A Hybrid Programming Model for Optimal Production Planning under Demand Uncertainty in Refinery

Abstract Production planning under uncertainty is considered as one of the most important problems in plant-wide optimization. In this article, first, a stochastic programming model with uniform distribution assumption is developed for refinery production planning under demand uncertainty, and then a hybrid programming model incorporating the linear programming model with the stochastic programming one by a weight factor is proposed. Subsequently, piecewise linear approximation functions are derived and applied to solve the hybrid programming model-under uniform distribution assumption. Case studies show that the linear approximation algorithm is effective to solve.the hybrid programming model, along with an error≤0.5% when the deviatiorgmean≤20%. The simulation results indicate that the hybrid programming model with an appropriate weight factor （0.1-0.2） can effectively improve the optimal operational strategies under demand uncertainty, achieving higher profit than the linear programming model and the stochastic programming one with about 1.3% and 0.4% enhancement, respectavely.

Risk-taking path choice behaviors under ATIS in transportation networks with demand uncertainty

A new travel time reliability-based traffic assignment model is proposed to investigate the effects of an advanced transportation information system (ATIS) on drivers' risk-taking path choice behaviours in transportation networks with demand uncertainty. In the model, drivers are divided into two classes. The first class is not equipped with ATIS, while the second class is equipped with ATIS. Different risk-taking path choice behaviours of the two classes are studied, respectively. A corresponding mixed equilibrium traffic assignment model is formulated as a variational inequality problem in terms of path flows, which is solved by a heuristic solution algorithm. Numerical results indicate that the ATIS can influence the drivers' risk-taking path choice behaviours and the total system travel time in transportation networks with demand uncertainty. It is also found that under higher demand levels, the benefits of ATIS for network performance enhancement may be more obvious.

Emergency Logistics Management for Hazardous Materials with Demand Uncertainty and Link Unavailability

Due to its harmful nature,any incident associated with hazardous material(hazmat)may cause tremendous impacts on the surrounding people and the environment.Focusing on the incident involving this specific type of good,we develop a reliable and robust emergency logistics network that considers both demand uncertainty and possible unavailability of particular links.A time-based risk measure is carefully designed upon the traditional risk assessment to reflect the stakeholder’s sensitivity to risk over response time.The disruption and uncertainty are modeled as two sets of scenarios which are integrated into a bi-objective robust model to evaluate the trade-offs between risk and cost.The effectiveness of the emergency response can be assured by expenditures that add extra capacities to certain links or establish additional facilities that aid recovery from incidents.We apply our model and approach to a real-world network in Guangdong China.Analytical results reveal the necessity of embedding consideration of uncertainty and unreliability into emergency network design problems;outline the importance of hedging against unpredictability by system redundancies;and indicate the impact of stakeholder’s orientation towards cost and risk on the location,allocation,and routing decisions in hazmat emergency response.

FUZZY-BASED NETWORK BANDWIDTH DESIGN UNDER DEMAND UNCERTAINTY

In communication networks （CNs）, the uncertainty is caused by the dynamic nature of the traffic demands. Therefore there is a need to incorporate the uncertainty into the network bandwidth capacity design. For this purpose, this paper developed a fuzzy methodology for network bandwidth design under demand uncertainty. This methodology is usually used for offiine traffic engineering optimization, which takes a centralized view of bandwidth design, resource utilization, and performance evaluation. In this proposed methodology, uncertain traffic demands are first handled into a fuzzy number via a fuzzification method. Then a fuzzy optimization model for the network bandwidth allocation problem is formulated with the consideration of the trade-off between resource utilization and network performance. Accordingly, the optimal network bandwidth capacity can be obtained by maximizing network revenue in CNs. Finally, an illustrative numerical example is presented for the purpose of verification.

Decision model for closed loop supply chain with uncertain demand and price-dependent returns

A single product closed-loop supply chain that satisfies an uncertain market demand with original and remanufactured products is considered.The yield of the recovery process is random and depends on the acquisition price offered for the end-of-life products.In such a stochastic setting,a firm needs to make production and procurement decisions so that the total expected profit is maximized.Both centralized and decentralized models are established depending on the party collecting the returns.The optimal acquisition price and production quantities of original and remanufactured products are determined for the firm.The contracts to coordinate the decentralized systems are chosen and the optimal contract parameters are determined.A computational experiment is given to show the effects of recovery parameters on the system performance.Results show that the recovery parameters have a high impact on the profitability of the manufacturer in the centralized model and on that of the collection agency in the decentralized model.

