Uncertainty Of Customer Demand Research Articles

An active multiphase probabilistic power flow based on a clustering approach

Probabilistic methods for analyzing power systems have become more widespread, mainly due to the increase in load and generation uncertainties and the current need for planning problems that are sufficiently accurate to take appropriate actions. However, some characteristics need to be better investigated in the Distribution Systems (DS) analysis, such as the input random variables’ modeling using real databases and, mainly, the probabilistic treatment of unbalance and scalability studies. This paper proposes an active Multiphase Probabilistic Power Flow (MPPF) evaluation, considering real measurement databases to represent customer demand uncertainties. It is called Multiphase Probabilistic Power Flow Based on K-Means (MPPFKM), and it encompasses a mixture of a clustering technique and an analytical method. The K-Means algorithm divides the input database into a set of clusters that models the scenarios to be evaluated in the multiphase power flow problem, which considers the distribution network with its inherent unbalance. Subsequently, the output variables’ higher order statistical moments are calculated using the obtained responses for each cluster. Finally, their PDFs and CDFs are recovered using the Gram-Charlier Expansion analytical method. While the clustering algorithm allows working with real data, giving the proposed work greater proximity to real-world results, the analytical tool provides an accurate recovery of the probability functions, because it detects their distortions caused by the nature of the multiphase problem. Estimating the correct number of clusters is a key factor in obtaining the precision required by power systems engineers and achieving significant computational time reduction, especially for large-scale unbalanced systems. Simulations are carried out using several DS feeders to test the proposed method, which specifically addresses DS issues not yet well explored in the literature, such as probabilistic simulations under different unbalance situations, applications with single-phase DGs, and scalability studies are conducted using the IEEE 8500 test feeder. Comparisons are made with usual MPPF algorithms, including Monte Carlo Simulation (MCS) and Point Estimation Methods (PEM). Simulations using large-scale unbalanced systems proved that the output variables' probability functions might present distortions, becoming more challenging for other methods. Under this perspective, MPPFKM presents better exactness in the calculation of higher order statistical moments and, consequently, can estimate the PDFs of the MPPF output variables with great precision, which is crucial for statistical models that use actual input data.

A multi-objective solution framework for the assembly inventory routing problem considering supply risk and carbon offset policies

This study aims to provide novel insights into the multi-objective optimisation of an inbound assembly inventory routing problem (IRP) by considering customer demand uncertainty, stochastic supply failure risks, and a carbon offset policy. An inbound IRP network generally comprises several geographically dispersed suppliers, vehicle depots, and assembly plants. In this study, a new mixed-integer nonlinear optimisation model is formulated considering two objective functions: (i) minimising the total supply network cost and (ii) maximising a low-risk supplier priority function with practical constraints. A hybrid non-dominated sorting genetic algorithm-II was developed to obtain near-optimal Pareto solutions for various small, medium, and large IRP instances. The Pareto solutions were compared with the efficient solution obtained using the ε-constraint method. Finally, the best Pareto solution was obtained using the multi-criteria decision-making technique. The results indicate that implementing supply risk-mitigation strategies leads to higher overall supply network costs and carbon emissions. However, a sensitivity analysis based on factorial simulation experiments indicates that a supply risk-mitigation strategy is advantageous if component prices are low and supply capacity, fuel price, and demand variations are high. In addition, the lower the demand variation, the lower the expected overall network cost and carbon emissions.

Antecedent configurations and performance of business models of intelligent manufacturing enterprises

Tackling climate change crises needs intelligent manufacturing and effective business models. This paper uses the adaptive structuration theory (AST) and configuration perspective to investigate the effects of digital infrastructure, digital orientation, top management team heterogeneity, servitization, government support, and customer demand uncertainty on the business model of intelligent manufacturing enterprises in China. The fuzzy set qualitative comparative analysis (fsQCA) was employed to analyze the data. The study found that there were five configurations of business model formation, which classified business models into five types: executive-led enhanced, digital leadership-enhanced, adaptive, extended, and complex business models. There was an intrinsic relationship between the five models with respect to the dimensions of digitalization and servitization. Further analysis revealed that executive-led, digital leadership-enhanced, and adaptive business models were positively associated with enterprise performance. The paper discusses the potential implications of these findings.

