Delay Penalty Research Articles

Risk-Aware Vessel Scheduling and Routing Optimization with CVaR and LSTM-MSNet Prediction

This paper proposes an integrated optimization model for vessel scheduling and routing. The objective is to maximize shipping company profits while considering profit volatility using the Conditional Value-at-Risk metric to master risks from demand fluctuations. Simultaneously, the model balances the spot and contract container allocation by optimally adjusting shipping speeds so as to minimize carbon emissions. We account for vessel deployment, chartering costs, delay penalties, fuel expenses, and weather conditions to ensure the model’s compatibility with the practical transporting environment. In particular, a hybrid demand prediction model, combining long short-term memory and multi-scale network techniques, predicts spot and contract container volumes at ports, facilitating real-time allocation and more precise scheduling optimization. Two hybrid heuristics, one adaptive large-neighborhood search algorithm, and the Gurobi solver are devised and compared based on the efficiency and accuracy of solving the model. The results indicate that our optimization offers practical insights for shipping companies, enabling them to achieve a better trade-off between profits and risks, promoting a promising maritime transport career.

New model to streamline customer order scheduling

The Customer Order Scheduling (COS) problem involves organizing a set of orders, each characterized by a release time, a due date, and a processing time, with the objective of minimizing the costs associated with delivery delays. This study introduces an innovative approach, the Streamline COS, which integrates order scheduling optimization with improved resource utilization and a reduction in production inefficiencies. We present a model aimed at minimizing an objective function that encompasses delay penalties and the optimization of resource usage. Additionally, we introduce an optimized COS algorithm, named SCOS (Streamline COS), designed to find the optimal solution. The efficacy of this approach is validated through experiments with several datasets of varying sizes and instances, demonstrating that the Streamline COS method significantly enhances resource management, reduces delays, and optimizes order scheduling, while offering flexible priority management. This approach provides a practical and effective solution to complex scheduling problems and opens new research avenues, particularly in integrating dynamic constraints and leveraging optimization and machine learning techniques for further performance improvements.

Evaluating the Impact of the Level of Robustness in Operating Room Scheduling Problems.

Managing uncertainty in surgery times presents a critical challenge in operating room (OR) scheduling, as it can have a significant impact on patient care and hospital efficiency. Objectives: By incorporating robustness into the decision-making process, we can provide a more reliable and adaptive solution compared to traditional deterministic approaches. Materials and methods: In this paper, we consider a cardinality-constrained robust optimization model for OR scheduling, addressing uncertain surgery durations. By accounting for patient waiting times, urgency levels and delay penalties in the objective function, our model aims to optimise patient-centred outcomes while ensuring operational resilience. However, to achieve an appropriate balance between resilience and robustness cost, the robustness level must be carefully tuned. In this paper, we conduct a comprehensive analysis of the model's performance, assessing its sensitivity to robustness levels and its ability to handle different uncertainty scenarios. Results: Our results show significant improvements in patient outcomes, including reduced waiting times, fewer missed surgeries and improved prioritisation of urgent cases. Key contributions of this research include an evaluation of the representativeness and performance of the patient-centred objective function, a comprehensive analysis of the impact of robustness parameters on OR scheduling performance, and insights into the impact of different robustness levels. Conclusions: This research offers healthcare providers a pathway to increase operational efficiency, improve patient satisfaction, and mitigate the negative effects of uncertainty in OR scheduling.

A cost-effective and highly robust triple-node-upset self-recoverable latch design based on dual-output C-elements

