European J. of Industrial Engineering (30 papers in press)

Regular Issues

• Risk-Averse Joint Facility Location-Inventory Optimization for Green Closed-Loop Supply  
  by Guodong Yu, Pengcheng Dong, Ying Xu, Xiao Zhao 
  Abstract: This paper considers a joint facility location-inventory optimisation for green closed-loop supply chain network design under demand uncertainty. Under the uncoordinated inventory policy, we propose a chance-constrained risk-averse bi-objective 01 mixed-integer nonlinear stochastic programming to minimise the total expected cost and CO2 emissions. To solve the model, we first present an equivalent reformulation with a single objective based on distributionally robust optimisation. Then, we provide a linear reformulation with some valid inequalities. We also provide a greedy heuristic decomposition searching rule to solve the large-scale problem. We finally present a numerical analysis to show the performance of our methods. Results illustrate that the risk-averse joint model can effectively improve service capability and reliability than independent and risk-neutral location and inventory problems. We also recommend that the incompletely uncoordinated strategy for the joint optimisation can be more cost-effective and generate fewer workloads. Besides, the proposed algorithm achieves a more desirable performance than CPLEX for large-scale problems.
  Keywords: green closed-loop supply chain; facility location; inventory; risk-averse; chance constraint; distributionally robust optimisation.
  DOI: 10.1504/EJIE.2023.10046132
   
  
• A survey on network design problems: main variants and resolution approaches  
  by Imen Mejri, Safa Bhar Layeb, Farah Zeghal 
  Abstract: Over the last decades, network design problems (NDPs) have been one of the most investigated combinatorial optimisation problems that are still catching the interest of both practitioners and researchers. In fact, NDPs pose significant algorithmic challenges, as they are notoriously NP-hard, and arise in several applications, mainly in logistics, telecommunication, and production systems. Based on the literature published mainly between 1962 and 2021, this paper provides a comprehensive taxonomy of NDPs and also identifies the most investigated variants as well as their main fields of application. This taxonomy highlights the diversity as well as the assets of this core class of operations research problems. Moreover, the main mathematical formulations and solution methods are reported. Finally, directions for future research on NDPs are derived.
  Keywords: network design problems; NDPs; literature review; survey; combinatorial optimisation.
  DOI: 10.1504/EJIE.2022.10046171
   
  
• A Hybrid Particle Swarm Optimisation Algorithm for Multi-Resource Constrained Flexible Job Shop Scheduling Problem with Transportation  
  by Deling Yuan, Zexi Yang 
  Abstract: In the flexible job processing environment, there exists an insufficient optimisation of spatial and transportation resources. To effectively solve the multi-resource constrained flexible job shop scheduling problem with transportation, it is imperative to consider factors such as the capacity of transportation equipment, limitations of the transportation equipment temporary storage area and job temporary storage area. This article focuses on the coordinated scheduling of processing and transportation tasks, aiming to minimise the makespan and the total equipment running time. A hybrid particle swarm optimisation algorithm (NHPSO) is designed, incorporating genetic algorithm's (GA) crossover and mutation functions to preserve particles' genetic information while enhancing global search capabilities. The simulated annealing (SA) mechanism is also included to boost early-to-mid-stage optimisation ability and broaden solution search range. A neighbourhood search strategy based on the critical chain concept is developed in three evolutionary directions to enhance algorithm effectiveness without getting stuck at local optimums. Finally, the convergence ability and effectiveness of the proposed algorithm are verified by experiments. [Submitted: 4 November 2023; Accepted: 20 July 2024]
  Keywords: flexible job shop scheduling; FJSP; limited resources; particle swarm optimisation; neighbourhood search; transportation.
  DOI: 10.1504/EJIE.2025.10067782
   
