2 Volume: 21 Year: 2026 Keywords: low-carbon supply chain; blockchain technology; carbon emission reduction; cost sharing; Stackelberg game model. File-URL: http://www.inderscience.com/link.php?id=151920 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:eujine:v:21:y:2026:i:2:p:137-168 Template-Type: ReDIF-Article 1.0 Author-Name: Jihen Issaoui Author-X-Name-First: Jihen Author-X-Name-Last: Issaoui Author-Name: Dorsaf Daldoul Author-X-Name-First: Dorsaf Author-X-Name-Last: Daldoul Author-Name: Nadia Bahria Author-X-Name-First: Nadia Author-X-Name-Last: Bahria Author-Name: Imen Harbaoui Author-X-Name-First: Imen Author-X-Name-Last: Harbaoui Title: A combined approach for machine rare failure detection and process monitoring using machine learning and multivariate control charts Abstract: Predictive maintenance is a powerful tool for reducing costly interruptions in modern manufacturing. One of its challenges is proactively detecting rare machine failures, which impact equipment health and process stability despite their infrequency. This paper introduces a combined approach to predicting rare machine failures and monitoring process stability using statistical and technological techniques. Initially, a data augmentation method is used to handle imbalanced data. Then, three machine learning algorithms (gradient boosting, K-nearest neighbour, and logistic regression) are tested and compared for their performance in detecting rare machine failures. Furthermore, principal component analysis is used to establish multivariate control charts, specifically TPCA2 and Q charts, to monitor manufacturing processes and equipment behaviour. The proposed approach, tested with real-world data, has demonstrated effective results in predicting rare failures and in monitoring equipment behaviour. [Received: 28 May 2024; Accepted: 30 April 2025] Journal: European J. of Industrial Engineering Pages: 277-305 Issue: 2 Volume: 21 Year: 2026 Keywords: predictive maintenance; machine learning classifiers; principal components analysis; PCA; rare failure prediction; multivariate statistical process control; MSPC. File-URL: http://www.inderscience.com/link.php?id=151921 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:eujine:v:21:y:2026:i:2:p:277-305 Template-Type: ReDIF-Article 1.0 Author-Name: Xuehong Gao Author-X-Name-First: Xuehong Author-X-Name-Last: Gao Author-Name: Xiaopeng Chen Author-X-Name-First: Xiaopeng Author-X-Name-Last: Chen Author-Name: Chanseok Park Author-X-Name-First: Chanseok Author-X-Name-Last: Park Author-Name: Bosung Kim Author-X-Name-First: Bosung Author-X-Name-Last: Kim Title: Facility location decision under data contamination: robustness properties and performance Abstract: The facility location decision problem conventionally assumes that demand locations are known to the decision maker with certainty. However, in many practical circumstances, information on demand locations is inaccurate or involves potential errors (which we refer to as contamination). We first investigate the robustness properties of popular location estimation methods (the centre of gravity method and methods based on <i>L</i><SUB align="right"><SMALL>1</SMALL></SUB> and <i>L</i><SUB align="right"><SMALL>2</SMALL></SUB> distances) for the Weber problem. To this end, we apply two important robustness metrics from statistics to the problem: infinitesimal robustness (sensitivity to contamination) and breakdown point (amount of robustness). We then numerically compare the performances of the location estimation methods by employing a new measure called relative performance. [Received: 3 June 2024; Accepted: 3 January 2025] Journal: European J. of Industrial Engineering Pages: 169-199 Issue: 2 Volume: 21 Year: 2026 Keywords: facility location decision; robustness; data contamination; supply chain. File-URL: http://www.inderscience.com/link.php?id=151924 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:eujine:v:21:y:2026:i:2:p:169-199