Two-Stage Production Planning Under Stochastic Demand:Case Study of Fertilizer Manufacturing

Agriculture is a key facilitator of economic prosperity and nourishes the huge global population.To achieve sustainable agriculture,several factors should be considered,such as increasing nutrient and water efficiency and/or improving soil health and quality.Using fertilizer is one of the fastest and easiest ways to improve the quality of nutrients inland and increase the effectiveness of crop yields.Fertilizer supplies most of the necessary nutrients for plants,and it is estimated that at least 30%-50%of crop yields is attributable to commercial fertilizer nutrient inputs.Fertilizer is always a major concern in achieving sustainable and efficient agriculture.Applying reasonable and customized fertilizerswill require a significant increase in the number of formulae,involving increasing costs and the accurate forecasting of the right time to apply the suitable formulae.An alternative solution is given by two-stage production planning under stochastic demand,which divides a planning schedule into two stages.The primary stage has non-existing demand information,the inputs of which are the proportion of raw materials needed for producing fertilizer products,the cost for purchasing materials,and the production cost.The total quantity of purchased material and produced products to be used in the blending process must be defined to meet as small as possible a paid cost.At the second stage,demand appears under multiple scenarios and their respective possibilities.This stage will provide a solution for each occurring scenario to achieve the best profit.The two-stage approach is presented in this paper,the mathematical model of which is based on linear integer programming.Considering the diversity of fertilizer types,themathematicalmodel can advise manufacturers about which products will generate as much as profit as possible.Specifically,two objectives are taken into account.First,the paper’s thesis focuses on minimizing overall system costs,e.g.,including inventory cost,purchasing cost,unit cost,and ordering cost at Stage 1.Second,the thesis pays attention tomaximizing total profit based on information from customer demand,as well as being informed regarding concerns about system cost at Stage 2.

Robust Design of Virtual Topology for WDM Networks under Bandwidth Demand Uncertainties

In this paper, a novel method is proposed to address the problem of designing virtual topology over wavelength division multiplexing （WDM） networks under bandwidth demand uncertainties. And a bandwidth demand model under uncertainties is presented. The optimization goal of virtual topology design is defined as minimizing the maximum value amongp percentiles of the bandwidth demand distribution on all Hght-paths. Correspondingly, we propose a heuristic algorithm called an improved decreasing multi-hop logical topology design algorithm （ID-MLTDA） that involves with a degree of uncertainties to design virtual topology. The proposed algorithm yields better performance than previous algorithms. Additionally, the simplicity and efficiency of the proposed algorithm can be in favor of the feasibility for topology design of large networks.

A predictive chance constraint rebalancing approach to mobility-on-demand services

This paper considers the problem of supply-demand imbalances in Mobility-on-Demand(MoD)services.These imbalances occur due to uneven stochastic travel demand and can be mitigated by proactively rebalancing empty vehicles to areas where the demand is high.To achieve this,we propose a method that takes into account uncertainties of predicted travel demand while minimizing pick-up time and rebalance mileage for autonomous MoD ride-hailing.More precisely,first travel demand is predicted using Gaussian Process Regression(GPR)which provides uncertainty bounds on the prediction.We then formulate a stochastic model predictive control(MPC)for the autonomous ride-hailing service and integrate the demand predictions with uncertainty bounds.In order to guarantee constraint satisfaction in the optimization under estimated stochastic demand prediction,we employ a probabilistic constraining method with user-defined confidence interval,using Chance Constrained MPC(CCMPC).The benefits of the proposed method are twofold.First,travel demand uncertainty prediction from data can naturally be embedded into the MoD optimization framework,allowing us to keep the imbalance at each station below a certain threshold with a user-defined probability.Second,CCMPC can be relaxed into a Mixed-Integer-Linear-Program(MILP)and the MILP can be solved as a corresponding Linear-Program,which always admits an integral solution.Our transportation simulations show that by tuning the confidence bound on the chance constraint,close to optimal oracle performance can be achieved,with a median customer wait time reduction of 4%compared to using only the mean prediction of the GPR.

Comparison of Group-Buying Online Auction and Posted Pricing Mechanism in an Uncertain Market

Demand uncertainty is a key factor for the seller's decision making, especially in the e-business environment, for the website to sell products through the online auction. In this paper, two kinds of demand uncertainties are considered: the consumer regime uncertainty and the inherent randomness of the market environment. Then, how to use a novel business model and group-buying auction (GBA) is analyzed in such a market environment. Based on the comparison of the GBA and the posted price mechanism, some conditions that favor the GBA are provided.

THE DEMAND DISRUPTION MANAGEMENT PROBLEM FOR A SUPPLY CHAIN SYSTEM WITH NONLINEAR DEMAND FUNCTIONS

This paper addresses the problem of handling the uncertainty of demand in a one-supplier-one-retailer supply chain system. Demand variation often makes the real production different from what is originally planned, causing a deviation cost from the production plan. Assume the market demand is sensitive to the retail price in a nonlinear form, we show how to effectively handle the demand uncertainty in a supply chain, both for the case of centralized-decision-making system and the case of decentralized-decision-making system with perfect coordination.

