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The integrated planning of several activities implicated in paper production can lead to remarkable gains in terms of raw materials and resource usage. However, activities such as order assignment, production sequencing and cutting planning are usually addressed separately while ignoring the interactions among these processes. But the quality of the solution resulting from the juxtaposition of the partial solutions is not guaranteed, and may have a significant impact in terms of inefficiency of global performance. This article considers production planning in a pulp and paper industry in order to meet a set of orders of diverse types of products, admitting the possibility of producing simultaneously in two or more paper machines with their own features. The developed approach, ASC-3Steps, considers not only cutting optimisation but also extends itself to the processes of assigning orders to paper machines and production sequencing at each machine. Minimisation of wasted paper is assumed to be the overall goal. A set of computational results based on real data is presented. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has triggered new research areas in supply chain resilience. One of these new areas is viability. Viability extends the resilience understanding from performance-based assessment of firm’s responses to disruptions towards survivability of both supply chains and associated ecosystems not only during some short-term disruptions but also under conditions of long-term crises. To explore the state-of-the-art knowledge on methods, models, capabilities, and technologies of supply chain viability, we edited this important IJPR special issue. To introduce the special issue, we review the existing literature on supply chain viability, conceptualise seven major pillars of supply chain viability theory (i.e. viable supply chain design, viability in process planning and control, ripple effect, intertwined and reconfigurable supply networks, ecosystems, digital supply chain, and Industry 5.0), and establish some associated future research directions. The findings of this editorial paper, as well as the articles in the special issue, can be used by researchers and practitioners alike to consolidate recent advances and practices of viability in supply chain networks and lay the solid foundation for further developments in this area. [ABSTRACT FROM AUTHOR]

Production planning and control (PPC) systems that employ aspects from both make-to-order (MTO) and make-to-stock (MTS) production control are known as hybrid MTS/MTO systems. While both MTO and MTS separately have been studied extensively, their combined use has received less attention. However, the literature on this topic is growing and this paper shows that the review performed in this paper is an important addition to the field. We categorise relevant literature according to a novel taxonomy and show that hybrid MTS/MTO production control can be used in different contexts. In addition, an overview of the modelling techniques and methods used in these papers is provided. Based on the reviewed literature, relevant research questions and directions for future research are identified. Finally, it is shown that hybrid MTS/MTO production control is prevalent in practice by discussing research with industrial applications. The paper contains an overview of research on hybrid MTS/MTO production control to be used as reference for researchers active in the field, and provides managerial insights and directions for future research on this topic. [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]

Due to today's competitive environment and information boom, companies are concerned about production planning in uncertain conditions. This paper integrates decision-making method with production scheduling model by considering limited resources. In this paper, a new mathematical model is extended for production project scheduling with multiple execution modes. The main aim of the formulation is to plan and schedule real production projects in uncertain environments. A new mixed-integer linear formulation is presented by considering trade-off of cost, time, as well as quality. Cost–time-quality trade-off is extended with the interval information. In the presented model, activity quality could be enhanced by reworking. The interval forms of some parameters, including duration, quality of activities, cost, and total available resources, are obtained by determining weights of experts and aggregating them. The presented group decision-making method is based on a bi-directional projection measure to deal with interval information. Since the mathematical model is multi-objective and some data are interval, a new modified solution method is developed for solving the model. The presented method for both decision-making and mathematical models is investigated by a real-world production project and two datasets to ascertain the accuracy of the model. Finally, an appropriate sensitivity analysis is proposed. [ABSTRACT FROM AUTHOR]

In the context of Industry 4.0, the manufacturing sector is moving from automation towards intelligence. The application of new generation information and communication technologies (ICTs) improves the interconnection and transparency of intelligent manufacturing (IM) systems, which will change how information interacts and work is done, thus changing how work should be managed. These changes require the following characteristics for IM production and operations management (POM): integration, flexibility and networking, autonomous and collaborative decision-making, learning-based operations management, self-optimisation and adaptability, and proactive decision-making. This paper presents the state of the art, current challenges, and future directions of IM-related POM research from the perspectives of these characteristics through a systematic literature review. Descriptive and thematic analyses of 208 research articles published between 2005 and 2020 are provided. The review and discussions focus on five research themes, i.e. value creation mechanisms, resource configuration and capacity planning, production planning, scheduling, and logistics. [ABSTRACT FROM AUTHOR]

Sales and operations planning (S&OP) has emerged as a planning approach that integrates tactical level decisions across functions and supply chains while aligning day-to-day operations with long-term strategy through these decisions. The extant knowledge on S&OP has evolved primarily based on the needs of mass production contexts, and applications of S&OP in engineer-to-order (ETO) contexts have not been explored by previous research. Arguing that the cross-functionally coordinated planning enabled by S&OP can improve the effectiveness of the challenging and competitively critical tendering process, this paper develops an S&OP framework for the tactical planning process design to support delivery date setting in ETO contexts. The paper adopts a systematic literature review approach for identifying the main tactical planning activities managers in ETO companies should consider while designing the S&OP process and the information inputs required for performing and coordinating these planning activities. The identified planning activities and planning inputs are synthesised to develop the proposed S&OP framework for delivery date setting in ETO contexts. The proposed framework can support managers in assessing which tactical planning activities are strategically essential in their respective companies and redesigning or reconfiguring existing planning processes to address the planning needs of their environment. [ABSTRACT FROM AUTHOR]