With the continuous reduction of feature size of transistors, single-event triple-node-upsets (TNUs) induced by the striking of radiation particles in nano-scale CMOS circuits have emerged as a significant reliability concern. To address the shortcomings of existing radiation-hardened designs, including low reliability and high overhead, this paper proposes a cost-effective and highly robust TNU self-recovery latch design called DOCTRL. The proposed DOCTRL latch primarily consists of six dual-output C-elements (DOCs) and two clocked DOCs. By utilizing DOCs with two independent outputs, the proposed DOCTRL latch achieves a smaller area overhead. In addition, a four-level circular interlock matrix connection is designed to recover all possible TNUs within the proposed DOCTRL latch. Meanwhile, the latch also incorporates clock gating technology and a high-speed path to minimize power consumption and delay penalties. Simulation results indicate that the proposed DOCTRL reduces area by an average of 32.43 %, power consumption by 46.84 %, delay by 14.43 %, and area-power-delay product (APDP) by 69.55 %, compared to the five typical TNU self-recovery latches (SCLCRL, TNUSH, LCTNUCR, ADTRL, TSRL). Furthermore, detailed process, voltage, temperature (PVT), and Monte Carlo simulations verify the robustness of the proposed DOCTRL latch.

Bi-level programming for joint order acceptance and production planning in industrial robot manufacturing enterprise

Effectively addressing the Order Acceptance and Production Planning (OAP) problem is crucial for industrial robot manufacturers, particularly considering the rapid expansion of the industrial robot market. A bi-level programming model is structured to enhance synergies in response to department reformation within an industrial robot manufacturer. The upper-level model focuses on order acceptance, considering rejection and delay penalty costs, aiming to maximize enterprise profit. The lower-level model concentrates on product production, aiming to minimize the total sum of production costs and Work-In-Progress (WIP) holding costs. Detailed constraints related to material readiness, including inventory levels and procurement lead times, are comprehensively examined. Given the NP-hard complexity of this multi-objective problem, a meta-heuristic algorithm Whale Optimization Algorithm (WOA) is implemented. To enhance WOA’s optimization capabilities, two heuristic algorithms, Solution Improvement policy (I-algorithm) and Heuristic-based acceleration strategy (H-algorithm), are introduced, resulting in the development of WOA-IH. The experimental evidence suggests that the proposed model facilitates decision coordination for manufacturers, particularly in order management, production planning, and material procurement. Furthermore, this study contributes to applying bi-level programming theory in managing supply chains for industrial robotics.

Application of the Least Cost Analysis Method to Determine the Optimal Cost and Duration for Delayed Projects

The construction project of the Polresta Denpasar Multi-Purpose Building experienced delays due to the Eid al-Fitr holiday, resulting in a 9.35% setback, equivalent to 19 days. In the 20th week, the planned progress was 66.52%, but actual progress was only 57.17%. With 76 days remaining for the project's completion, the least cost analysis method was applied to find the optimal cost and time for completing the remaining work by increasing the workforce. This approach aims to ensure the project is completed on time and the contractor avoids penalties. The penalty is 1/1000 (1 ‰) of the contract value. The analysis used primary data such as interviews and project observations, and secondary data including contractor unit price analysis, budget plans (RAB), and S-curves. The data was analyzed from the remaining project work, crashing duration, to finding the cost slope for each task, and then using the least cost analysis method to determine the optimal cost and time. The analysis results showed that the optimized project cost after acceleration was Rp 3,077,713,130.90, with the project duration reduced from 76 days to 57 days, accelerated by 19 days. This acceleration saved costs by Rp 18,428,493.93. By applying this method, the project is expected to be completed faster without compromising quality, and the contractor can avoid delay penalties. This approach helps in time efficiency and budget management, ensuring all resources are used optimally and the project is completed on target.

How to improve efficiency for prefabricated component production scheduling with worker allocation and order outsourcing?

Prefabricated building is an advanced building mode that is actively promoted in China owing to its high-efficiency, energy-saving and labour-saving characteristics. However, because of the limited production capacity and production resources of the final assembly parts production plant, it is usually impossible for a factory to accept all orders. Therefore, the factory will consider outsourcing part of the orders to reduce delay penalties. In addition, the allocation of workers in the production process will significantly affect the processing time of orders. Regarding those two issues, a production scheduling integrated optimization model, considering worker allocation and order outsourcing, is established to maximize net profit. To address the complexity of problem solving caused by the NP-hard nature of this problem, a hybrid improved genetic–accelerated iterative greedy search algorithm is developed. Finally, a numerical example is delivered to verify that the proposed algorithm has improved solution quality and convergence.