An enhanced solution approach for multi-period discrete manufacturing product assembly inventory routing problem with supply failure risks and customer demand uncertainty

PurposeThis study primarily aims to develop and solve an enhanced optimisation model for an assembly product multi-period inbound inventory routing problem (IRP). The many-to-one (inbound) model considers the bill of materials (BOM), supply failure risks (SFR) and customer demand uncertainty. The secondary objective is to study the influence of potential time-dependent model variables on the overall supply network costs based on a full factorial design of experiments (DOE).Design/methodology/approachA five-step solution approach is proposed to derive the optimal inventory levels, best sourcing strategy and vehicle route plans for a multi-period discrete manufacturing product assembly IRP. The proposed approach considers an optimal risk mitigation strategy by considering less risk-prone suppliers to deliver the required components in a specific period. A mixed-integer linear programming formulation was solved to derive the optimal supply network costs.FindingsThe simulation results indicate that lower demand variation, lower component price and higher supply capacity can provide superior cost performance for an inbound supply network. The results also demonstrate that increasing supply capacity does not necessarily decrease product shortages. However, when demand variation is high, product shortages are reduced at the expense of the supply network cost.Research limitations/implicationsA two-echelon supply network for a single assembled discrete product with homogeneous vehicle fleet availability was considered in this study.Originality/valueThe proposed multi-period inbound IRP model considers realistic SFR, customer demand uncertainties and product assembly requirements based on a specific BOM. The mathematical model includes various practical aspects, such as supply capacity constraints, supplier management costs and target service-level requirements. A sensitivity analysis based on a full factorial DOE provides new insights that can aid practitioners in real-life decision-making.

A two-stage stochastic planning model for locating product collection centers in green logistics networks

In this article, a model for locating product collection centers considering risk and environmental factors is presented. At first, a mixed integer programming model in deterministic conditions for the closed loop supply chain is presented to prevent the suboptimality resulting from considering these two chains separately by considering the forward and backward supply chain as a closed loop supply chain. Then, the effect of uncertainty in customer demand and the rate of returned products from customers in the network structure investigated by means of two-stage stochastic programming and based on the scenario-based method. Finally, the numerical results obtained from both models are presented, and the ability of the proposed stochastic model in using non-deterministic data has been examined.

A Review of Demand Side Management Strategies and Electricity Tariffs in Distributed Grids

A major challenge in renewable energy planning and integration with existing systems is the management of intermittence of the renewable resources and customer demand uncertainties. In emerging distributed grids, state-of-the-art optimization techniques were used for cost and reliability objectives. In the existing literature, power dispatch and demand side management schemes were implemented for various techno-economic objectives. In renewable energy-based distributed grids, power dispatch is strategic to distributed grid operations. However, demand side management is preferred, as it allows more options for customer participation. Moreover, the demand side management can simply follow supplies. This paper investigates the functions of demand side management as it affects the planning and operations of renewable energy-based distributed grids. The traditional demand side management strategies were inflexible and affect customer comforts. The time-of-use methods on the other hand are utility-centered and do not allow customer participation. Hence, integration of demand side management with tariff regimes through hybridizations such as time-of-use based real-time pricing were conceptualized for improved economic performance, reliability enhancement, and mitigation of environmental implications.

Designing of Integrated Tactical Production-Quality Planning in a Rolling Horizon Under Demand Uncertainty: Case Study in the Automotive Wire Harness Industry

To minimize waste and scrap, reworking becomes an integral part of many manufacturing companies. In this study, the integrated production-quality planning in the tactical horizon considering the customer demand uncertainty is presented. A mixed-integer programming model is developed to find optimal dynamic planning production which minimizes costs and reduces the scrap. After each production cycle, the quality indicators and relative costs for good and poor quality are performed. These indicators are introduced into the developed model to determine the optimal production orders and stock levels considering the reworked and scrapped products. A real case study is presented to illustrate the applicability of the proposed model. By comparing the obtained results with real results from studied company, we proved that this new model gives an optimal production plan.