As assembly systems move into the era of mass customisation, the complexity of design processes, (re)configurations and operations rises. Well-structured data are key in keeping this complexity manageable. Here to, this paper presents a multidimensional formal skill model designed to deliver generic descriptions of needs and capacities with skills as the connector between products, processes and resources. The model formalises resource structures in relation to the processes they master and products they can produce. This paper discusses the case-based evaluation in a reconfigurable assembly system and highlights the added-value of a skill-based modelling approach. The presented formal model combines concepts coming from both offline and online modelling perspectives and allows for various applications and levels of detail. The resource structures embedded in the prerequisites of a skill enable matchmaking of resources for workspace design and reconfigurations. The mapping of the model to standardised ISA-95 models couples production needs to the resources allowing for more optimal production planning, control and a structured interface between enterprise and control systems. The possibility to couple states to the assembly environment allows for optimal runtime orchestration. [ABSTRACT FROM AUTHOR]

The Industry 4.0 revolution is changing the manufacturing landscape. A broad set of new technologies emerged (including software and connected equipment) that digitise manufacturing systems. These technologies bring new vitality and opportunities to the manufacturing industry, but they also bring new challenges. This paper focuses on the impact of Industry 4.0 on production planning approaches and software. We first propose a digital twin framework that integrates production planning systems and frontier technologies. The frontier technologies that may impact production planning software are the internet of things, cloud manufacturing, blockchain, and big data analytics. Second, we provide a state-of-the-art on the application of each technology in the production planning, as well as a detailed analysis of the benefit and application status. Finally, this paper discusses the future research and application directions in the production planning. We conclude that Industry 4.0 will lead to the construction of data-driven models for production planning software. These tools will include models built accurately from data, account for uncertainty, and partially actuate the decision autonomously. [ABSTRACT FROM AUTHOR]

This paper provides a literature review and an analysis of the studies related to workforce reconfiguration strategies as a part of workforce planning for various production environments. The survey demonstrates that these strategies play a crucial role in the resilience and flexibility of manufacturing systems since they help industrial companies to quickly adapt to frequent changes in demand both in terms of volume and product mix. Five strategies are considered: the use of utility, temporary, walking, cross-trained workers, and bucket brigades. They are analysed in the context of mixed and multi-model manual assembly lines, dedicated, cellular, flexible, and reconfigurable manufacturing systems. The review shows that most of the researches on these reconfiguration strategies focus on multi- or mixed-model assembly lines. At the same time, few studies consider workers team reconfiguration in flexible and reconfigurable manufacturing systems. Finally, this paper reveals several promising research directions in workforce reconfiguration planning, namely, the use of both machine and workforce reconfigurations, consideration of the ergonomic aspects, the combination of multiple workforce reconfiguration strategies, the study of workforce reconfiguration in human-robot collaborative systems, and the use of new technologies in human-machine industrial environments. [ABSTRACT FROM AUTHOR]

Electricity demand-response programmes, such as the incentive-based and price-based programmes, have been used by utilities to induce customers to reduce their electricity consumption during peak periods. This study investigates the production decisions of a two-stage production system under these programmes in a situation where peak periods arrive randomly in the manufacturing cycle. Analytical results show that under demand-response programmes, the manufacturer, who aims at minimising the total operational cost, usually selects a lower production rate during peak periods and a higher one during non-peak periods. Notably, the uncertainty of the peak periods also has a significant influence on the manufacturer's production plans under these programmes. This paper further investigates the efficiency of different demand-response programmes in reducing the inventory holding cost and electricity cost. The results indicate that participating in the demand-response programmes does not always result in a higher inventory holding cost, which goes against the manufacturer's intuition about these programmes. In addition, this paper evaluates the manufacturers' preference for these demand-response programmes by comparing the operational cost savings generated from participating in the two programmes. It turns out that both the demand-response signals and the inventory holding cost substantially influence the manufacturer's willingness to participate in the programmes. [ABSTRACT FROM AUTHOR]

As the core link of intelligent manufacturing, the process planning of aviation parts still faces the challenges such as relying on manual experiences for process decision-making and lack of linkage between process design and manufacturing for process optimisation. Process knowledge could support scientific decision-making on process issues, while twin data, namely high-fidelity simulation data and feedback information of manufacturing site, could further verify the process plans and optimise process parameters, so as to continuously improve the quality of process plans. Consequently, this paper proposes a general framework for twin data and knowledge-driven intelligent process planning (TDKIPP) of aviation parts, and analyses four standard procedures that support the above-mentioned reference framework, namely mechanism-data fusion process digital twin model, dynamic process knowledge base, process decision-making and evaluation, machining quality prediction and process feedback optimisation. A thus constructed test bed of TDKIPP and its four application examples about the process planning of a micro turbojet engine integral impeller demonstrate the feasibility and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Shortening product development cycles and fully customisable products pose major challenges for production systems. These not only have to cope with an increased product diversity but also enable high throughputs and provide a high adaptability and robustness to process variations and unforeseen incidents. To overcome these challenges, deep Reinforcement Learning (RL) has been increasingly applied for the optimisation of production systems. Unlike other machine learning methods, deep RL operates on recently collected sensor-data in direct interaction with its environment and enables real-time responses to system changes. Although deep RL is already being deployed in production systems, a systematic review of the results has not yet been established. The main contribution of this paper is to provide researchers and practitioners an overview of applications and to motivate further implementations and research of deep RL supported production systems. Findings reveal that deep RL is applied in a variety of production domains, contributing to data-driven and flexible processes. In most applications, conventional methods were outperformed and implementation efforts or dependence on human experience were reduced. Nevertheless, future research must focus more on transferring the findings to real-world systems to analyse safety aspects and demonstrate reliability under prevailing conditions. [ABSTRACT FROM AUTHOR]