  
• Robust Monitoring of Contaminated Data in Detecting the Change Point with the Shewhart Mean Chart  
  by Kooi Huat Ng, Chan Hoong Lee, Jeng Young Liew, Kok Haur Ng, Yann Ling Goh 
  Abstract: The Shewhart mean (X) chart is a powerful device for distinguishing special cause variations. The change point is often slower than the X charts signalling time in practice. Knowing the actual process change time reduces the opportunity window for locating the special cause. This paper exploits the robust monitoring of contaminated data concerning the X charts change point detection. The estimated expected run lengths, average change point estimates, and related standard errors of change point estimates for different process shifts under various contamination percentages are computed using Monte Carlo simulation to assess the X charts performance. The findings reveal that the average estimated process change time with its corresponding standard error of estimate declines proportionally when the contamination percentage increases. In contrast, the expected run length increases proportionally when several contamination levels occur, hinting that the X charts process shift detection efficiency decreases with an increase in contamination. [Submitted: 27 November 2023; Accepted: 6 October 2024]
  Keywords: Shewhart mean chart; change point; robust; contamination; expected run length.
  DOI: 10.1504/EJIE.2025.10068461
   
  
• Multi-Level Uncapacitated Facility Location Problem with Clients' Preferences  
  by Stefan Miskovic, Olivera Stancic, Zorica Stanimirovic, Raca Todosijevic 
  Abstract: This paper introduces the multi-level uncapacitated facility location problem with clients’ preferences (MLUFLP-CP), which represents a generalization of the well-known multi-level uncapacitated facility location problem (MLUFLP). The MLUFLP-CP is first modeled as a bi-level mathematical program, and then reformulated to four integer linear programs. Due to the NP-hardness of the MLUFLP-CP, the problem instances of real-world dimensions are unsloved to optimality by CPLEX solver. Therefore, we have designed a general variable neighborhood search (GVNS) metaheuristic as an efficient solution approach to the MLUFLP-CP. The GVNS concept and its parameters are adapted to the multi-level nature of problem, and a novel VND variant, denoted as multi-level VND, is used as a local search improvement procedure. Computational experiments on MLUFLP-CP instances show that the proposed GVNS quickly reaches all known optimal solutions, improves upper bounds obtained by CPLEX and efficiently provides solutions for large-scale instances that were out of reach for CPLEX.
  Keywords: Multi-level facility location problem; Clients' preferences; Bi-level mathematical program; Integer linear programming; CPLEX solver; Variable neighborhood search; Variable neighborhood descent.
  DOI: 10.1504/EJIE.2025.10068899
   
  
• Container Vehicle Scheduling Problem with Port Congestion by using a Knowledge-based Greedy Heuristic  
  by Sang-u Song, Jun-Hee Han, Yoonjea Jeong 
  Abstract: This paper addresses a container vehicle scheduling problem within the context of congestion at port terminals and formulates a mathematical model aimed at minimizing vehicle usage time by efficiently organizing 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 optimization 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 utilization 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.
  Keywords: port terminal; port congestion; container truck; binary integer program; knowledge-based greedy heuristic; vehicle scheduling problem.
  DOI: 10.1504/EJIE.2026.10069239
   
  
• Combination of Berth Allocation and Storage Assignment in Dry Bulk Ports Considering Consolidated Stacking Strategies  
  by Hongpeng Hou, Shiheng Zhu, Zhiyou Li, Ruiyou Zhang 
  Abstract: An integrated problem of berth allocation and stockyard assignment in dry bulk ports is investigated. The neighborhood storage slots with the same type of cargo can be consolidated to hold a whole stockpile in the problem. The optimization criteria of the problem are to minimize the total transshipment and storage costs of all the cargoes, as well as the total berthing time of all the ships. The problem is mathematically formulated as a mixed-integer nonlinear programming model. A genetic algorithm is proposed to solve the problem. Experiments based on instances with different scales validate both the mathematical model and the genetic algorithm. The experimental results illustrate that the algorithm can provide better solutions within a shorter time than directly solving the mathematical model for large-scaled instances.
  Keywords: Berth allocation; stockyard assignment; dry bulk port; mathematical modeling; genetic algorithm.
  DOI: 10.1504/EJIE.2025.10069262
   
