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Parcel hub (PH), as one of the logistic system entities, plays a significant role in delivering packages. PH operations are identical to a cross-docking system. Numerous studies were conducted to examine solutions in PH and cross-docking in general. This paper aims to investigate scientific references published within the last fifteen years through a systematic literature review (SLR). The objectives are to determine the critical decisions related to PH problems, classify the applied solution approach, and identify the future challenges regarding the transformation towards intelligent logistics. This study implements an SLR methodology that consists of five stages: planning, searching, screening, analysing, and reporting. This study utilises two publication databases and one reference management system. More than four thousand documents were collected in the searching stage. Then, five assessments are conducted to sort the most relevant papers. One hundred and forty-six articles are included in the further analysis. This study classifies four important decisions associated with PH problems: truck scheduling, internal process planning, facility planning, and technology selection. In addition, this study categorises ten solution approaches employed to find the best decision in the prior research. Moreover, this study explains the future research directions and managerial implications of managing parcel hub systems. [ABSTRACT FROM AUTHOR]

This paper deals with a problem of tactical planning of production activities. It considers the case of the world's largest phosphate production site, with several mines, several washing plants, and a drying plant, with transport of merchantable phosphate ores (MOs) by rail or pipeline. The tightening of product quality requirements by customers prevents from continuing to plan its production with the current rules. Currently, the plants are managed quite independently, source ores (SOs) are pre-assigned to the MOs for blending, and the MO production routings in the washing plants are predetermined. We propose a model that ensures flexibility in the production of MOs through the choice of dynamic blends and routings. Our approach goes beyond any pre-allocation of resources and aims at synchronising the flows of the washing plants. This specific problem of blending production and planning has not been addressed in the literature. Still, it is widely encountered in many mining industries and offers perspectives for the application of the approach proposed. Numerous tests show that this model ensures the quality constraints related to the composition of several chemical components, with a generalised use of the most economical routings while ensuring horizontal and vertical alignment of decisions. [ABSTRACT FROM AUTHOR]

This study conducts a comprehensive analysis of deep reinforcement learning (DRL) algorithms applied to supply chain inventory management (SCIM), which consists of a sequential decision-making problem focussed on determining the optimal quantity of products to produce and ship across multiple capacitated local warehouses over a specific time horizon. In detail, we formulate the problem as a Markov decision process for a divergent two-echelon inventory control system characterised by stochastic and seasonal demand, also presenting a balanced allocation rule designed to prevent backorders in the first echelon. Through numerical experiments, we evaluate the performance of state-of-the-art DRL algorithms and static inventory policies in terms of both cost minimisation and training time while varying the number of local warehouses and product types and the length of replenishment lead times. Our results reveal that the Proximal Policy Optimization algorithm consistently outperforms other algorithms across all experiments, proving to be a robust method for tackling the SCIM problem. Furthermore, the (s, Q)-policy stands as a solid alternative, offering a compromise in performance and computational efficiency. Lastly, this study presents an open-source software library that provides a customisable simulation environment for addressing the SCIM problem, utilising a wide range of DRL algorithms and static inventory policies. [ABSTRACT FROM AUTHOR]

Lot-sizing and scheduling in a job shop environment is a fundamental problem that appears in many industrial settings. The problem is very complex, and solutions are often needed fast. Although many solution methods have been proposed, with increasingly better results, their computational times are not suitable for decision-makers who want solutions instantly. Therefore, we propose a novel greedy heuristic to efficiently generate production plans and schedules of good quality. The main innovation of our approach represents the incorporation of a simulation-based technique, which directly generates schedules while simultaneously determining lot sizes. By utilising priority rules, this unique feature enables us to address the complexity of job shop scheduling environments and ensures the feasibility of the resulting schedules. Using a selection of well-known rules from the literature, experiments on a variety of shop configurations and complexities showed that the proposed heuristic is able to obtain solutions with an average gap to Cplex of 4.12%. To further improve the proposed heuristic, a cooperative coevolutionary genetic programming-based hyper-heuristic has been developed. The average gap to Cplex was reduced up to 1.92%. These solutions are generated in a small fraction of a second, regardless of the size of the instance. [ABSTRACT FROM AUTHOR]