The design of reverse logistics and remanufacturing processes and the recovery of end-of-life products have been well-studied in the literature. Quality, reliability, maintenance and warranty for recovered products and the remanufacturing activities that extend their life are integral issues in reverse logistics. This paper reviews recent and relevant literature on these issues in closed-loop supply chains, with a focus on remanufactured or second-hand products. The published literature is first classified into domain areas of research and practice. The wide array of mathematical tools and techniques used in the literature are then identified and mapped. Finally, the findings are summarised and the main research gaps are highlighted. [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]

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]

This paper deals with the capacity planning and production network planning in plastics processing factories. We lay emphasis on the opportunity and – potentially – necessity to move machines, tools and material. Furthermore, we consider the option to outsource production to external suppliers. Demand is deterministic and has to be satisfied in the same period. We present a linear mixed integer programming model representing the problem to find a minimum-cost production plan where total cost accounts for variable production cost, setup cost, transportation cost, relocation cost and cost caused by machine purchases. The decisions to be made are the allocation of machines to plants. This includes to decide for investments or relocating machines between plants. In addition, the allocation of products to machines and the determination of the corresponding optimal quantities of production, purchase and transportation within the network of suppliers, plants and customers are decided. We provide a computational study validating the model and conduct a sensitivity analysis of the run time using an off-the-shelf solver. Finally, we present a real-world case of an international automobile supplier. Tackling the instance with an off-the-shelf solver the problem cannot be solved in reasonable time. Thus we apply a simple decomposition scheme to provide decision support for the real-world case. [ABSTRACT FROM AUTHOR]

This paper outlines the main idea and approach of the H2020 ASSISTANT (LeArning and robuSt deciSIon SupporT systems for agile mANufacTuring environments) project. ASSISTANT is aimed at the investigation of AI-based tools for adaptive manufacturing environments, and focuses on the development of a set of digital twins for integration with, management of, and decision support for production planning and control. The ASSISTANT tools are based on the approach of extending generative design, an established methodology for product design, to a broader set of manufacturing decision making processes; and to make use of machine learning, optimisation, and simulation techniques to produce executable models capable of ethical reasoning and data-driven decision making for manufacturing systems. Combining human control and accountable AI, the ASSISTANT toolsets span a wide range of manufacturing processes and time scales, including process planning, production planning, scheduling, and real-time control. They are designed to be adaptable and applicable in a both general and specific manufacturing environments. [ABSTRACT FROM AUTHOR]

Sawmills are key elements of the forest product industry supply chain, and they play important economic, social, and environmental roles. Sawmill production planning and control are, however, challenging owing to several factors, including, but not limited to, the heterogeneity of the raw material. The emerging concept of digital twins introduced in the context of Industry 4.0 has generated high interest and has been studied in a variety of domains, including production planning and control. In this paper, we investigate the benefits digital twins would bring to the sawmill industry via a literature review on the wider subject of sawmill production planning and control. Opportunities facilitating their implementation, as well as ongoing challenges from both academic and industrial perspectives, are also studied. [ABSTRACT FROM AUTHOR]

This paper studies the multi-item stochastic capacitated lot-sizing problem with stationary demand to minimise set-up, holding, and backorder costs. This is a common problem in the industry, concerning both inventory management and production planning. We study the applicability of the Proximal Policy Optimisation (PPO) algorithm in this problem, which is a type of Deep Reinforcement Learning (DRL). The problem is modelled as a Markov Decision Process (MDP), which can be solved to optimality in small problem instances by using Dynamic Programming. In these settings, we show that the performance of PPO approaches the optimal solution. For larger problem instances with an increasing number of products, solving to optimality is intractable, and we demonstrate that the PPO solution outperforms the benchmark solution. Several adjustments to the standard PPO algorithm are implemented to make it more scalable to larger problem instances. We show the linear growth in computation time for the algorithm, and present a method for explaining the outcomes of the algorithm. We suggest future research directions that could improve the scalability and explainability of the PPO algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we address a new optimal plate design problem in steel production, in which slab selection is jointly considered. On the basis of the underlying features, the problem is formulated as a mixed integer nonlinear programming (MINLP) model with generalized disjunctive programming (GDP) constraints. A logic-based outer approximation (L-OA) algorithm is proposed to solve the problem. Specifically, a two-stage heuristic method is designed to initialise the L-OA algorithm. Numerical results are presented to demonstrate that the proposed L-OA algorithm and the heuristic method are effective and computationally efficient. [ABSTRACT FROM AUTHOR]

