Reasonable CPU Time Research Articles

Dynamic fluid flow model for phosphate slurry pipeline: OCP main pipeline as case study

The transportation of phosphate slurry through large-scale pipelines presents significant challenges due to the complex behavior of multiphase flows, particularly with varying solid content, density, and dynamic viscosity. Efficient and accurate prediction of flow behavior is critical for optimizing the operation of such pipelines. This work aims to develop a dynamic computational model to simulate phosphate slurry flow in pipelines. The case study focuses on the OCP Group’s main slurry pipeline, which links the mining sites at Khouribga to the industrial plants at Jorf Lasfar, Morocco. This pipeline system spans a total length of 187.124 km, consisting of 5237 pipes with an inner diameter ranging from 0.8546 m to 0.8578 m, and features several elevation changes along its ground level. Using a section-averaged, dynamic approach and the Finite Volume scheme, the model computes essential flow parameters, including density, dynamic viscosity, and pressure, for the incompressible and non-Newtonian slurry and process water flows. The model’s accuracy is validated against on-site measured data, showing an average deviation of ±0.32% for outlet density, and below 10% for pressure along the pipeline, underscoring the model’s reliability. Additionally, a sensitivity analysis was conducted to illustrate the impact of key parameters on the predicted head losses and pressures along the pipeline. This analysis shows that the slurry viscosity is the most critical parameter, significantly influencing these predictions. This model provides high accuracy and reasonable CPU time for real-time simulation and monitoring, while also offering significant potential for optimizing pipeline operations and ensuring the reliability of phosphate transport.

A MILP-based approach to address the production and distribution planning of large industrial gas supply chains

This paper focuses on addressing the Production Routing Problem (PRP) of industrial gas supply chains (SC). The work introduces a Mixed-Integer Linear Programming (MILP) based approach to effectively tackle this challenging problem. The proposed framework aims to increase company profits by optimizing both production and distribution activities simultaneously. For the distribution part, a route generation algorithm is employed to initially generate a set of feasible routes, ensuring that only routes with practical significance are considered. Subsequently, the mathematical model is developed considering only the set of routes generated. The formulation includes decisions associated with plants production levels, plant selection to supply the different consumers, clients to be visited per trip and day, quantity to be delivered and routes to be used. As part of the problem, it is considered that trucks can make several trips per day, as well as trips of several days. To take into account the last feature, the approach accounts for the explicit effect of the lead time on production and distribution decisions. In this way, the work allows for different delivery times for customers included in the same route, according to their inventories and usage levels. The model considers fleet unavailability until they return to the plants after finishing a trip. Finally, two case studies, one motivating and the other industrial size, are presented and solved. The results obtained demonstrate the effectiveness of the proposed approach to solve the case studies in reasonable CPU times.

Possible application of quantum computing in the field of ocean engineering: optimization of an offshore wind farm layout with the Ising model

AbstractAs a possible application of quantum computing in the field of ocean engineering, an attempt is made to identify the optimum layout of an offshore wind farm with the quasi-quantum annealing method while assuming a wake model that conforms to the standard Jensen–Katic wake model but can be implemented in the Ising model. The optimum layouts obtained in the present study are compared with those identified with other conventional methods such as the genetic algorithm. It is confirmed that the quasi-quantum annealing method works as well as other methods in identifying the optimum layouts, while the optimum layout of an offshore wind farm composed of even more than 100 wind turbines can be identified with reasonable CPU time.

