Industrial Instances Research Articles

Unravelling the carbon emissions compliance in sustainable supply chains: The impacts of carbon audit cooperation

As one of the 17 Sustainable Development Goals, reducing carbon emissions is crucial to combat climate change. It has also prompted companies to comply with emission regulations and evaluate the environmental impacts of their supply chains. Yet, news and reports occasionally highlight industrial instances of noncompliance. In particular, supplier’s noncompliance is often mistakenly attributed to its downstream manufacturers. Due to this misconception, manufacturers might conduct audits to protect their reputation and sales. Moreover, because a supplier may provide components to multiple competing manufacturers, they may collaborate to share audit findings regarding the common supplier’s compliance with carbon emissions regulations. However, studies do not reveal how this audit cooperation affects stakeholder interests. Here, we introduce a stylised model to examine the effects of carbon audit cooperation on the environment, competing manufacturers, and their supplier. We identify two main effects: the free-riding and amplifying effects. The former benefits the supplier but harms the environment and competing manufacturers, while the latter presents the opposite effect. The net impact depends on the balance between these two effects, which challenges conventional beliefs about carbon emissions compliance and highlights the importance of sustainability in the industry. Finally, we explore various extensions to validate the robustness of our findings.

Discrete cutting path problems: a general solution framework and industrial applications

The optimal tool routing for cutting machines, also known as cutting path optimisation is an important problem in production research. This problem is relevant in various manufacturing environments such as aeronautic, automotive, garment and semiconductor industries. In this paper, we introduce a general solution framework for the discrete Cutting Path Problem which includes: (i) the universal approach to reduce numerous settings of this problem to the appropriate auxiliary instances of the well-known Precedence Constrained Generalized Traveling Salesman Problem; (ii) the proposition of efficient solution methods for finding (sub-) optimal solutions. We carry out extensive computational experiments in order to evaluate performance of the proposed framework and the obtained results demonstrate its efficiency for real-life industrial instances.

Automated placement of analog integrated circuits using priority-based constructive heuristic

This paper presents a heuristic approach for solving the placement of Analog and Mixed-Signal Integrated Circuits. Placement is a crucial step in the physical design of integrated circuits. During this step, designers choose the position and variant of each circuit device. We focus on the specific class of analog placement, which requires so-called pockets, their possible merging, and parametrizable minimum distances between devices, which are features mostly omitted in recent research and literature. We formulate the problem using Integer Linear Programming and propose a priority-based constructive heuristic inspired by algorithms for the Facility Layout Problem. Our solution minimizes the perimeter of the circuit’s bounding box and the approximated wire length. Multiple variants of the devices with different dimensions are considered. Furthermore, we model constraints crucial for the placement problem, such as symmetry groups and blockage areas. Our outlined improvements make the heuristic suitable to handle complex rules of placement. With a search guided either by a Genetic Algorithm or a Covariance Matrix Adaptation Evolution Strategy, we show the quality of the proposed method on both synthetically generated and real-life industrial instances accompanied by manually created designs. Furthermore, we apply reinforcement learning to control the hyper-parameters of the genetic algorithm. Synthetic instances with more than 200 devices demonstrate that our method can tackle problems more complex than typical industry examples. We also compare our method with results achieved by contemporary state-of-the-art methods on the MCNC and GSRC datasets.

Linear Lexicographic Optimization and Preferential Bidding System

Some airlines use the preferential bidding system to construct the schedules of their pilots. In this system, the pilots bid on the different activities and the schedules that lexicographically maximize the scores of the pilots according to their seniority are selected. A sequential approach to solve this maximization problem is natural: The problem is first solved with the bids of the most senior pilot, and then it is solved with those of the second most senior without decreasing the score of the most senior, and so on. The literature admits that the structure of the problem somehow imposes such an approach. The problem can be modeled as an integer linear lexicographic program. We propose a new efficient method, which relies on column generation for solving its continuous relaxation and returns proven optimality gaps. To design this column generation, we prove that bounded linear lexicographic programs admit “primal-dual” feasible bases, and we show how to compute such bases efficiently. Another contribution on which our method relies is the extension of standard tools for resource-constrained longest path problems to their lexicographic versions. This is useful in our context because the generation of new columns is modeled as a lexicographic resource-constrained longest path problem. Numerical experiments show that this new method is already able to solve to proven optimality industrial instances provided by Air France, with up to 150 pilots. By adding a last ingredient in the resolution of the longest path problems, which exploits the specificity of the preferential bidding system, the method achieves for these instances computational times that are compatible with operational constraints. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0372 .