The concept of reconfigurable manufacturing systems (RMSs) is a current subject that has attracted intensive research. This latter covers the entire RMS life cycle, from the design to the exploitation phase, and includes several important problems requiring the use of optimisation. The objective of this paper is to survey research publications related to RMS optimisation problems and their solution methods. For this, the types of RMS and their components are described. Subsequently, relevant objective functions and performance indicators of RMS are presented. In addition, an overview of the most used solution approaches and a classification of optimisation problems are proposed. Finally, a detailed analysis, our conclusions, and suggestions for future research are provided. [ABSTRACT FROM AUTHOR]

In many seasonal industries, customer demands are constantly changing over time, and accordingly the facility layout should be re-optimized in a timely manner to adapt to changing material handling patterns among manufacturing departments. This paper investigates the aperiodic facility layout problem (AFLP) that involves arranging facilities layout and re-layout aperiodically in a dynamic manufacturing environment during a given planning horizon. The AFLP is decomposed into a master problem and a combination set of static facility layout problems (FLPs, the slave problems) without loss of optimality, and all problems are formulated as mixed-integer linear programming (MILP) models that can be solved by MIP solvers for small-sized problems. An exact backward dynamic programming (BDP) algorithm with a computational complexity of O(n2) is developed for the master problem, and an improved linear programming based problem evolution algorithm (PEA-LP) is developed for the traditional static FLP. Computational experiments are conducted on two new problems and twelve well-known benchmark problems from the literature, and the experimental results show that the proposed solution approach is promising for solving the AFLP with practical sizes of problem instances. In addition, the improved PEA-LP found new best solutions for five benchmark problems. [ABSTRACT FROM AUTHOR]

We consider an integrated batching problem for steel-making and continuous casting process of steel plate manufacturing. For steel-making, slab batching problem is defined to make charges from slabs, and for continuous casting, charge batching problem is defined to make casts from charges. Our batching problem is regarded as a combination of slab batching and charge batching problems. Since steel-making and continuous casting are processes that must be synchronised, two batching problems must be integratedly handled. However, because of their difficulties, most papers solved each batching problem sequentially. We deal with a batching problem under bi-strand casting environment where slabs are cast with two strands. We also consider practical aspects that were barely considered before such as slab length difference constraints between strands and casts having few charges. We propose two mixed integer programming models. The first one is to create frames to define the overall structure of the solution and the second one is to fill the created frames with slabs to concretise the solution. The proposed matheuristic algorithm solves the problem in an integrated manner and finds a near-optimal solution in a reasonable time. It outperforms the company's current practice, as shown in experiments with real and randomly generated data. [ABSTRACT FROM AUTHOR]

After grinding, the machine tool spindle with high surface integrity has a significant impact on its subsequent service life. Therefore, it is necessary to create a reasonable process plan for the grinding process of the machine tool spindle. A hybrid method based on case-based reasoning (CBR) and process reasoning (PR) was proposed in this paper. The subjective and objective weights of feature attributes are determined in the CBR by the AHP method and the CRITIC method, respectively, and the similarities between the latest case and the retrieved case are calculated on the basis of the nearest neighbour algorithm. The most suitable process solution can be selected from the case database by means of CBR. PR is applied to solve the problem that the case cannot be retrieved through CBR or is unsatisfactory. In the case of the grinding machine spindle, the applicability of this technology was demonstrated. Consequently, the results revealed that the surface consistency of the spindle after grinding was substantially increased. A decision-making system based on the proposed approach was developed by using Qt 4.8.7 and SQLite 3. The results demonstrate the viability and efficacy of the hybrid CBR-PR method to rapidly generate process planning for specific parts. [ABSTRACT FROM AUTHOR]

This paper deals with the more realistic version of the lot sizing and scheduling problem where we have a single machine that process a number of different products and for each product we have a set of delivery dates and the quantities to deliver at each of these dates. The objective is to minimise the sum of setup costs and inventory holding costs including the holding cost of products from the finish time of each production lot to the next delivery date. In addition, a setup can be carried over from a production run to the immediately succeeding one if the same product is processed during these two runs. A mathematical formulation of this problem is given as well as two specially designed solution heuristics. As the literature does not provide any other method to solve this problem, only the results obtained by these two heuristics are compared. [ABSTRACT FROM AUTHOR]

Forming product platforms is an effective strategy to offer products variety economically. The short products life span and new variants pose a challenge to designing platforms, which satisfy the changing customer demands. There is a need to design platforms that can adapt to changes. In addition, it is important to account for the partially completed platforms inventory held in each production period which could be utilised in subsequent production periods. An important contribution of this work is the use of combined additive and subtractive manufacturing in customising the product platform by adding and/or removing features to suit the changing product features and demands for different product variants. In this paper, a holistic non-linear model is presented for designing optimal multi-period additive/subtractive product platforms and managing their inventory. The model provides the optimal product platform design for each production period, macro process plans for customisation, the number of each platform stored as inventory, and the product variant platform assignment. The initial model is subsequently linearised to reduce computation time. A gear shaft family of products is used as a demonstration example. The model redesigns the product platform as needed to meet the changing demand of each product variant while minimising the costs. [ABSTRACT FROM AUTHOR]