The use of Additive Manufacturing (AM) technology has largely increased in the last years. Because of its large differences from conventional technologies, the use of AM in production systems might call for new strategies in production planning and control. To this aim, this paper proposes analytical models to predict aggregate performance measures such as flow time, work in process, and production throughput, for production systems characterised by Laser Powder Bed Fusion AM technology. These indicators could be used both in operations strategy development and in technology comparison. The proposed models differentiate for their detail of the analysis and the number of input parameters that need to be estimated. The results show that the level of detail of the model affects the analysis leading to quite different values of the performance measures, especially in the case of highly saturated systems. Also, a discussion about the applicability of the proposed model to other AM technologies show whether and to what extent the proposed models can be applied for modelling other AM technologies. [ABSTRACT FROM AUTHOR]

Cloud Manufacturing (CMfg) is a promising approach that leverages the sharing economy to reduce costs and enhance supply chain flexibility. Particularly, when utilised alongside Additive Manufacturing (AM), CMfg is considered a key enabler for collaborative production (CP) systems. However, there is still a lack of planning models that reduce entry barriers for CP. Therefore, we propose a decentralised CP planning framework for AM. In our approach, machines autonomously select jobs from an existing production plan to forward them to other suppliers that can produce these parts more efficiently. A CMfg platform facilitates job forwarding and creates promising part bundles and manufacturing machines autonomously places bids on the packages via a combinatorial $ 2^{nd} $ 2 nd price reverse auction. Costs of the reallocated bundles are shared throughout a Shapley value-based approach without the need to disclose critical information. We benchmark our proposed framework against a centralised planning approach and find that it achieves comparable effectiveness as the benchmark solution. We also show that this mechanism promotes individual rationality and that agents particularly benefit when participating in both offering and acquiring production jobs through the auction. [ABSTRACT FROM AUTHOR]

Assemble-to-order approaches deal with randomness in demand for end items by producing components under uncertainty, but assembling them only after demand is observed. Such planning problems can be tackled by stochastic programming, but true multistage models are computationally challenging and only a few studies apply them to production planning. Solutions based on two-stage models are often short-sighted and unable to effectively deal with non-stationary demand. A further complication may be the scarcity of available data, especially in the case of correlated and seasonal demand. In this paper, we compare different scenario tree structures. In particular, we enrich a two-stage formulation by introducing a piecewise linear approximation of the value of the terminal inventory, to mitigate the two-stage myopic behaviour. We compare the out-of-sample performance of the resulting models by rolling horizon simulations, within a data-driven setting, characterised by seasonality, bimodality, and correlations in the distribution of end item demand. Computational experiments suggest the potential benefit of adding a terminal value function and illustrate interesting patterns arising from demand correlations and the level of available capacity. The proposed approach can provide support to typical MRP/ERP systems, when a two-level approach is pursued, based on master production and final assembly scheduling. [ABSTRACT FROM AUTHOR]

The lot-sizing problem aims at determining the products to be produced and their quantities for each time period, which is a difficult problem in production planning. This problem becomes even more complicated when practical aspects such as limited production capacity, bill of materials, and item substitution are considered. In this paper, we study a new variant of the lot-sizing problem, called the multi-level capacitated lot-sizing problem with substitution and backorder. Unlike previous studies, this variant considers substitutions at both the product and component levels, which is based on the real needs of manufacturers to increase planning flexibility. Backorders are allowed, but should be delivered within a certain time limitation. We formulate this problem using a mathematical programming model. A matheuristic approach is proposed to solve the problem. This first generates an initial feasible solution using a relax-and-fix algorithm, and then improves it using a hybrid fix-and-optimise algorithm. The proposed algorithm is calibrated with a full factorial design of experiments, and its efficiency is well validated. Finally, through extensive numerical experiments, we analyse the properties of this new lot-sizing problem, such as the effect of substitution options, and the influence of backorder time limitation, and provide several useful managerial insights for manufacturing companies to save costs in production planning. [ABSTRACT FROM AUTHOR]