Electroconductometer via Arduino for measurement in aqueous suspensions

Real-time electroconductivity monitoring is important in several industry instances. However, this monitoring in aqueous solutions or suspensions (e.g. mineral slurries), applying direct current, is problematic due to electrode polarization, which is the accumulation of ions at the electrode-solution interface. In this study, a low-cost circuit developed is based on Arduino, allowing real-time measurement. It has annular electrodes and operates at a frequency of 1.0 kHz, simulating an alternating current signal, in order to diminish the deleterious effect of interfacial polarization and electrolysis on the electrodes. Through calibration using standard KCl solutions, a measurement range of 0.002 S/m to 1,000 S/m was obtained with good accuracy and precision. Its use is exemplified here by predicting the porosity of suspended binary granular systems.

Solving a 3D bin packing problem with stacking constraints

We consider a novel variant of the three-dimensional bin packing problem (3DBPP) arising from a maritime shipping port. Unlike the classical bin packing problem, our 3DBPP requires all items to be stacked vertically during the packing process. To tackle this problem effectively, we divide it into two subproblems: the stack packing problem (SPP) and the two-dimensional bin packing problem (2DBPP). We first formulate the SPP as an integer programming model to minimize the total bottom area of the stacks, which is further solved by a branch-and-price method. Then, in the 2DBPP, to pack all stacks into a minimum number of bins, we follow the left-down most extreme point placement strategy. An encoder–decoder model was developed to optimize the packing sequence and orientation of the stacks simultaneously. Industrial instances from shipping ports were collected and extended. The computational results show that our two-stage algorithm performs well in solving the stacking-constrained 3DBPP.

Solving a Continent-Scale Inventory Routing Problem at Renault

This paper is the fruit of a partnership with Renault. Their reverse logistic requires solving a continent-scale multiattribute inventory routing problem (IRP). With an average of 30 commodities, 16 depots, and 600 customers spread across a continent, our instances are orders of magnitude larger than those in the literature. Existing algorithms do not scale, so we propose a large neighborhood search (LNS). To make it work, (1) we generalize existing split delivery vehicle routing problems and IRP neighborhoods to this context, (2) we turn a state-of-the-art matheuristic for medium-scale IRP into a large neighborhood, and (3) we introduce two novel perturbations: the reinsertion of a customer and that of a commodity into the IRP solution. We also derive a new lower bound based on a flow relaxation. In order to stimulate the research on large-scale IRP, we introduce a library of industrial instances. We benchmark our algorithms on these instances and make our code open source. Extensive numerical experiments highlight the relevance of each component of our LNS. Funding: This work was supported by Renault Group. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0342 .

Spatial–Temporal Load Balancing and Coordination of Multi-Robot Stations

Cycle time minimization in multi-robot manufacturing stations is computationally challenging. This is due to the many aspects that need to be accounted for, including assigning process tasks to robots, specifying robot configurations at tasks, sequencing, planning motions, and coordinating the robots to avoid collisions. Hence, to find good solutions, often some assumptions are made and/or the problem is divided into subproblems—often limiting the set of solutions with the risk of excluding the best ones. In this study, we generalize the completely disjoint solution method that challenges the so-called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">shortest path</i> assumption, i.e., to let each robot use its shortest collision-free motion between any two configurations, regardless of the other robots. We devise a generalized method called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">spatial–temporal load balancing and coordination</i> , which prevents robot–robot collisions by a sequence of disjoint solutions, guiding task assignments, sequences, and robot motions (path and velocity). We study both artificial and industrial instances. For some of them, our suggested method is superior to methods based on the shortest path assumption, with as much as a 28% reduction in cycle time. Moreover, for problem instances with no special structure, we establish that the shortest path assumption is often reasonable. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work is motivated by a particular industrial problem instance of a spot-welding station with two robots and where welds are placed along the edge of a workpiece. Due to the special geometry of the instance one robot can only perform welds in the middle of the edge and the other only at the ends. As a result, if the robots use their shortest motions between welds, then waiting times are required to prevent collisions. Moreover, the tasks are too close to each other to allow for a completely disjoint solution. Hence, we suggest a method based on sequence of disjoint solutions.

