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.

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/)

The e-grocery sector has experienced a significant boost since the COVID-19 pandemic, dramatically changing consumer buying behaviours. As demand for faster and more efficient delivery options grows, e-grocery retailers face increasing pressure to optimize home delivery operations. Collaborations with third-party logistics providers (3PLs), although still overlooked, have emerged as promising, offering operational flexibility and environmental benefits. This work introduces an optimization model that supports the design of an on-demand delivery fleet conjunctly with delivery routings and schedules, while considering both cost and environmental impact. To this aim, a vehicle routing problem with time windows (VRPTW) is extended to incorporate on-demand fleet design and three different objective functions embodying a cost-efficient, an environmentally-effective and a cost-environmental balanced perspective respectively. Numerical experiments based on an Italian case study show that prioritizing environmental objectives reduces emissions by over 90%, with marginal increases in annual costs. Besides, on-demand vehicles enable flexibility that facilitates the adoption ofsustainable delivery options without requiring challenging investments such as delivery fleet. Several contributions are provided: insights into using on-demand vehicles are proposed; a mathematical model jointly optimizing fleet design and delivery routing and scheduling, while considering both costs and environmental objectives, is developed and its practical application is demonstrated using real-world data. The findings highlight the significant impact of environmental considerations on fleet composition and operational efficiency, offering actionable strategies for e-retailers to reduce emissions while maintaining service quality.

As the quest for healthier lifestyles intensifies, there is a growing demand for customized nutritional options, challenging the traditional food supply chain with its perishable goods and unpredictable demand patterns. This paper explores the potential of additive manufacturing, or 3D printing, as an innovative solution to these challenges. We present a conceptual framework to assess the impact of AM on the supply chain of edible products, including food and medicine, through the lens of sustainability—encompassing environmental, social, and economic perspectives. Our systematic literature review identifies five key strategies through which AM can enhance supply chain sustainability: distributed localized manufacturing, in-house production, delayed production, mass customization, and on-demand production. This research contributes valuable insights for industry practitioners and policymakers, guiding them toward exploiting AM's potential to revolutionize the sustainable supply chain management of edible products. Our findings highlight the transformative capabilities of AM and set the stage for future research directions in the nexus of additive manufacturing and sustainable supply chain practices.

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.

With recent Industry 4.0 developments, companies tend to automate their industries. Warehousing companies also take part in this trend. A shuttle-based storage and retrieval system (SBS/RS) is an automated storage and retrieval system technology experiencing recent drastic market growth. This technology is mostly utilized in large distribution centers processing mini-loads. With the recent increase in e-commerce practices, fast delivery requirements with low volume orders have increased. SBS/RS provides ultrahigh-speed load handling due to having an excess amount of shuttles in the system. However, not only the physical design of an automated warehousing technology but also the design of operational system policies would help with fast handling targets. In this work, in an effort to increase the performance of an SBS/RS, we apply a machine learning (ML) (i.e., Q-learning) approach on a newly proposed tier-to-tier SBS/RS design, redesigned from a traditional tier-captive SBS/RS. The novelty of this paper is twofold: First, we propose a novel SBS/RS design where shuttles can travel between tiers in the system; second, due to the complexity of operation of shuttles in that newly proposed design, we implement an ML-based algorithm for transaction selection in that system. The ML-based solution is compared with traditional scheduling approaches: first-in-first-out and shortest process time (i.e., travel) scheduling rules. The results indicate that in most cases, the Q-learning approach performs better than the two static scheduling approaches.

Recent dramatic increase in e-commerce has also increased the adoption of automation technologies in warehouses. Autonomous mobile robots (AMRs) are from those technologies widely utilized in warehouse operations. It is important to design the operation of those robotic systems in such a way that, they meet the current and future system requirements correctly. In this paper, we study flexible travel of AMRs in warehouses by developing smart deadlock and collision prevention algorithms on agent-based modelling. By that, AMR agents can interact with each other and environment, so that they can make smart decisions maximizing their goals. We compare the performance of the developed flexible travel system with non-flexible designs where there is a single AMR dedicated to a specific zone so that no deadlock or collision possibility takes place. The results show that AMRs may provide up to 39% improvement in the flexible system compared to its non-flexible design.

This paper studies e-grocery order fulfillment policies by leveraging both customer and e-grocery-based data. Through the utilization of historical purchase data, product popularity trends, and delivery patterns, allocation strategies are informed to optimize performance metrics such as fill rate, carbon emissions, and cost per order. The study aims to conduct a sensitivity analysis to identify key drivers influencing these performance metrics. The results highlight that fulfillment policies optimized with the utilization of the mentioned data metrics demonstrate superior performance compared to policies not informed by data. These findings underscore the critical role of integrating data-driven models in e-grocery order fulfillment. Based on the outcomes, a grocery allocation policy, considering both proximity and product availability, emerges as promising for simultaneous improvements in several performance metrics. The study recommends that e-grocery companies leverage customer data to design and optimize delivery-oriented policies and strategies. To ensure adaptability to new trends or changes in delivery patterns, continual evaluation and improvement of e-grocery fulfillment policies are emphasized.

