Optimal pricing policy for a deteriorating product by dynamic tracking control

We study the optimal selling price of a deteriorating product under a deterministic situation in a finite time horizon where the time horizon is either known or unknown. Inventory holding cost is expressed as a quadratic function of the current inventory level. Given a known time horizon, we develop a model by considering the deterioration dynamics of the product, and show its equivalence to a generalised optimal control problem of a linear quadratic form, i.e. an optimal dynamic tracking problem with constraints on the control variable. An optimal pricing policy is derived based on the maximum value principle. The control policy takes a state feedback form; it exhibits a closed-loop relationship between the optimal selling price (control variable) and the optimal inventory level (state variable). Given an unknown time horizon, an optimal pricing policy is derived through a similar approach when the initial inventory level meets certain conditions. Numerical situations are conducted to illustrate the effectiveness of the derived price control policies. Some interesting managerial insights are discussed.     

Lockout/tagout and optimal production control policies in failure-prone non-homogenous transfer lines with passive redundancy

This paper considers a production problem for a transfer line subject to random failures and repairs, and differs from other studies on transfer lines. It considers a manufacturing system consisting of three machines (two machines with passive redundancy, and one in series with the previous ones) producing one part type. The control problem is subject to non-negative constraints on work-in-process (WIP). The decision variables are the production rates of two main machines and a standby machine, and influence the WIP levels, the inventory levels and the system's capacity, which is assumed to be described by a finite-state Markov chain. The objective of this paper is to minimise WIP and finished goods inventory costs; it also aims to respect the essential space–time during intervention on machine down, in order to minimise the possibility of the circumvention of protection devices or of the retraction of lockout/tagout procedures through a passive redundancy system. This paper therefore verifies the effect of passive redundancy on optimal stock levels. Given that an analytical or even a numerical solution of the problem is very difficult to find, and that we want to have a more realistic model for industries, we present a combined approach, which is presented based on a combination of analytical formalism, simulation modelling, design of experiments, and response surface methodology to optimise a transfer line with passive redundancy, producing one part type. The usefulness of the proposed approach is illustrated through a numerical example and a sensitivity analysis.     

Reduced order modelling and predictive control of multivariable nonlinear process

In this paper, an efficient model-predictive control strategy that can be applied to complex multivariable process is presented. A reduced order generalized predictive algorithm is proposed for online applications with reduction in complexity and time elapsed. The complex multivariable process considered in this work is a binary distillation column. The reduced order model is developed with a recently proposed hybrid algorithm known as Clustering Dominant Pole Algorithm and is able to compute the full set of dominant poles and their cluster centre efficiently. The controller calculates the optimal control action based on the future reference signals, current state and constraints on manipulated and controlled variables for a high-order dynamic simulated model of nonlinear multivariable binary distillation column process. The predictive control algorithm uses controlled auto-regressive integrated moving average model. The performance of constraint generalized predictive control scheme is found to be superior to that of the conventional PID controller in terms of overshoot, settling time and performance indices, mainly ISE, IAE and MSE.     

The consignment stock of inventories under buyer's warehouse space limitation

In this paper, coordination between a single vendor (or manufacturer) and a buyer (or retailer) via the delivery schedule in a production and distribution system is presented. A continuous deterministic model with centralised decision process is developed. To satisfy the buyer's demands, the product is delivered in discrete batches from the vendor's stock to the buyer's stock subject to consignee's warehouse space capacity constraint. A more general type of consignment stock (CS) policies is analysed. The inventory patterns and the cost structure of production distribution cycles (PDC) are described under the constraint – when the ratio α of the sizes of delayed to just in time (JIT) for the vendor deliveries is given. Also, an analysis how an upper buyer's warehouse space limit can act as a key factor for implementing the generalised CS-policies successfully. Numerical examples are presented to illustrate the performance of model and the results. A comparative study of the results shows that the generalised CS-policies performs better.     