Given the high demand for energy in the manufacturing industry and the increasing use of renewable but volatile energy sources, it becomes increasingly important to coordinate production and energy availability. With the help of incentive-based programmes, grid operators can incentivise consumers to adjust power demand in critical situations such that grid stability is not threatened. On the consumer side, energy-efficient scheduling models can be used to make energy consumption more flexible. This paper proposes a bi-objective job-shop scheduling problem with variable machine speeds that aims on minimising the total energy consumption and total weighted tardiness simultaneously. We use a genetic algorithm to solve the model and derive Pareto frontiers to analyse the trade-off between both conflicting objectives. We gain insights into how incentive-based programmes can be integrated into machine scheduling models and analyse the potential interdependencies and benefits that result from this integration. [ABSTRACT FROM AUTHOR]

This paper highlights the tight relationship between the picking and packing processes in warehouse management and the need to consider them as an integrated problem. The study describes and models this integrated problem as a mixed-integer programming model, to optimise overall labour costs by determining the assignment of the subsets of orders, i.e. batches, for picking and packing. To address the issue of model complexity, the paper presents a statistical-based framework for generating approximate models and selecting the optimal one through examination. Based on the examination results, a pair-swapping heuristic is additionally proposed to be combined as a hybrid algorithm. Numerical experiments based on a real-world case demonstrate the effectiveness of the framework-proposed and selected hybrid algorithm by comparison with other framework-proposed approximate models, a solver, and existing heuristics. Our findings indicate that the combined usage of integrated picking and packing processes planning and the hybrid algorithm proposed and selected within the statistical-based framework can effectively reduce the cost of warehouse management. [ABSTRACT FROM AUTHOR]

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A production plan gives the quantity of products to release on the shop floor in each period, where a period may represent a week or a month. The plan is the basis for negotiating order acceptance and delivery dates with customers and suppliers. The production plan must respect the available capacity on the shop floor, as underloading the shop floor leads to a loss of opportunity, and thus, a loss of competitiveness for the company. To properly manage the production capacity while negotiating with suppliers and customers, the production planners need a tool to accurately estimate the capacity consumption in each period. The computation of capacity consumption requires creating a detailed production schedule which is a complex task. Algorithms that find close to optimal schedules in a complex manufacturing environment are often time-consuming, which is impractical in a negotiation context. We investigate machine learning models to predict capacity consumption. We consider a flexible job-shop as commonly encountered in practice, and proposed several machine learning models. Namely, several variants of linear regression, decision trees, and artificial neural networks. Numerical experiments showed that our models outperform those found by both an exact approach and dispatching rules when computation time is short. [ABSTRACT FROM AUTHOR]

To optimise production planning, maintenance strategies, and quality control in imperfect manufacturing systems, most existing works set the buffer stocking time at the cycle start time. However, excess inventory holding costs will be incurred if the buffer is stocked too early. This means that while the abovementioned setting can make the optimisation model simple, it might increase costs. Taking the uncertain practical buffer stocking time of an imperfect manufacturing system into account, this study aimed to find the production cycle, maintenance frequency, quality inspection cycle, and number of inspections that will minimise the expectation unit cost of the system. To this end, we developed a new model to optimise production, maintenance, and quality control considering timely replenishment. First, excess inventory holding and shortage costs were considered and the production process was divided into five scenarios based on the buffer stocking time and inspection time for assignable causes. Second, the link between production, maintenance, and quality was addressed by capturing the dynamic and random behaviour of production systems. An x-bar control chart was integrated into the model to monitor quality. A case study and sensitivity analysis were undertaken to verify the effectiveness and superiority of the proposed optimisation strategy. [ABSTRACT FROM AUTHOR]