  
• Design of Incomplete Hub Location-Routing Networks in an Intra-City Metro Logistics System  
  by Qing-Mi Hu, Shihao Guo 
  Abstract: This paper addresses the design of incomplete hub location-routing networks in an intra-city metro logistics system, in which flows of mail and parcels are exchanged among customers via the integrated ground road and underground metro transport modes. The network design is modelled as a two-stage optimization decision. In the first stage, candidate hubs are determined by an improved K-means clustering algorithm and the E-TOPSIS model. In the second stage, a mixed-integer programming formulation is proposed to determine hub locations, demand allocation, metro route selection, and ground distribution routes. Moreover, to tackle large-scale instances, a hybrid heuristic algorithm based on Tabu search and adaptive large neighbourhood search is proposed, and extensive numerical experiments with randomly generated instances are conducted to validate the effectiveness of model and algorithm. Finally, we present a realistic case study on the Shanghai metro in China, and analyze the impact of different parameters on network configurations.
  Keywords: incomplete hub location-routing; intra-city metro logistics; E-TOPSIS model; mixed-integer programming; hybrid heuristics.
  DOI: 10.1504/EJIE.2025.10069268
   
  
• Evaluation of the Challenges of Blockchain Implementation for Supply Chain Management of the Iranian Lighting Industry  
  by Mahnaz Naghsh Nilchi, Morteza Rasti-Barzoki 
  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, categorizing challenges into six areas: social, organizational, economic, technical, regulatory, and security. Utilizing 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.
  Keywords: Blockchain; supply chain management; decision making; DEMATEL; Ordinal Priority Approach; Challenges.
  DOI: 10.1504/EJIE.2026.10069839
   
  
• Machine Learning-Based Hybrid Preprocessing Techniques for UAV Spare Parts Demand Forecasting  
  by Jae-Dong Kim, Ji-Hoon Yu, Jung-Ho Choi, Hyoung-Ho Doh 
  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. Unmanned Aerial Vehicle spare consumption data were used to develop a classification model for predicting future demand.
  Keywords: Unmanned Aerial Vehicles (UAV); Time series; Machine learning; Deep learning; Demand Forecasting; Preprocessing.
  DOI: 10.1504/EJIE.2026.10070259
   
  
• Scheduling Wagons to Optimise the Remanufacturing Time of a Train  
  by Ayoub Tighazoui, Michael Schlecht, Roland De Guio, Bertrand Rose 
  Abstract: This study first investigated the problem of scheduling the wagons in facilities for maintenance with the objective of minimizing 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 optimization 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.
  Keywords: maintenance scheduling; rolling stock; railway system; Makespan; MILP.
  DOI: 10.1504/EJIE.2026.10070495
   
  
• Strategies for Smart Port Development Based on an ISM-MICMAC Approach  
  by Kevin X. Li, Yuhan Zhu, Yi Xiao, Chen Qian, Xiao Zhang 
  Abstract: The concept of smart ports has rapidly evolved in recent years, driven by both theoretical advancements and practical applications. This research aims to identify both macro-level and practical strategies for smart ports by clarifying the influential factors and their interactions. Using Interpretive Structural Modeling (ISM) and Matrices Impacts Cross-Multiplication Appliance Classmen (MICMAC), the study establishes the hierarchical relationships among key factors identified through a systematic bibliometric review. The analysis reveals that Intelligent Infrastructure and Free Trade Zone (FTZ) Policy have the most significant foundational impact, driving the development of upper-level factors such as decision-making platforms. These influences eventually manifest in the practical implementation of systems like terminal operating systems, equipment management, and energy consumption management. Based on the findings, this paper proposes macro-level strategies emphasizing financial stability, resource management, and comprehensive support frameworks, alongside practical strategies targeting economic development, intelligent technology, data integration and collaboration.
  Keywords: smart ports; ISM-MICMAC analysis; bibliometric review; hierarchical relationships; port development strategies.
  DOI: 10.1504/EJIE.2026.10070544
   
  
• Optimal Allocation Strategies in Two-Warehouse Systems: Managing Expiry Dates, Trade Credit, Price-Advertisement Dependent Demand, and Shortages  
  by Mrudul Jani, Manish Betheja, Urmila Chaudhari 
  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.
  Keywords: Maximum fixed life-span; Price-advertisement dependent demand; variable holding cost; Two-layer trade credit; Two-warehouse; Shortages.
  DOI: 10.1504/EJIE.2026.10071221
   