Multiobjective Planning for Logistics Distribution of Consumer Electronic Items Based on Improved Genetic Algorithm

With the increasing uncertainty of customer demand, order management that takes into account the uncertainty of customer demand is important in the supply chain between distribution bases (distribution centers, RDCs, etc.) and large retailers. A multisite delivery planning system is recommended to scale past to handle a single product. Aiming at the logistics distribution problem of consumer electronic products, the problem is attributed to a multiobjective optimization problem, and an improved genetic algorithm is used to deal with it. Combined with the situation, this paper proposes a multiproject (multiproject delivery mass production planning system) and studies the narrative problem description, formula, solution sequence, and case analysis results. The experimental results show that this method can effectively improve the logistics distribution efficiency by 18% and reduce the distribution cost by 34%, by finding the optimal distribution path, reducing the total cost of low-carbon distribution of fresh products, improving customer satisfaction, reducing energy consumption, and achieving economic and social benefits for enterprises.

Resilient Supply Chain Planning for the Perishable Products under Different Uncertainty

In this research, the perishable products’ closed-loop supply chain network design problem is assessed by considering the disruption in production and distribution capacity and taking into account the uncertainty in customer demand. The main contribution of this research is modeling perishable products’ supply chain optimization and providing intelligent solution methods. In this regard, a mixed-integer mathematical model is proposed. This mathematical model consists of three objective functions. The first objective function is related to profit maximization, the second objective function is to minimize delivery time, and the third objective function is to reduce lost business days. Moreover, non-dominated sorting genetic algorithm II (NSGAII) and Multi-Objective Evolutionary Algorithm (MOEA) have been applied to optimize the proposed model. The research results show that the proposed meta-heuristic algorithm can provide a complete set of Pareto solutions in a reasonable amount of time. Moreover, based on different criteria, MOEA provides the non-dominated solutions with a higher quality, while NSGAII presents the Pareto boundary with more solutions than MOEA.

Meta Heuristic Algorithm for Vehicle Routing Problem with Uncertainty in Customer Demand

The Vehicle Routing Problem is an optimization problem with number of applications in the transportation field. In this paper, Vehicle Routing Problem (VRP) with uncertainty in customer demand is considered. Often in real world situation, the information on demand on each customer is not accurate enough. Here, in this article we are assuming that the customer demands in each time period are uncertain and are considered as fuzzy variables. This kind of problem is known as an NP-hard problem. The main purpose of this article is to give conceptual and practical idea on how fuzzy logic with meta heuristic algorithm can be implemented to improve the optimization of vehicle routing problem with uncertainty in customer demand. To solve this problem, a hybrid Meta heuristic algorithm (HMHA) including clustering is proposed, with the objective of determining an optimal route to minimize the total cost.

Robust Electric Vehicle Routing Problem with Time Windows under Demand Uncertainty and Weight-Related Energy Consumption

Vehicle routing problem with time windows (VRPTW) is a core combinatorial optimization problem in distribution tasks. The electric vehicle routing problem with time windows under demand uncertainty and weight-related energy consumption is an extension of the VRPTW. Although some researchers have studied either the electric VRPTW with nonlinear energy consumption model or the impact of the uncertain customer demand on the conventional vehicles, the literature on the integration of uncertain demand and energy consumption of electric vehicles is still scarce. However, practically, it is usually not feasible to ignore the uncertainty of customer demand and the weight-related energy consumption of electronic vehicles (EVs) in actual operation. Hence, we propose the robust optimization model based on a route-related uncertain set to tackle this problem. Moreover, adaptive large neighbourhood search heuristic has been developed to solve the problem due to the NP-hard nature of the problem. The effectiveness of the method is verified by experiments, and the influence of uncertain demand and uncertain parameters on the solution is further explored.

A branch-and-price algorithm for the robust single-source capacitated facility location problem under demand uncertainty

We consider the robust single-source capacitated facility location problem with uncertainty in customer demands. A cardinality-constrained uncertainty set is assumed for the robust problem. To solve it efficiently, we propose an allocation-based formulation derived by Dantzig–Wolfe decomposition and a branch-and-price algorithm. The computational experiments show that our branch-and-price algorithm outperforms CPLEX in many cases, which solves the ordinary robust reformulation. We also examine the trade-off relationship between the empirical probability of infeasibility and the additional costs incurred and observe that the robustness of solutions can be improved significantly with small additional costs.

