Browsing by Author "Ekren, Banu Y."

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Citation - WoS: 49
Citation - Scopus: 61
A simulation-based experimental design for SBS/RS warehouse design by considering energy related performance metrics
(Elsevier B.V., 2020-01) Banu Yetkin Yetkin Ekren; Ekren, Banu Y.
This paper deals with an experimental study for shuttle-based storage and retrieval system (SBS/RS) design to identify significant factors affecting the performance metrics in the system. SBS/RS is an automated warehouse technology developed to overcome recent order profile resulting from increased e-commerce. Recent order profile has been shaped towards more product variety with less volume and short response time. In SBS/RS technology each tier of an aisle typically has a dedicated shuttle processing orders hence it has a very high transaction rate compared to its opponents. Neverthless there can be several design options that can improve the performance of these systems. In this study first the significant design factors affecting the pre-defined SBS/RS performance measures (e.g. average cycle time and energy consumption for a transaction, amount of energy regeneration for a transaction etc.) are identified. Secondly the best levels of these critical design factors improving the performance measures are identified. For this purpose a statistical test namely Tukey's test is utilized. For the modelling purpose simulation modelling using ARENA 14.0 is utilized. The statistical analyses from the simulation results are performed using Minitab 17 statistical software. © 2019 Elsevier B.V. All rights reserved.
Citation - WoS: 60
Citation - Scopus: 69
A tool for time variance and energy related performance estimations in a shuttle-based storage and retrieval system
(Elsevier Inc. usjcs@elsevier.com, 2018-11) Banu Yetkin Yetkin Ekren; Anıl Akpunar; Zaki Sari; Tone Lerher; Akpunar, Anil; Lerher, Tone; Ekren, Banu Y.; Sari, Zaki
The aim of this study is to present a tool that can calculate critical performance measures of different Shuttle-Based Storage and Retrieval System (SBS/RS) designs. Specifically our aim is to develop an analytical model-based tool that can estimate the mean and variance of travel time of lifts and shuttles as well as the mean amount of energy consumption and energy regeneration per transaction for a pre-defined SBS/RS design. The methodology illustrated in this paper can also be utilized for any system having a straight discrete travel pattern with equal distance stop points. We are motivated in this study by a relatively new automated storage and retrieval technology SBS/RS. By the provided tool one can evaluate these systems’ performance promptly by changing the input design parameters (e.g. discrete travel lengths velocity of vehicles acceleration/deceleration of vehicles number of tiers number of bays etc.) of these systems. The analytical models used in the tool are verified via simulation for several SBS/RS design scenarios. As a result it is found that the proposed tool produces highly accurate results specifically less than 1% Absolute Percentage Error from the simulation results. © 2018 Elsevier B.V. All rights reserved.
Citation - Scopus: 2
Additive manufacturing and its impact on pharmaceutical supply chains
(Elsevier, 2024) Wenqi Li; Banu Yetkin Yetkin Ekren; Emel Aktas; Li, Wenqi; Aktas, Emel; Ekren, Banu Y.
Additive manufacturing (AM) also known as 3D printing has the potential to improve the performance of the pharmaceutical supply chain (PSC). By using 3D printing for manufacturing drugs pharmaceutical companies can reduce waste by using only the required number of raw materials and eliminating excess inventory. This chapter will provide a systematic literature review of the state of the art of AM in PSC and develop a conceptual framework to explain their interconnections. It was found that 3D printing impacts the SC in three main ways: reducing complexity moving manufacturing facilities closer to the end user and shifting production from make-to-stock to make-to-order. These changes influence the inventory level which in turn affects SC sustainability efficiency responsiveness and resilience. This study provides a conceptual framework that illustrates the interrelationships between various variables in the medical SC impacted by 3D printing technology. © 2024 Elsevier B.V. All rights reserved.
Citation - WoS: 38
Citation - Scopus: 46
An open queuing network-based tool for performance estimations in a shuttle-based storage and retrieval system
(ELSEVIER SCIENCE INC, 2021-01) Banu Y. Ekren; Antl Akpunar; Akpunar, Antl; Ekren, Banu Y.