  
• Strategic Optimisation of Service Systems with Cold-Standby Parts using Inventory Policy  
  by Zeyu Luo, Zhaotong Lian, Zhixin Yang 
  Abstract: Ensuring the reliability of servers is a cardinal concern in today's business operations. 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. In this paper, we pioneer the development and detailed study of an M/M/1 queueing model equipped with cold standby parts under the regulation of an (r, q) ordering policy. We go 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 offering companies a refined strategy to maximise profit against server breakdowns.
  Keywords: Queueing; cold standby; inventory; customer strategy; optimisation.
  DOI: 10.1504/EJIE.2026.10071222
   
  
• Research on Supply Chain Collaboration Carbon Emission Reduction Strategies Embedded in a Blockchain Under Cost-Sharing Contracts  
  by Changbin Chen, Qiaobo Xu, Zhengtao Wang, Yaoxing Xie 
  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.
  Keywords: low-carbon supply chain; blockchain technology; carbon emission reduction; cost sharing; Stackelberg game model.
  DOI: 10.1504/EJIE.2026.10071361
   
  
• Exploratory Performance Evaluation and Ranking for Complex Network Systems based on the Extension of GFA-DEA Approach  
  by Mengdie Huang, Tangbin Xia, Guojin Si, Yutong Ding, Ershun Pan, Li-Feng Xi 
  Abstract: In complex systems with expansive operations, the connections between inputs and outputs are complicated and multifaceted, requiring deep insights into network systems to extend to multi-stage evaluation. Meanwhile, the inherent uncertainty including data insufficiency and interrelation may hinder the applicability of conventional efficiency evaluation models. And it is crucial to figure out the appropriate inputs and outputs from a wide range of potential indicators. Therefore, this paper proposes an exploratory performance analysis and ranking model for complex systems with network structures and uncertainties based on the extension of the combination of data envelopment analysis (DEA), grey theory, and factor analysis (GFA-DEA). It can be used as a supplementary tool for exploratory analysis of complex systems as the requirements for basic data are not excessive. The illustrative application shows the extended model concludes relatively consistent results with network DEA models and provides complementary information with simplified model formulation and computational complexity.
  Keywords: Performance evaluation and ranking; Network structure; Exploratory analysis; Complex systems.
  DOI: 10.1504/EJIE.2025.10071474
   
  
• The Integrated Data-Driven and Graph Technology-Based Network Analysis Method for Abnormal States and Risk in Automated Container Terminals Operations  
  by Zixin Wang, Qingcheng Zeng, Xingnan Zhang 
  Abstract: Automated container terminals (ACTs) are critical to the global supply chain, enhancing efficiency but facing substantial challenges from complex environments and equipment. This study introduces a framework combining data-driven and graph technologies to analyse ACT anomalies and risks. Data from various sources are processed to ensure accuracy, using BERT and BiLSTM for entity and relation extraction and Neo4j for constructing a coupled network. The framework identifies anomalous events and assesses their risk impacts, revealing intrinsic links between anomalies and risks. The research suggests improvements for risk management strategies in the current ACT operating environment, offering new theoretical insights and practical methods for enhancing operational stability and safety.
  Keywords: Automated Container Terminal (ACT); Operation abnormality; Risk network analysis; Operational optimization; Data-driven.
  DOI: 10.1504/EJIE.2026.10071828
   
  
• A Combined Approach for Machine Rare Failure Detection and Process Monitoring using Machine Learning and Multivariate Control charts  
  by Jihen Issaoui, Dorsaf Daldoul, Nadia Bahria, Imen Harbaoui 
  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 Neighbor, 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 behavior. The proposed approach, tested with real-world data, has demonstrated effective results in predicting rare failures and in monitoring equipment behaviour.
  Keywords: Predictive maintenance; machine learning classifiers; principal components analysis (PCA); rare failure prediction; multivariate statistical process control (MSPC).
  DOI: 10.1504/EJIE.2026.10072019
   