An Internet-of-Things-Based Dynamic Scheduling Optimization Method for Unreliable Flexible Manufacturing Systems under Complex Operational Conditions

The dynamic scheduling problem (DSP) in unreliable flexible manufacturing systems (UFMSs) with concurrency, conflicts, resource sharing, and sequential operations is a complex optimization problem that requires the use of efficient solution methodologies. The effectiveness of scheduling UFMSs relies on the quality of equipment maintenance. Currently, UFMSs with consistently large queues of parts awaiting service employ a repair-after-failure approach as a standard maintenance procedure. This method may require unexpected resources, incur costs, consume time, and potentially disrupt the operations of other UFMSs, either partially or fully. This study suggests using a predictive maintenance (PdM) strategy that utilizes the Internet of Things (IoT) to predict and avoid early mechanical equipment failures before they happen in UFMSs, thereby reducing unplanned downtime and enhancing reliability. Therefore, the objective of this paper is to construct timed Petri net (TPN) models using the IoT for the PdM configuration of mechanical equipment in the dynamic scheduling problem of UFMSs. This necessitates that users represent the specific problem using TPNs. The process of PN modeling requires the utilization of domain knowledge pertaining to the target problems as well as to machine information. However, it is important to note that the modeling rules for PNs are straightforward and limited in number. Consequently, the TPN model is applied to generate and formulate mixed-integer linear programming (MILP) instances accurately. This is done to identify the optimal production cycle time, which may be implemented in real-life scenarios. Several UFMS instances are used to demonstrate the applications and effectiveness of the proposed method. The computational results demonstrate that the proposed method shows superior solution quality, effectively solves instances for a total of 10 parts and 6 machines, and achieves a solution in a reasonable CPU time.

Twitter-sentiment analysis of Moroccan diabetic using Fuzzy C-means SMOTE and deep neural network

Effectively managing diabetes as a lifestyle condition involves fostering awareness, and social media is a powerful tool for this purpose. Analyzing the content of tweets on platforms like Twitter can greatly inform health communication strategies aimed at raising awareness about diabetes within the Moroccan community. Unfortunately, the corpus of tweets is imbalanced and the feature extraction leads to data sets with a very high dimension which affects the quality of sentiment analysis. This study focused on analyzing the content, sentiment, and reach of tweets specifically related to diabetes in Morocco. The proposed strategy processes in five steps: (a) data collection from Twitter platforms and manual labilization, (b) feature extraction using TF-IDF technique, (c) dimension reduction using deep neural network, (d) data balancing using Fuzzy C-Means SMOTE, and (e) tweets classification using five well-known classifiers. The proposed approach was compared with the classic system, which works directly on very large, unbalanced tweets. In terms of recall, precision, F1-score, and CPU time, the proposed system can perform highly accurate sentiment analysis in a reasonable CPU time.

A multi-agent resource bidding algorithm for order acceptance and assembly job shop scheduling

This study uses an agent-based approach with a combinatorial auction mechanism to solve the joint order acceptance and assembly job shop scheduling problem. A set of jobs is offered. Each job has a revenue, ready time, due date, deadline, and consists of a set of operations with precedence relationships. Jobs that deviate from their due dates incur earliness/tardiness penalties. An operation may require several units of capacity per time unit and a resource could have multiple units of capacity. The manufacturer can reject any job to satisfy the capacity constraints and maximise the overall profit. We develop a mathematical model for the problem, then use an agent-based approach to solve it. First, the relaxed problem is decomposed into a set of job-level subproblems. Each job is optimised individually without considering the capacity constraints. Profitable jobs at the individual level submit their optimal schedules as combinatorial bids to an auctioneer to acquire combinations of resource capacity-time units. Then, the auctioneer records the profit upper bound, resolves capacity conflicts to reach a feasible solution, records the profit lower bound, and updates the dual variables. Experimental results show that the proposed methodology can solve large-sized problems in reasonable CPU times.

Integrated optimization of assortment, inventory and pricing considering omnichannel retailer’s risk aversion and customer’s time preference

This paper mainly discusses the joint optimization of product assortment, inventory and pricing for an omnichannel retailer under uncertain demand, seeking to maximize the retailer’s Worst-case Conditional Value-at-Risk (WCVaR) based profit and customer’s expected utility. The risk aversion of decision-maker and the time preference of customers are depicted by the WCVaR and quasi-hyperbolic discounting function, respectively. A bi-objective stochastic optimization model is developed. To alleviate the computational burden, a linearization method is applied to convert the problem into a mixed integer linear programming that proves to be solvable within reasonable CPU times using the augmented ε-constraint method. Numerical studies are conducted to investigate the applicability of the proposed model and the efficiency of the solution approach. Our experimental results show that offering customers with high expected utility typically requires low prices and small assortments. We also make other counterintuitive observation that the price of products sometimes increases when the expected utility increases. Furthermore, WCVaR has superiority compared with the risk-neutral model in terms of robustness and stability. The time preference of customers has non-negligible impacts on decision-making results. In addition, the further sensitivity analyses investigate the impacts of various key parameters on the performance of omnichannel retailing.