Distributionally robust front distribution center inventory optimization with uncertain multi-item orders

<p style='text-indent:20px;'>As a new retail model, the front distribution center (FDC) has been recognized as an effective instrument for timely order delivery. However, the high customer demand uncertainty, multi-item order pattern, and limited inventory capacity pose a challenging task for FDC managers to determine the optimal inventory level. To this end, this paper proposes a two-stage distributionally robust (DR) FDC inventory model and an efficient row-and-column generation (RCG) algorithm. The proposed DR model uses a Wasserstein distance-based distributional set to describe the uncertain demand and utilizes a robust conditional value at risk decision criterion to mitigate the risk of distribution ambiguity. The proposed RCG is able to solve the complex max-min-max DR model exactly by repeatedly solving relaxed master problems and feasibility subproblems. We show that the optimal solution of the non-convex feasibility subproblem can be obtained by solving two linear programming problems. Numerical experiments based on real-world data highlight the superior out-of-sample performance of the proposed DR model in comparison with an existing benchmark approach and validate the computational efficiency of the proposed algorithm.</p>

An improved learnable evolution model for solving multi-objective vehicle routing problem with stochastic demand

The multi-objective vehicle routing problem with stochastic demand (MO-VRPSD) is much harder to tackle than other traditional vehicle routing problems (VRPs), due to the uncertainty in customer demands and potentially conflicted objectives. In this paper, we present an improved multi-objective learnable evolution model (IMOLEM) to solve MO-VRPSD with three objectives of travel distance, driver remuneration and number of vehicles. In our method, a machine learning algorithm, i.e., decision tree, is exploited to help find and guide the desirable direction of evolution process. To cope with the key issue of ”route failure” caused due to stochastic customer demands, we propose a novel chromosome representation based on priority with bubbles. Moreover, an efficient nondominated sort using a sequential search strategy (ENS-SS) in conjunction with some heuristic operations are leveraged to handle the multi-objective property of the problem. Our algorithm is evaluated on the instances of modified Solomon VRP benchmark. Experimental results show that the proposed IMOLEM is capable to find better Pareto front of solutions and also deliver superior performance to other evolutionary algorithms.

Data-Driven Robust Optimization for Solving the Heterogeneous Vehicle Routing Problem with Customer Demand Uncertainty

In practice, the parameters of the vehicle routing problem are uncertain, which is called the uncertain vehicle routing problem (UVRP). Therefore, a data-driven robust optimization approach to solve the heterogeneous UVRP is studied. The uncertain parameters of customer demand are introduced, and the uncertain model is established. The uncertain model is transformed into a robust model with adjustable parameters. At the same time, we use a least-squares data-driven method combined with historical data samples to design a function of robust adjustable parameters related to the maximum demand, demand range, and given vehicle capacity to optimize the robust model. We improve the deep Q-learning-based reinforcement learning algorithm for the fleet size and mix vehicle routing problem to solve the robust model. Through test experiments, it is proved that the robust optimization model can effectively reduce the number of customers affected by the uncertainty, greatly improve customer satisfaction, and effectively reduce total cost and demonstrate that the improved algorithm also exhibits good performance.

Optimal design for component capacity of integrated energy system based on the active dispatch mode of multiple energy storages

The hourly cooperation of production units and storage units in integrated energy system (IES) improves its performance on energy generation and supply, but increases the complexity in the capacity design. Conventional methods are flawed in terms of inappropriate operation mode and inaccurate performance evaluation. To improve the reliability of IES design, this research adopts operation trajectory prediction, energy dispatch schedule, and real time correction into operation mode, as well as annual continuous active dispatch optimization into capacity design method. IES components are divided into production unit, storage unit, and backup units. The proposed operation mode operates storage units actively to shift peak demand and resist uncertainty. The proposed design method optimizes both the capacity and cooperation of all units through an annual continuous operation, which improves the stability and reliability of the system performance. Compared with conventional methods, IES designed by the proposed method reduces energy consumption by 2.22%, annual cost by 1.40%, and carbon emission by 3.87%. In addition, this IES presents stability and robustness under different renewable energy and customer demand uncertainties. The proposed operation mode and design method contribute to accurate and speedy optimization, and provide reliable suggestions and feedbacks for IES investment and management.