This work considers an industrial production site partially powered by a decentralised energy system based on intermittent renewable energy sources. Our objective is to simultaneous plan the industrial production and the energy supply in this site so as to minimise the total cost. A new way of modelling this combinatorial optimisation problem is proposed: it relies on the extension of a multi-product single-resource small-bucket lot-sizing model called the proportional lot-sizing and scheduling problem. This extension involves among others sequence-dependent changeover times overlapping multiple periods and energy-related constraints. Our numerical results show that the resulting mixed-integer linear programming model enables to obtain good-quality production and energy supply plans with a computational effort much smaller than the one required by a previously published large-bucket lot-sizing model. [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]

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]

Order release optimisation is essential in production planning, especially in discrete manufacturing. Order release planning models with load-dependent lead times must anticipate the time-dependent work-in-process and output for any given release schedule and thus require an anticipation model that approximates the time-dependent behaviour of queueing systems. We present a generic optimisation model for order release planning in stochastic, non-stationary manufacturing systems that includes a well-defined interface for the anticipation model. We develop two stationary backlog carryover (SBC) approaches to approximate time-dependent queueing behaviour and prove their consistency with the order release model. The resulting nonlinear programming model is shown to be a special case of the well-known clearing function models. A numerical study demonstrates that the optimised order releases for different demand patterns are close to the optimum that results from simulation-based optimisation even for extreme demand and release patterns. The resulting output closely matches the simulated output with some deviations. [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]

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]

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]

Manufacturing companies strive to identify and manage the effects of unexpected disruptions (risks) on their production processes, which affect their performance and resilience. In this study, we propose a decision framework to capture the impact of interconnected risk sources, on the efficiency of manufacturing companies. The proposed framework utilises a novel mixed-integer linear programming (MILP) model to minimize the time of satisfying the orders while it considers the risk associated with suppliers and manufacturers. The MILP model considers the relationships among (i) material and suppliers and (ii) work centers to measure the propagation of risks throughout the production system. The proposed framework also utilises the Monte Carlo simulation to calculate the associated likelihood of delay and the distribution of the delivery time of orders. To show the complication of the propagation of risk, two distinct scenarios are compared. The first scenario considers zero risks, while the second one assigns probabilistic risk to the suppliers and work centers. The results highlight the magnitude and the complexity of the risk propagation from various interconnected sources through the production system. It also identifies the most vulnerable components of the production system affected more by various types of risk. [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]

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]

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]

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]

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]

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]

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]

We study the single item capacitated lot sizing problem with multiple resources and periodic carbon emission constraints that impose an upper bound for the average emission per product produced in any period. Although the uncapacitated version of this problem can be solved in polynomial time, generalisation of the problem including the resource capacities is NP-Hard, in general. We present important structural properties for the optimal solutions of the problem. We consider the special cases with two resources and under non-speculative costs, construct the piecewise linear total production cost function when the resource capacities, and the emission and cost parameters are time-invariant, and develop a polynomial time dynamic programming algorithm (DP) to solve them. Then, we generalise the procedure to construct the total production cost function and the DP for the general setting with fixed number of capacitated resources. We test our algorithm for different problem instances, and compare it with a commercial solver and a DP available in the literature for solving the lot sizing problem with piecewise concave production cost functions. The results reveal that our DP outperforms the other one, and it performs better than the commercial solver when the number of breakpoints of the total production cost function is small. [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]

While humans typically saccade every ~250 ms in natural settings, studies on vision tend to prevent or restrict eye movements. As it takes~50 ms to initiate and execute a saccade, this leaves only~200 ms to identify the fixated object and select the next saccade goal. How much detail can be derived about parafoveal objects in this short time interval, during which foveal processing and saccade planning both occur? Here, we had male and female human participants freely explore a set of natural images while we recorded magnetoencephalography and eye movements. Using multivariate pattern analysis, we demonstrate that future parafoveal images could be decoded at the feature and category level with peak decoding at ~110 and ~165 ms, respectively, while the decoding of fixated objects at the feature and category level peaked at~100 and~145 ms. The decoding of features and categories was contingent on the objects being saccade goals. In sum, we provide insight on the neuronal mechanism of presaccadic attention by demonstrating that feature- and category-specific information of foveal and parafoveal objects can be extracted in succession within a ~200 ms intersaccadic interval. These findings rule out strict serial or parallel processing accounts but are consistent with a pipeline mechanism in which foveal and parafoveal objects are processed in parallel but at different levels in the visual hierarchy. [ABSTRACT FROM AUTHOR]

