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This paper introduces novel mathematical optimisation models for the 2-Dimensional guillotine Cutting Stock Problem with Variable-Sized Stock that appears in a Spanish company in the honeycomb cardboard industry. This problem mainly differs from the classical cutting stock problems in the stock, which is considered variable-sized, i.e. we have to decide the panel dimensions, width, and length. This approach is helpful in industries where the stock is produced simultaneously with the cutting process. The stock is then cut into smaller rectangular pieces that must meet the customers' requirements, such as the type of item, dimensions, demands, and technical specifications. Furthermore, in the problem tackled in this paper, the cuts are guillotine, performed side to side. The proposed mathematical models are validated using real data from the company, obtaining results that drastically reduce the produced material and leftovers, reducing operation times and economic costs. [ABSTRACT FROM AUTHOR]

In recent years, the way that maintenance is carried out has evolved due to the incorporation of digital tools and Industry 4.0 concepts. By connecting to and communicating with their production system, companies can now gather information about the current and future health of the equipment, enabling more efficient control through a process called predictive maintenance (PdM). The goal of PdM is to reduce unplanned downtimes and proactively address maintenance needs before failures occur. However, it can be challenging for industrial practitioners to implement an intelligent maintenance system that effectively manages data. This paper presents a methodology for developing and implementing a PdM system in the automotive industry, using open standards and scalable data management capabilities. The platform is validated through the presentation of two industry use cases. [ABSTRACT FROM AUTHOR]

Rapid advances in new-generation information technologies have been the main driving force for the transformation of manufacturing enterprises in Industry 4.0. Digital twin (DT), as a key technology to promote intelligent manufacturing, has shown great potential for manufacturing enterprises to create an industrial intelligence-driven production equipment through in-depth integration of cyber-physical systems. However, the lack of a systematic effective DT modelling method with a supporting evaluation metric is the most important factor restricting the application of DT in manufacturing enterprises. To bridge the gap, this paper proposes a novel multi-level modelling and fidelity evaluation (MLM&FE) method of DT for creating smart production equipment in manufacturing enterprises, which could help enterprises establish an industrial intelligence-driven production environment to quickly respond to changes in the customised global market, thus greatly improving competitiveness of the enterprises. Specifically, this paper firstly designs a reference framework for DT-enhanced smart production equipment, on which an MLM&FE architecture is proposed. Then, key implementation methodologies and tools for MLM&FE are introduced from the perspective of data space modelling, virtual space modelling, knowledge space modelling, model integration and evaluation. Finally, the developed smart production equipment prototype demonstrates the feasibility and effectiveness of DT MLM&FE. [ABSTRACT FROM AUTHOR]

Production systems have to be adapted continuously to changing circumstances. This means that the responsible production planner has to frequently make decisions about reconfiguring complex systems under uncertainty whose outcome greatly affects the company's business success. Discrete-event simulation is one powerful tool to support the necessary analysis and scenario evaluation, but still remains time-consuming and tricky to set up and to maintain. When implemented as a digital twin of the production system, the simulation model can maintain a high degree of accuracy over a long time period. An approach to realise this potential is presented and illustrated in this paper with a use case from the automotive industry. This paper contributes several new methods and findings to the development of digital twins of production systems: Firstly, it demonstrates how exceptional events in the validation of the digital twin can be handled. Secondly, it shows how structural changes in the system can be discovered using data on machine activity and process mining. Thirdly, the paper introduces a possibility on how to assess the accuracy of the digital twin. Furthermore, it demonstrates how to assess the robustness of the digital twin to estimation errors in machine processing times. [ABSTRACT FROM AUTHOR]

This paper provides a systematic review of the Genetic Algorithm (GA)s proposed to solve planning and scheduling problems in Engineer-To-Order (ETO) contexts. Our review focuses on how the key characteristics of ETO projects affect both the problem studied and the GA algorithmic features. Typical ETO projects consist of one-of-a-kind products with complex structures and uncertain designs. A deep analysis of the papers published between 2000 and 2022 enables identifying 10 main characteristics of ETO projects, six activity types, 10 decision types, eight groups of constraints, and 10 optimisation objectives. Our study shows that none of the reported papers integrates all 10 ETO characteristics. The less studied ETO characteristics are incorporating design and engineering information in the problem definition and the design uncertainty. Our review also identifies 10 recurrent encoding formats and emphasises the most frequently used genetic operators. We observed that most planning and scheduling problems consider objectives and decisions related to product customisation or supply chain configuration yielding multi-objective problems. Most multi-objective GAs use a weighted sum or are based on NSGAII. Diversity maintenance methods, adaptive and parameter tunning mechanisms, or hybridisation with machine learning models are still not used in this context. A systematic review of genetic algorithms dedicated to industrial planning and scheduling Analysis on how the characteristics of ETO projects impact the design of genetic representation and operators Recommendation on approaches employed to reach high-quality solutions [ABSTRACT FROM AUTHOR]

Digitalization is constantly altering company paradigms and expanding cross-border supply chain prospects. Maritime transportation plays an increasingly essential part in the global supply network. Since maritime shipping services need to exchange a huge number of papers and paperwork across numerous companies, the usage of a unified platform for inter-organisational communication and information sharing is required. To develop an integrative, adaptive, and intelligent container booking system, a multi-agent architecture is designed in this article. The proposed architecture will aid the maritime industry in establishing real-time information interchange between autonomous agents, shippers, freight forwarders, and shipping lines. The process outlined in this paper reveals how the agents communicate with one another to resolve underlying inconsistencies. With the multi-agent framework, the article also presents a container slot optimisation problem considering market segmentation, different booking periods, heterogeneous containers and port congestion scenarios. Using this model the managers can find the booking limit for each type of containers and accordingly they can accept or reject the incoming booking requests. Furthermore, a simulated case study is also provided to validate the model. [ABSTRACT FROM AUTHOR]