Recently growth in e-commerce has resulted in substantially increased investment in robotic technologies in warehouses especially for storage and retrieval devices. A shuttle-based storage and retrieval system (SBS/RS) is one of the robotic automated storage and retrieval technologies mostly used for a miniload material handling system in e-commerce warehouses. This new technology has been developed as an alternative to the traditional miniload crane-based automated storage and retrieval system. Because it has an autonomous order picker robot in each tier of the racking system the process rate for this new technology is very high. To evaluate several design options for an SBS/RS warehouse quickly we have developed analytical models for estimating important performance outputs of these systems. Specifically this study presents an open queuing network-based software tool that can quickly estimate some important performance metrics for a predefined SBS/RS warehouse design. Once the user enters the required input data values in the tool (e.g. the arrival rate of transactions in the system warehouse metrics number of tiers aisles and bays and velocity profiles of devices) the performance metric estimates can be obtained in terms of the mean cycle time of a transaction the mean energy consumption and energy regeneration per transaction the mean waiting time of a transaction the mean number of transactions waiting in a server (lift/shuttle) queue and the mean utilization of servers. (C) 2020 Elsevier Inc. All rights reserved.
An Open Vehicle Routing Problem for Daily Shipment Plan of a Local Bedding Company in Turkey
(Springer Science and Business Media Deutschland GmbH, 2019-10-25) Bartu Arslan; Özlem Erdin; Deniz Gülce Dağıstanlıoğlu; Ozan Bolel; Çağlar Su; Banu Yetkin Yetkin Ekren; Cansu Yurtseven; Yurtseven, Cansu; Su, Çağlar; Ekren, Banu Y.; Arslan, Bartu; Erdin, Özlem; Dağıstanlıoğlu, Deniz Gülce; Bolel, Ozan; M.N. Osman Zahid , R. Abd. Aziz , A.R. Yusoff , N. Mat Yahya , F. Abdul Aziz , M. Yazid Abu , N.M. Durakbasa , M.G. Gençyilmaz
This study deals with a daily shipment plan for a bedding company in Turkey. We develop a decision support system optimizing total travel distance of vehicles completing daily shipment plans. In detail the developed models both assign the shipment goods to capacitated vehicles as well as minimize the total travel distance of these vehicles by considering an open vehicle routing algorithm. The decision support system is coded by using the MS excel-macro and a user friendly tool is developed for users’ convenience. In the developed tool there are two different solution approaches: a mixed integer mathematical programming and a heuristic solution approaches. For the small size plans (i.e. less than 25 demand points) mathematical modelling guaranteeing the optimal solution can be utilized. For the large size cases the heuristic algorithm providing a near-optimal result promptly can be utilized. The macro-based tool is designed such a flexible way that the user can make any revisions on the provided plan whenever necessary. © 2022 Elsevier B.V. All rights reserved.
Comfort-Aware Distributionally Robust Chance-Constrained Scheduling of PV-Assisted Heat Pumps under Dynamic Tariffs: A DOE–ANOVA Interaction Analysis
(Pergamon-Elsevier Science Ltd, 2026-06) Huo, Da; Balta-Ozkan, Nazmiye; Ekren, Banu Y.
Purpose: This paper develops an uncertainty-aware optimisation framework for smart heat pump (HP) operation that minimises electricity cost while maintaining thermal comfort under time-varying tariffs and uncertain PV generation. Design/methodology/approach: A Day-ahead scheduling model is formulated using distributionally robust chanceconstrained programming (DR-CCP) to control the probability of comfort-constraint violation under distributional ambiguity in renewable forecast errors. Thermal comfort is represented using either (i) conventional indoor-temperature (IT) bounds or (ii) Predicted Mean Vote (PMV) constraints. A full-factorial design of experiments (DOE) is conducted across building scale, tariff type, probability of comfort constraint violation (PoCCV) level, and comfort formulation, and ANOVA is applied to quantify statistically significant main and interaction effects on IT, coefficient of performance (COP), and operating cost. Findings: Across the experimental scenarios, dynamic tariffs reduce operating cost relative to fixed tariffs through load shifting, while stricter PoCCV settings increase cost by inducing more conservative schedules. PMV-based comfort constraints achieve lower operating costs than temperature-only bounds, indicating that comfort representation materially changes the feasible operating region for cost-effective flexibility. ANOVA results show that cost outcomes are strongly driven by building scale, tariff type, and comfort formulation, with significant interaction effects demonstrating that tariff benefits and comfort-modelling benefits are context-dependent rather than uniform across settings. Originality/value: The paper contributes an integrated robust optimisation + statistical inference framework for smart HP scheduling: DR-CCP provides tunable protection against distributional misspecification in renewable uncertainty, while DOE/ANOVA yields interpretable and reproducible evidence on which factors and interactions dominate the cost-comfort-efficiency trade-off. The results offer actionable guidance for deploying comfort-aware flexibility strategies for electrified heating.