  
• Choosing from the Menu of Beer Game Adaptations: a General Framework based on a Narrative Review  
  by Nimmy J.S, Justin Sunny, Dony S. Kurian, V. Madhusudanan Pillai 
  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 started developing similar games and still, it is continuing. Till this time, these developments are 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.
  Keywords: Beer Distribution Game; Operation Simulation; Role-play Game; Supply Chain Management; System Dynamics; Decision Making.
  DOI: 10.1504/EJIE.2026.10072129
   
  
• Minimising Yard Congestion in Container Ports with Autonomous Transportation Vehicles  
  by Jeong-Hwa Lee, Dong-Hyun Kang, Hoon Lee, Min-Chan Kim, Tae-Sun Yu 
  Abstract: We examine operational-level port optimization models with an objective of improving the transportation efficiency of autonomous yard trucks. We aim to provide managerial insights into how job assignment and path finding decisions affect the operational efficiency of transportation resources in the presence of yard congestion. Through this research we assess whether an alternative detouring method can surpass conventional vehicle optimisation methods based on the shortest path approach. Furthermore, we develop a physics-based port digital twin environment to precisely measure and quantify yard congestion induced by the spatial interference among transportation vehicles. We also introduce a modified network flow model that facilitates the identification of a vehicle operational schedule to minimise yard congestion.
  Keywords: Port optimisation; autonomous yard trucks; yard congestion; port digital twin; network model.
  DOI: 10.1504/EJIE.2026.10072386
   
  
• Using Digital Twin Technology to Conduct Dynamic Simulation of Industry-Education Integration  
  by Zhenyun Chang, Xiaohong Li, Minmin Shao, Dongmei Qian, Lin Zhao 
  Abstract: This study evaluates the effectiveness of the Digital Twin-Construction Safety Training Framework (DT-CSTF) using a sample of 100 construction workers. The workers underwent safety training in a Virtual Reality Training Environment (VRTE), integrated with real-time data from a Digital Twin (DT) model, Building Information Modeling (BIM), and safety protocols. Key performance metrics, including Hazard Detection Rate (HDR), Compliance Rate (CR), and Worker Stress Level (WSL), were analyzed to measure the system's effectiveness. Results showed that the DT-CSTF significantly enhanced worker safety awareness, with HDR and CR metrics consistently exceeding 95%, even in complex, high-stress scenarios. The integration of real-time physiological monitoring allowed the system to dynamically adjust training, ensuring high performance without compromising safety, even under stress. The cost-effectiveness of the DT-CSTF was evident in its ability to replicate hazardous situations in VR without the need for physical setups, reducing training costs while maintaining high safety standards. This data-driven approach promises to reduce accidents in actual construction projects by improving hazard detection and compliance in real-world conditions.
  Keywords: Digital Twin (DT); Virtual Reality (VR); Workers' Safety; Artificial Intelligence (Al).
  DOI: 10.1504/EJIE.2026.10072994
   
  
• Improving Supply Chain Performance by Production Scheduling in a Pharmaceutical Company  
  by Faten Ben Chihaoui, Frank Werner 
  Abstract: This paper discusses the single machine production scheduling of multi-product batches in a pharmaceutical plant, focusing on sequence-dependent setup times, minimal plant activities, waiting times, delivery deadlines, and release date restrictions. The packaging phase of the tab-let is considered. A mathematical model is proposed to minimize the makespan and total weighted tardiness time. A genetic algorithm is developed to resolve the problem. The algorithm's performance is tested on three classes of real-world problems, demonstrating its efficacy. The obtained solution reduces the objective function compared to the company expert schedule and is computationally efficient. Production scheduling is crucial for supply chain performance. The genetic algorithm improves OEE to 70% due to an improved packing machine performance and availability. It aims to reduce the waste in production scheduling to optimise the resources. A 10% increase in OEE can identify inefficiencies, reduce downtime, and boost output, boosting profitability and ensuring supply chain performance.
  Keywords: pharmaceutical industry; supply chain sustainability; KPI; performance; OEE; Scheduling; Setup times; Storage; Availability constraints; Genetic algorithm; Makespan and Total tardiness.
  DOI: 10.1504/EJIE.2026.10073030
   
