International Journal of Production Research

This paper analyzes a Multi-Period Stochastic Vehicle Routing Problem in the healthcare sector. Patients with unknown locations and demands ask for domiciliary care services with unknown temporal distributions. Requests from patients arrive over time to a nurse agency that has to plan the activities of a fleet of nurses over several days. Based on daily information, the agency decides if a new patient can be accepted, earning the corresponding revenue, or assigned to an external provider. To guarantee high service satisfaction, the agency schedules the nurses' routing by guaranteeing consistency in nurse-patient assignments. The problem aims to plan nurses' routing to satisfy all requests of accepted patients while maximising the total profit measured as the difference between collected revenue and travelling costs. We propose different solution methodologies that either sequentially make short-sighted decisions or use a scenario-based strategy, leveraging historical data to predict future requests. All algorithms make use of an Adaptive Large Neighborhood Search and are validated on medium-sized instances. Managerial insights on the impact of consistency on the profit and its relation to date flexibility in patients' requests are provided. Interesting rules of thumb are derived from a case study conducted in the city of Brescia, Italy.

Implementing collaborative robots in warehouse operations requires employees to engage in order picking alongside robots, which raises concerns about employees’ perception of being ‘robotised’. This study explores the interplay between workload and autonomy in the context of Automated Guided Vehicle (AGV)-assisted order picking, aiming to understand their joint impact on employees’ boredom and performance. In a unique controlled laboratory experiment conducted within an experimental warehouse environment, 352 order pickers interacted with an actual AGV to retrieve items from various aisles and deliver them to a depot station. Using a 2 × 2 between subject design, participants were assigned to either pick 77 products (low workload) or 231 products (high workload), and to walk behind the AGV (low autonomy) or walk in front of the AGV (high autonomy). Participants in the high-workload low-autonomy condition were less bored but performed poorer than those in the low-workload low-autonomy condition. No significant differences in boredom and performance between the low-workload high-autonomy condition and the high-workload high-autonomy condition were found. Our findings emphasise the importance of considering the effects on employees when implementing AGV-assisted order picking. To alleviate boredom among order pickers due to such tasks, it is important to provide autonomy while carefully managing workload levels to maintain optimal performance.

Effective work package sizing and project scheduling are crucial for construction project management. However, existing studies often address them as separate optimisation problems, neglecting the interactive nature of these processes. In practical scenarios with limited resources, there is an increasing demand for integrating work package sizing and project scheduling to enhance project management efficiency. This research aims to bridge this gap by developing an integer non-linear programming model that incorporates work package sizing and project scheduling while considering their interaction within a resource-constrained environment. Moreover, we introduce a novel look-ahead heterogeneous reinforcement learning approach that dynamically adjusts work package sizes and project schedules based on observed information at each decision step. A look-ahead sequential decision mechanism is proposed to effectively address the interdependencies and constraints inherent in the joint model. We extend the heterogeneous agent mirror learning technique to our problem to improve sample efficiency and ensure progressive enhancement of the joint policy. To evaluate our approach's effectiveness, we conduct experiments using datasets of 15, 30, 60, 90, and 120 tasks from Rangen and PSPLIB and validate our method in a real-world modular integrated construction project. The experimental results reveal that a joint model integrating work package sizing and project scheduling offers a more comprehensive understanding of their interplay, leading to better resource utilisation and improved project schedules. A comparative analysis against existing state-of-the-art reinforcement learning and priority rule-based methods further substantiates the superior effectiveness of our proposed approach, yielding an approximate 5% reduction in total project cost. ARTICLE HISTORY

The Product-as-a-Service (PaaS) model offers the opportunity to implement circular actions such as repairing, reusing, collecting end-of-life products, and recycling. However, adopting circular practices causes more complexities in managing the inventory flow due to repetitive product subscriptions. Accordingly, this paper aims to optimise a PaaS model's order quantity and profit, considering circular economy practices and various quality levels for subscription products. In the proposed model, the subscription firm defines a quality check and repair procedure at the end of each quality period before sending the product to another subscriber. Moreover, the firm recycles end-of-life products and sells the recycled material to the supplier. This study aims to compare a closed-loop PaaS model with the traditional economic order quantity model in terms of operational costs, revenue, and inventory flow. The results show that factors such as the difference between the demand rates of the consecutive periods, the relationship between the recycling capacity and the final collection rate, and the difference between the screening and demand rates have essential roles in alleviating the extra inventory costs related to circular economy actions like reusing, repairing, and recycling.

Modern supply chains are characterised by high complexity, requiring effective managementthrough coordinated activities across interrelated functions. This study aims to move from isolatedoptimisation to integrated decision-making, which offers new potential for efficiency. We investi-gate an integrated procurement-production problem based on a real case study from a Germancompany specialising in printed circuit board assembly. We propose a novel solution approach thatcombines a genetic algorithm with a neural network to increase computational efficiency. Our com-prehensive evaluation scheme demonstrates the viability of the approach in generating integrateddecisions within a limited time frame. Specifically, we quantify the benefits of integrated over sepa-rated decision-making at the operational level, extending previous research focussed on the tacticallevel. The results indicate considerable benefits of integrated decision-making across a wide range ofcost factors, although the exact savings depend on specific cost parameters. In addition, we evaluateour model on a rolling horizon planning basis, which is crucial for modelling realistic supply chainbehaviour and remains underrepresented in the literature.

Historically the notion of resilience capability has primarily been conceptualised as a holistic construct in the domain of supply chain networks – referred to as SCRES. The field of SCRES has since evolved gaining prominence among scholars and practitioners. However, the existing SCRES literature inadequately delves into the embedded resilience harboured by their constituent components, practices, or internal routines, including the aggregate production planning (APP) process. Therefore, the purpose of this article is to examine the interplay between medium-term pure strategies for APP and the resilience of manufacturing facilities, assuming demand to be the sole source of uncertainty. To accomplish this, a realistic Monte-Carlo simulation model integrated with a robust heuristic procedure for pure strategies is merged with a factory resilience index-based Cobb–Douglas function. The results of this research suggest that manufacturing facilities achieve a higher level of resilience when certain ‘zero-inventory plans’ are implemented. Based on this key finding, production/demand planners might incorporate the resilience dimension alongside the customary manufacturing success factors. Thus, to the best of the authors’ knowledge, this study represents the first attempt to establish a clear linkage between the implementation of pure APP strategies and a quantitative measure of factory resilience.