With the rise of Internet of Things (IoT) technology, the seamless connection between the physical and digital worlds has been realized. This review paper aims to conduct a science mapping review of IoT applications in industrial management and to identify mainstream research topics, research gaps, and future research directions. Using VOSviewer as a visualization tool, 142 articles retrieved from the Scopus database were quantitatively discussed using scientometric analysis. Additionally, a follow-up qualitative discussion was focused on mainstream research topics, existing research gaps, and future research directions as the main research goals. The results revealed influential findings for the co-occurrence of keywords, journals, countries, authors, and documents analyses. Moreover, it was found that the existing research mainly focused on four main research topics including (1) application of IoT in manufacturing based on cyber-physical systems, (2) IoT-related technologies on logistics and supply chain management, (3) The impact of IoT on business models, and (4) Industrial IoT (IIoT) in the context of Industry 4.0. On this basis, the existing research gaps and future research directions are proposed. This review paper would help relevant practitioners and researchers to better understand the existing body of knowledge and lay the foundation for further research. [ABSTRACT FROM AUTHOR]

Under servitization (one paradigm of non-ownership-transfer) business model such as sharing, a manufacturer is responsible not only for product design but also for maintenance. This paper deals with joint decision of product quality and maintenance under servitization. A model minimising total cost is formulated to obtain the optimal product quality, maintenance effort and maintenance times. We find that the total cost first decreases then increases with product quality, maintenance effort and maintenance times. Moderate quality and maintenance effort level are the best for the manufacturer, because too high or too low quality and effort cause too high total cost. However, preventive maintenance should be implemented as many times as possible until the failure cost is reduced to zero as long as each maintenance can save cost. Moreover, in the optimal joint decision, the product quality, maintenance effort and maintenance times are negatively correlated in pairs. In addition, we explore the impact of unit failure cost, product lifecycle, production cost, maintenance cost, and failure intensity on the manufacturer's optimal decisions. In extensions, we further take penalty cost, exponential failure distribution, discount rate, and customer usage into account. This paper provides insight for the manufacturer to jointly consider product design and maintenance under servitization. [ABSTRACT FROM AUTHOR]

Simultaneous allocation of service times and buffer capacities in manufacturing systems in a random environment is a NP-hard combinatorial optimisation problem. This paper presents a sophisticated simulation-based optimisation approach for the design of unreliable production lines to maximise the production rate. The proposed method allows for a global search using a Genetic Algorithm (GA), which is coupled with Finite Perturbation Analysis (FPA) as a local search technique. Traditional techniques based on perturbation analysis optimise decision variables of the same nature (e.g. service time only, buffer capacity only), whereas the proposed technique simultaneously provides an allocation of service times and buffer capacities. One of the main focuses of this paper is the investigation of the persistence or absence of the buffer and service rate allocation patterns which are among the most essential insights that come from designing production lines. The results show the superiority of the combined GA-FPA approach regarding GA and FPA in terms of solution quality and convergence behaviour. Moreover, considering instances ranging from 3 to 100 machines, our numerical experiments are in line with the literature for small instances (as similar allocation patterns are identified in our work), but important differences are highlighted for medium/large instances. [ABSTRACT FROM AUTHOR]

Issues 1 and 2 of 2024 are composed of papers selected for the special section in memory of Dr. Jean-Marie Proth. Some papers are extended versions of articles presented at the 10th IFAC MIM 2022 conference (hub.imt-atlantique.fr/mim2022/) in the invited sessions in memory of Dr. Proth. [ABSTRACT FROM AUTHOR]

In a companion paper (Date, K., and R. Nagi. [(2023) Optimal Placement of Multiple Finite-size Rectangular Facilities in an Existing Layout. International Journal of Production Research, THIS ISSUE]) we investigated a new problem of optimal placement of multiple finite-size rectangular facilities with known dimensions in the presence of existing rectangular facilities. We introduced the requisite theory to arrive at a solution by dividing the feasible region into sub-regions whose boundaries provide the candidates for the optimal placement. In this paper, we focus on developing optimal and heuristic solution methods for solving problem instances with various facility counts. To solve small instances with fewer facilities, we develop two optimal methods: (1) efficient explicit enumeration based on a tree state-space representation and (2) an implicit enumeration scheme that uses a flow-decomposition-based lower bound to reduce the search space and time. To solve industrial-sized instances, we have to resort to a family of construction and improvement heuristics given that the problem is NP-hard. The heuristic procedures perform well for non-pathological cases with an acceptable optimality gap. The main contribution of this paper is a range of optimal to heuristic methods that can be applied by the practitioner depending on their problem characteristics and desired efficiency. [ABSTRACT FROM AUTHOR]

The industry has embraced digitalisation leading to a greater reliance on models derived from data. Understanding and getting insights into the models generated by machine learning algorithms is a challenge due to their non-explicit nature. Explainable artificial intelligence (XAI) is to enhance understanding of the digital models and confidence in the results they produce. The paper makes two contributions. First, the XRule algorithm proposed in the paper generates explicit rules meeting user's preferences. A user may control the nature of the rules generated by the XRule algorithm, e.g. degree of redundancy among the rules. Second, in analogy to federated learning, the concept of federated explainable artificial intelligence (fXAI) is proposed. Besides providing insights into the models built from data and explaining the predicted decisions, the fXAI provides additional value. The user-centric knowledge generated in support of fXAI may lead to discovery of previously unknown parameters and subsequently models that may benefit the non-explicit and explicit perspectives. The insights from fXAI could translate into new ways of modelling the phenomena of interest. A numerical example and three industrial applications illustrate the concepts presented in the paper. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to shed light on the relationship between environmental, social and financial performance. In particular, based on the literature, we hypothesise the existence of a 'sand cone' model of sustainability that implies cumulative and sequential effects among the three performance dimensions. The hypothesis is tested using the fixed effects model on a longitudinal dataset of secondary data covering the period 2010–2019. The dataset includes 661 companies operating in the manufacturing industry and located in Europe, North America and Asia. The findings show that environmental, social and financial performance do not conflict but cumulate. The sequence of performance improvements that outlines a 'sand cone' starts with environmental sustainability and ends with financial performance, while social performance fully mediates the relationship between the two. Any other sequence is not confirmed by the data. These results are however valid only in Western countries (i.e. Europe and North America). Based on these findings, this research informs managers that the three pillars of sustainability have a cumulative effect, but only if a specific implementation sequence is followed. However, managers should be aware that these cumulative effects need some time to show up. [ABSTRACT FROM AUTHOR]