  
• Facility Location Decision under Data Contamination: Robustness Properties and Performance  
  by Xuehong Gao, Xiaopeng Chen, Chanseok Park, Bosung Kim 
  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 L1 and L2 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.
  Keywords: Facility location decision; robustness; data contamination; supply chain.
  DOI: 10.1504/EJIE.2026.10073237
   
  
• An Online-Offline Sustainable Hybrid Production Supply Chain Inventory Model with controllable Lead Time  
  by Nayantara Ghosh, Balaji Roy 
  Abstract: This article considers a green supply chain inventory model consisting of a single manufacturer and a single retailer, who uses a hybrid-channel to sell the product. To promote sustainability, the manufacturer uses a hybrid manufacturing facility. The demand of the product depends on price and greenness. The retailer invests to reduce the lead time, which depends on the production, transportation, and set-up times. Carbon emissions occur mainly from production, warehousing, and transportation activities. This study also considers carbon tax and greenness-based government subsidy. Initially, the decentralized model is developed, followed by the construction of the centralized model. Finally, a hybrid revenue and cost sharing contract is proposed. A Stackelberg game model from the retailer's viewpoint is analysed, and a solution algorithm is suggested to find the optimal outputs. The findings suggest that the hybrid production system benefits more than the single production system, and the hybrid contract boosts individual profits.
  Keywords: online-offline channel; variable demand; lead time; carbon emission reduction.
  DOI: 10.1504/EJIE.2026.10073416
   
  
• Joint Operations Management, Pricing and Greenness Decisions based on Government Subsidies under VMI with Consignment Stock Contract, Cap-and-Trade Regulation, and Margin Effects of Greening  
  by Harun Öztürk, Fatih Ahmet Şenel 
  Abstract: This paper introduces the concept of investment in green technology, government subsidy, margin effects and emission reduction regulation, and provides a more realistic insight into the present literature on VMI with consignment stock. The manufacturer is responsible for the shipment of each production batch in accordance with the geometric batch shipment model, which entails a direct delivery to the retailer's warehouse. The products distributed via this particular chain are aimed at a green-conscious market. The level of investment in green technology by the manufacturer has an influence on the variable costs incurred. Furthermore, the retailer has demonstrated an investment in green processes. Each shipment dispatched to the retailer contains items that are faulty to some degree. The results demonstrate that if decisions are made in conjunction with the government's subsidy programme for the reduction of green investment costs, it will facilitate an increase in system profit.
  Keywords: vendor managed inventory; consignment stock; government subsidy; cap-and-trade regulation; remanufacturing; margin effects.
  DOI: 10.1504/EJIE.2026.10073428
   
  
• Optimisation of Green Investment under Shipping Alliance and Hub-and-Spoke Collusion  
  by Ying Yi, Gang Dong, Zongtuan Liu 
  Abstract: As global climate change and environmental pollution issues intensify, ports and the shipping industry are accelerating their transition towards green and low-carbon practices. Consequently, this paper contends that ports and shipping firms will undertake green investments, defined as activities promoting environmental sustainability and mitigating pollution and carbon emissions. Specifically, shipping firms will adopt clean energy, while ports will establish shore power infrastructure to aid in emission reductions within the industry. This paper constructs a game theory model involving a port and two shipping companies to optimise the green investment under shipping alliance and hub-and-spoke collusion, and their impact on profits. The study analyses the strategic equilibrium in the context of green investment. The findings indicate that green investment can effectively enhance the profits of shipping companies across different cooperative models, but excessive investment may lead to increased costs, which in turn affects the final profit. Additionally, this paper identifies two critical regions, hub-and-spoke dominance zone and shipping alliance dominance zone, and highlights that the relative advantages of hub-and-spoke collusion versus shipping alliances vary under specific green investment coefficients and the degree of substitutability between shipping companies.
  Keywords: green investment; hub-and-spoke collusion; shipping alliance; supply chain; strategy optimisation.
  