Driven by globalization and diverse market demands, modern multi-echelon supply chains face significant challenges. Effective coordination of resources and information flows between levels is crucial for improving supply chain efficiency and competitiveness. Traditional scheduling methods often fail to capture inter-level synergies and struggle with the dynamic changes and uncertainties in supply chains. To overcome these issues, a coordination strategy based on discrete event simulation was proposed. This strategy includes a multi-objective optimization model that focuses on production efficiency, inventory cost, and delivery time. Discrete event simulation models the production and scheduling processes, enhancing decision support for complex environments. This approach aims to boost the overall operational efficiency and flexibility of the supply chain, providing substantial theoretical and practical benefits. [ABSTRACT FROM AUTHOR]

Gender considerations have been part of climate change adaptation planning in the Global South for the last two decades. Despite this, studies have reported a gap in understanding how organisations incorporate people's diverse experiences of climate risks into planning and implementing adaptation strategies, particularly for women disproportionately impacted by climate risks. Taking the case of Bangladesh, this study contributes to this knowledge gap by exploring the representation of power in organisational decision‐making arenas concerning adaptation planning. The investigation involved an analysis of five major national adaptation guidelines and 22 projects conducted in Bangladesh, in addition to in‐depth interviews with 36 development practitioners. This article argues that adaptation planning is a top‐down organisational process in Bangladesh. Decisions are undertaken in 'inner circles' involving experts, bureaucrats and top officials of major national NGOs and often fail to include the voices of diverse social groups affected by intersecting inequalities, including ethnicity, disability, religion, locality and, in particular, gender. The study elaborates on the need for a major change to planning and decision‐making processes to achieve adaptation planning and strategies that effectively reflect women's diversified and localised realities and allow them to respond to climate risks adequately. [ABSTRACT FROM AUTHOR]

This paper aims to design and integrate virtual power plant (VPP) into a geographically dispersed production-distribution network for attaining environmental sustainability goal. The VPP is comprised wind turbines, photovoltaics, combined heat and power, and energy storage units. The proposed VPP-based supply chain design model makes strategic allocations on renewable capacity as well as scheduling production, inventory, transportation, and energy generation and trading decisions. The model represents a first-of-its-kind in planning a multi-layer supply chain network in which renewable-based electric-thermal cogeneration is coordinated in production-climate nexus. The study yields the following findings. First, products with larger energy use opt to be made in locations with stronger sunshine or wind profile despite higher transportation costs. Second, the time-of-use rate greatly stimulates the installation of onsite photovoltaics and energy storage. Third, under flat utility rate, reducing energy storage cost has limited benefit to the cost savings of the supply chain operations. [ABSTRACT FROM AUTHOR]

Milling is the primary machining method for manufacturing thin-walled workpieces. Due to the low rigidity, these workpieces are easy to deform during the milling process, which significantly affects machining accuracy. This paper proposes a fusion model to predict the milling deformation based on the U-NET neural network and the physical model. Firstly, a deformation measurement experimental platform is designed to capture the deformation distribution on the surface of the thin-walled workpiece. Secondly, an approximate solution for the theoretical deformation caused by axial cutting force is obtained based on the elastic deformation behavior of the thin-walled workpiece. At last, the captured original deformation and the theoretical deformation are then used as inputs for the fusion model to predict the relative deformation distribution of the machined thin-walled workpiece. The end milling experiment of the thin wall cavity is conducted to validate the accuracy of the prediction results and the effectiveness of the fusion model, the deformation in the milling process is predicted by the U-NET model and fusion model, and then compared with the measured deformation. The result shows that the proposed fusion model achieved an average root mean square error of less than 2.65% on the normalized dataset, providing accurate predictions close to measurements and with the speed of the physical model. Furthermore, compared to the data-driven U-NET model, the fusion model exhibited significant improvement in prediction performance on the test set, demonstrating good physical interpretability and generalization ability. The fusion model is used to realize the high-precision and high-speed milling deformation prediction, to guide the milling process planning of thin-walled parts, provide a basis for realizing the online machining compensation of thin-walled parts, and help to improve the machining quality and processing efficiency of thin-walled parts. [ABSTRACT FROM AUTHOR]