Robust decision trees for the multi-mode project scheduling problem with a resource investment objective and uncertain activity duration

In this paper, we advocate the use of robust decision trees for a multi-mode resource constrained project scheduling problem with uncertain activity duration and a resource investment objective, coming from an industrial assembly line scheduling application. This work takes place in the context of multi-stage optimization where uncertainty is revealed progressively across a succession of decision time points. In a robust decision tree, a node represents a robust partial schedule from the time origin to a specific decision time point. At this point, the decision maker has access to some information, which partitions the uncertainty scenario set, yielding for each scenario subset a child node and an associated extended partial robust schedule up to the next decision point. Considering that the level of uncertainty is lowered, the new partial schedule is less conservative and improves the robustness guarantee. However, since all accessible information may not be relevant, we turned the information selection part into an optimization problem. An algorithm is proposed to solve the robust decision tree problem. Experimentation is provided to study the influence of decision tree parameters as well as highlighted recommendations. The interest of the decision tree is shown through an experimental comparison with classical approaches of the literature on benchmark instances and industrial instances.

From atomic attrition to mild wear at multi-asperity interfaces: The wear of hard Si3N4 repeatedly contacted against soft Si

Wear causes surfaces to be irreversibly damaged, thereby incurring significant economic cost, for instance in the semiconductor industry. Much progress has been made in describing wear at single asperity interfaces between silicon based materials (Si, SiOx, Si3N4), translating the fundamental understanding of wear into wear predictions and control over wear. Yet, predicting and controlling wear at industrially relevant multi-asperity interfaces remains a challenge, especially when considering the wear of the harder material subjected to repeated, nanometric scale displacement. We studied pre-sliding Si3N4-on-Si wear using the atomic force microscopy topography difference method and showed that the harder Si3N4 wears through either atomic attrition or ductile removal enhanced by subsurface damage, depending on the magnitude of the local Si3N4-on-Si contact pressure. Our methods and results bridge fundamental insight into wear based on nanoscale studies to industrial applications.

A two-step methodology for product platform design and assessment in high-variety manufacturing

The delayed product differentiation (DPD) recently rose as a hybrid production strategy able to overcome the main limits of make to stock (MTS) and make to order (MTO), guaranteeing the management of high variety and keeping low storage cost and quick response time by using the so-called product platforms. These platforms are a set of sub-systems forming a common structure from which a set of derivative variants can be efficiently produced. Platforms are manufactured and stocked following an MTS strategy. Then, they are customized into different variants, following an MTO strategy. Current literature proposes methods for platform design mainly using optimization techniques, which usually have a high computational complexity for efficiently managing real-size industrial instances in the modern mass customization era. Hence, efficient algorithms need to be developed to manage the product platforms design for such instances. To fill this gap, this paper proposes a two-step methodology for product platforms design and assessment in high-variety manufacturing. The design step involves the use of a novel modified algorithm for solving the longest common subsequence (LCS) problem and of the k-medoids clustering for the identification of the platform structure and the assignment of the variants to the platforms. The platforms are then assessed against a set of industrial and market metrics, i.e. the MTS cost, the variety, the customer responsiveness, and the variants production cost. The evaluation of the platform set against such a combined set of drivers enhancing both company and market perspectives is missing in the literature. A real case study dealing with the manufacturing of a family of valves exemplifies the efficiency of the methodology in supporting companies in managing high-variety to best balance the proposed metrics.