This paper introduces a novel autonomous vehicle-based storage and retrieval system that utilizes movable lifts (AVS/RS/ML), proposed as an alternative to the tier-captive shuttle-based storage and retrieval system (SBS/RS). The newly proposed system aims to provide an affordable solution with highly utilised AGVs, that can also perform operations out of warehouse. The performance of this novel system is compared with the equivalent tier-captive SBS/RS warehouse design, where each shuttle is dedicated in a specific tier in that design. The comparison is based on the initial system investments costs, throughput rates, and average utilisation of lifts/MLs in the system. Collision prevention rules are also applied to AVS/RS/ML, and its performance is tested through simulation. The results show that the tier-captive SBS/SR system becomes cost-efficient under high throughput rate requirements, while the AVS/RS/ML technology is preferred for relatively moderate and low process rate requirements. The unit-cost per month performance metric of AVS/RS/ML is less sensitive to an increase in number of tiers in the system, compared to the tier-captive SBS/RS case, indicating that AVS/RS/ML may be promising for high-tier warehouse system designs.

Purpose The ongoing pandemic has gravely affected different facets of society and economic trades worldwide. During the outbreak, most manufacturing and service sectors were closed across the globe except for essential commodities such as food and medicines. Consequently, recent literature has focused on studying supply chain resilience and sustainability in different pandemic contexts. This study aims to add to the existing literature by exploring the economic, environmental and societal aspects affecting the food supply chain and assessing the impact of COVID-19 on food sustainability. Design/methodology/approach A survey method has been adopted with a questionnaire instrument investigating the role of technology, government policies, geopolitics and intermediaries on sustainable organisational management. A five-point Likert scale (i.e. 1 = strongly disagree; 5 = strongly agree) is used to evaluate the responses. The findings are based on 131 responses from entry-level workers and senior executives of different food supply chains across Asia and Europe. The data has been analysed to derive insights into the impacts of this pandemic. Findings The survey concludes with the significant impact of COVID-19 on the three pillars of sustainability, i.e. economic, social and environmental dimensions. The empirical analysis shows digitalisation and its applications help mitigate the negative effect of COVID-19 on sustainability. In addition, the supportive government policies and intermediatory interventions were helpful in improving sustainability at each level. Research limitations/implications The findings have implications for businesses and policymakers. Companies can learn from the advantages of digitalisation to counter the challenges imposed by the pandemic or similar situations in the future in maintaining the sustainability of their supply chains. Managers can also learn the importance of effective organisational management in driving sustainability. Finally, policymakers can devise policies to support businesses in adopting sustainable practices in their supply chains. Originality/value This study adds to the limited literature exploring the impact of COVID-19 on food supply chain sustainability through the triple bottom line lens. To the best of the authors’ knowledge, this is also one of the first empirical studies to examine the effect of technology, government and organisational management practices on the sustainability of food supply chains.

Food trade restrictions pose a serious risk for countries that are heavily reliant on food imports, potentially leading to food crises, inequality, and geopolitical conflicts on a global scale. However, such restrictions may also have transformative effects in promoting food supply chain resilience, security, and self-sufficiency. In this study, a novel econometric analysis is presented, utilizing a data-driven analytical model to investigate the impact of a food embargo on the industry, using Qatar as a case study. A structured and automated food trade database is created using Microsoft Management Server Studio and data visualization software is integrated for automated data discovery. By using a global, trade-based sustainability assessment model, which combines the multi-region input-output (MRIO) analysis with transportation mode-based (sea, road, and air) emissions, the carbon footprint of the dairy food production sector could be estimated. The study shows that the trade embargo on Qatar’s food industry can lead to significant reductions in the annual import of food products, promoting self-sufficiency, and reducing the net carbon emissions of the dairy food sector by nearly 40%. This reduction is not only achieved through food supply chain changes, such as transportation modes, but also by restrictions pushing the country to increase domestic production. Overall, the study demonstrates that a trade embargo, with the support of a well-designed national food security strategy, trade/import diversification, and the use of different modes of transportation for food products, can improve the resilience of global supply chains, self-sufficiency, and environmental sustainability.

This paper provides an overview of the complex structure of the e-grocery industry, highlighting recent trends and challenges including the increasing customers’ expectations. Customers’ satisfaction can be driven by multiple objectives, which can create significant trade-offs. We propose a new approach as a future work for e-grocery businesses to leverage multi-objective perspectives, maximizing product availability and sustainability and minimizing cost. Specifically, we propose an e-grocery store assignment policy while consumers are using apps, which is developed on a real-time data-driven approach from customer ordering behaviors. With the help of data availability and data analytic tools, data-based solutions can foster continuous improvement in businesses. In a simulation study, imitating different demand profiles and online ordering behaviors might help develop a good solution approach for a multi-objective perspective.

This paper studies the utilization of digital twins (DTs) as a decision support tool in supply chains (SCs) by providing a framework. DT is an emerging technology-based modeling approach reflecting a virtual representation of an object or system that can help organizations monitor operations, perform predictive analytics, and improve their processes. For instance, it may provide a digital replica of operations in a factory, communications network, or the flow of goods through an SC system. In this paper, by focusing on SC systems, we explore the critical decisions in SCs and their related data to track, to make the right decisions within DTs. We introduce six main functions in SCs and define frequent decisions that can be taken under those functions. After defining the required decisions, we also identify which data/information would help to make correct decisions within those DTs.

Food waste reduction and security are the main concerns of agri-food supply chains, as more than thirty-three percent of global food production is wasted or lost due to mismanagement. The ongoing challenges, including resource scarcity, climate change, waste generation, etc., need immediate actions from stakeholders to develop resilient food supply chains. Previous studies explored food supply chains and their challenges, barriers, enablers, etc. Still, there needs to be more literature on the innovations in supply chains that can build resilient food chains to last long and compete in the post-pandemic scenario. Thus, studies are also required to explore supply chain innovations for the food sector. The current research employed a stepwise weight assessment ratio analysis (SWARA) to assess the supply chain innovations that can develop resilient food supply chains. This study is a pioneer in using the SWARA application to evaluate supply chain innovation and identify the most preferred alternatives. The results from the SWARA show that ‘Business strategy innovations’ are the most significant innovations that can bring resiliency to the food supply chains, followed by ‘Technological innovations.’ The study provides insights for decision makers to understand the significant supply chain innovations to attain resilience in food chains and help the industry to survive and sustain in the long run.

This paper studies micro-fulfilment centres (MFCs) as a response to rising e-grocery sales and customer expectations from decreased delivery time and cost requests. MFC is a business solution that allows orders to be picked and packed in a hyper-local facility. The study’s aim is to provide an overview of this subject from two research questions: i) how MFCs affect the last-mile delivery challenges? and ii) what design decisions are critical in building MFCs? While we evaluate the advantages and disadvantages of centralised versus decentralised warehousing strategies in the first question, we discuss the critical decisions in designing MFCs in the second question. In that, we discuss location and technology selection decisions as well as other warehousing design criteria. Further, this study provides future research directions at the end of this study.

This paper studies performance comparison of two shuttle-based storage and retrieval system (SBS/RS) configurations developed on flexible or non-flexible travel policies of shuttles in the system. In the non-flexible SBS/RS, a shuttle is dedicated to a tier so that it cannot travel out of its dedicated aisle and tier. A lifting mechanism is installed in each aisle to provide vertical travel for loads. In flexible SBS/RS, shuttles can travel between tiers by a separate lifting mechanism installed on the other edge point of each aisle. The advantage of that flexible design is that there might be decreased number of shuttles settling in the system compared to the non-flexible design. We simulate the two system configurations and conduct an experimental design for the comparison purpose. Based on the three-performance metrics: total investment cost, throughput rate and energy consumption per transaction, the results show that mainly the flexible system provides better results which might be considered as future system investment for SBS/RS.

In light of recently increased e-commerce, also a result of the COVID-19 pandemic, this study examines how additive manufacturing (AM) can contribute to e-commerce supply chain network resilience, profitability and competitiveness. With the recent competitive supply chain challenges, companies aim to decrease cost performance metrics and increase responsiveness. In this work, we aim to establish utilisation policies for AM in a supply chain network so that companies can simultaneously improve their total network cost and response time performance metrics. We propose three different utilisation policies, i.e. reactive, proactive – both with 3D printing support – and a policy excluding AM usage in the system. A simulation optimisation process for 136 experiments under various input design factors for an (s, S) inventory control policy is carried out. We also completed a statistical analysis to identify significant factors (i.e. AM, holding cost, lead time, response time, demand amount, etc.) affecting the performance of the studied retailer supply chain. Results show that utilising AM in such a network can prove beneficial, and where the reactive policy contributes significantly to the network performance metrics. Practically, this work has important managerial implications in defining the most appropriate policies to achieve optimisation of supply network operations and resilience with the aid of AM, especially in times of turbulence and uncertainty.

This paper studies a Deep Q-Learning (DQL) method for transaction sequencing problems in an automated warehousing system, Shuttle-based Storage and Retrieval System (SBSRS), in which shuttles can move between tiers flexibly. Here, the system is referred to as tier-to-tier SBSRS (t-SBSRS), developed as an alternative design to tier-captive SBSRS (c-SBSRS). By the flexible travel of shuttles between tiers in t-SBSRS, the number of shuttles in the system may be reduced compared to its simulant c-SBSRS design. The flexible travel of shuttles makes the operation decisions more complex in that system, motivating us to explore whether integration of a machine learning approach would help to improve the system performance. We apply the DQL method for the transaction selection of shuttles in the system to attain process time advantage. The outcomes of the DQN are confronted with the well-applied heuristic approaches: first-come-first-serve (FIFO) and shortest process time (SPT) rules under different racking and numbers of shuttles scenarios. The results show that DQL outperforms the FIFO and SPT rules promising for the future of smart industry applications. Especially, compared to the well-applied SPT rule in industries, DQL improves the average cycle time per transaction by roughly 43% on average.