变频空调多变量模糊控制

家用变频空调蒸发压缩循环各反馈回路存在强耦合,使得传统的基于模型的控制器和解耦模糊控制器控制性能受到限制.提出一种非模型多变量模糊控制器,调节蒸发压缩循环的蒸发温度和过热度以改善其控制性能. 实验结果表明所提出的MFC能有效提高系统性能和改善能效,且该控制器设计和实现简单方便.     

Superstructure-based synthesis and optimisation of an oil palm eco-industrial town: a case study in Iskandar Malaysia

Malaysia is one of the world’s top edible oil producers, having more than 5.23 million hectares of palm oil plantations and more than 400 palm oil mills. The oil palm industry produces millions of tonnes of biomass waste during harvesting and mill processing. This paper presents an oil palm eco-industrial town (EIT) that integrates a palm oil mill with nine downstream oil palm-based industries, as well as a community. The downstream industries produce various types of products such as crude palm oil, bio-fertiliser, bio-gas, bio-diesel, bio-pellet, medium-density fibreboard (MDF), and are also involved in the paper industry, and livestock production. Through the concept of industry symbiosis, the oil palm EIT promotes energy and material sharing among the industries and the community to reduce energy consumption, virgin material consumption, and waste generation. Therefore, this concept could provide economic and environmental benefits to upstream industries (utilisation of biomass), downstream industries (conversion of biomass to valuable products), and the community (job creation). In this work, a multi-objective linear programming model is formulated to maximise economic performance, while minimising waste generation in the oil palm EIT. The applicability of the model is demonstrated using a case study in Iskandar Malaysia (IM). The optimised model suggests that the most efficient way to utilise abundant oil palm biomass is via the production of crude palm oil, MDF, bio-paper, paper, bio-gas, and bio-diesel. The model could assist decision makers to identify the sub-industries in the EIT that would promote sustainability in the oil palm industry.     

Inventory Management of a Plant Spare Parts Supply Chain

In spare parts industries, firms are dealing with a situation which is more and more uncertain due to the supply chain structure and variable demands. This paper presents a Bayesian approach to forecast demand and subsequently determine the appropriate parameter S of an (S - 1; S) inventory system for controlling plant spare parts. We apply the Bayesian approach in an innovative way to specify the initial prior distributions of the failure rates, using the initial estimates and the failure history of similar items. According to the proposed method, a lower base stock than the one currently used is sufficient to achieve the desired service level.     

A constrained EPSAC approach to inventory control for a benchmark supply chain system

The design of an appropriate inventory control policy for a supply chain (SC) plays an essential role in tempering inventory instability and bullwhip effect. Several constraints are commonly encountered in actual operations so managers are required to take these physical restrictions into account when designing the inventory control policy. Model predictive control (MPC) appears as a promising solution to this issue, due to its capability of finding optimal control actions for a constrained SC system. Therefore, the inventory control problem for a benchmark SC is solved using the extended prediction self-adaptive control approach to MPC. To extend methodologies in our previous work, the control framework relies on generic process model and incorporates the physical constraints arising from practical operations to form the general constrained optimisation problems. The managers can choose from decentralised and centralised control structures according to specific informational and organisational factors of their SCs. The proposed control schemes in this study may be appropriate for industrial practice because the designed policy can bring a reduction of over 30% in operating cost and a significant increase of customer satisfaction level compared with that of the conventional policy.     

A supply chain model with vendor-managed inventory,consignment, and quality inspection errors

This paper considers the integration between quality control and production inventory control in supply chain management. Specifically, we study the effect of inspection errors on the costs incurred in a supply chain system with a single vendor and multiple buyers. In this system, the vendor enters into a vendor-managed inventory (VMI) and a consignment stock (CS) partnership with several buyers. We assume that the items made by the vendor are not in perfect quality, but they contain a given proportion of defective units. We also assume that quality inspection of these items by the buyers is subject to sampling errors. Three cases indicating to different levels of supply integration are considered: VMI–CS system, traditional system and integrated system. For each case, a mathematical model is formulated, an optimum solution is developed, and a numerical example is solved.     