Numerical estimation via remeshing and analytical modeling of nonlinear elastic composites comprising a large volume fraction of randomly distributed spherical particles or voids

We investigate numerically via finite element (FE) simulations and analytically the nonlinear behavior of a soft matrix comprising a large volume fraction (up to 55%) of monodisperse spherical inclusions, which can be either rigid particles or voids. To address the issue of severe mesh distortion at large strains, we employ a successive remeshing and solution mapping technique that allows us to achieve a large macroscopic deformation, with the maximum attained stretch being at least two to three times when compared to that without remeshing. A general algorithm allowing to read complex arbitrary particle geometries is proposed and implemented allowing to remesh a finitely strained domain with randomly distributed inclusions of arbitrary shape. The present simulation results for a neo-Hookean elastic matrix filled with rigid particles show that the nonlinear stress–stretch uniaxial tension response can be well approximated by a neo-Hookean material with an effective shear modulus that depends on the volume fraction of the inclusions. Additionally, a study of the number of particles demonstrates that sixty four monodisperse spheres is a good compromise to reach accurate results for large deformations and maintain a reasonable CPU-time. The FE data serve as a powerful assessment tool for analytical homogenization models. Newly developed and existing models for small and large strains are proposed and assessed, respectively, for both rigid particles and voids. These results fill the gap for large volume fractions of inclusions and serve as a reference for the homogenization of the microstructures of interest.

Shipment planning and safety stock placement in maritime supply chains with stochastic demand and transportation times

This paper introduces and studies shipment planning and safety stock placement in bulk and semi-bulk maritime supply chains. This problem jointly optimizes the assignment of destinations to origins, shipment quantities of multiple products by determining the share of the contract by sea transport options, sailing speed, and multi-echelon safety stocks with the objective to minimize the expected total transportation and inventory cost. In the case of a serial system supplying a single product, we analytically characterize the effect of sailing speed and different time and cost parameters on optimal safety stock placement. For the general case, the integrated problem can be solved in four different ways. We present a mixed integer nonlinear program (MINLP) and the superior reformulation of the mixed integer conic quadratic integer program (MICQP) that can be solved using state-of-the-art solvers. Then, we propose two exact solution procedures that iteratively approximate the nonlinear terms in the objective function and constraints. Our numerical experiments illustrate the interdependence between safety stock placement, assignment decisions, and sailing speed. Results also show that it is important to adopt multi-stage inventory optimization and there is an added value in integrating shipment planning and safety stock placement. Furthermore, computational experiments demonstrate that all the solution approaches are alternatives to solving small instance sizes. For medium and large instances, the solvers are unable to provide quality solutions within one hour while the iterative procedures find optimal or near-optimal solutions in reasonable CPU times.

The transportation problem with packing constraints

We address a new variant of the transportation problem, where a set of different commodities, available at several supply nodes with different prices, are distributed to demand nodes using a set of heterogeneous vehicle fleets available at each supply node. Since each commodity has a specific weight, there is a packing constraint due to the total capacity of each vehicle. The objective of this problem is to minimize the total supply cost, which includes the purchase cost of commodities and the fixed transportation cost based on the traveled distance. This novel variant of the transportation problem poses computational challenges due to its combinatorial structure. To tackle these challenges and solve large-scale instances to near-optimality in reasonable CPU times, we propose a variable neighborhood search algorithm. The proposed algorithm partitions the problem into smaller, more manageable problems to be solved. In order to initialize this methodology, we also propose a decomposition heuristic that prescribes initial feasible solutions in CPU times ranging from 0.6 s to 133.2 s on average with average optimality gaps ranging from 0.09% to 4.62%. Our proposed methodology provides solutions with 0.06% to 3.78% optimality gaps on average within average CPU times ranging from 0.2 s to 386.2 s, while the commercial solvers are unable to find integer solutions or to solve most of the instances to optimality within one CPU-hour time limit.