Multi-Agent Simulation Approach for Modular Integrated Construction Supply Chain

The shift from traditional on-site to off-site construction marks a significant evolution in the construction industry, characterized by increasing levels of prefabrication. These advancements enhance construction efficiency, reduce lead times, and mitigate environmental impacts, leading to modular integrated construction (MiC). However, MiC presents complex supply chain challenges, particularly in the transportation of prefabricated components and fully integrated modules. This study addresses these challenges by employing a multi-agent simulation using AnyLogic to optimize MiC transport logistics. The simulation models the interactions of various agents involved in the MiC process to improve operational efficiency and reduce costs. Results demonstrate that using three vehicles per supplier minimizes total transport costs, effectively balancing fixed and variable expenses while eliminating penalties for project delays. The findings highlight the cost efficiency of MiC, showing potential savings due to centralized assembly and optimized logistics. These significantly reduce material transportation and related costs, contributing to the overall efficiency and sustainability of construction projects. These insights underscore the value of multi-agent simulation in addressing the complexities of MiC supply chains.

INDIDO: A novel low-power approach for domino logic circuits

Power dissipation in Nanoelectronic circuits at advanced technology nodes is dominated by leakage power dissipation. This is due to an increase in short channel effects in the scaled transistors.The VLSI industry is seeking alternative options to enhance the performance of portable electronic systems by ensuring higher speed and reduced power dissipation. In the ultra-deep submicron (DSM) regime, a new device called the fin-shaped field effect transistor (FinFET) was developed to replace CMOS technology. FinFET, a multi-gate device, significantly reduces power dissipation compared to planer MOS (Metal Oxide Semiconductor ) transistor, but it doesn’t entirely resolve the issue. To further reduce power dissipation in the ultra DSM regime, low power approaches are required. Domino logic, a widely-used dynamic logic, is commonly utilized in high-speed VLSI architectures. In this research work, a novel INput Dependent Inverter DOmino (INDIDO) logic approach for low-power domino logic circuits using FinFET devices is proposed. A comparative analysis between the proposed INDIDO method and the existing approaches is performed for domino logic circuits for various performance metrics at the 16 nm technology node. In this research, various circuits like domino buffer, domino OR, domino AND, domino XOR and domino half adder are designed using the proposed INDIDO approach.The proposed INDIDO FinFET buffer circuit offers significant improvement in energy efficiency and FOM (Figure of Merit) by 53.18% and 82% respectively as compared to conventional FinFET footed domino buffer circuit. Beside this, proposed circuits like INDIDO OR, INDIDO AND, INDIDO XOR and INDIDO Half adder circuit follow the same trend as INDIDO buffer circuit and show better performance parametres in comparison to the existing low power domino approaches as well. In addition to this, the proposed INDIDO buffer circuits are also analyzed for PVT(process voltage and temperature) variability.This whole analysis makes us to conclude that proposed INDIDO approach reduces power dissipation and delay penalty, have high noise tolerance capacity, more immunity against PVT variations and high energy efficiency in comparison to the already existing techniques.

Waiting Strategy for the Dynamic Meal Delivery Routing Problem with Time-Sensitive Customers Using a Hybrid Adaptive Genetic Algorithm and Adaptive Large Neighborhood Search Algorithm

In this paper, we study the dynamic meal delivery routing problem (MDRP) with time-sensitive customers. The multi-objective MDRP optimization model is developed to maximize customer satisfaction and minimize delay penalty cost and riding cost. To solve the dynamic MDRP, a novel waiting strategy is proposed to divide the dynamic problem into a series of static subproblems. This waiting strategy utilizes the decision threshold to determine rerouting points based on the number of dynamic meal orders. Meanwhile, time-sensitive priority is introduced to accelerate assignment and routing decisions for orders from customers with high time sensitivity. For each static subproblem, a hybrid AGA–ALNS algorithm that incorporates the adaptive genetic algorithm and adaptive large neighborhood search is developed to improve both the global and local search capabilities of the genetic algorithm. We validate the performance of the proposed waiting strategy and the AGA–ALNS algorithm through numerical instances. In addition, managerial insights are obtained from sensitivity analysis experiments.