In the context of great changes in the food industry, the aim of this research is to investigate if and how the implementation of 4.0 enabling technologies can enhance the economic, environmental and social sustainability of the food sector. A systematic literature review, using a combination of 12 keywords, was carried out on Scopus database with defined inclusion and exclusion criteria in order to answer four selected research questions. Overall, 50 relevant papers were retrieved and analysed by Mendeley and Excel with descriptive statistics. VOSviewer was used for co-occurrence and co-authorship analysis. Results illustrate that the interest in the topic has grown, in particular in Italy, and resume the benefits achievable by the implementation of 4.0 technologies in food industries. Social impacts are new job positions, ergonomic design of workplaces, changes in educational institutions, improved nutrition and better animal welfare. Positive aspects are related even to economic growth, improving food chain performances and decreasing companies' costs. Finally, it allows energy, water, CO2 emissions and food savings. Overall, the work provides a helpful overview to food manufacturers and producers, recommending the introduction of I4.0 technologies to positively influence the sustainable development of the sector and remain competitive in the market. [ABSTRACT FROM AUTHOR]

Balancing conflicting goals among different stakeholders is a challenging problem in various application domains. In this paper, we analyse it in the context of home healthcare. Fairness objective functions for nurses and patients are combined with system-level governance goals of the territorial centers in charge of the assistance service. From the solution of several multi-objective problems including one objective for each actor hierarchically ordered, the best goal for each stakeholder is identified and interesting managerial conclusions are drawn. To efficiently solve large-size instances, we introduce a parallel Adaptive Large Neighborhood Search algorithm with destroy and repair operators customised to solve multi-objective multi-actor problems. The algorithm proves to be highly efficient and effective when compared to a commercial Mixed Integer Programming solver, either in its plain form or enforced by a mild start and a primal heuristic. Additionally, we devise a metaheuristic method to generate the Pareto frontier of an instance. [ABSTRACT FROM AUTHOR]

Online ride-hailing services provide additional transportation capability by recruiting private vehicles to meet people's growing travel demand. To ensure the profitability of drivers and platforms, pick-up efficiency and frequency must be maintained at high levels. Therefore, consistency between the spatial distribution of drivers and that of travel demand becomes a key issue to address. This paper proposes a prediction-based iterative Kuhn-Munkres approach for service vehicle reallocation in the context of large-scale online ride-hailing. Firstly, preliminaries are formally defined and a novel mathematical model for the problem is proposed. Secondly, a deep spatio-temporal residual perception network is designed to accurately predict travel demand. Thirdly, an iterative Kuhn-Munkres approach combined with an improved A-Star algorithm is developed to reallocate service vehicles to spatial locations according to their distinct travel demand densities. Finally, extensive experiments are conducted to evaluate and verify the performance of the proposed approach. [ABSTRACT FROM AUTHOR]

Unlike mass production manufacturing systems, where configurations are rarely changed after the initial design, reconfigurable cyber-physical systems (RCPMS) self-change their structures throughout missions and thus self-adjust production in response to demand requirements. Accordingly, such a paradigm requires enhancing selective maintenance strategy to optimise scheduling maintenance actions, selecting configuration layouts for capacity and product family changes, and achieving maintenance cost reduction and reliability maximisation. This paper is the first to propose a robust model for a selective maintenance problem with imperfect repairs in the RCPMS context. The model also integrates uncertainties originating from the imperfect observations of components' health status. The model's objectives are to maximise the expected reliability and minimise the variance and maintenance cost under maintenance resource constraints. Moreover, we propose a new deep reinforcement learning framework for solving the resulting multi-objective and combinatorial optimisation problem. In addition, we use decision values to enhance the scalarisation process by permitting the priorities of specific objectives to be adjusted after the learning process. Furthermore, we employ Analytical Hierarchy Process to adjust the static priorities with respect to the objective functions and the actual learning context. Finally, broad experiments are conducted to highlight the performance of the proposed model and resolution framework. [ABSTRACT FROM AUTHOR]

Following the recent advances in Industry 4.0 and the upcoming Industry 5.0, the use of multiple UAVs for indoor tasks has risen, particularly in real-time remote monitoring, wireless coverage, and remote sensing. As a result, UAVs can be viewed as proactive problem solvers and can support Internet of Things (IoT) platforms by collecting and monitoring data cost-effectively and efficiently, leading to better decision-making. Moreover, sophisticated drone operations require specialised software and data processing abilities. However, the utilisation of drones has been mainly focused on outdoor environments, thus creating a literature gap regarding indoor navigation and operation. Therefore, the design and development of a method for remote planning and control of drones based on the utilisation of AR is presented in this paper. The proposed method is based on the utilisation of drones for remote monitoring. The suggested approach involves engineers designing a sequence of actions and transmitting them wirelessly to the drone, eliminating the need for human intervention. Thus, the proposed method contributes towards enabling engineers visualise the drone path with the use of Augmented Reality and provides the flexibility of adding multiple way points. The applicability of the developed framework is tested in a laboratory-based machine shop. [ABSTRACT FROM AUTHOR]