To react on increasing customer demand uncertainty, production systems have to be flexible concerning the provided capacity. With respect to labour, one opportunity to gain such flexibility is to assign workers to different work stations which often require different skills to be operated. Therefore, cross-trained workers are needed to enable this flexibility. Since the qualifying workforce implies costs, a relevant problem is how much skills and what mix of skills is optimal for a production system. In addition, the workforce may be on vacation or have a sick leave and hence is not always available. In this paper, we study the effect of different predefined workforce qualification profiles for a streamlined production system with simulation and compare the results with simulation-based optimisation using a genetic algorithm. Specifically the effect of stochastic worker absence, in comparison to workers being always available, is evaluated for different production system scenarios. The results show that cross-trained workers can significantly improve the service level achieved and that simulation-based optimisation can provide a much better worker specific mix of skills than predefined qualification profiles. Another managerial insight is that there is a trade-off between number of skills and number of workers needed to obtain the same service level. [ABSTRACT FROM AUTHOR]

Decision-making on chatter-free machining parameters is essential for process planning since chatter significantly affects production quality and efficiency. Stability lobe diagram (SLD) is commonly used for selecting chatter-free machining parameters, but its analytical prediction often has poor accuracy and experiment-based prediction is time-consuming. This paper proposes a multi-fidelity (MF) surrogate model and transfer learning-based method to improve the milling stability analysis. Firstly, an analytical stability model is constructed to predict low-fidelity (LF) SLDs for key combinations of radial cutting width (ae) and feed rate per tooth (ft). A few spindle speeds (ns) are selected from each key LF SLD to detect high-fidelity (HF) stability limits (aplim) through milling experiments. Subsequently, sufficient LF and limited HF combinations of ns, ae, ft, and aplim are taken to construct additive scaling function-based MF stability models. Predicted MF combinations of ns, ae, ft, and aplim are combined with limited HF combinations to construct more accurate stability models through transfer learning. Then, a neural network is ultimately trained to predict aplim values for arbitrary combinations of ns, ae, and ft. A detailed experimental validation indicates that the proposed method can provide more accurate lobe boundaries for machining parameters selection by introducing fewer experimental samples. [ABSTRACT FROM AUTHOR]

Catastrophe-related insurance (e.g. business interruption insurance) is an effective financing tool for global corporations to reduce economic losses caused by high impact events. Flexible operational planning is an often-used tool enabling rapid adjustment of operational plans for reducing catastrophe-related damage costs. The interaction between catastrophe insurance and flexible operations planning has rarely been studied. In this paper, we develop a stochastic programming model for a multi-echelon global supply chain network that we solve to investigate the impact of purchasing catastrophe insurance on supply chain operational planning in a catastrophe-prone environment. Computational simulations are developed for evaluating solution quality and measuring catastrophe-related damage costs. We find that it may be optimal for supply chains to scrap redundant products in catastrophes when customer demand falls below the expected level. Purchasing catastrophe insurance may encourage supply chains to scrap more products, which results in more catastrophe-related damages. From analysing supply chain costs, catastrophe-related damage costs, and operational plans, we find that a higher compensation rate of catastrophe insurance triggers more production activities being planned at the vulnerable node just before the vulnerable time period, especially for low residual value products. Finally, we give managerial insights to help reduce unnecessary damages in catastrophes. [ABSTRACT FROM AUTHOR]

Planning decisions are generally subject to some level of uncertainty. In forestry, data describing the resources available have a major impact on operations performance and productivity. This paper aims to present a method to improve decision-making in the forest supply chain by taking supply uncertainty into account using the results of data quality assessments. The case study describes the operations planning process of a Canadian forest products company dealing with an uncertain volume of wood supply. Three approaches to constructing probability distributions based on data quality are tested. Each approach offers a different level of precision: (1) a frequency distribution of accuracy, (2) a normal distribution based on average accuracy, and (3) a normal distribution based on data quality classification. Using stochastic programming to plan transport and production shows that lower costs can be achieved with a general characterisation of the data accuracy. Not considering uncertainty when planning operations leads to a significant replanning transportation cost. Using classes of data quality to include uncertainty in operations planning contributes to reducing the transportation cost from $15.90/m3 down to $15.32/m3 representing 3.6%. [ABSTRACT FROM AUTHOR]

This paper delves into the impacts of an ongoing global crisis on the resilience of supply chains. Furthermore, it proposes measures to address and mitigate the disruptions caused by the prevailing uncertainties. For example, while the economy has started to recover after the pandemic and demand has increased, companies have not fully returned to their pre-pandemic levels. To enhance their supply chain resilience and effectively manage disruptions, one viable strategy is the implementation of flexible/hybrid manufacturing systems. This research is motivated by the specific requirements of Vestel Electronics, a household appliances company, which seeks a flexible/hybrid manufacturing production setup involving dedicated machinery to meet regular demand and the utilisation of flexible manufacturing system (FMS) to handle surges in demand. We employ a scenario-based approach to model demand uncertainty, enabling the company to make immediate and adaptive decisions that take advantage of the cost-effectiveness of standard production and the responsiveness of FMS. To solve the problem, we propose a heuristic algorithm based on column generation. The numerical results demonstrate that our optimisation model provides solutions with an average optimality gap of less than 6% while also reducing the average cost of standard production schemes without FMS by over 12%. [ABSTRACT FROM AUTHOR]