Impact of Variability on Novel Transistor Configurations in Adder Circuits at 7nm FinFET Technology

This research work focuses on implementation of the FinFET-based complementary metal-oxide-semiconductor (CMOS) Full Adder circuits for different transistor configurations using ASAP7 FinFET model. First, this work examines FinFET-based AND-OR-invert (AOI) gates using different topologies, and second, a FinFET-based CMOS Full Adder circuit at the 7[Formula: see text]nm technology node is analyzed with respect to its process, voltage and temperature (PVT) variability effect measured in terms of the normalized standard deviation of different performance metrics. The comparison is made between conventional (CFFA1) and proposed (FFA2, FFA3, FFA4, FFA5, FFA6, FFA7 and FFA8) FinFET-based CMOS full adder circuits. The aim is to determine the optimal design configuration of the FinFET full adder circuit with the minimum impact of PVT variability. On examining the power delay product (PDP) variability, it is found that variations in FFA2, FFA3, FFA4, FFA5, FFA6, FFA7 and FFA8 are significantly lower than CFFA1 by 6.30%, 4.68%, 10.30%, 65.48%, 68.05%, 65.61% and 17.20%, respectively. Among all the proposed configurations, normalized standard deviation [Formula: see text] for the PDP metric is lowest in FFA6, followed by FFA7, FFA5, FFA8, FFA4, FFA2 and FFA3. The normalized standard deviation [Formula: see text] for power dissipation, however, is lowest in FFA8. In addition, a layout comparison analysis of conventional and proposed full adder circuits reveals that FFA7 has the least area, followed by FFA8, FFA6, FFA5, FFA3, FFA4, FFA2 and CFFA1. The area of FFA2, FFA3, FFA4, FFA5 and FFA6 has decreased by 3.29%, 3.51%, 3.50%, 5.14% and 5.52%, while FFA7 and FFA8 have experienced a decrease in area by 13.87% and 14.36%, respectively, as compared to conventional CFFA1. The proposed layout of FinFET-based CMOS Full Adders can be directly transferred into the foundry’s production line for manufacturing purposes. The overall investigation led us to conclude that FFA8 is the most efficient of all due to the lowest power variation, lower delay variation and lower PDP variation, moreover it has a reduced layout area among all the discussed designs. However, there is a tradeoff in terms of penalty in nominal power, propagation delay and PDP in the proposed topologies.

Loan Recovery in Maimana Islamic Investment and Finance Cooperative Microfinance Institution

MIIFC is one of the financial institutions operating in financial sector in Faryab province of Afghanistan. One of the most important problems of financial institutions is the recovery of the loans from the borrowers. The purpose of this study is to find the determinants of loan recovery in MIIFC micro finance institution. The statistical population of this research is the borrowers of MIIFC micro finance institution, the sample size is 300 and the stratified random sampling technique was employed. The study used both descriptive and inferential statistic, the quantitative method has been analysed by descriptive statistic with statistical package SPSS (24) and empirical analysis is conducted by using binary logistic regression model to stablish of all explanatory variables. The results show that from these variables, only economic and political situation, number of loans taken, penalty of delay, gender and debt guarantee have a significant relationship with loan repayment. The marital status, education level, age, monthly expenditure, loan utilization, literacy, income-increasing debt, relative location, type of loan, group loan, amount of repayment, repayment period, interest rate on the loan, number of families, loan utilization training and pre-loan purpose have no relationship with loan recovery.