A criterion space decomposition approach to generalized tri-objective tactical resource allocation

We present a tri-objective mixed-integer linear programming model of the tactical resource allocation problem with inventories, called the generalized tactical resource allocation problem (GTRAP). We propose a specialized criterion space decomposition strategy, in which the projected two-dimensional criterion space is partitioned and the corresponding sub-problems are solved in parallel by application of the quadrant shrinking method (QSM) (Boland in Eur J Oper Res 260(3):873–885, 2017) for identifying non-dominated points. To obtain an efficient implementation of the parallel variant of the QSM we suggest some modifications to reduce redundancies. Our approach is tailored for the GTRAP and is shown to have superior computational performance as compared to using the QSM without parallelization when applied to industrial instances.

Aggregating disjoint partial sub-orders – an internal logistics application

The problematic addressed in this work concerns an internal logistics issue that arises in warehousing. It first concerns the aggregation of a several ordered flows of loads (cartons, totes, containers, etc.) into a single flow, called the load sequencing problem. Then, this flow should be injected into the system at a maximal throughput, which gives the load injection problem. From the theoretical perspective, the load sequencing problem belongs to the class of partial-order aggregation problems, known to be NP-Hard. We describe a use-case application in logistics and formulate it in mathematical terms as a specific partial-order aggregation problem, namely disjoint partial sub-orders aggregation. We then present two exact solution methods in detail, one based on integer linear programming and the other using dynamic programming combined with a Branch & Cut scheme. All methods perform very well for randomly generated industrial instances. At the end, we provide a heuristic based on the dynamic programming algorithm. This heuristic is implemented for industrial use and gives near-optimal solutions in a very short computation time. The load injection problem falls in the category of scheduling problem (Job shop problem). In practice, this comes to compute the injection dates of loads in the collector with goal of ensuring a maximal debit exit flow with respect to the capacity of the system. We provide the formula for the injection date which achieves maximal debit exit flow. With respect to the application in hand, the proposed method outperforms the existing used method in terms of flow management and brings in practice a significant increase (up to 20%) of a number of loads proceeded for the same amount of time. Our findings are supported by numerical results.

An improved Pareto local search for solving bi-objective scheduling problems in hot rolling mills

Hot rolling scheduling is a complex decision-making optimization problem in the production management of the iron and steel industry. Comprehensively considering temporal and technical constraints of hot rolling production, we formulate a prize-collecting capacitated vehicle routing problem with time windows (PC-CVRPTW) involving special constraints and multiple objectives. Furthermore, this paper develops a Pareto local search (PLS)-based solving algorithm via leveraging problem-specific properties and implements three improvement mechanisms: (1) solution initialization with a greedy strategy; (2) promising solution selection with optimistic hypervolume contribution; (3) hybrid neighborhood exploration integrated variable neighborhood search with constraint programming. To validate the proposed method, computational experiments are conducted on a set of randomly synthetic and realistic industrial instances. The results compared with other variants of PLS and state-of-art algorithms demonstrate that the improved PLS algorithm is effective and can provide promising solutions for bi-objective hot rolling scheduling problems.

Fracture Characterization of Advanced High Strength Steel USS CR980XG3 Using Phase-Field Diffusive Approach in Ductile Solids

In this work, the utilization of a phase-field ductile fracture model in the failure analysis of advanced steels is investigated. The importance of advanced steels is potentially proven, for instance in automotive industry, due to its light weight, which entails the crucial role of fracture analysis in these structures. A third generation advanced high strength USS CR980XG3™️ AHSS material is considered to perform fracture analyses. For this purpose, the necessary data regarding the stress distribution and fracture patterns from digital image correlation tests are utilized for subsequent numerical experimentations.A recent phase-field model of ductile fracture is employed herein for the analysis of crack advance in this class of materials. The significance of the choice of material properties using this model is shown through the analysis of an experimental fracture benchmark called Shear Fracture specimen, through assessment of crack evolution and force diagrams.