  
• Single-machine slack due window assignment with position-dependent weights, learning effect, convex resource allocation and past-sequence-dependent delivery times  
  by Yurong Zhang, Ziming Mao, Xiaokai Shi, Xin-Na Geng 
  Abstract: In this paper, we address a single-machine slack due window scheduling problem within a past-sequence-dependent delivery times and learning effect setting. Here, the actual processing time of a job is defined as a convex function of the resource allocation amount. We investigate both the linear combination model and constrained models, which incorporate scheduling costs (including earliness, tardiness, and due window costs) and resource costs with position-dependent weights, where the weights are solely determined by the job's position within a sequence. Our objective is to determine the optimal slack due window, resource allocation, and job schedule in order to minimize the specified objective function. We prove that these problems can be solved within polynomial time and present the corresponding algorithms. Additionally, we provide examples of various models and conduct an analysis of the relevant parameters.
  Keywords: single-machine; convex resource allocation; slack due window; past-sequence-dependent delivery times; learning effect.
  
  
• The Effect of Local Forecasting Improvements on Supply Chain Performance  
  by Geng Cui, Imura Naoto, Katsuhiro Nishinari, Takahiro Ezaki 
  Abstract: We examine the impact of local demand forecasting improvements on both local and supply chain performance in a serial supply chain with five echelons. In our setup, one echelon adopts a high-precision forecasting method, while the remaining four continue to use a traditional method (i.e., moving average). Our analysis shows that machine learning methods, though capable of outperforming traditional approaches in terms of prediction accuracy, may inadvertently deteriorate the performance of upstream echelons. This effect is particularly pronounced when the advanced forecasting method achieves high predictive accuracy but induces large order fluctuation (i.e., the bullwhip effect). From a supply chain perspective, this highlights that minimising order fluctuation should take priority over reducing inventory fluctuation, since the former propagates upstream and impacts multiple echelons, whereas the latter remains localised.
  Keywords: Supply chain management; Bullwhip effect; Demand forecasting; Moving average; Machine learning.
  
  
• Research on the Supply Chain Coordination Strategy of Fermented Foods Considering Quality and Efficiency Improvement Effort  
  by Shuo Cheng, Mohuan Chen, Taiye Luo, Haoran Zhang, Wen Luo, Gongliang Zhang 
  Abstract: The changing quality of fermented foods is affected by microorganisms, which has a direct impact on consumer utility and the decision-making processes of related enterprises. This paper explores the intricate decision-making processes within the fermented foods supply chain that arise from the quality variations during fermentation processing. We propose a two-phase profit model in a fermented foods supply chain incorporating dynamic changes in both price and quality. By using the Stackelberg game paradigm, the optimal quality, efficiency improvement initiatives, and pricing strategies are obtained with centralised and decentralised decision-making methods. Furthermore, we design a new contract mechanism to combine quantity discount, wholesale price adjustment, and quality and efficiency cost sharing. The results demonstrate that the agreement improves consumer utility and successfully aligns the supply chain for fermented foods. Finally, numerical experiment is provided to demonstrate the usefulness of this coordination technique, and important insights about how to optimize decision-making in supply chains for fermented foods are also provided.
  Keywords: fermented foods; time-varying quality; consumer utility; quality and efficiency improvement effort; supply chain coordination.
  
  
• Optimisation of Police Patrol Routing and Scheduling  
  by Chun-Ying Chen, Shangyao Yan, Pin-Tzu Huang 
  Abstract: This study presents a model for police patrol routing and scheduling, utilising the time-space network flow technique to represent potential movements of police vehicles along patrol routes in both temporal and spatial dimensions. The mathematical formulation involves an integer multiple-commodity network flow problem with side constraints, a challenging task due to its NP-hard nature. In practical situations, the problem's large size often hinders the identification of optimal solutions within feasible timeframes. To overcome this issue, a heuristic algorithm is introduced, employing problem decomposition and collapsing techniques to efficiently handle the computational complexity. The effectiveness of the model is assessed through a case study, utilising authentic data obtained from police authorities in Taiwan. The results demonstrate the potential utility of the proposed model and solution algorithm in enhancing police patrol planning.
  Keywords: Police patrol routing and scheduling; optimization; time-space network; integer multiple-commodity network flow problem with side constraints; heuristic.