Ordered Escape Routing via Assignment of Routability-Driven Detouring Paths

In board-level routing, ordered escape routing becomes a key problem. In this paper, given a set of escape pins inside a pin array, a set of available boundaries and the capacity constraint between two adjacent pins, an efficient algorithm can be proposed to solve the routing problem in ordered escape routing under capacity constraints. Firstly, based on the mapping process of the detouring paths of the untangled nets in one-sided net untangling, the routability-driven pins of the untangled nets can be assigned onto feasible positions and the target pins of the escape nets can be assigned on some available boundaries. Furthermore, based on the assignment of the routability-driven detouring paths with the capacity consideration, the global wires of the escape nets in a pin array can be assigned in single-layer global routing and the routing paths of the escape nets in a pin array can be assigned in single-layer detailed routing. Compared with Luo’s SAT-based algorithm, Jiao’s flow-based algorithm and Yan’s heuristic algorithm for the 6 tested examples with capacity as 1, our proposed algorithm can obtain the same 100% routability of the escape nets. Compared with Yan’s heuristic algorithm for the other 6 tested examples with capacity as 2, our proposed algorithm can use reasonable CPU time to improve 1.9% of the total length of the routed escape nets and 0.25% of routability of the routed escape nets on the achievement of the 100% routability on the average.

New nurse scheduling model considering nurses’ seniority and the possibility of consecutive shifts under COVID-19 (A real case study)

ABSTRACT The nurse scheduling problem is the assignment of nurses to different working shifts while considering the existing constraints and rules. In recent years, medical centers have faced the challenge of resource planning due to the COVID-19 pandemic. In this study, a new mathematical model is formulated for nurse scheduling under the pandemic. In this model the nurses are categorized based on seniority and the auxiliary nurses are used because of the staff shortage. Apart from the hospital rules, conditions imposed during the COVID-19 pandemic are also considered. The proposed model is formulated in such a way that undesirable shifts are minimized, and also the violation of the schedule in the case of the possible absence of a nurse is prevented due to the model's robustness. The formulated model is applied in a real case in Iran, and the Genetic Algorithm (GA) is used as the solution method. To justify the Genetic Algorithm, different examples are generated and solved using this algorithm and the exact method. The results indicate that the Genetic Algorithm can provide near-optimal solutions with a mean optimality gap of 0.65% in a reasonable CPU time. The resulting timetable minimizes undesirable shifts and the utilization of auxiliary nurses.

Scheduling and Routing of Mobile Charging Stations With Stochastic Travel Times to Service Heterogeneous Spatiotemporal Electric Vehicle Charging Requests With Time Windows

This article presents an alternative service of mobile charging stations for the large-scale charging of electric vehicles, which consider the spatiotemporal heterogeneity of charging requests. As charging infrastructure is the key determinant for the large-scale adoption of electric vehicles, state-of-the-art scheduling and control strategies need to be explored. The charging of electric vehicles in a conventional charging station even with the fast dc–dc chargers takes around 30 min, which results in congestion and large waiting queues at public charging stations. To account for this issue, a novel strategy of routing and scheduling mobile charging stations to charge electric vehicles without the constraints of time and space is discussed in detail. Furthermore, the traveling times of mobile charging stations in reality are stochastic in nature. We formulate the optimization problem to minimize the cost of charging and show that the problem formulated is a combination of a bin packing problem and a multicity traveling salesman problem; hence, it is NP-hard and cannot be solved in reasonable CPU time, unless P = NP. We, thus, present modified saving’s heuristic and modified genetic algorithm metaheuristic to solve the optimization problem. Furthermore, numerical simulations show that the proposed scheduling and routing algorithm requires less number of mobile charging stations and can appreciably reduce the cost of charging.

