Jingchun Wang

Adaptive State Feedback Predictive Control and Expert Control for a Delayed Coking Furnace

Abstract An adaptive state feedback predictive control (SFPC) scheme and an expert control scheme are presented and applied to the temperature control of a 1200 kt·a −1 delayed coking furnace, which is the key equipment for the delayed coking process. Adaptive SFPC is used to improve the performance of temperature control in normal operation. A simplified nonlinear model on the basis of first principles of the furnace is developed to obtain a state space model by linearization. Taking advantage of the nonlinear model, an online model adapting method is presented to accommodate the dynamic change of process characteristics because of tube coking and load changes. To compensate the large inverse response of outlet temperature resulting from the sudden increase of injected steam of a particular velocity to tubes, a monitoring method and an expert control scheme based on heat balance calculation are proposed. Industrial implementation shows the effectiveness and feasibility of the proposed control strategy.

Nonlinear dynamic matrix control based on multiple operating models

Abstract This paper presents a method of nonlinear dynamic matrix control based on multiple operating models. Its closed-loop stability and asymptotically unbiased tracking properties are proved. The application software (SMART-NLS) is developed using this method, and the simulation results of two nonlinear benchmark processes verify the feasibility and effectiveness of the proposed method.

Supply Chain Coordination with Sales Effort Effects and Impact of Loss Aversion on Effort Decision

Abstract A new supply contract based on sharing the sales profits as well as the cost of effort was developed to coordinate the supply chain with sales effort effects. The contract coordinates the suppliers actions with voluntary compliance; the contract is symmetric in the sense that both the suppliers and retailers profits are linearly correlated and is more easily implemented in some situations. The impact of the retailers loss aversion on his effort is investigated based on the contract. After characterizing the retailers optimal solutions, this paper demonstrates that contrary to intuition, loss aversion weakens incentives for retailers sales effort and the retailers optimal effort decreases as the loss aversion increases.

Coordinating a loss-averse newsvendor with vendor managed inventory

Supply chain inefficiencies can result from incompatible incentives provided by independent decision-makers and also their risk aversion effect. This paper investigates how vendor managed inventory (VMI) affects a supply chain formed by a risk-neutral supplier and a loss-averse retailer. Under no coordination schemes, the supply chain inefficiencies result from the double marginalization combined with the retailers risk effect. Coordination can be achieved by a modified quantity flexibility contract. Moreover, the supplier can share the risk of the retailer through VMI policy. It is shown that the optimal order quantity with VMI is higher than that without VMI and VMI improves the performance of the supply chain and achieve a win-win outcome.

An Advanced Selective Hydraulic Control system for the Heavy-duty Rough Grinding Machine

An Advanced Selective Hydraulic Control system (ASHC) for a Heavy-duty Rough Grinding Machine (HRGM) is discussed. The HRGM has some special features which are difficult to handle, such as heavy moving part with great inertia, multiple disturbances with known and unknown sources, and nonlinear characteristics. So it is necessary to design an appropriate control system with good parameters tuning to achieve the manipulation goal. Through the theoretical analysis and experiments, some key features and models of the HRGM are revealed and built separately, and then an ASHC system is proposed, which combined a cascade control system and a simple loop control system. Both the two systems have nonlinear compensation model and feedforward control as the assisted modules to get better control performance. With this ASHC system, the constant grinding force is achieved. Steady and good quality products are produced by the HRGM for more than one year. It shows that the economic benefits and working condition improvements are brought to the plant.

Take-or-pay contracted assembly system with random demand

An assembler needs sets of components, each produced by a different supplier. To buy the components from the components suppliers and assemble the final product, the assembler must order before observing the actual demand. Under a take-or-pay contract given by the supplier, the assembler can procure the component directly or prepay for the option first and buy the component when demand observed. All parts are only purchased for the current demand season. The assembler faces a random demand and spot market exists for the components if necessary. The objective is to maximize the total expected profit. We present some structural results of optimal procurement decision and numerical results given.

Trajectory Planning for Automated Driving Based on Ordinal Optimization

This paper proposes an approach based on Ordinal Optimization (OO) to solve trajectory planning for automated driving. As most planning approaches based on candidate curves optimize the trajectory curve and the velocity profile separately, this paper formulates the problem as an unified Non-Linear Programming (NLP) model, optimizing the trajectory curve and the acceleration profile (acceleration is the derivative of velocity) simultaneously. Then a hybrid optimization algorithm named OODE, developed by combining the idea of OO and Differential Evolution (DE), is proposed to solve the NLP model. With the acceleration profile optimized “roughly”, OODE computes and compares “rough” (biased but computationally-easier) curve evaluations to select the best curve from candidates, so that a good enough curve can be obtained very efficiently. Then the acceleration profile is optimized again “accurately” with the selected curve. Simulation results show that good enough solutions are ensured with a high probability and our method is capable of working in real time.

Lead time and quantity decision in assembly system with spot market

A two-stage assembly system is considered. An assembler procures associated component parts and assembles final products to fulfill a random quantity of demand. The final product must be delivered in a certain time; otherwise a delay cost is incurred. Lead times of the components are independent stochastic variables. And, in our model except regular channels from which the components is procured before the demand reason, components spot markets exist for the need when ordered quantity is not enough. That is, we consider random demand quantity and random operation times at the same time in an assembly system with components spot market. The objective is to minimize the total expected cost. We investigate the structure of optimal operation time plan and quantity decision. The analytical and heuristics numerical result manifest some management insights of the system.

Pricing and replenishment strategies in supply chains with competing loss averse retailers

This paper investigates a single-period supply chain model with competing loss-averse retailers who decide their pricing and order quantity simultaneously. Firstly, the unimodality property of the retailers expected utility and the uniqueness property of its decisions are studied under non-competing circumstances. In comparison to a risk-neutral retailer, a loss-averse retailer orders less and charges a higher price under some conditions. Then, the competing impact among the loss-averse retailers is considered. After investigating the equilibrium behavior of the decentralized supply chain, a pull contract is constructed to coordinate the decentralized supply chain, which allows the decentralized chain to perform as well as a centralized one.