Scheduling is an important decision-making problem in production planning and the resulting decisions have a direct impact on reducing waste, including energy and idle capacity. Batch scheduling problems occur in various industries from automotive to food and energy. This paper introduces the parallel p-batch scheduling problem with batch delivery, content-dependent loading/unloading times and energy-aware objective function. The problem has been motivated by a real system used for freezing products in a food processing company. A mixed-integer linear programming model (MILP) has been developed and explained through a numerical example. As it is not practical to solve large-size instances via a mathematical model, the discrete differential evolution algorithm has been improved (iDDE) and hybridised with the genetic algorithm (GA). A release-oriented vector generation procedure and a heuristic batch formation mechanism have been developed to efficiently solve the problem. The performance of the proposed approach (iDDEGA) has been compared with CPLEX, iDDE and GA through a comprehensive computational study. A case study was conducted based on real data collected from the freezing process of the company, which also verified the practical use and advantages of the proposed methodology. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) introduces a set of technology-specific problems, such as the proper orientation of parts or the placement of several heterogeneous parts in the same build cycle, which are not addressed by traditional approaches to production planning and scheduling. Although these new production subproblems have been implicitly addressed by several works according to generic nesting and scheduling concepts, a literature review revealed that there is no uniformity in identifying and, thus, solving all these subproblems. For this reason, and as a result of an in-depth analysis of the existent literature on AM production planning and an analogy with classic cutting and packing typologies, the present paper offers a framework to formalise the production planning problem in AM at the operational level. This framework can be used as a reference to focus on and address these AM-related problems for efficient production planning. It is designed at the subproblem level and centres on production order processing in AM. A coding strategy is specifically developed for the framework, which is applied to a review of relevant works that propose models for the production planning of AM systems. Finally, the review results are discussed and possible extensions of the framework are proposed. [ABSTRACT FROM AUTHOR]

Production planning and scheduling for companies with divergent processes, where a single component can be transformed into several finished products, are challenging as planners might face material misallocation issues. In this paper, we address the problem of managing a divergent process with DDMRP stock buffers, where different finished products are bottled with the same component having a fixed batch size. An allocation decision needs to be made to determine the quantities of finished products to be bottled. This study is motivated by a real-life problem faced by a dermo-cosmetic company. We compare and analyze by simulation nine different policies triggering allocation decisions. The first policy is the classic DDMRP rule, while the others are new policies, including a virtual buffer of a generic finished product and ConWIP loops, delaying the allocation decision. Our results show that the policy combining the classic DDMRP rule and a ConWIP loop surrounding a part of the process reduces the work-in-process by 34% compared to the classic DDMRP while ensuring high customer service rates and control of flow times. [ABSTRACT FROM AUTHOR]

This paper considers a multi-period multi-product capacitated lot-sizing problem. It develops an integrated predictive maintenance and production planning framework using deep learning and mathematical programming. The objective is to minimise the sum of maintenance, setup, holding, backorder, and production costs, while satisfying the demand for all products over the horizon under consideration. Based on a rolling horizon approach, the model dynamically integrates data-driven predictive maintenance and production planning. The used maintenance policy includes replacements and minimal repairs that are considered as preventive and corrective maintenance, respectively. To select preventive maintenance actions, a long short-term memory model is employed to accurately predict the health condition of the machine. Each rolling horizon consists of ordinary and forecast stages, and by collecting new sensor data, the maintenance and production decisions are simultaneously updated. The resulting integrated framework is validated using a benchmarking data set. The results are compared for different approaches to highlight the advantages of the proposed framework. [ABSTRACT FROM AUTHOR]

Order picking (OP) is one of the most labour- and time-intensive processes in internal logistics. Over the last decades, researchers have developed various mathematical planning models that help to increase the efficiency of OP systems, for example, by optimising storage assignments or by specifying routes for the order pickers that minimise travel distance in the warehouse. Human characteristics that are often a major determinant of OP system performance have, however, widely been ignored in this stream of research. This paper systematically evaluates the literature on manual OP systems and conducts a content analysis to gain insights into how human factors (HF) have been considered and discussed in the scientific literature. The results of the analysis indicate that management-oriented efficiency criteria dominated prior research on OP, and that there is a clear lack of attention to HF in the design and management of OP systems. This poses an opportunity for research and design of manual OP systems. [ABSTRACT FROM AUTHOR]

Supply chain management issues have become increasingly important to the semiconductor industry over the last two decades due to the global distribution of facilities and increasing numbers of firms specialising in particular stages. This series of three papers reviews the literature on modelling and analysis of the larger semiconductor supply chain. After describing the structure of semiconductor supply chains to provide context for the research efforts, we propose a classification scheme for the relevant literature. The remainder of this paper (Part I) then focuses on Strategic Network Design models for this industry, supply chain coordination through contracting and semiconductor supply chain simulation. Part II discusses Demand Planning, Inventory Management and Capacity Planning, while Part III addresses Master Planning, Production Planning and Demand Fulfilment. [ABSTRACT FROM AUTHOR]