Order picking optimization in a robotic mobile fulfillment system

The Robotic Mobile Fulfillment System (RMFS) is a parts-to-picker warehouse system where robots are used to fetch inventory goods from the storage area and transport them to the workstation. To improve the overall performance of the order picking process, this paper investigates an integrated order and robot scheduling optimization problem. We formulate this problem as a mixed-integer non-linear programming (MINLP) model with an objective of minimizing the total cost, containing the transportation cost of robots, the cost of loading and unloading operation, the incentive cost, and the penalty cost. And a Variable Neighborhood Search algorithm is presented to solve the MINLP model. Numerical experiments are conducted on 30 test cases based on real-world data to validate effectiveness of the proposed model and algorithm. Results show that the formulated model contribute to cost saving, and the proposed algorithm not only solves the model within a reasonable CPU time, but also yields near-optimal solutions with about 0.67% relative gap. Moreover, the study find that the reasonable robot configuration has a significant impact on the overall system performance.

Bus Assignment Considering Flexible Escape Routing for Layer Minimization in PCB Designs

It is necessary for cost consideration to minimize the number of used layers in a PCB design. Traditionally, the number of used layers outside components in bus assignment depends on the locations of the bus pins inside components in escape routing in a PCB design. In this article, the concept of introducing the flexible escape directions in escape routing is considered in bus assignment outside components in a PCB design. Clearly, the flexible consideration of the escape directions inside components can lead to the reduction on the number of used layers in bus assignment outside components. Given a set of buses on a set of components in a PCB design, based on the introduction of the flexible escape directions in escape routing and the construction of the possible bus connections in bus assignment, the two upper bounds of the layer numbers inside and outside components can be first computed. By eliminating the redundant bus connections for the given buses, an integrated algorithm can be further proposed to minimize the number of used layers in a PCB design. Based on the assignment constraints from the intersection relations inside components, the physical connections of the given buses can be assigned onto a minimal set of used layers. Compared with the two-phase algorithm using Yan’s routing algorithm in direction-constrained rectangle escape routing and Yan’s assignment algorithm in bus assignment, the experimental results show that our proposed integrated algorithm uses reasonable CPU time to reduce 32.1% of the layer number for ten tested examples in a PCB design on the average.

Tree-Based Clock Distribution of Multiple-Stage Pipelined Architecture in Rapid Single-Flux-Quantum Circuits

It is known that rapid single-flux-quantum (RSFQ) circuit technology and its energy-efficient derivatives are considered as one promising technology in superconducting digital applications. In this article, given the placement result of a multiple-stage pipelined architecture with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${n}$ </tex-math></inline-formula> gate columns and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula> gate components in an RSFQ circuit inside a rectangular layout, it is assumed that signals can be propagated from the primary inputs on the left boundary to the primary outputs on the right boundary. First, a lower bound on the routed width of one PTL region can be defined as the estimated width of one PTL region. Furthermore, the sum of the estimated widths of all the PTL regions in an RSFQ circuit can be defined as the estimated region width in an RSFQ circuit and the estimated width can be used as the objective function for the design of the clock distribution in an RSFQ circuit. Based on the flexibility of using the clock splitters (SPLs) inside the gate components for the propagation of clock signals, an iterative assignment algorithm can be proposed to complete the assignment of the tree-based clock connections with minimizing the estimated width of an RSFQ circuit. From viewpoint of numerical experiments, the experimental results show that the reduction of the estimated region width can lead to the reduction of the final width of the PTL regions in an RSFQ circuit. Compared with the design of Kito’s tree-based clock distribution for 5 tested RSFQ circuits, the experimental results show that the design of our proposed tree-based clock distribution with two capability parameters, 2 and 3, on a clock SPL can use reasonable CPU time to reduce 47.03% and 54.33% of the estimated region width and 44.83% and 45.25% of the final width for 5 tested RSFQ circuits on the average, respectively.