The diffusion process and residual stock in periodic review,coordinated control of families of items

Inventory control models which provide coordinated control ( or joint replenish ment) of families of items are very useful in practice. These models require expressions for the residual stock levels for each item in the family when a replenishment is placed. In this paper expressions are developed for the periodic review residual stock probability density function, when inventory position is modelled as a diffusion process, and heuristics are presented for quick calculation of these expressions. The importance of residual stock in setting inventory control model parameters is illustrated by showing how residual stock is taken into account in establishing item reorder points. The relationship of these residual stock pdf expressions to other components in a new class of coordinated control models is also discussed. This class of models has been shown to outperform existing models (IBM's IMPACT Inventory Control package).     

Innovative vendor-managed inventory strategy exploiting interconnected logistics services in the Physical Internet

Traditional supply chain networks are often designed in the interests of a company. Once the network has been defined, the storage and distribution of goods are usually fixed and restricted within the network. This is assumed to be an inherent limit of current inventory control research. Instead of specialised hierarchical storage networks, this paper proposes an innovative vendor-managed inventory strategy exploiting the Physical Internet (PI), which is an open, universal, interconnected logistics system. In such a system, facilities and means of transport are shared and can be allocated according to demands of users. As a result, the PI allows users to stock anywhere in the network and also provides open multisourcing options for orders with on-demand warehousing services within the PI. Inventory decisions can be made dynamically by each player to minimise networkwide inventory levels. A non-linear, simulation-based optimisation model was developed for the vendors’ inventory decision-making when confronted with stochastic demands. A metaheuristic using simulated annealing was applied to solve the problem, and then, the optimised inventory decisions were validated using simulation. The results suggest that the proposed PI inventory model can reduce the total logistics cost while maintaining a comparable or better level of end customers’ services.     

一种基于PSO算法的非线性模型预测控制方法

将微粒群优化(PSO)算法用于输入受限非线性系统,提出了一种基于PSO的非线性模型预测控制算法.该算法采用双模控制策略,将保证预测控制稳定性的终端等式约束转化为终端不等式约束,推导出使系统稳定的不变可行集.在不变集外,利用PSO算法优化求解预测控制律,使系统状态进入不变集;在不变集内,利用线性状态反馈使系统状态渐近稳定.同时对算法的稳定性进行了分析.仿真结果证明了该算法的可行性和有效性.     

An inventory model with emergency orders under explicit energy cost considerations

The inventory model in this paper is targeted to production systems with constant production rates but underlying possibilities for undesirable circumstances to threaten the production schedule. The inventory policy proposed explicitly considers energy cost when determining optimal size for order quantity, safety stock and inventory cycle length such that the total expected cost per unit time is minimised. The results are compared to a traditional inventory policy that does not consider the direct impact of energy cost. An analysis of the model reveals three production environment characteristics in which inventory policies are most significantly affected by changes in energy cost: heavy product weight, high regular product demand or high emergency product demand. If any one of the three key factors increases, then changes of the inventory decisions or related logistics costs become more significant. The cost effectiveness of implementing the proposed inventory policy also becomes more significant as any one of the three key factors increase with respect to energy cost.     

An application of cost-effective fuzzy inventory controller to counteract demand fluctuation caused by bullwhip effect

This paper develops a fuzzy inventory model to counteract the demand fluctuation in supply demand networks, which combines fuzzy logic controller with (s,?S) policy based on economic order quantity (EOQ) model. Following a literature review and a discussion of counteractions to the bullwhip effect and the obstruction of general counteraction in supply demand networks, a multi-echelon fuzzy inventory model in supply demand networks is proposed. A simulation model with one- and two-echelon supply demand network is built and tested for (s, S) policy based on the classical EOQ model and the proposed fuzzy inventory model. Based on the simulation, results of the relevance performance are presented and discussed, which show that the proposed multi-echelon fuzzy inventory model provides not only a cost-effective management of inventory (e.g. lower inventory levels and cost) in market uncertainty, but also another effective alternative for counteracting demand fluctuation. In particular, the proposed multi-echelon fuzzy inventory model shows benefit in counteracting demand fluctuation in multi-echelon supply demand networks. