Equity in humanitarian logistics (HL) has received considerable attention. However, its implementation remains highly context-specific, resulting in heterogeneous, nonstandard, and ad hoc practices. This study systematically reviews two decades of HL research on equity and proposes a structured four-step framework comprising identifying equity metrics, constructing equity functions, formulating equity models, and selecting optimization techniques to help researchers and practitioners design and advance equity strategies tailored to specific needs. We develop a multidimensional taxonomy of equity metrics and propose strategies for switching and aligning metrics across the disaster lifecycle. We provide selection guidance for equity functions from two complementary perspectives—mathematical properties and application scenarios—and set out procedures for multi-objective settings that prevent equity dilution and address high-dimensional Pareto challenges. We compare three equity-related optimization approaches and derive choice rules with actionable thresholds based on problem scale and data availability. We also deliver scenario-specific checklists for earthquakes, floods, droughts, and pandemics within the four-step framework. Finally, we outline future directions from five equity-related perspectives to enhance both the theoretical understanding and practical application of equitable resource allocation in complex humanitarian contexts.

Amid growing environmental concerns, recycling and remanufacturing are vital for efficient supply chains of ‘reusable products’, such as batteries and electronic equipment. Thus, to enable customers to return old products and receive cash to buy new or remanufactured products, two types of trade-in programmes are considered: trade-old-for-new (TON) and trade-old-for remanufactured (TOR). In this study, an economic production quantity model was developed, whereby manufacturers receive the trade credit offered by their suppliers to offer trade-in programmes to consumers. The objective is to optimise the manufacturer’s production cycle time while reaching the maximum total profit under two-level trade credit and trade-in programmes. The results of the numerical examples show that the cycle time in the TON programme can be up to 40% longer than that in the TOR programme, providing advantages for manufacturers to reduce storage costs and the risk of changing market demand. Without considering trade-in programmes, stakeholders’ profits may drop if only two-level trade credit is applied. Our analysis revealed that the simultaneous implementation of TON and TOR programmes is most beneficial in reducing trade-in prices and cycle times, thereby increasing the manufacturer’s profit, which has not been considered in previous studies.

This study systematically examines the role of advanced technologies in enhancing healthcare logistics within Sierra Leone's dynamic landscape, focusing on operational efficiency, supply chain resilience, product integrity, and timely delivery of pharmaceuticals and medical equipment. Analyzing data from 16 district hospitals, including 625 validated responses from healthcare professionals (a 78% response rate), we employed a mixed-methods approach (surveys and interviews) to triangulate findings, paired with Structural Equation Modelling (SEM) for quantitative analysis. Key findings reveal distinct technology-performance relationships: specifically, AI-driven inventory management systems reduced stockouts by 42% (β = 0.38, p < 0.01) in high-adoption facilities such as Connaught Hospital. These results highlight how targeted technological integration addresses critical logistics challenges in resource-constrained settings, while offering actionable insights to optimize healthcare delivery through enhanced operational efficiency and service reliability.

This study considers a stochastic inventory relocation problem for a one-way, station-based car-sharing system that utilises electric vehicles (EV), where customers' rental demands and rented vehicles' travel distances are uncertain and temporal-spatial imbalance. Workers are hired to relocate vehicles between stations. To maximise the total expected profits that can be collected by the system, a worker must determine whether to relocate an EV, which vehicle to choose, and which station to move the EV to upon their arrival at a rental station. The problem is formulated as a Markov decision process (MDP). A reinforcement learning algorithm is proposed to develop dynamic policies for the problem. The reinforcement learning algorithm uses an approximate value iteration (AVI) algorithm to overcome the computational challenges arising from the extensive state and action space. Action-space restriction and state-space aggregation schemes are developed to enhance the performance of the AVI algorithm. The effectiveness of the proposed modelling and solution methodologies is demonstrated through a comparison of the dynamic policies against benchmark solutions. Additionally, sensitivity analyses are conducted to investigate whether parameter configurations will impact the performance of the dynamic policies.

The rapid advancement in IoT-based industrial systems emphasizes maximizing important quality parameters like reliability, latency, and throughput. While previous researches focus on individual factors, an integrated approach is required to assess the influence of Six Sigma on the quality of service (QoS) in IoT environments. The present study leverages Six Sigma's DMAIC process to drive significant improvements in the quality of service for IoT systems, explicitly targeting critical sectors such as manufacturing, logistics, and energy. This research used a quasi-experimental design involving 30 IoT systems before and after implementing Six Sigma. Based on the Stratified Random Sampling method, basic information on QoS issues, was captured in milliseconds, percentage, and Mbps, respectively. Qos results have shown significant improvement of 22% in latency, 15% in system reliability, and 25% in throughput rates. The practical implications of this research are significant, involving greater productivity, rationalization of costs, and enhanced competitive advantage for industries dependent on IoT networks, thus successfully demonstrating the value of Six Sigma in managing industrial systems.

Widespread use of chemicals and fertilisers in agriculture and food manufacturing has contaminated food products and groundwater across numerous regions worldwide. This study examines organic food supply chains as a sustainable alternative, introducing the novel concept of ‘organic level’ (OL) as a metric to assess environmental impact throughout production stages. Moving beyond traditional profit-maximisation models, we analyze a complex two-echelon supply chain comprising organic and inorganic producers alongside a retailer specialising in organic distribution. The decision-making framework strategically distributes responsibilities: organic producers determine organic levels, inorganic producers manage sales efforts, and retailers control both pricing and distribution organic levels. The model incorporates demand uncertainty for both organic and inorganic products while accounting for deterioration rates that vary according to organic levels and product characteristics. Our analysis reveals strategic competition between producers that resembles a Nash equilibrium game, leading us to develop innovative centralised decision-making approaches and coordination mechanisms. Through carefully structured contractual agreements, retailers can effectively support organic producers, demonstrating a fundamental shift toward sustainability-driven business models in the food industry.

Production systems frequently face dynamic disruptions, such as job arrivals or cancellations, requiring schedule adjustments to maintain efficiency and stability. This paper addresses a multi-objective permutation flow shop rescheduling problem with mixed blocking constraints and time-dependent job weights. A multi-objective model and predictive-reactive rescheduling strategy based on a posteriori optimisation are proposed. In the reactive phase, the objective is to minimise the Total Weighted Waiting Time (TWWT) for efficiency and the Average Completion Time Deviation (ACTD) for stability. The use of population-based stochastic algorithms enables the generation of approximate Pareto fronts, allowing decision-makers to explore trade-offs before selecting a new schedule. Additionally, their stochastic nature makes them well suited for highly disrupted systems, where exact methods are computationally expensive. To validate the proposed rescheduling strategy, we implemented it using three population-based algorithms (NSGA-II, NSGA-III and U-NSGA-III). Their performance was assessed through the Mean Hypervolume metric. Statistical analysis confirmed the superior performance of NSGA-II. These findings support the robustness of the proposed strategy and its potential for application in dynamic scheduling environments.

In this paper, we develop a mathematical model of a technician routeing and scheduling problem with time windows (TRSPTW) and overnight shifts, which we title the ‘overnight TRSPTW’. This problem is motivated by a real application in the telecommunications industry in Saskatchewan, Canada. A mixed-integer nonlinear programming (MINLP) model is employed to achieve two key objectives: (1) minimising the total costs associated with technicians and subcontractors, including travel costs, accommodation costs and penalty costs caused by late starts and (2) minimising imbalanced workloads. Furthermore, the present work aims to determine the daily assignment of technicians to communities, depots and routes; find a high-quality schedule for technicians’ start times in communities and lunch breaks and determine the daily assignment of tasks to the subcontractor. The bi-objective MINLP model used to solve the overnight TRSPTW is a typical NP-hard problem in combinatorial optimisation. As such, we introduce a new hybrid category of TRSPTW that combines centroid-based clustering, which is an unsupervised machine learning (ML) approach, with a bi-objective zebra optimisation algorithm (BOZOA). The resultant algorithm blends the advantages of the ZOA and ML to strike a balance between the exploration and exploitation of the solution region. Finally, we compare our results with those obtained using an exact solver for small-, medium-, and large-sized instances. The performance evaluation and validation results revealed that the proposed ML-based BOZOA provides very good performance in solving TRSPTWs at a variety of scales with respect to the optimality criteria, including, number of taken iterations, infeasibility, optimality error and complementarity compared with both an exact solver and two inspired algorithms from ZOA. Highlights An ML-based bi-objective zebra optimisation algorithm to treat large-scale TRSPs Centroid-based clustering on the population of zebras to avoid bias towards a specific search space Making a trade-off between exploration and exploitation of the feasible region in the developed algorithm A new MINLP model of a weighted bi-objective TRSP with limited capacity depots Workload function, penalty function for lateness, subcontracts, time windows for tasks and breaks Experiments using real data to show the performance of the model and solution method

While production and sales are booming of NEVs, the issue of recycling retired power batteries from NEVs has also emerged. A ‘Recycling credit' has been introduced on the basis of the ‘Dual Credit' policy, forming a ‘Tri-Credit' policy. Targeting the secondary closed-loop supply chain of power batteries composed of manufacturers and retailers, Stackelberg game theory models were constructed for two recycling modes. The results show that: (1) Both NEV credits and CAFC credits can enhance the attractiveness of NEVs to consumers, but they do not promote the recycling of power batteries; (2) Recycling credits and recycling ratio requirement both contribute to the attractiveness of NEVs and the recycling of power batteries. However, the retailer recycling mode is more conducive to boosting NEV sales, the manufacturer recycling mode is more effective in promoting power battery recycling. Under the retailer recycling mode, the pricing of NEVs and power battery recycling is more sensitive to changes in recycling credits and recycling ratio requirement; (3) In both recycling modes, the prices of NEVs are more sensitive to changes in recycling ratio requirement than to recycling credit. At last, based on the results, specific strategies are proposed at the government and NEV manufacturer levels, respectively.

This study addresses two key challenges in optimising Truck Appointment System (TAS) at ports: establishing an optimal appointment quota plan and effectively managing unpunctual trucks. Conducted at Yan Port, China, the study begins by analyzing truck operation data to identify a quantitative link between truck arrival patterns and their turnaround time. This insight enables the formulation of a mathematical optimisation model to minimise the trucks’ overall turnaround time by optimising their arrivals, ultimately guiding the development of an optimal appointment quota plan. Additionally, this study proposes a Q-learning-based strategy to handle unpunctual trucks. A simulation method is developed to model truck arrival patterns based on the optimised quota plan, which is employed to create the state space of the Q-learning method. The method is then guided by a reward function designed to minimise the total related costs, enhancing decision-making for unpunctual trucks’ re-servicing. The proposed Q-learning method is then tested against five alternative approaches, with results demonstrating its superior performance in terms of turnaround efficiency and TAS optimisation. Overall, the study shows that an optimised appointment plan significantly improves operational efficiency, while the Q-learning approach effectively addresses unpunctuality, enhancing the TAS’s performance and contributing to smoother port operations.