A proper investigation of geomechanical properties of reservoir sediments allows accurate prediction of both magnitude and direction of different stress regimes. These are crucial for hydrocarbon production and development particularly in deformed and structurally controlled petroliferous basins. The rift basins in the north Western Desert (NWD) provide analogues for these structurally controlled prolific regions, where the syn-rift Jurassic sedimentary successions host prolific reservoir targets. However, a detailed assessment of the geomechanical behavior of the Jurassic reservoirs has never been investigated. Thus, the present study utilizes wireline log data to determine the geomechanical properties of the syn-rift Jurassic facies of the Khatatba Formation in Shushan Basin in the NWD. Mechanical earth model was constructed and formation microimager logs (FMI) were interpreted for to investigate the geomechanical behavior and wellbore stability of the studied deformed facies. The stress direction was determined from FMI by identifying drilling-induced fractures, breakout zones and formation fractures. Resistive, conductive and partially conductive fractures are oriented in NE–SW, ESE–WSW, NW–SE, NE–SW and SW–NE directions coinciding with the Jurassic syn-rift structures. The ENE–WSW direction of breakout and minimum horizontal stress (SHmin) are the same as that of the primary fault in the study region contrasting with the NNW–SSE direction of maximum horizontal stress (SHmax) and induced fractures. The present study highlights the significance of integrating wireline logging results in interpreting the critical and non-critical stress orientations which are necessary for optimal production plans in structurally controlled prolific basins around the globe. [ABSTRACT FROM AUTHOR]

Integrating the United Nations Sustainable Development Goals (SDGs) into national legislation includes a need for their localization. The authors posit that this concept of localizing the SDGs is achieved if the goals are appended to an existing policy process with local implications, termed a "policy vehicle." For this study, Q-methodology was used to gather local perspectives on the legislative process for coastal planning in Norway (the "policy vehicle"), the "proxy" legislation through which the SDGs are localized for the case study municipality of Andøya, Norway. The overall aim of the study was to understand potential pathways for enabling approaches to societal transformations where focus is placed on fostering human agency and capacities. The authors demonstrate how Q-methodology can be applied for enhanced stakeholder engagement in local decision-making processes as a starting point to enable social transformations for sustainability in a social-ecological system. [ABSTRACT FROM AUTHOR]

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In the realm of production systems, determining the optimal segment allocation remains a central concern. While several existing models address this issue, a significant gap remains as many overlook the critical role of innovation and lack a holistic perspective. This paper presents a model that emphasizes innovation capabilities and introduces the concept of a "Technology Multiplier" underscoring the compounding influence of technology and innovation on production segment allocation decisions. Within this work, we focus on preliminary studies to establish the "Technology Multiplier" concept employing an Analytical Hierarchy Process (AHP) with sensitivity analysis. The validity of our approach is demonstrated through four case studies from three industries, illustrating the relevance of our elaborated metrics for the concept of "Technology Multipliers". In particular, a leading automotive company uses our findings to reach a more appropriate strategic decision aligned with innovation and production growth, compared to its previous decisions. These results not only demonstrate a robust fit with our proposed metrics but also indicate that our framework lays the foundation for further research on the "Technology Multiplier", enriching the decision-making process for production segment allocation. [ABSTRACT FROM AUTHOR]