Citation - WoS: 36
Citation - Scopus: 43
Cost and performance comparison for tier-captive and tier-to-tier SBS/RS warehouse configurations
(Blackwell Publishing Ltd, 2020-08-12) Melis Küçükyaşar; Banu Yetkin Yetkin Ekren; Tone Lerher; Y. Ekren, Banu; Küçükyaşar, Melis; Lerher, Tone; Ekren, Banu Y.
This paper compares two shuttle-based storage and retrieval system (SBS/RS) configurations developed on travel policy of shuttles in the systems. Specifically we compare two system configurations of SBS/RS: SBS/RS with tier-captive shuttles and SBS/RS with tier-to-tier shuttles. In a tier-captive SBS/RS warehouse design there is a dedicated shuttle in each tier that cannot travel between tiers. Due to a large number of shuttles in such a configuration lifts are always bottlenecks and the utilizations of shuttles are very low. In this study we propose an alternative SBS/RS design with tier-to-tier shuttles in which shuttles can travel between tiers by using a separate lifting mechanism dedicated for them. The advantage of this design would be reduction in investment cost due to the decreased number as well as higher utilizations of shuttles in the system. The two configurations were compared on the following performance metrics: initial investment costs throughput rates per hour and average energy consumption per transaction when there is a regeneration mechanism in the system. In a tier-to-tier SBS/RS design allowing shuttles to travel between tiers increases operational complexity of the system. Thus we simulate the two system designs and compare their performance metrics by conducting several rack design experiments based on the number of aisles tiers and bays. The results of this study suggest that there may be a better configuration in terms of total investment cost and throughput rate in a newly proposed tier-to-tier SBS/RS design that could be considered as an alternative to a tier-captive SBS/RS design. © 2021 Elsevier B.V. All rights reserved.
Citation - Scopus: 1
Design and Operation of Singleand Multi-deep Shuttle-Based Storage and Retrieval Systems (SBS/RS)
(Springer International Publishing, 2024) Tone Lerher; Jakob Marolt; Fabio Sgarbossa; Banu Yetkin Yetkin Ekren; Goran Dukic; Marolt, Jakob; Sgarbossa, Fabio; Dukic, Goran; Lerher, Tone; Ekren, Banu Y.
With the rapid growth of e-commerce practices the efficient management of warehouses and fulfilment centres has become crucial in supply chain operations. One effective strategy for achieving this efficiency is the digital transformation of warehouses through automation. In particular shuttle-based storage and retrieval systems (SBS/RS) have gained significant attention due to their ultra-high transaction process capability. However ongoing research focuses on enhancing the operating performance and cost efficiency of these SBS/RS designs. This chapter presents mathematical modelling approaches for estimating the performance of single- and multi-deep SBS/RS designs. These models provide valuable insights for designing warehouses that optimize transaction process rates while ensuring a balanced utilization of elevators and shuttles in the system tailored to meet the specific requirements of the warehouse. By leveraging these modelling techniques warehouse managers can make informed decisions regarding the design and configuration of SBS/RSs to improve overall operational efficiency and productivity. © 2025 Elsevier B.V. All rights reserved.
Citation - Scopus: 10
Engineering education towards industry 4.0
(IEOM Society ieom-society@iieom.org, 2020) Banu Yetkin Yetkin Ekren; Vikas Kumar; Kumar, Vikas; Ekren, Banu Y.
Many countries are now entering the stage of the Fourth Industrial Revolution also referred to as Industry 4.0 in which technological advances enable disruptive changes in the industry. Industry 4.0 development changes the way people work and the work is organized. Currently there is a lack of qualified workers capable of undertaking the jobs of the Industry 4.0 and unfortunately many schools and universities are still training students behind the Industry 4.0. The new industry 4.0 developments have created requirement of new cross-functional roles on the emerging knowledge and skills combining information technology (IT) and production. Therefore it is important to understand the characteristics of knowledge and skills required for the future of jobs and engineering profiles to determine the emerging patterns in the delivery of new education requirements of Industry 4.0. This work aims to address how the future of higher education should be towards Industry 4.0 developments for engineering faculties. We also aim to present what the technological trends in education as well as how the universities are required to integrate the new innovative and multidisciplinary approaches in teaching and how to stimulate entrepreneurial and digital skills in higher education and boost curricula adjustments. © 2020 Elsevier B.V. All rights reserved.