Scale-Free Random SAT Instances

We focus on the random generation of SAT instances that have properties similar to real-world instances. It is known that many industrial instances, even with a great number of variables, can be solved by a clever solver in a reasonable amount of time. This is not possible, in general, with classical randomly generated instances. We provide a different generation model of SAT instances, called scale-free random SAT instances. This is based on the use of a non-uniform probability distribution P(i)∼i−β to select variable i, where β is a parameter of the model. This results in formulas where the number of occurrences k of variables follows a power-law distribution P(k)∼k−δ, where δ=1+1/β. This property has been observed in most real-world SAT instances. For β=0, our model extends classical random SAT instances. We prove the existence of a SAT–UNSAT phase transition phenomenon for scale-free random 2-SAT instances with β&lt;1/2 when the clause/variable ratio is m/n=1−2β(1−β)2. We also prove that scale-free random k-SAT instances are unsatisfiable with a high probability when the number of clauses exceeds ω(n(1−β)k). The proof of this result suggests that, when β&gt;1−1/k, the unsatisfiability of most formulas may be due to small cores of clauses. Finally, we show how this model will allow us to generate random instances similar to industrial instances, of interest for testing purposes.

Robust optimization of a bi-objective tactical resource allocation problem with uncertain qualification costs

In the presence of uncertainties in the parameters of a mathematical model, optimal solutions using nominal or expected parameter values can be misleading. In practice, robust solutions to an optimization problem are desired. Although robustness is a key research topic within single-objective optimization, little attention is received within multi-objective optimization, i.e. robust multi-objective optimization.This work builds on recent work within robust multi-objective optimization and presents a new robust efficiency concept for bi-objective optimization problems with one uncertain objective. Our proposed concept and algorithmic contribution are tested on a real-world multi-item capacitated resource planning problem, appearing at a large aerospace company manufacturing high precision engine parts. Our algorithm finds all the robust efficient solutions required by the decision-makers in significantly less time than the approach of Kuhn et al. (Eur J Oper Res 252(2):418–431, 2016) on 28 of the 30 industrial instances.

The Global Carbon Footprint and How New Carbon Mineralization Technologies Can Be Used to Reduce CO2 Emissions

Carbon dioxide is a byproduct of our industrial society. It is released into the atmosphere, which has an adverse effect on the environment. Carbon dioxide management is necessary to limit the global average temperature increase to 1.5 degrees Celsius and mitigate the effects of climate change, as outlined in the Paris Agreement. To accomplish this objective realistically, the emissions gap must be closed by 2030. Additionally, 10–20 Gt of CO2 per year must be removed from the atmosphere within the next century, necessitating large-scale carbon management strategies. The present procedures and technologies for CO2 carbonation, including direct and indirect carbonation and certain industrial instances, have been explored in length. This paper highlights novel technologies to capture CO2, convert it to other valuable products, and permanently remove it from the atmosphere. Additionally, the constraints and difficulties associated with carbon mineralization have been discussed. These techniques may permanently remove the CO2 emitted due to industrial society, which has an unfavorable influence on the environment, from the atmosphere. These technologies create solutions for both climate change and economic development.

Reinforcement Learning of Dispatching Strategies for Large-Scale Industrial Scheduling

Scheduling is an important problem for many applications, including manufacturing, transportation, or cloud computing. Unfortunately, most of the scheduling problems occurring in practice are intractable and, therefore, solving large industrial instances is very time-consuming. Heuristic-based dispatching methods can compute schedules in an acceptable time, but construction of a heuristic allowing for a satisfactory solution quality is a tedious process. This work introduces a method to automatically learn dispatching strategies from only a few training instances using reinforcement learning. Evaluation results obtained on real-world, large-scale instances of a resource-constrained project scheduling problem taken from the literature show that the learned dispatching heuristic generalizes to unseen instances and produces high-quality schedules within seconds. As a result, our approach significantly outperforms state-of-the-art combinatorial optimization techniques in terms of solution quality and computation time.

Minimizing saw cycles on the cutting stock problem with processing times depending on the cutting pattern

In this paper the cutting stock problem minimizing saw cycles is considered. In this context where objects can be cut simultaneously the trade-off between raw material and machine utilization costs is of major interest. Different processing times for each cutting pattern are considered, according to the number of items on it, different from previous works of the literature. The optimization aims to minimize both machine utilization costs due to the saw cycles and raw material usage costs. An integer mathematical formulation is proposed, and a solution method based on a column generation procedure is used. Computational tests were performed to assess the impact on costs for illustrative and real industry instances. A set of instances were also randomly generated to analyse the behaviour of the outcome of the problem in face of different technical parameters.