A multi-location inventory system with occasional allocation under uncertain defective rates

Probabilistic seismic demand models are developed for bridges elevated with steel pedestals by adding correction and error terms to commonly used models. Separate probabilistic demand models are developed for the force demand on steel pedestals and the shear and deformation demands on concrete columns. Nonlinear time history analyses on detailed, three-dimensional finite-element models are used to generate virtual experimental data. By applying a Bayesian updating method to the generated data, parameters of the probabilistic models and their correlations are estimated. Comparisons between the demands from the developed probabilistic demand models and the demands from their corresponding demand models without correction and error terms reveal that the developed probabilistic models provide more accurate and unbiased predictions of the demands of interest. As an illustration of the developed framework, fragilities are estimated for a two-span bridge. The results show that pedestals are more vulnerable in the longitudinal direction, and columns are more vulnerable in the transverse direction. A sensitivity analysis on the studied bridges shows that decreasing the pedestal height, increasing the length of the pedestal anchor bolts within the concrete bent, and increasing the concrete cover on the anchor bolts are the most effective ways to decrease the probability of failure.

Developing deterministic and probabilistic prediction models to evaluate high-temperature performance of modified bitumens

A hybrid stochastic-robust bidding model for wind-storage system in day-ahead market considering risk preference

In group decision-making, ignoring the existence of uncertain factors causes the decision-making problem to lose its practical significance. Based on the maximum expert consensus model (MECM), we considered the uncertainty of the opinions of the three participating roles by introducing noncooperators. Additionally, three different opinion uncertainty sets were constructed to describe the characteristics of opinion uncertainty more accurately. Furthermore, by applying a robust optimization (RO) method to process uncertain sets, we propose mixed-integer robust MECMs, which reduce the risk of uncertain opinions to decision-makers (DMs). Moreover, numerical experiments used in the passenger satisfaction survey of the Shanghai Metro verified the validity of the models proposed in this article. The characteristics of the models were revealed through sensitivity analysis. Finally, to overcome the relatively highly conservative results of the classic RO method, we construct data-driven opinion uncertainty sets and propose data-driven RO models. Hence, DMs with different risk preferences can choose RO models with different risk levels according to the situation.

The strategic behavior of users is significantly influenced by their hidden information such as private valuations, risk preferences, and price sensitivities. Contextual behavioral model learning refers to learning the dependence of users' hidden information on their observable context information. While many existing studies use offline data to learn contextual behavioral models, we study how to design sequential experiments to collect the most informative user behavioral data for learning. We propose a basic inference-then-design method. In each experimental period, it infers a probabilistic contextual behavioral model using historical experimental data, and then designs the new experiment to maximize the gain of information about the probabilistic model. We further improve the basic method in two aspects. First, we improve the inference step by specifying a more informative prior for learning the probabilistic contextual behavioral model. Second, we integrate the inference and design steps instead of conducting them separately. Our rigorous theoretic analysis reveals that the optimization objective of the inference step can be modified to account for the downstream experimental design step. Numerical experiments show that our methods lead to more effective experiments, i.e., the collected experimental data can help in learning a more accurate behavioral model.

Motivated by various applications in inventory management, this article is devoted to the stochastic monotonicity and comparability of two special mean-risk models, called mean-conditional value-at-risk (abbreviated as MCVaR) measures. Firstly, we characterize the two MCVaR measures by the second quantile function of a random variable, and show that the two MCVaR measures are consistent with ascending stochastic dominance (abbreviated as ASD) or descending stochastic dominance (abbreviated as DSD) for risk lovers by using then relations between the second quantile function and two stochastic dominance. We also show that the two MCVaR measures have loss-aversion property for risk-aversion case, and have subadditivity (superadditivity) and convexity (convexity) for risk-seeking (risk-aversion), respectively. We obtain similar results by using a linear transformation of a random variable with a location and a scalar parameters, the transformation is corresponding to ASD and DSD as location parameter changes. The obtained results with respect to the stochastic monotonicity for the two MCVaR measures are used to solve a inventory decision problem with bi-objective maximization expected utility considering risk preference (including risk-aversion and risk-seeking) and stockout cost. We obtain the close solution and optimal expected utility for this problem. Due to the complexity of the solution, we provide several upper and lower bounds for the optimal order quantity, which are corresponding value in risk-neutral or without stockout cost cases. The obtained results in this paper has a certain insights and help for enterprises facing market uncertainty and considering decision maker’s risk preference.