Current studies on scheduling of machines and transporters assume that either a single transporter or an infinite number of homogeneous transporters such as AGVs or mobile robots are available to transport semi-finished jobs, which seems very restrictive in practice. This paper addresses this gap by studying a job-shop scheduling problem that incorporates a limited number of heterogeneous transporters, where the objective is to minimize the makespan. The problem is modelled using mixed-integer linear programming and constraint programming. Different structure-based branch-and-bound algorithms with two lower-bounding strategies are also developed. A comprehensive numerical study evaluates the proposed models and algorithms. The research demonstrates that the adjustment of the proposed MILP model outperforms the existing formulation when applied to the homogeneous case. The study also uncovers interesting practical implications, including the analysis of the impact of different transporter types in the system. It shows that utilizing a fleet of heterogeneous transporters can improve the overall performance of the job shop compared to a relevant homogeneous case. The importance of the study is emphasized by highlighting the negative consequences of disregarding transporters' differences during the scheduling phase. [ABSTRACT FROM AUTHOR]

Demand shocks and fluctuations underscore the need for new approaches to coordinate collaboration between firms to scale up production. This paper proposes an approach to formalise product and process requirements via a collaboration ontology and applies semantic reasoning techniques for process formation. Our approach contributes to production research by providing flexibility in coordinating firms engaged in demand-driven collaboration. The proposed approach has four core dimensions: (1) The Collaboration ontology builds on a set of product assembly requirements, process steps, their input/output resources and semantic rules; (2) the ontology reasoner derives resource dependencies between the steps; (3) the java tool interprets resource dependencies as possible transitions in Business Process Management Notation (BPMN); (4) a workflow engine executes the generated product assembly process. The approach and the ontology were validated in an industrial aerospace tendering scenario demonstrating its practical relevance for firms seeking demand-driven collaborations to react to production changes. Finally, we position and explain our contributions to the body of knowledge in collaborative production engineering. [ABSTRACT FROM AUTHOR]

For automobile manufacturers, reducing vehicle interior noise is essential for increasing customer satisfaction and vehicle quality. Windshield wipers are one of the major components that generate such noises, and faulty wipers could negatively affect passengers' psychological and physiological perceptions while driving. Thus, identifying faulty wipers during the manufacturing process would improve the driving experience and vehicle and road safety as well as reduce driver distraction. However, the existing windshield wiper noise-detection process is entirely manual, relies upon human subjectivity, and is time-consuming. Accordingly, this paper develops a novel automated windshield wiper fault-detection system. First, a novel binarization approach is used to effectively binarize the transformed spectrograms of sound signals from windshield wiper operation to segment nAoisy regions. Then, a new matrix-factorisation approach called orthogonal binary singular value decomposition is proposed to decompose binarized mel spectrograms into uncorrelated binary eigenimages to extract meaningful features and identify faulty wipers. Then, the $ k $ k -nearest neighbour classifier is utilised to classify the extracted features into normal or faulty windshield wipers. Finally, to demonstrate the effectiveness of the proposed system, it was validated on real-life windshield wiper reversal and squeal noise datasets, where it outperformed existing methods and achieved accuracies of 95% and 94%, respectively. [ABSTRACT FROM AUTHOR]

Modern manufacturing is the world's largest and most automated industrial sector. The rise of Industry 4.0 technologies such as Big Data, Internet of Things (IoT) devices, and Machine Learning has enabled a better connection with machines and factory systems. Data harvesting allowed for a more seamless and comprehensive implementation of the knowledge-based decision-making process. New models that provide a competitive edge must be created by combining the Lean paradigm with the new technologies of Industry 4.0. This paper presents novel computer-based vision models for automated detection and classification of damaged packages from intact packages. In high-volume production environments, the package manual inspection process through the human eye consumes inordinate amounts of time poring over physical packages. Our proposed three different computer-based vision approaches detect damaged packages to prevent them from moving to shipping operations that would otherwise incur waste in the form of wasted operating hours, wasted resources and lost customer satisfaction. The proposed approaches were carried out on a data set consisting of package images and achieved high precision, accuracy, and recall values during the training and validation stage, with the resultant trained YOLO v7 model. [ABSTRACT FROM AUTHOR]

Visibility across supply chains has been a key concern for organisations for many years, but the tools and information systems to achieve real-time visibility have not been available until recently. In response to uncertainty and complexity, advanced information and communication technologies have been explored for supply chain visibility (SCV). However, managerial perspectives are largely absent from the current literature. In response, this paper systematically documents managerial factors influencing SCV and information that should be collected and shared among supply chain partners for better visibility. A multi-stage Delphi analysis was conducted with 26 supply chain experts from various globally recognised enterprises with manufacturing units located worldwide. The results provide details on prioritised managerial perspectives and experiences within (1) factors affecting SCV (drivers, enablers, challenges and contingencies), (2) SCV content (supplier, internal and customer information) and (3) implications of SCV (capabilities and performance effects). One observation was that forecasting is not deemed as important due to severe disruptions in supply chains. Real-time visibility for better predictability emerged as the top priority. This study is among the few that empirically explores factors influencing supply chain visibility and generates new insights into why barriers can be difficult to overcome in complex supply chain settings. [ABSTRACT FROM AUTHOR]

In this paper, stochastic optimisation of CVaR is applied to maintain risk-averse viability and improve resilience of a supply chain under propagated disruptions. In order to establish the risk-averse boundaries on supply chain viability space, two stochastic optimisation models are developed with the two conflicting objectives: minimisation of Conditional Cost-at-Risk and maximisation of Conditional Service-at-Risk. Then, the risk-averse viable production trajectory between the two boundaries is selected using a stochastic mixed integer quadratic programming model. The proposed approach is applied to maintain the supply chain viability in the smartphone manufacturing and the results of computational experiments are provided. The findings indicate that when the decision-making is more risk-aversive, the size of the viability space between the two boundaries is greater. As a result, more room is available for selecting viable production trajectories under severe disruptions. Moreover, the larger is viability space, the higher is both worst-case and average resilience of the supply chain. Risk-neutral, single-objective decision-making may reduce the supply chain viability. A single-objective supply chain optimisation which moves production to the corresponding boundary of the viability space, should not be applied under severe disruption risks to avoid greater losses. [ABSTRACT FROM AUTHOR]