This paper studies a common planning problem encountered in steelmaking production. The problem is to group different customer orders into a set of batches to accommodate the mass production mode of steelmaking furnaces. We formulate the problem as a novel mixed-integer programming model by considering the practical technological requirements. To solve the problem, we propose a cooperative coevolution framework in which an effective decomposition scheme based on graph partitioning is developed. The decomposition scheme first explores the problem structure by considering the production process rules and then exploits the batch information of the best-so-far solution to identify the potentially better decompositions. To solve each decomposed subcomponent, we propose a new differential evolution algorithm which incorporates a subpopulation-based classification mechanism and local search with an external archive strategy to balance the abilities of exploration and exploitation. Computational tests on a set of real production data as well as on a more diverse set of randomly generated problem instances show that our method is effective and efficient in practical application and outperforms other benchmark algorithms. [ABSTRACT FROM AUTHOR]

Manufacturing in the age of Industry 4.0 can be characterised by a high product variety and complex material flows. The increasing individualisation of products requires adaptive production planning and control systems. Research in the area of Machine Learning demonstrates the applicability and potential of Reinforcement Learning (RL) systems for the control of complex manufacturing. However, a major disadvantage of RL-methods is that they are usually considered as 'black box' models. For this reason, this paper investigates methods of explainable reinforcement learning in production control. Based on a comprehensive literature review an approach to increase the plausibility of RL-based control strategies is presented. The approach combines the advantages of high prediction accuracy (e.g. neural networks) and high explainability (e.g. decision trees). In doing so, understandable control strategies such as heuristics can be generated, and an advanced RL-system can be designed including specific domain expertise. The results are demonstrated based on a real-world system, taken from semiconductor manufacturing, which is investigated in a simulated approach. [ABSTRACT FROM AUTHOR]

Due to various factors of flexibility introduced into manufacturing systems, researchers have gradually shifted their focus to the integrated process planning and scheduling (IPPS) problem to improve productivity. The previous literature rarely associates IPPS with constraint programming, even though constraint programming has achieved success in the scheduling field. Furthermore, existing approaches are usually customized to certain types of IPPS problems and cannot handle the general problem. In this paper, with a view to obtaining the optimal AND/OR graph automatically, a depth first search generating algorithm is designed to convert the type-1 IPPS problem into our approach's standard input format. Moreover, we propose an approach based on enhanced constraint programming to cope with the general problem, employing advanced schemes to enhance the constraint propagation and improve the search efficiency. Our approach is implemented on ORTOOLS, and its superiority is verified by testing on 15 benchmarks with 50 instances. Experimental results indicate that 41 instances are solved optimally, among which the optimality of the solutions for 20 instances is newly confirmed, and the solutions of six instances are improved. Our approach is the first method to reach the overall optimum in the most influential benchmark with 24 instances. [ABSTRACT FROM AUTHOR]

Introduction of digital solutions has made dentistry more efficient and effective in response to patients' demands. Moreover, digital solution helps participants to manage daily workflow simpler than traditional practices. Scanners, CAD/CAM software, CNC machines and 3D printers are components of digital solutions in current dentistry. Responding to patients as quickly as possible is essential in medical fields and dentistry has similar situations. On the other hand, demands in dentistry are highly customized that it makes managing orders more difficult because each order needs special design and production. Thus, this paper assesses orders in digital dentistry and develops a mathematical model to optimize production planning and scheduling of orders by considering different objectives and requirements that are common in dentistry. In addition, a metaheuristics algorithm was developed based on PSO to respond to NP-hard challenges of the model. The developed algorithm includes two steps that PSO metaheuristics consider in the second step to search new solutions. Finally, through case studies, performance of the developed model and proposed algorithm was investigated. Developed algorithm generates solutions with proper quality considering makespan and total completion time objectives and required time to obtain a solution with high quality depends on problem data and can increase. [ABSTRACT FROM AUTHOR]

This paper deals with improving the lead-time performance of a large crystal manufacturer that uses a state-of-the-art commercial Enterprise Resource Planning system. Since the company encountered some limitations of the standard production planning and control (PPC) system it sought for improvements by implementing an order release mechanism based on workload control (WLC). WLC employs certain rules for releasing orders in order to maintain a certain level of work in process to achieve a certain utilisation of the production system and thus control the flow times in order to meet the required due dates of the orders. We describe the successful implementation of an order release mechanism based on the WLC concept in this make-to-stock company. The paper describes the implemented order release mechanism, the implementation process and its impact on the company’s performance. We show that the core function of WLC - the order release mechanism - can be integrated successfully into an existing PPC system. Furthermore, this study highlights the applicability of WLC to a wider range of companies, especially to make-to-stock manufacturers. [ABSTRACT FROM AUTHOR]

The key to radical innovation lies in technological breakthroughs, and the key to technical breakthroughs lies in the application of cross-domain knowledge. However, in the conceptual design stage of new product development, the designer's cognition and knowledge are limited and the retrieval and evaluation of cross-domain technologies is often subjective, making it difficult to generate radical concepts. To address the above issues, this paper proposes a method for cross-domain technology recommendation based on patent analysis. In this method, feature words in various domains of patents are used to generalise the abstract functions of product core subsystems, where designers can obtain more comprehensive cross-domain technologies. This is conducive to breaking the design fixation of designers. Subject–Action–Object structural and functional similarity are combined to quickly extract cross-domain technologies in patents. Then multiple technical indicators are used to objectively evaluate and rank cross-domain technologies. This approach helps designers objectively select cross-domain technologies to change the way next-generation products work, enabling early planning of production conditions and a head start in the market. The method has been verified in a new product development project on peeling equipment, a new product that meets radical indicators is finally obtained. [ABSTRACT FROM AUTHOR]