Citation - Scopus: 1
Evaluation of fuel production technologies by using renewable energy for smart cities
(Elsevier, 2022) Celal Hakan Canbaz; Orhan Ekren; Banu Yetkin Yetkin Ekren; Vikas Kumar; Ekren, Orhan; Canbaz, Celal Hakan; Kumar, Vikas; Ekren, Banu Y.
Fuels that are environment-friendly resources play a vital role in the development of economies. Each fuel resource has intensive however inadequate specifications in terms of security sustainability capacity to compete with other sources. Although oil and gas resources lead the way of fuel production due to their low cost alternative sources such as wind and solar renewable energy and nuclear energy have also gained popularity forcing the technological development of fuel production technologies. Hydrogen is a source for the production of energy by using any energy resource. In this chapter we investigate the latest fuel production technologies providing energy outputs from renewable energy resources for their usage in smart cities. We study the role of renewable resources under the current dynamics of the global energy market situation as well as the effects of fuel production technologies to increase the efficiency and sustainability for the future of smart cities. This review includes a broad spectrum that spans from the technical energy resources literature with very comprehensive research by using SCOPUS database to nontechnical but renowned resources including journals and other publications including raw data as well as forecasts and opinions that respected experts have performed. Specifically solar and wind production technologies as well as future forecasts compared with the conventional fuel resources are reviewed comprehensively. By that we aim to shed a light on the current status of fuel production technologies in renewable energy resources and their usage in smart cities. © 2022 Elsevier B.V. All rights reserved.
Citation - Scopus: 2
Intelligent Supply Chains Through Implementation of Digital Twins
(Springer Science and Business Media Deutschland GmbH, 2022) Oray Kulaç; Banu Yetkin Yetkin Ekren; Ayhan Özgür Toy; Toy, A. Ozgur; Kulac, Oray; Ekren, Banu Y.; C. Kahraman , S. Cevik Onar , B. Oztaysi , I.U. Sari , A.C. Tolga , S. Cebi
Data-driven decision-making process can be defined to be the sequential activities of real-time data collection data analytics optimization and decision making. Developments in Industry 4.0 technologies have made it possible to realize that new quality decision-making process. When that decision-making process is performed under the simulation model of a system developed on real-time data-based and end-to-end connection manner to prevent the disruption risks and to improve resilience in a system then it constitutes a digital twin (DT). A DT is a virtual representation of an object or system that can help organizations monitor operations perform predictive analytics and improve processes. For instance a DT could provide a digital replica of the operations of a factory communications network or the flow of goods through a supply chain system. In this work we focus on DT implementations in supply chain networks. We present state of the art implementation of DTs in supply chains and their prospective utilizations towards creating intelligent supply chains. © 2022 Elsevier B.V. All rights reserved.
Next generation digital engineering education: Moocs
(IEOM Society, 2020) Banu Yetkin Yetkin Ekren; Vikas Kumar; Kumar, Vikas; Ekren, Banu Y.
Recent Industry 4.0 and technological developments have created disruptive changes in industries as well as our lives. Digitalization is one of the innovative results of recent Industry 4.0 developments improving the quality of our life increasing the productivity in industries. Those technological developments have also affected the educational system by transforming it to more digitized environment. For instance according to the 2016 Chronicle of Higher Education report the new “learning economy” of the future is defined on some technological trends. New trends in higher education systems and institutions are: developing innovative curricula searching alternative learning pathways that can be implemented by online distance open and blended-based learnings as well as courses such as Massive Online Open Courses (MOOCs) and Open Education Resources (OERs). MOOCs is an online learning platform by which higher education gains new paths as well as lifelong learning opportunities. It also provides flexibility in education alternatives for students and cost efficiency for institutional education. In this work we spotlight the digitization in higher education by focusing on digital learning platforms at universities. © 2020 Elsevier B.V. All rights reserved.
Citation - Scopus: 1
Reimagining Salmon Supply Chains: A Sustainability Comparison of 3D-Printed and Traditional Production
(Elsevier, 2025) Li, Wenqi; Aktas, Emel; Ekren, Banu Y.