Multi-modal transport of perishable products with demand uncertainty and empty repositioning: A scenario-based rolling horizon framework

We study the planning of a multi-modal transportation system with perishable products, demand uncertainty and repositioning of the empty Returnable Transport Items (RTIs). We propose a rolling horizon framework where we periodically re-optimize. As such, relevant responses and actions to new occurred demand are taken, and possible updates to the transportation and repositioning plans can be made. Our rolling horizon framework considers the uncertainty of customer demand, formulated as a Scenario-based Two-Stage Program (STSP) for which a set of scenarios is generated. An Adaptive Large Neighborhood Search (ALNS) algorithm is used to solve this scenario-based problem. Our proposed ALNS algorithm employs new operators and strategies to solve this complex and large problem. We give detailed computational analysis on the properties of our framework, evaluating the effects of stochastic demand, and we provide practical insights.

MIMOA: A membrane-inspired multi-objective algorithm for green vehicle routing problem with stochastic demands

Abstract Nowadays, an increasing number of vehicle routing problem with stochastic demands (VRPSD) models have been studied to meet realistic needs in the field of logistics. In this paper, a bi-objective vehicle routing problem with stochastic demands (BO-VRPSD) was investigated, which aims to minimize total cost and customer dissatisfaction. Different from traditional vehicle routing problem (VRP) models, both the uncertainty in customer demands and the nature of multiple objectives make the problem more challenging. To cope with BO-VRPSD, a membrane-inspired multi-objective algorithm (MIMOA) was proposed, which is characterized by a parallel distributed framework with two operation subsystems and one control subsystem, respectively. In particular, the operation subsystems leverage a multi-objective evolutionary algorithm with clustering strategy to reduce the chance of inferior solutions. Meanwhile, the control subsystem exploits a guiding strategy as the communication rule to adjust the searching directions of the operation subsystems. Experimental results based on the ten 120-node instances with real geographic locations in Beijing show that, MIMOA is more superior in solving BO-VRPSD to other classical multi-objective evolutionary algorithms.

Service level improvement due to worker cross training with stochastic worker absence

To react on increasing customer demand uncertainty, production systems have to be flexible concerning the provided capacity. With respect to labour, one opportunity to gain such flexibility is to assign workers to different work stations which often require different skills to be operated. Therefore, cross-trained workers are needed to enable this flexibility. Since the qualifying workforce implies costs, a relevant problem is how much skills and what mix of skills is optimal for a production system. In addition, the workforce may be on vacation or have a sick leave and hence is not always available. In this paper, we study the effect of different predefined workforce qualification profiles for a streamlined production system with simulation and compare the results with simulation-based optimisation using a genetic algorithm. Specifically the effect of stochastic worker absence, in comparison to workers being always available, is evaluated for different production system scenarios. The results show that cross-trained workers can significantly improve the service level achieved and that simulation-based optimisation can provide a much better worker specific mix of skills than predefined qualification profiles. Another managerial insight is that there is a trade-off between number of skills and number of workers needed to obtain the same service level.

Initial Coin Offerings, Speculation, and Asset Tokenization

Initial coin offerings (ICOs) are an emerging form of fundraising for blockchain-based startups. We examine how ICOs can be leveraged in the context of asset tokenization, whereby firms issue tokens backed by future assets (i.e., inventory) to finance growth. We (i) make suggestions on how to design such “asset-backed” ICOs—including optimal token floating and pricing for both utility and equity tokens (a.k.a. security token offerings)—taking into account moral hazard (cash diversion), product characteristics, and customer demand uncertainty; (ii) make predictions on ICO success/failure; and (iii) discuss implications on firm operating strategy. We show that in unregulated environments, ICOs can lead to significant agency costs, underproduction, and loss of firm value. These inefficiencies, however, fade as product margins and demand characteristics (mean/variance) improve, and they are less severe under equity (rather than utility) token issuance. Importantly, the advantage of equity tokens stems from their inherent ability to better align incentives and thus continues to hold even absent regulation. This paper was accepted by Vishal Gaur, operations management.