A well-configured spare parts supply chain (SC) can reduce costs and increase the competitiveness of spare parts retailers. A structured method for configuring spare parts SCs should be used to determine whether to centralise or decentralise inventory management, also considering hybrid configurations. Moreover, such a method should define whether or not to switch the production of spare parts from Conventional Manufacturing (CM) technologies to Additive Manufacturing (AM) ones. Indeed, AM is considered the next revolution in the field of spare parts, and the adoption of AM technologies strongly affects the characteristics of SCs. However, the choice between centralisation and decentralisation is not the subject of much scientific research, and it is also not clear when AM would be the preferable manufacturing technology for spare parts. This paper aims to assist managers and practitioners in determining how to design their spare parts SCs, thus defining both the spare parts SC configuration and the manufacturing technology to adopt through the development of a decision support system (DSS). The proposed DSS is a user-friendly decision tree, and, for the first time, it allows comparison of the total costs of SCs characterised by different degrees of centralisation with both AM and CM spare parts. [ABSTRACT FROM AUTHOR]

This paper studies the universal point scheme with multiple retailers and a platform that implements the points within a channel. First, we analysed the equilibriums of the channel under the decentralised control and the mode of lateral cost-sharing among retailers. We found that retailers are willing to set the lowest point conversion ratios under the lateral cost-sharing mode compared with the decentralised and centralised control modes. The optimal conversion ratio of a retailer under the decentralised control is greater than the one under the centralised control when the cost spillover and double marginalisation are not significant. Next, we extended the model by considering the participation of new retailer. In this dynamic channel, the impact of the cost spillover phenomenon on the channel members' preferences is discussed. Third, we showed that the wholesale price contract for point management used in the real world has a limitation in terms of profit split. Following this, we proposed a buyback contract and showed the optimal contract parameters under which the channel's profit could be maximised and arbitrarily split. The managerial insights we obtained shed light on how to design a universal point scheme and select channel members to achieve an all-win scenario. [ABSTRACT FROM AUTHOR]

Reconfigurable manufacturing systems (RMS) intend to bridge the gap between dedicated and flexible manufacturing systems. If the literature is mainly focused on the design step and tactical planning of such systems, few research projects have addressed scheduling at the operational level. While setup times may occur in flexible manufacturing systems, reconfiguration times considered in RMS may affect several resources at once, and hence require specific modelling and solving approaches to be considered. This paper first formalises the problem at hand through integer linear programming. An iterative search method is then provided to obtain solutions to larger-scale instances. Results obtained on generated instances show that managing even few possible configurations can yield significant improvements in solutions' quality. Meanwhile, the extended search space implied by the increase in available configurations hinders the convergence to a good solution in a reasonable computation time, which suggests further investigations. [ABSTRACT FROM AUTHOR]

This paper studies the multiple period inventory replenishment problem for a capacitated warehouse when procurement prices and market demands are both uncertain, with the goal to minimise the procuring and holding costs. We analyse this problem under the framework of competitive analysis, where neither probabilistic distributions nor sets are available to characterise the unknown price and demand parameters. An efficient online real-time replenishment algorithm is developed, which is free of any distribution assumption, and the decisions are made based entirely on past and present information. We derive an instance-independent competitive ratio of the algorithm, which provides a worst-case theoretical performance guarantee, and shows that the proposed algorithm performs well for situations with high volatility uncertainty over time and is naturally risk-averse. Finally, a set of numerical experiments further verifies the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

The paper presents an analytical model for travel time evaluation in double-deep, triple-deep, quadruple-deep and fivefold-deep tier-captive shuttle-based storage and retrieval system (SBS/RS). Under the assumption of the deepest location rule for the storage process and the nearest neighbour rule for the retrieval process, the expressions for single-command and dual-command cycle times of the shuttle vehicle and elevator are developed, from which the SBS/RS performance can be estimated. The model based on the probability theory enables the evaluation of all individual times that make up the cycle times and assessment of the average number of totes relocated during a retrieval process. Special attention is devoted to the modelling of the relocation of totes which block access to the requested tote during the retrieval process. The validity of the analytical model is examined via the developed simulation model. The given case study shows that the proposed analytical travel time model results match the results of the simulation model. The presented modelling strategy could be applied to bigger system racking depths following the same modelling logic. [ABSTRACT FROM AUTHOR]

In this paper, we are interested in modelling and solving a flow and resource planning problem related to the trade-in, treatment and resale of pre-owned consumer electronic products. This problem is motivated and inspired by the flows and the processes of the company Recommerce Group, which mainly handles traded-in used smartphones in Europe, but also used tablets, smartwatches, game consoles, laptops, etc. Collected products must be directed to refurbishment centres. After having been recorded, cleaned, tested and reset, they may be refurbished, or remanufactured by a subcontractor. They may also remain in their initial state. Finally, they are sold on diversified channels. This problem can be generalised to other refurbishment and remanufacturing activities of used consumer electronic products. We first formulate the problem as a mixed-integer linear program and show its NP-hardness. In order to solve large size real-life instances, mixed-integer programming based heuristic approaches are proposed. Extensive computational experiments on realistic instances are conducted to show the advantages and the limits of the proposed approaches. Several managerial insights are investigated and analysed. [ABSTRACT FROM AUTHOR]