The seafood industry faces growing sustainability challenges, including overfishing, resource inefficiency, and environmental degradation, necessitating innovative production alternatives. While traditional SCs benefit from established infrastructure and consumer trust, their high resource demand and operational inefficiencies highlight the need for sustainable alternatives. This study compares traditional and 3D-printed salmon SCs, using process mapping by flowcharting and sustainability metrics to evaluate their environmental, economic, and social impacts. Findings indicate that 3D -printed salmon reduces carbon emissions by up to 86% and freshwater consumption by 95%, primarily by eliminating farming, feed production, and long-distance cold storage. Additionally, localized production lowers logistical costs and enhances resource efficiency. Despite challenges related to consumer acceptance, regulatory approval, and scalability, 3D printing presents a promising complement to aquaculture, supporting long-term sustainability in seafood production. Copyright (C) 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Reimagining Salmon Supply Chains: A Sustainability Comparison of 3D-Printed and Traditional Production
(Elsevier, 2025) Li, Wenqi; Aktas, Emel; Ekren, Banu Y.
The seafood industry faces growing sustainability challenges, including overfishing, resource inefficiency, and environmental degradation, necessitating innovative production alternatives. While traditional SCs benefit from established infrastructure and consumer trust, their high resource demand and operational inefficiencies highlight the need for sustainable alternatives. This study compares traditional and 3D-printed salmon SCs, using process mapping by flowcharting and sustainability metrics to evaluate their environmental, economic, and social impacts. Findings indicate that 3D -printed salmon reduces carbon emissions by up to 86% and freshwater consumption by 95%, primarily by eliminating farming, feed production, and long-distance cold storage. Additionally, localized production lowers logistical costs and enhances resource efficiency. Despite challenges related to consumer acceptance, regulatory approval, and scalability, 3D printing presents a promising complement to aquaculture, supporting long-term sustainability in seafood production. Copyright (C) 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Citation - WoS: 14
Citation - Scopus: 17
Simulation-based lateral transshipment policy optimization for s S inventory control problem in a single-echelon supply chain network
(Balikesir University, 2019-09-16) Banu Yetkin Yetkin Ekren; Bartu Arslan; Arslan, Bartu; Ekren, Banu Y.
Since it affects the performance of whole supply chain significantly definition of correct inventory control policy in a supply chain is critical. Recent technological development enabled real time visibility of a supply network by horizontal integration of each node in a supply network. By this opportunity inventory sharing among stocking locations is also possible in the effort of cost minimization in supply chain management. Hence lateral transshipment gained popularity and studies seeking the best lateral-transshipment policy is still under research. In this study we aim to compare different lateral-transshipment policies for an s S inventory control problem for a single-echelon supply chain network system. In this work we consider a supply network with three stocking locations which may perform lateral transshipment among them when backorder takes place. We develop the simulation models of the systems in ARENA 14.5 commercial software and compare the performance of the policies by minimizing the total cost under a pre-defined fill rate constraint by using an optimization tool OptQuest integrated in that software. The results show that lateral transshipment works well compared to the scenario when there is no lateral transshipment policy in the network. © 2023 Elsevier B.V. All rights reserved.
Citation - Scopus: 2
Smart transaction picking in tier-to-tier SBS/RS by deep Q-learning
(IEOM Society, 2021) Bartu Arslan; Banu Yetkin Yetkin Ekren; Arslan, Bartu; Ekren, Banu Y.
By the rapid growth of e-commerce the intralogistics sector is facing new challenges. Intralogistics sector requires more flexible scalable processes with maximum reliability and availability. They are complicated and interconnected systems whose all components are required to be perfectly coordinated with each other for optimal functionality. In this work we study an intralogistics technology shuttle-based storage and retrieval system (SBS/RS) where shuttles are tier-to-tier. In this novel system design in an effort to increase shuttle utilization as well as decrease initial investment cost shuttles are designed in a more flexible travel manner so that they can change their tiers within an aisle by using a separate lifting mechanism. Due to the complexity of such system design as well as aiming to obtain fast transaction process time by the decreased number of shuttles in the system we implement a Deep Q-Learning (DQL) approach to let shuttles select the best transaction to process based on its targets. We compare the performance of the DQL by the average cycle time per transaction performance metric with the other well-known selection rules First-in-First-Out (FIFO) and Shortest Process Time (SPT). Results show that Deep Q-Learning approach produces better results than those FIFO and SPT. © 2021 Elsevier B.V. All rights reserved.