Efficient resource management methods are essential for spare parts used in the maintenance and repair of equipment. Forecasting plays a critical role in planning, especially under demand uncertainty. Existing works regarding spare parts with intermittent demand focus on the mere forecasting model while integrating the planning and forecasting models are not sufficiently investigated. We examine the interaction between two models to optimise planning and forecasting decisions and prevent sub-optimality. This paper presents two mathematical models, including a planning model that determines stock level, spare part order assignment to suppliers, equipment repair assignment, and the number of intervals over the planning horizon. The second model is the forecasting model by Support Vector Machine (SVM). Considering uncertainty, demand estimation is performed by piecewise linearisation considering the optimal number of intervals in the planning model used in forecasting. An interactive procedure is developed to optimise models. We use an empirical investigation from an oil company providing the spare part supply chain data. The analyses show that demand estimation by piecewise method and integrating the decisions optimises the cost, improves the forecasting accuracy, and planning performance. Moreover, we offer several insights to practitioners that shed light on spare part planning and forecasting decisions. [ABSTRACT FROM AUTHOR]

In this paper, a practical relevant operational production planning problem in complex manufacturing systems is addressed. In this problem, lots are planned individually to provide a more detailed plan than approaches that only consider production quantities. A three-step approach, which is currently fully integrated and used in a Decision Support System, is then introduced. This work follows the one of Mhiri et al. [2018. "Heuristic Algorithm for a WIP Projection Problem at Finite Capacity in Semiconductor Manufacturing." IEEE Transactions on Semiconductor Manufacturing 31 (1): 62–75] who addressed this problem. We push the approach a step further by introducing new optimisation possibilities through new smoothing rules, whose performance is studied according to different indicators. Furthermore, we present the production planning process in which the decision support tool is embedded and how it bridges the gap between the upper and lower planning levels. [ABSTRACT FROM AUTHOR]

In this paper, we address a model for supply chain coordination. There are m manufacturers modelled as single machines, each of which processes a specific set of jobs (products). After processing is completed, jobs are batched, and batches are shipped to a customer by means of vehicles. The problem consists in concurrently finding a production schedule of the jobs, a partition of jobs into delivery batches and an assignment of delivery batches to vehicles, so that jobs are delivered within their deadlines and total costs are minimised. We focus on a scenario characterised by fixed departure times and inventory holding costs. For each departure time there is a given number of vehicles, possibly having limited capacity. Each job incurs a cost proportional to the time from job completion to delivery departure. In this paper, we show that the problem is NP-hard even for a very restricted case, and report various polynomiality results for two scenarios, namely: (i) when the production sequence of each manufacturer is fixed in advance, and (ii) when there is a single manufacturer and processing times are all equal to 1. We also point out several open problems. [ABSTRACT FROM AUTHOR]

Managing production level after the launch of a new product is a challenging problem and is critical to the overall profit of manufacturing firms. The problem involves concepts from different fields including production planning, manufacturing flexibility, forecasting, and marketing. In this paper, the gaps in the existing literature are first illustrated. With the goal of addressing the identified research gaps, agent-based modeling and simulation is employed to analyze the performance of different production planning strategies under various levels of volume flexibility and consumer social network structures. The key distinguishing feature of the developed model is the capability of the manufacturing firm to adjust its production level by forecasting the future demand. The analysis of the simulation outputs yields substantial results by challenging some intuitive and traditional understandings of manufacturing systems. The paper also provides a discussion on managerial implications of the results in order to provide the managers with guidelines on the implementation of the model in real-world diffusion environments. [ABSTRACT FROM PUBLISHER]

This paper addresses a joint production and maintenance problem under environmental constraints and reliability issues in a manufacturing/remanufacturing context. The manufacturing system is composed of one machine producing one type of product. The remanufacturing system, also composed of one machine, retrieves returned products from the market in order to refurbish them. The manufacturing and remanufacturing systems aim to satisfy random demands under a given service level. Moreover, the entire system generates harmful emissions. Exceeding carbon emission limits defined by authorities may risk sanctions. We aim to propose a compromise between ecologic and economic production and maintenance plan by calling on green subcontracting in order to satisfy the demand and avoid emission excess. Three models are proposed in this paper. These models tackle mainly the basic production problems and propose alternative equivalent solution schemas for future extensions. The robustness and usefulness of the proposals are illustrated with various examples and sensitivity analyses. [ABSTRACT FROM AUTHOR]

Production and distribution are the two primary internal elements of the supply chain. This paper presents a systematic literature review (SLR) of quantitative approaches for the integration of production and distribution planning (IPDP) in the supply chain. We provide a classification framework with eight dimensions and give a brief overview of the quantitative approaches such as modelling approaches and solution approaches for the IPDP problem in the supply chain. The SLR has been carried out using two basic search databases Scopus and Web of Science. In all, we identify relevant articles in the period from 2000 to 2019. We also highlight certain research opportunities, suggestions, and research gaps for possible future research by assessing the current knowledge on the quantitative approaches for IPDP problems in the supply chain. [ABSTRACT FROM AUTHOR]