Environmental deterioration is one of the current hot topics of the business world. To cope with the negative environmental impacts of corporate activities, researchers introduced the concept of closed-loop supply chain (CLSC) management and remanufacturing. This paper studies joint inventory and pricing decisions in a multi-echelon CLSC model that considers online to offline (O2O) business strategy. An imperfect production process is examined with a random defective rate that follows a probability distribution. The results show that the O2O channel increases the profit of the system. For the defective rate, three different distributions are considered and three examples are solved. The results of the three examples conclude that the highest profit is generated when the defective rate follows a uniform distribution. Furthermore, based on the salvage value of defective items, two cases were studied. Results and sensitivity analysis show that the increase in defective rate does not reduce total profit in every situation, as perceived by the existing literature. Sensitivity analysis and numerical examples are given to show robustness of the model and draw important managerial insights.

This paper describes the simulation model of supply chain and its implementation using general purpose tool and the simulation package. The output of Monte Carlo experiments taken both from spreadsheet formulas in Microsoft Excel and from graphical environment of Extend software was confronted and revised and then model was used to find the minimal inventory cost. A BRIEF DESCRIPTION OF THE INVENTORY MANAGEMENT'S BASIC CONCEPTS According to (Heizer and Render 2001) there are four types of firm’s inventory: • Raw material inventory, which are at command of the firm. The main purpose of maintain of these items is to eliminate supplier variability in quality, quantity or delivery time. • Work-in-process inventory, which are processed inside the firm. It means that changes has been made but not ended yet. • Inventories which are devoted to maintenance, repair and operating. It refers to assure current running of plants and devices etc. • Finished goods inventory: the completed products waiting for shipping. They arise because values of future demand are unknown. There are two types of models of inventory control in relation to demand for other goods: dependent (for instance portable FM receivers and batteries) and independent (for instance TV receivers and FM receivers). In our paper, we will study the issues related only to independent demand. The quality of implementation of inventory control policy is evaluated according to total inventory cost over certain time. Total inventory cost is composed of holding costs (also including insurance cost, handling etc.), ordering cost (also transportation cost, packing and setup cost, administration cost) and stockout cost (storage cost). The main purpose of model analysis is generally to minimize the total costs. One can assume sometimes that the demand is known or constant but most often this assumption have to be rejected and it is necessary to specify the probability distribution of demand. This leads to a probabilistic model. That sort of models are sometimes evaluated by service level, which usually can be measured at a number of points in the supply chain e.g. X service levels means that the products is available X percent of the time or X out of a hundred customers will buy the product. To reduce stockouts one can increase inventory in comparison with results from non probabilistic model. Size of safety stock depend on stockout and maintain costs, including ordering and storage cost. There are two patterns (approaches) of inventory controlling and scheduling – push (for instance MRP, based on production schedules, developed for production stages according to demand forecasts) and pull system (for instance just in time and kanban). We will present the simulation model of a pull system. It is based upon the direct and immediate ordering products or components by the customer from the supplier who delivers it at required time and amount. IMPLEMENTATION OF THE INVENTORY MANAGEMENT BUSINESS GAME Authors investigated the process of implementation of business game, based upon kanban approach which applies the probabilistic model of goods demand and time of fulfilling the order by the supplier. Our considerations were based on inventory management model from (Heizer and Render 2001) but very similar approach is presented in (Lawrence and Pasternack 2001; Jensen and Bard 2003). Guidelines of the Inventory Management Business

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A probabilistic model and software tool for evaluating the long-term performance of landfill covers

SummaryWhile probabilistic risk assessment (PRA) is an explicit methodology for complying with the performance requirements of the Building Code of Australia (BCA) or similar codes, it traditionally focuses only on technical risks of fire safety systems in a building. There are growing concerns that performance‐based fire engineering designs underestimate safety risk levels in high‐rise residential buildings. Existing fire risk models account for failures of technical systems but ignore human and organizational errors (HOEs) and the complex interactions among these variables. Probabilistic models in other applications, such as offshore platforms and nuclear plants, demonstrate the importance of HOE inclusion in risk models and the resulting impacts on overall risk. This paper proposes a comprehensive technical‐human‐organizational risk (T‐H‐O‐Risk) methodology to enhance the PRA approach by quantifying human and organizational risks in a probabilistic model using Bayesian Network (BN) analysis of HOEs and System Dynamics (SD) modelling for dynamic characterization of risk variations over time. While risk modelling itself is not novel, the current research develops unique and specific enhancements to existing risk approaches by integrating HOE risks with technical risks in a comprehensive dynamic and probabilistic model for high‐rise residential buildings. Three case studies are conducted to demonstrate the application of this comprehensive approach to the designs of various high‐rise residential buildings ranging from 18 to 24 storeys. Societal risks are represented in F‐N curves. Results show that in general, fire safety designs that do not consider HOEs underestimate overall risks generally by~20%—and can reach up to 42% in an extreme case. Furthermore, risks over time due to HOEs vary by as much as 30% over a 10‐year period. A sensitivity analysis indicates that deficient training, poor safety culture and ineffective emergency plans have significant impact on overall risk.