Developing an effective emergency collaborative distribution system is critical to improve on-time response performance, especially considering secondary disasters. To address the challenge, this paper investigates a bi-objective robust optimisation model on relief collaborative distribution among three echelons of authorities, the province, the municipality, and the county, which aims to minimise the total travel time and the total humanitarian logistics cost simultaneously. The optimal location of relief supply facilities and the relief distribution schemes will be determined by the optimisation model, which considers uncertain demand and travel time. Moreover, two robust optimisation methods are utilised to deduce the robust counterparts of the proposed model. An epsilon-constraint-based approach is used to solve the bi-objective optimisation model. A real-world case study based on an earthquake and aftershocks with different magnitudes in Yunnan Province is provided. The results show that incorporating secondary disaster scenarios contributes to reducing the total travel time and cost. For making full use of emergency resources and preventing situations from worsening, the centralised decision scheme is more effective than the decentralised one. The uncertainty of demand of primary disaster relief has a higher impact on the optimal solution than that of travel time. [ABSTRACT FROM AUTHOR]

This paper studies a multi-period dynamic production planning problem in a hybrid manufacturing and remanufacturing system (HMRS), where new and remanufactured products are perfect substitutes. The HMRS encounters uncertain return amounts due to products' random usage durations. As returns are from previous sales, production planning in each period impacts both current sales and future returns. The random return amount and the correlation among different periods make it a complex system. To solve the problem, we first utilise the hazard rate function and in-use products' information to derive an estimator of the return amount. Then, we formulate a dynamic programming model and prove a threshold policy is optimal under uniformly distributed demand. We employ marginal analysis to derive an approximation of the optimal threshold value. Through simulating all alternatives, the derived threshold is verified to be a good approximation as it achieves more than 99% of the optimal revenue in most scenarios. In addition, the calculated return amount based on the hazard rate function is almost identical to the return amount obtained via simulation. Compared to other return measurements, our method achieves the highest revenue in all considered scenarios, including heterogeneous usage durations and general demand distributions. [ABSTRACT FROM AUTHOR]

Reconfigurable Manufacturing Systems (RMS) have gained attention in the aerospace industry in the past years, as post-pandemic context shows drastic production capacity changes and new environmental regulations to which it must adapt quicker than before to maintain competitiveness. Nevertheless, current RMS design methods have not thoroughly considered this industry specificities, nor industrial resources requirements in the current concurrent design practice. These limitations have been identified in several recent research works. Ontology-based Engineering (OBE) systems can stand overly complex collaborative design processes involving multidisciplinary stakeholders and various digital tools, integrating different levels of decision. Models for Manufacturing (MfM) is an OBE methodology aiming to enable industrial design and decision-making in manufacturing by preserving the company knowledge in ontology models, usable as knowledge base to generate and integrate the aircraft and manufacturing systems design. This paper presents an MfM application for RMS design in aerospace, introducing innovative design concepts that allow implementing RMS in a collaborative engineering process of an aerospace product. An implementation is shown designing the RMS of a model aircraft family to illustrate the concepts introduced and considerations are given to transfer this knowledge base into an OBE system to support complex real-life applications. [ABSTRACT FROM AUTHOR]

This paper addresses the semi-online scheduling problem of minimising the total weighted completion time on a single machine, where a combination of information on jobs release dates and processing times is considered. In this study, jobs can only arrive at known future times and a lower bound on jobs processing times is known in advance. A new semi-online algorithm is presented and is shown to be the best possible for the considered problem. In order to make this statement, a new lower bound on the competitive ratio of any semi-online algorithm for the problem is developed and, using competitive analysis, the proposed semi-online algorithm is shown to have a competitive ratio that matches the lower bound. [ABSTRACT FROM AUTHOR]

Planning and scheduling approaches in real manufacturing environments entail the need to cope with random attributes and variables to match the characteristics of real scheduling problems where uncertain events are frequent. Moreover, the capability of devising robust schedules, which are less sensitive to the disruptive effects of unexpected events, is a major request in real applications. In this paper, a branch-and-bound approach is proposed to solve the two-machine permutation flow shop scheduling problem with stochastic processing times. The objective is the minimisation of the value-at-risk of the makespan, to support decision-makers in the trade-off between the expected performance and the mitigation of the impact of extreme scenarios. A Markovian Activity Network (MAN) model is adopted to estimate the distribution of the makespan and assess the value-at-risk for both partial and complete schedules. Phase-type distributions are used to enable general distributions for processing times while maintaining the capability to exploit a Markovian approach. The effectiveness and performance of the proposed approach are demonstrated through a set of computational experiments. [ABSTRACT FROM AUTHOR]

Production companies are facing increasing production complexity and demographic change. Therefore, with its decreasing physiological and mental performance prerequisites, the aging workforce is often confronted with overload at the workplace leading to fatigue and work errors in the short term and increasing sick days in the long term. Integrating physiological and mental aspects into existing employee scheduling is one way to maintain employees' health and performance. Incorporating these aspects is often very complex due to the need for multiple disciplines and the lack of supporting methods and tools. Therefore, this paper provides a practical-oriented overview for including physiological and mental aspects in existing employee scheduling. The intercorrelation between workload, individual performance prerequisites and strain, their measurement methods, and standardised alternatives are explained to support industrial practitioners in production management, like planners. A four-step method for integrating physiological and mental aspects in employee scheduling is presented. With the help of this method, the interaction between the ergonomics/human factors departments and production management can be improved, resulting in a more human-oriented employee scheduling. A practical example from manual assembly illustrates the application of the presented approach. [ABSTRACT FROM AUTHOR]

The price of oil is highly complex to predict as it is impacted by global demand and supply, geopolitical events, and market sentiment. The accuracy of such predictions, however, has far-reaching implications for supply chain performance, portfolio management, and expected stock market returns. This paper contributes to the oil price prediction literature by evaluating the predictive impact of the US President's communication on Twitter, while benchmarking various Natural Language Processing (NLP) techniques, including Term Frequency-Inverse Document Frequency (TF-IDF), Word2Vec, Doc2Vec, Global Vectors for Word Representation (GloVe), and Bidirectional Encoder Representations from Transformers (BERT). These techniques are combined with a deep neural network Long Short-Term Memory (LSTM) architecture using a five-day lag for both the oil price and the textual Twitter data. The data was collected during the term of US President Donald Trump, resulting in 1449 days of crude oil price prediction and a total of 16,457 tweets. The study is validated for Brent and West Texas Intermediate blends, using the daily price of a barrel of crude oil as the target variable. The results confirm that including the US President's tweets significantly increases the predictive power of oil price prediction models, and that an LSTM architecture with BERT as NLP technique has the best performance. [ABSTRACT FROM AUTHOR]