Collaborative and participatory planning processes that are interactive, thoughtful, and efficient have become increasingly important elements of post-disaster recovery. Meanwhile, rapid action remains critically important to quickly restore destroyed urban systems, but creating long-term resilience demands new ideas that are also implementable. These various tensions create a daunting paradox, and while many communities have attempted to use collaborative planning approaches in recovery, useful assessments and guidance on collaborative recovery planning is sparse. To better understand the nuances of a collaborative recovery, we document and evaluate the effectiveness of the collaborative processes behind the planning of the ESCR resilience project, initiated as BIG U in New York City, which emphasizes community participation and collaborative decision-making. The project is unique because of its innovative and interactive multi-stakeholder processes. The authors have studied the case longitudinally over the past decade, using a variety of research methods to gather relevant information. The ESCR collaborative efforts suggest that, first, there is a disconnect in the dominant rationalities behind climate resilience efforts between the planning and implementation phases. Second, given the disconnect between the planning and implementation phases, collaborative efforts must be refined to remain relevant in the implementation phase. Third, even after project implementation, there are different types of rationality employed, and thus varying levels of satisfaction, depending on the benefits that participants receive. While planners must recognize the disconnect between collaborative planning and project implementation and seek ways to mitigate any abrupt shifts, we conclude that collaborative approaches to recovery planning can be effective and manageable, even in unique recovery situations. [ABSTRACT FROM AUTHOR]

The use of Third-Party Logistics (TPL) is a common practice among manufacturing companies seeking to increase profitability. However, the tender process in selecting a TPL service provider can be challenging, requiring significant effort from both the tendering company and the service provider. The latter must meticulously plan processes and calculate pricing positions while running the risk of losing the bid. This risk impedes verifying logistical feasibility and comparing different logistic concepts extensively, such as layouts, which are often work-intensive. With the ongoing progress of research toward automatic simulation model generation for material flow, it is left to answer whether such approaches can improve the planning processes of TPL service providers by using planning data to generate simulation models. Therefore, this work presents a system with an underlying ontology to generate material flow simulations by developing a model transformation methodology. The system's functions are tested to determine whether they can support the planning process using defined case studies that cover everyday planning decisions. As a result, the system is capable of verifying the performance of planned logistic systems with minimal manual modelling efforts. This encompasses the evaluation of alternative logistical concepts for configuring the planned systems. [ABSTRACT FROM AUTHOR]

Optimization of the machines allows you to increase their productivity and reduce the labor costs for performing individual elements of the technological cycle of their work in the forest. In this regard, the authors offer theoretical studies that take into account the peculiarities of the work of one of the most popular logging machines today, namely, a machine for collecting and forwarding logs (forwarder). The article discusses the technological features of the functioning of these machines, identifies ways to reduce the cost of collecting logs, justifies the rational width of apiaries, which allows collecting logs with a minimum number of movements between work positions. The main purpose of the study is to substantiate the mathematical dependence for determining the rational values of such interrelated parameters of the forwarder's operation as the distance between the working positions of collecting logs and the width of apiaries. The authors propose an analytical approach to solving the problem. On the basis of the standard technological scheme of the harvester's movement through the swath, geometric regularities of the trajectory of the manipulator movement, dimensional characteristics and location of the prepared logs are revealed. As a result of the work, mathematical dependencies are constructed that make it possible to determine the rational values of the desired parameters. The results can be used by research organizations in planning the production process of logging operations. The introduction of author's recommendations into production will contribute to improving the efficiency and operation of forestry equipment. [ABSTRACT FROM AUTHOR]

This paper focuses on lean management in Engineering-To-Order (ETO) companies. The objective is to streamline the management of an order and to reduce the time to market, from order acceptance to delivery. To this aim, we propose a visual tool that allows a better organisation and coordination of all the activities carried out by the departments involved in the management of an order. The proposed tool is a readaptation of the 'scrumban' methodology, which originated from an 'action research-based project', carried on in cooperation with an important ETO manufacturer. Specifically, using this implementation as use case, the proposed tool and its benefits are analysed in detail, showing that it allows to: (i) reorganise and operate the shop floor in a lean way, (ii) encourage a continuous improvement of the manufacturing processes and, above all, (iii) achieve a full coordination of physical and information flows. A step-by-step implementation framework is also provided, aiming to support and guide managers and practitioners willing to introduce visual tools, for a lean-oriented management of shop floor activities in ETO. [ABSTRACT FROM AUTHOR]