This study was performed to evaluate the probabilistic characteristics of the flexural strength of reinforced concrete (RC) flexural members adopted for underground box culverts. These probabilistic models were developed to be adopted for the development of limit state load combination formats for underground RC box culverts. The probabilistic models of uncertainties inherent in the basic design variables were developed to evaluate flexural strength using field material test data as well as field survey data collected from various domestic construction sites of underground box culverts in Korea. The basic design variables include concrete strength, steel rebar strength, and section dimensions, such as slab thickness and rebar locations. Some design variables are assumed to have inherent construction error characteristics, which may be different from those inherent in the RC members for buildings and bridges. The bias models on flexural strength were evaluated based on the experimental results of four-point flexural tests on one-way RC slabs, which were fabricated following the general practice adopted in the local underground box culvert construction process. Based on the probabilistic models of basic design variables, as well as the bias models of flexural strength, Monte Carlo simulations were performed to examine the probabilistic characteristics of both ultimate flexural strength and yield moment strength of RC slab members. Some sensitivity analyses were performed to confirm the soundness of various probability models and the assumptions adopted in the development procedure. The proposed procedure may be applied to develop probabilistic resistance models for structural members, in which the construction error characteristics are assumed to be different from other practices.

This study investigates overestimations in defect inspections performed by imperfect inspectors, particularly in scenarios involving random defective rates. Mathematical models are developed under two key assumptions: (1) inspection errors are either constant or uniformly distributed and (2) defective rates follow a random uniform distribution. Four analytical models are used to evaluate the probability of overestimation (PO) and identify critical defect rate thresholds (CFBs). The findings reveal that the PO approaches 100% as defect rates approach zero, irrespective of inspection error characteristics. Sensitivity analysis demonstrates model robustness under varying error distributions and parameter changes. Addressing practical concerns, this research highlights the need to revise inspection schemes to mitigate biases, especially in industries with stringent quality control standards, such as electronics and pharmaceuticals. Recommendations include integrating probabilistic error models and adopting dynamic calibration systems to improve inspection accuracy. By providing a theoretical foundation for tackling overestimation, this study has significant implications for improving fairness and efficiency in global supply chains.

ABSTRACTIn this paper, a derivative-free trust region methods based on probabilistic models with new nonmonotone line search technique is considered for nonlinear programming with linear inequality constraints. The proposed algorithm is designed to build probabilistic polynomial interpolation models for the objective function. We build the affine scaling trust region methods which use probabilistic or random models within a classical trust region framework. The new backtracking linear search technique guarantee the descent of the objective function, and new iterative points are in the feasible region. In order to overcome the strict complementarity hypothesis, under some reasonable conditions which are weaker than strong second order sufficient condition, we give the new and more simple identification function to structure the affine matrix. The global and local fast convergence of the algorithm are shown and the results of numerical experiments are reported to show the effectiveness of the proposed algorithm.

A probabilistic model (SHEDS-Wood) was developed to examine children's exposure and dose to chromated copper arsenate (CCA)-treated wood, as described in Part 1 of this two-part article. This Part 2 article discusses sensitivity and uncertainty analyses conducted to assess the key model inputs and areas of needed research for children's exposure to CCA-treated playsets and decks. The following types of analyses were conducted: (1) sensitivity analyses using a percentile scaling approach and multiple stepwise regression; and (2) uncertainty analyses using the bootstrap and two-stage Monte Carlo techniques. The five most important variables, based on both sensitivity and uncertainty analyses, were: wood surface residue-to-skin transfer efficiency; wood surface residue levels; fraction of hand surface area mouthed per mouthing event; average fraction of nonresidential outdoor time a child plays on/around CCA-treated public playsets; and frequency of hand washing. In general, there was a factor of 8 for the 5th and 95th percentiles and a factor of 4 for the 50th percentile in the uncertainty of predicted population dose estimates due to parameter uncertainty. Data were available for most of the key model inputs identified with sensitivity and uncertainty analyses; however, there were few or no data for some key inputs. To evaluate and improve the accuracy of model results, future measurement studies should obtain longitudinal time-activity diary information on children, spatial and temporal measurements of residue and soil concentrations on or near CCA-treated playsets and decks, and key exposure factors. Future studies should also address other sources of uncertainty in addition to parameter uncertainty, such as scenario and model uncertainty.