Valuation of hospital resources: an optimization approach using clearing functions

We propose an approach to estimating the time-dependent marginal values of hospital resources facing heterogeneous patient demand over time using the dual variables of a novel dynamic patient admission and flow planning model maximizing hospital revenue. Clearing functions are used to represent the queuing behavior of the patients within the hospital. Using a large data set containing 17,483 patients treated over one year in a 400-bed hospital, we undertake a computational study where we derive the value of hospital resources under different demand and resource scenarios. Our results show that large instances of the model can be solved in reasonable CPU times, and that the model yields resource valuations that are qualitatively different from conventional approaches ignoring queueing costs.

A novel multi-objective green vehicle routing and scheduling model with stochastic demand, supply, and variable travel times

We propose an optimization approach to the routing and scheduling problem for a heterogeneous transportation network that considers (i) stochastic values of demand and supply at the nodes, (ii) variable travel times between nodes conditioned by the fatigue of drivers, (iii) maximum allowed continuous driving time, and (iv) soft service time windows per customer at the nodes. The problem minimizes energy consumption and maximizes customer satisfaction. The subsequent stochastic multi-objective mixed integer programming model is solved using a hybrid approach based on chance- and epsilon-constraint methods. Given the NP-hard quality of the model, we introduce a hybrid meta-heuristic method based on genetic algorithm (GA) and simulated annealing (SA). This novel technique, named MOGASA, combines the global and local search capacities of both meta-heuristic algorithms, providing an intuitive solution approach that allows to solve problem instances considering large distribution networks with multiple types of vehicles in reasonable CPU time. We illustrate how MOGASA improves upon the hybrid chance- and epsilon-constraint exact solution method, particularly when dealing with large problem instances that cannot be solved by the latter. A medium instance scenario is used to analyze the reaction of the objective functions and the subsequent Pareto frontiers to modifications in the main structural parameters defining the transportation network. Potential applications of our stochastic framework to different types of logistic structures and retail supply chains are highlighted.

Permutation Flowshop Scheduling Problem Considering Learning, Deteriorating Effects and Flexible Maintenance

Availability constraints, machine condition as well as human behavior phenomena were recently introduced in the study of scheduling problems in order to get closer to the industrial reality. In this context, the permutation flowshop scheduling problem (PFSP) under flexible maintenance planning is investigated by incorporating machine deteriorating and human learning effects. The objective is to minimise the expected makespan by optimising simultaneously job sequence and maintenance decisions. To study the different problem configurations with respect to machine and human related effects, two studies are carried out. In the former study, the learning effect (human effect) is applied on maintenance activities, where durations are assumed to be time varying. While in the later, besides applying the learning effect on maintenance operations, time-dependent deteriorating jobs are also considered. Given the NP-completeness of the PFSP, an artificial bees colony algorithm (ABC) based metaheuristic is proposed, complemented with a maintenance insertion heuristic and adaptive local search procedures, to provide good solutions with reasonable CPU time. To prove the effectiveness of our proposed algorithm, intense computational experiments are carried out on Taillard's well-known benchmarks, expanded with flexible maintenance data.

A genetic algorithm for integrated hub covering-routing problem considering forbidden paths

In this study, a new mathematical model is provided for the hub covering-routing problem (HCRP) through an extension of the travelling salesman problem (TSP). The objective of HCRP is to minimise the total related costs. This model assumes that if existence nodes are at a certain distance from a given node, they will be covered. The problem is a complete graph, so the seller must enter and leave once on each node. The main decision variables are to determine the tours and how to use the hubs in the way that total cost can be minimised. Since the proposed model is NP-hard, a genetic algorithm (GA) is developed for solving the model in real-world sizes. The results show that the proposed model and solution method are useful for dealing with real-world problems. The computational results show that in large-scale instances, GA solves the problem in a reasonable CPU time where GAMS is unable to find even a feasible solution. The average running time of GA is 62.69 seconds for large scale instances.