Deep learning models for groundwater level prediction based on delay penalty

Abstract In irrigation agriculture, predicting groundwater level (GWL) using deep learning models can help decision-makers coordinate surface water and groundwater usage, thus aiding in the sustainable development and utilization of groundwater. However, when making a long sequence prediction, prediction sequences often have severe delays affecting the availability of prediction results. In this paper, a new loss function is proposed to minimize the lag and oversmoothing on the prediction of GWLs. GWL, meteorology, and pumping data are collected via an irrigation Internet of Things system in Hutubi County, Xinjiang. Through Pearson's correlation analysis, historical potential evapotranspiration (ET0), groundwater extraction, and GWL were chosen to predict GWLs. Datasets were constructed through the proposed spatiotemporal data fusion method; then, the best model from the six deep learning models was selected by comparing the prediction capability of the datasets. Finally, the mean-squared error (MSE) loss function is replaced by the proposed loss function. Compared to the mean absolute error, MSE, and predicted sequence graphs, the new loss function significantly depresses the time delay with similar prediction accuracy.

Ferroelectric FET-based context-switching FPGA enabling dynamic reconfiguration for adaptive deep learning machines.

Field programmable gate array (FPGA) is widely used in the acceleration of deep learning applications because of its reconfigurability, flexibility, and fast time-to-market. However, conventional FPGA suffers from the trade-off between chip area and reconfiguration latency, making efficient FPGA accelerations that require switching between multiple configurations still elusive. Here, we propose a ferroelectric field-effect transistor (FeFET)-based context-switching FPGA supporting dynamic reconfiguration to break this trade-off, enabling loading of arbitrary configuration without interrupting the active configuration execution. Leveraging the intrinsic structure and nonvolatility of FeFETs, compact FPGA primitives are proposed and experimentally verified. The evaluation results show our design shows a 63.0%/74.7% reduction in a look-up table (LUT)/connection block (CB) area and 82.7%/53.6% reduction in CB/switch box power consumption with a minimal penalty in the critical path delay (9.6%). Besides, our design yields significant time savings by 78.7 and 20.3% on average for context-switching and dynamic reconfiguration applications, respectively.

Study on cruise speed optimization and refueling strategy considering emission control areas

In order to alleviate the cost pressure caused by navigation restrictions and rising fuel prices faced by cruise companies, on the basis of considering the constraints of emission control areas and the arrival time constraints of each port, according to the relationship between the speed and fuel consumption of ships during navigation, a mixed integer nonlinear programming model with the minimum sum of the sum of ship fuel replenishment cost, arrival delay penalty cost and carbon tax cost is established to optimize the speed and refueling strategy of cruise ships. The results show that with the change of fuel price and time constraints, the fuel replenishment strategy will also change.

A hybrid genetic algorithm based on reinforcement learning for the energy-aware production scheduling in the photovoltaic glass industry

Recently, the growing solar energy capacity has played a significant role in developing a clean energy supply system in China. However, the resulting rapid expansion of photovoltaic component (e.g., glass) manufacturing intensifies the energy demand in the locality of the plant. Therefore, this paper considers the energy-aware production scheduling of a deep-processing line in the photovoltaic glass plant, whose layout is a hybrid flow shop with batch and non-batch machines. Firstly, we establish a mixed integer programming model with the minimization of the energy consumption and the penalty for excess of the due date. Then, we propose a hybrid genetic algorithm (GA) based on reinforcement learning to solve the problem. Specifically, the expected Sarsa is used to extract critical knowledge about algorithmic parameters during the population evolution to guide the exploration of the GA. Finally, we conduct extensive numerical experiments to validate the effectiveness of the proposed algorithm by comparing it with a commercial optimization solver and other metaheuristics. The numerical results show that the average gap between the solver and the proposed algorithm is around 4% in small-sized instances. Compared with the heuristic used in the plant, the improvements of this paper are about 16%∼18% and 17%∼21% in practical-sized instances for the delay penalty and energy consumption objectives, respectively. In addition, the computational results provide managerial insights for managers in further pursuing energy efficiency from higher-level decision-making, e.g., planning over multiple periods from a tactical perspective, and changing production line configurations and introducing new processing techniques from a strategic perspective.