STOCHASTIC COMPARISONS IN A PRICE-QUANTITY SETTING FIRM WITH UNCERTAIN DEMAND AND EMERGENCY PROCUREMENT

We consider a newsvendor problem with price-dependent demand, in either additive or multiplicative format. The newsvendor has two modes of purchasing： regular ordering at the beginning of the selling season and emergency ordering （if the realized demand exceeds the initial order quantity） at the end of the selling season. By stochastic comparisons, we systematically investigate the effects of demand magnitude and demand randomness on pricing and ordering quantity decisions as well as expected profit of the newsvendor, under both usual stochastic order （first order stochastic dominance） and convex order （less variable）. Our key findings include： （i） in contrary to the case where price is exogenous, a stochastically larger demand shock may even lead to a lower order quantity; （ii） a stochastically larger demand shock leads to a higher price for the additive demand case, but may lead to a lower price for the multiplicative demand case; （iii） a stochastically larger demand shock leads to a higher expected profit for both demand models; （iv） in general, a less variable demand leads to a higher expected profit for both demand models; and furthermore, a less variable demand shock has no effect on price for the additive demand model, but leads to a higher price for the multiplicative demand model. The implications of all these findings for pricing and order quantity are discussed in detail.

Optimal procurement strategy of fresh produce retailer under stochastic product qualification and market demand

Consider an optimal procurement strategy for fresh produce,which is a type of perishable product.Due to the different quality provided by each manufacturer,the fresh produce qualification rates are dissimilar.Simultaneously,consumers demand is influenced by the freshness and price of products,as a result,the demand in the market is not fixed.In this scenario,how a particular retailer should develop an optimal procurement strategy will be a core issue in supply chain management.In order to address the above questions,the Bayesian approach is adopted to explore retailer optimal procurement strategies with uncertainty about product supply and market demand.Finally,simulation data are used to analyse the results of the proposed model and expected non-random model to illustrate the validity and feasibility of the proposed model.

Mechanisms of Collaboration in the Hotel Supply Chain:Two-Stage Ordering Contract and Option Contract

This paper considers a hotel supply chain consisting of a hotel and an online travel agent （OTA） for the distribution of a limited number of rooms through both offline and online channels. Under the merchant cooperation assumption, to overcome the disadvantages of the decentralized decision model and demand uncertainty, two collaboration mechanisms, namely a two-stage ordering contract and an option contract, are introduced to increase the profit in the hotel supply chain. This paper investigates the optimal decisions of the hotel and the OTA under different contract models. Moreover, the authors analyze the effect of demand uncertainty and the interaction of demand variability and the hotel＇s capacity on the profits of the hotel and the OTA under different models. The results show that both the two-stage ordering contract and the option contract can increase the profits of the entire supply chain and the hotel; however, the profit of the OTA can be increased through the two-stage ordering contract and option contract only when the hotel＇s capacity is relatively small and the demand variability is big; otherwise, the two collaboration mechanisms cannot increase the OTA＇s profit.

GROUP-BUYING ONLINE AUCTION AND OPTIMAL INVENTORY POLICY IN UNCERTAIN MARKET

In this paper we consider a group-buying online auction (GBA) model for a monopolistic manufacturer selling novel products in the uncertain market. Firstly, we introduce the bidder's dominant strategy, after which we optimize the GBA price curve and the production volume together. Finally, we compare the GBA with the traditional posted pricing mechanism and find that the GBA is highly probable to be advantageous over the posted pricing mechanism in some appropriate market environments.

Major government customer and management earnings forecasts

This paper examines whether customer base composition in the US,that is,whether a firm's major customers are government entities or publicly traded companies,affects the properties of its management earnings forecasts(MEFs).Using a sample of 1,168 MEFs from 1998 to 2014,we find that firms whose major customers are government entities(i.e.,govemment suppliers)issue more precise and more accurate MEFs than firms whose major customers are public companies(i.e.,corporate suppliers).Moreover,when managers disclose negative information to the market,earnings forecasts issued by government suppliers have greater price impact than those issued by corporate suppliers.Collectively,our empirical results suggest that having major govemment customers has a positive impact on the quality of MEFs.

Probabilistic steady-state and dynamic security assessment of power transmission system

Two-level system model based probabilistic steady-state and dynamic security assessment model is introduced in this paper.Uncertainties of nodal power injection caused by wind power and load demand,steady-state and dynamic security constraints and transitions between system configurations in terms of failure rate and repair rate are considered in the model.Time to insecurity is used as security index.The probability distribution of time to insecurity can be obtained by solving a linear vector differential equation.The coefficients of the differential equation are expressed in terms of configuration transition rates and security transition probabilities.The model is implemented in complex system successfully for the first time by using the following effective measures:firstly,calculating configuration transition rates effectively based on component state transition rate matrix and system configuration array;secondly,calculating the probability of random nodal power injection belonging to security region effectively according to practical parts of critical boundaries of security region represented by hyper-planes;thirdly,locating non-zero elements of coefficient matrix and then implementing sparse storage of coefficient matrix effectively;finally,calculating security region off-line for on-line use.Results of probabilistic security assessment can be used to conduct operators to analyze system security effectively and take preventive control.Test results on New England 10-generators and 39-buses power system verify the reasonableness and effectiveness of the method.