This paper proposes a white-box perspective that portrays a supply chain delivery process as a network of related activities which remains to be improved. It addresses a critical disadvantage of supply chain delivery performance models, namely considering a delivery process as a whole and ignoring characteristics and relationships between activities in the delivery process. A delivery process is modeled using the Graphical Evaluation and Review Technique based on the characteristic function (CF-GERT). Based on the CF-GERT model, a framework for applying managerial effort to activities to improve overall delivery performance is proposed. Then, particle swarm optimization (PSO) based on the penalty function is used to solve the delivery performance improvement framework. Finally, a numerical case shows how applying efforts to activities can effectively improve delivery performance and demonstrates the influence of several parameters on the related costs. [ABSTRACT FROM AUTHOR]

Business models play a key role in companies' profit growth and competitiveness. A growing number of companies not only sell products but also provide leasing services. In traditional leasing models, companies usually charge customers for the time they occupy the product, but ignore differences in how long and how often customers actually use the product. In practice, customers have different product usage patterns, which will affect their purchase choice. Considering the pooling effect in the leasing service network, this paper describes customers' usage patterns in two dimensions (product occupancy time and utilisation rate) and determines the optimal pricing under three models (selling, leasing, and their hybrid model) to maximise corporate profits. Through comparative analysis, the optimal business model selection decision is obtained, and its market performance is also analysed. The results show that when the leasing service operational capability is strong, the pure leasing service model is superior; otherwise, the result also depends on the product value creation capability. When this capability is high, the hybrid model will be selected; otherwise, the pure selling model will be better. This result differs from the generally held result in previous studies that the hybrid model is more profitable in most cases. [ABSTRACT FROM AUTHOR]

In contemporary manufacturing and supply chain environments, there are various challenges that companies have to face. Many companies are facing challenges to respond quickly to customers' requirements and to provide customised products cost effectively. Mass customisation (MC) can help companies to provide customised products and services quickly at a low price. Integrated decision-making can obtain performance improvement in many situations. This paper investigates the problem of scheduling integration of flexible manufacturing systems (FMSs) for mass customisation. A mathematical model is formulated for the problem. A set-based dynamic algorithm is developed. Numerical studies are made on the proposed algorithm by simulation and by statistical analysis. Conclusions, managerial implications, and future research suggestions are provided. [ABSTRACT FROM AUTHOR]

This paper presents novel models for the Integrated Procurement and Lot-Sizing Problem with multiple customers and backlogging. Since allowing backlog in a traditional cost minimisation model involves dealing with intangible costs of not fulfilling the demand on time, we propose optimising service-levels while keeping the costs minimal using a budget constraint. The motivation of this study emanates from a manufacturing company that assembles commercial and industrial refrigeration equipment, using both purchased materials and in-house products. Instead of considering an aggregate demand, we consider the specific demand from various customers. This allows us to incorporate demand fulfilment decisions into the model in case of stock-outs by deciding which customers will have their orders backlogged. The fill-rate, or β service-level, is considered both globally and for customers and products individually. Computational experiments show that the service-level-driven models improve service when compared to the traditional cost model and also end up enforcing service equity among different customers at the expense of a deterioration in the global fill-rate. [ABSTRACT FROM AUTHOR]

Inspired by real-life applications, mainly in hand-intensive manufacturing, the incorporation of learning effects into scheduling problems has garnered attention in recent years. This paper deals with the flowshop scheduling problem with a learning effect, when minimising the makespan. Four approaches to model the learning effect, well-known in the literature, are considered. Mathematical models are providing for each case. A solver allows us to find the optimal solution in small problem instances, while a Simulated Annealing algorithm is proposed to deal with large problem instances. In the latter, the initial solution is obtained using the well-known Nawaz-Enscore-Ham algorithm, and two local search operators are evaluated. Computational experiments are carried out using benchmark datasets from the literature. The Simulated Annealing algorithm shows a better result for learning approaches with fast learning effects as compared to slow learning effects. Finally, for industrial decision makers, some insights about how the learning effect model might affect the makespan minimisation flowshop scheduling problem are presented. [ABSTRACT FROM AUTHOR]

Throughput analysis plays an important role in the operations and management of automotive manufacturing. Predicting how the system throughput changes over time helps the plant managers to make timely operational decisions to meet the daily production requirement. In today's automotive production systems, the availability of sensing data reflecting process variables and machine status across the plant floor raises new opportunities for improving the throughput prediction accuracy. However, challenges exist in extracting the most important features from the high-dimensional data and capturing the complicated time-vary interdependency among different assets in the system. To overcome such challenges, in this paper we propose a hierarchical Recurrent Neural Network (RNN)-based framework that is composed of clustering, dimension reduction and feature selection, regression, and prediction pruning/adjustment. In addition to predicting end-of-line throughput, our framework then identifies the associations for low throughput to facilitate downtime prevention and maintenance decision-making. The proposed framework is applied to an automotive production system, and its effectiveness is demonstrated by comparison with conventional methods. [ABSTRACT FROM AUTHOR]

Configuring supply chains (SCs) is critical to spare parts retailers' success, entailing two key aspects: stock deployment into distribution centres (DCs) (i.e. inventory centralisation or decentralisation) and stock supply in each DC (how many spare parts to supply and how often). Given the unpredictability of spare parts demand, stock deployment and supply policies should be regularly reviewed, adapting to fluctuations in customer needs. A viable way to do this is to adopt a multi-criteria ABC criticality classification. However, the multi-criteria ABC criticality classification has often been used to plan stock supply policies in a single DC, but only once to plan spare parts deployment. Nevertheless, the available literature methodology presents major limitations, being not applicable in real companies. Therefore, this paper provides a novel methodology, called SP-LACE, which first reviews the configuration of spare parts SCs based on a multi-criteria criticality classification. Then, allows, for the first time, to evaluate the economic benefits of the reviewed SC configuration. SP-LACE was tested on two case studies and compared with the literature methodology. The results indicate that it provides economic benefits (in terms of total SC cost), overcoming the limitations of the literature methodology and ensuring high service levels. [ABSTRACT FROM AUTHOR]