Social Implications of Autonomous Vehicle (AV) Adoption in Supply Chains
(Springer Nature, 2025) Gu, Chengzhen; Reefke, Hendrik; Yates, Nicky; Ekren, Banu Y.; Abushaikha, Ismail
Social Implications of Autonomous Vehicle (AV) Adoption in Supply Chains
(Springer Nature, 2025) Gu, Chengzhen; Reefke, Hendrik; Yates, Nicky; Ekren, Banu Y.; Abushaikha, Ismail
Citation - WoS: 1
Citation - Scopus: 2
Warehousing 5.0 for the Future of the Logistics Industry
(Taylor & Francis Ltd, 2026-01-21) Sgarbossa, Fabio; Grosse, Eric H.; Venkatadri, Uday; Ekren, Banu Y.
This editorial introduces and contextualises the International Journal of Production Research Special Issue on 'Warehousing 5.0 for the Future of the Logistics Industry'. Building on the principles of Industry 5.0, the concept of Warehousing 5.0 redefines warehouse operations as human-centric, intelligent, sustainable, and resilient systems. It emphasises the integration of advanced automation and analytics with human well-being, environmental stewardship, and data responsibility - shifting the focus from efficiency alone to a balanced socio-technical paradigm. The Special Issue received 45 submissions, from which 12 papers were accepted after rigorous peer review. Together, these studies advance understanding across four interconnected themes: (T1) Human Factors and Human-Centric Design, (T2) Optimisation and Efficiency in Robotics and Automation, (T3) Energy Efficiency and Sustainable Operations, and (T4) Data-Driven and AI-Enabled Warehousing. The contributions highlight innovations in ergonomic design, collaborative robotics, energy-aware scheduling, stochastic and multi-objective optimisation, wearable sensing, and AI-enabled vision systems, demonstrating how operational efficiency can coexist with human welfare and environmental responsibility. Synthesising across these themes, the editorial identifies key insights on human-technology symbiosis, sustainable digitalisation, and cyber-physical-social integration in warehouses. It also outlines future research directions on adaptive human-robot collaboration, circular logistics, responsible AI, and integrative modelling. The practical and policy implications discussed provide a framework for managers and decision-makers to implement Warehousing 5.0 principles effectively. Collectively, the Special Issue contributes to shaping a new generation of resilient, sustainable, and human-aware warehouses, reinforcing IJPR's leadership in advancing innovative and responsible production and logistics systems.
Warehousing 5.0 for the Future of the Logistics Industry
(Taylor & Francis Ltd, 2026-01-21) Sgarbossa, Fabio; Grosse, Eric H.; Venkatadri, Uday; Ekren, Banu Y.
This editorial introduces and contextualises the International Journal of Production Research Special Issue on 'Warehousing 5.0 for the Future of the Logistics Industry'. Building on the principles of Industry 5.0, the concept of Warehousing 5.0 redefines warehouse operations as human-centric, intelligent, sustainable, and resilient systems. It emphasises the integration of advanced automation and analytics with human well-being, environmental stewardship, and data responsibility - shifting the focus from efficiency alone to a balanced socio-technical paradigm. The Special Issue received 45 submissions, from which 12 papers were accepted after rigorous peer review. Together, these studies advance understanding across four interconnected themes: (T1) Human Factors and Human-Centric Design, (T2) Optimisation and Efficiency in Robotics and Automation, (T3) Energy Efficiency and Sustainable Operations, and (T4) Data-Driven and AI-Enabled Warehousing. The contributions highlight innovations in ergonomic design, collaborative robotics, energy-aware scheduling, stochastic and multi-objective optimisation, wearable sensing, and AI-enabled vision systems, demonstrating how operational efficiency can coexist with human welfare and environmental responsibility. Synthesising across these themes, the editorial identifies key insights on human-technology symbiosis, sustainable digitalisation, and cyber-physical-social integration in warehouses. It also outlines future research directions on adaptive human-robot collaboration, circular logistics, responsible AI, and integrative modelling. The practical and policy implications discussed provide a framework for managers and decision-makers to implement Warehousing 5.0 principles effectively. Collectively, the Special Issue contributes to shaping a new generation of resilient, sustainable, and human-aware warehouses, reinforcing IJPR's leadership in advancing innovative and responsible production and logistics systems.