A robust-fuzzy multi-objective optimization approach for a supplier selection and order allocation problem: Improving sustainability under uncertainty

Attaining sustainability objectives has received wide attention in the supplier selection and order allocation (SSOA) literature. This paper aims to investigate an SSOA problem under multiple items, multiple suppliers, multiple price levels, and multiple period using a robust-fuzzy multi-objective programming in which: (a) transportation cost, delay penalty cost, and demand are uncertain; (b) four objectives are proposed to minimize total costs and the number of defective items and to maximize environmental and social impacts; and (c) all objectives of the problem have a fuzzy membership degree that is determined by the decision-makers. A robust optimization approach is elaborated as a solution procedure to address the uncertainty of the decision variables. The significance of each objective in practice is discussed based on seven distinct scenarios that produce a specific membership degree to help practitioners make efficient decisions in selecting the suppliers and allocating the orders. Two numerical examples with different sizes are conducted to validate the mathematical model. Thereafter, the sensitivity of each scenario on objectives and total satisfaction degree is analyzed. The results of the numerical solution compare the value of four objective functions under each developed scenario to provide a trade-off insight between different objectives for practitioners. Eventually, the credibility and efficiency of the proposed solution procedure are evaluated to validate the findings.

Project scheduling cost optimization based on resource transfer costs and robustness

Under an uncertain environment, the reasonable arrangement of project scheduling and resource allocation can help managers save more on project costs. This paper focuses on the project scheduling cost optimization problem based on resource transfer costs and robustness under an uncertain environment. The optimization model of the problem is constructed with the objective to minimize the resource transfer costs and maximize the robustness. After accounting for the costs of transferring resource units across activities, we present a new project network graph consisting of unit resource flows. In addition, the robustness index is set based on the cost. To further ensure the robustness of the project, a time buffer insertion mechanism based on activity integrated delay penalty cost is designed. Then a developed non-dominated sorting genetic algorithm II (NSGA-II) is constructed to make the algorithm have better solving performance. The results of numerous experimental instances also serve as proof of the developed algorithm's effectiveness. Finally, we construct three uncertainty environments to further illustrate the performance of obtained solutions. The research results can provide a reference for project managers to make schedules and optimize project costs under uncertain environments.

Optimal scheduling of vessels passing a waterway bottleneck

We develop a novel schedule optimization model for vessels passing a waterway bottleneck. From the system-optimal perspective, the model aims to minimize the total vessel bunker cost and delay penalties at destinations by incorporating the nonlinear relationship between bunker consumption and sailing speed into its calculations. The nonlinear model is linearized via two commonly used approximation techniques. The first one linearizes the bunker consumption function using a piecewise linear lower bound, while the second does so by discretizing the time. Numerical case studies are conducted for a real-world waterway bottleneck, the Three Gorges Dam Lock. Results reveal how the optimal cost components, vessel schedules, and delays are affected by key operating parameters, including the fuel prices, delay penalty rates, and the tightness of sailing time windows. Comparison against two simpler benchmark scheduling strategies (one with no vessel coordination and the other adopting a naïve coordination) manifests the sizeable benefit of optimal vessel scheduling.This paper presents the first investigation into the system-optimal scheduling strategy for vessels navigating a shared bottleneck, considering bunker costs, schedule delay penalties, and varying sailing speeds. The results highlight the significant potential of system-optimal scheduling and potential coordination strategies that enable approximation of the system-optimal solution. Additionally, our numerical experiments uncover the limitations of the outer-approximation method, while demonstrating that the discrete-time approach surpasses it in terms of both solution quality and computational efficiency.

DELAYS IN EXECUTION OF BUILDING PROJECTS AND THEIR FINANCIAL CONSEQUENCES FOR CONTRACTORS - POLISH VIEW

The execution of building projects should be planned in a realistic time, at the assumed cost and quality, and take into account the risk of the contract parties. However, even the best-planned construction projects are exposed to the risk of delays. The article reviews causes of delays in the construction projects identified both in the world and in Poland. The consequences of delays, regardless of the responsible party, should be clearly spelled out in the contracts. Financial penalties for delays are commonly used in agreements for construction works, and their level, according to the presented analyses, is usually higher in the public than in the private sector. Attention has been also paid to the few models presented in the literature that make it possible to predict delays and prevent their effects. It seems that this is an interesting direction for further research.