As instability becomes the norm, supply chain management is becoming increasingly complex and critical. As a result, supply chain managers must adapt to complex situations. Managing instability is a key expectation for these managers. One way to help them to manage this instability is to study resilience. Resilience is related to in the literature as the ability of a system to resist, adapt and recover from disruptions. Measuring and controlling supply chain resilience has therefore become a key issue for managers, especially in a context of instability. In 2013, the World Economic Forum [2013. Global Risks 2013. Davos, Switzerland: World Economic Forum] highlighted in its study, this priority for the surveyed companies to master this concept of resilience. To address this need, this paper presents an innovative approach to disruption and resilience management based on physics principles. It considers disruptions as forces that impact supply chain performance. These forces are created as a result of changes in the internal or external attributes of the supply chain. In this approach, supply chain performance is represented and visualised as a physical trajectory modelled in the framework of its performance indicators. Thus, disturbances are considered as forces that displace and deviate the supply chain's performance trajectory in its performance framework. [ABSTRACT FROM AUTHOR]

Human–robot collaborative technology maximises the advantages of the capabilities of humans and robots, and provides diverse operating scenarios for the remanufacturing industry. Accordingly, this paper proposes an innovative human–robot collaborative disassembly line balancing problem (HRC-DLBP). First, a mixed-integer programming (MIP) model is devised for the HRC-DLBP to minimise the number of workstations, smoothness index, and various costs. Second, a hybrid local search genetic algorithm (HLSGA) is developed to solve the proposed HRC-DLBP efficiently. According to the problem characteristics, a four-layer encoding and decoding strategy was constructed. The search mechanism of the local search operator was improved, and its search strategy was adjusted to suit the genetic algorithm structure better. Furthermore, the accuracy of the proposed MIP model and HLSGA is verified through two HRC-DLBP examples. Subsequently, three HRC-DLBP examples are used to prove that the HLSGA is superior to five other excellent algorithms. The case of the two-sided disassembly line problem reported in the literature is also solved using the HLSGA. The results are found to be significantly better than the reported outputs of the improved whale optimisation algorithm. Besides, HLSGA also outperforms the results reported in the literature in solving EOL state-oriented DLBP. Finally, the HLSGA is applied to a power battery disassembly problem, and several optimal allocation schemes are obtained. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper presents an evaluation of the usability, functionality, and usefulness of the Warehouse Error Prevention (WEP) tool that consists of seven modules. The WEP tool is framed in a simple yes/no form, which can be used to identify human factors related to sources of pick errors in a warehouse. Thirty-three participants in 27 organisations from three different countries participated in a trial application and evaluation of the tool. The evaluation included a survey study and semi-structured interviews. Survey results show that participants agreed on the usability and functionality of the WEP tool. In the interviews, participants generally reported the WEP tool as being both accurate and functional with the potential to support engineers, ergonomists, and warehouse managers to improve order picking quality. Further quantitative field testing of the WEP tool's potential to identify costly warehouse errors is needed. [ABSTRACT FROM AUTHOR]

As manufacturers continue to provide new and unique products and services for their customers, we argue that warranty menus with differentiated options are an often-overlooked unique selling point. Meanwhile, there is a huge swathe of customers who own durable products which are still in use even after the expiration of a warranty. It is in this area that we develop a research perspective which urges customers to consider warranty plans and post-warranty maintenance strategies simultaneously when making a purchasing decision. In this paper, through the lens of a consumer, we propose an integrated model of warranty and post-warranty maintenance under a flexible renewable warranty. This model takes into account repair limits and multiple failure types with time-varying probabilities. As part of this, a new post-warranty maintenance strategy is designed, in which the repair time-based corrective replacement and the age-based preventive replacement are combined. The life cycle cost rate of the product is minimised by a synthetic decision-making about the consumer's warranty purchase and post-warranty maintenance strategy. Numerical studies show that there exists a joint optimal solution, which can help the customer to develop an optimal overall warranty-maintenance strategy under the warranty menu with minimised cost rate. An integrated model of warranty and post-warranty maintenance is developed. The warranty product has minor and catastrophic failures with time-varying probabilities. A flexible renewable warranty is considered for the consumer. A post-warranty maintenance strategy with the repair time limit is proposed. The optimal warranty-maintenance strategy is derived to minimise the Life cycle cost rate. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, e-commerce retailers have had trouble satisfying the growing demand because of limited warehouse capacity constraints. Fortunately, an on-demand warehousing system has emerged as a new alternative to mitigate warehouse capacity issues. In recent years, several studies have focused on the supply chain problem considering on-demand warehousing. However, there is no study that deals simultaneously with inherent uncertainties and the property of commitment, which is the main advantage of on-demand warehousing. To fill these research gaps, this paper presents an e-commerce supply chain network design problem considering an on-demand warehousing and decisions for commitment periods. We propose the two-stage stochastic programming model that captures the inherent uncertainties to formulate the presented problem. We solve the proposed model utilizing sample average approximation combined with the Benders decomposition algorithm. Of particular note, we develop a method to generate effective initial cuts for improving the convergence speed of the Benders decomposition algorithm. Computational results show that the developed method could find an effective feasible solution within a reasonable computational time for problems of practical size. Furthermore, we show the significant cost-saving effects, based on experiment results, that occur when an on-demand warehousing system is used for designing supply chain networks. [ABSTRACT FROM AUTHOR]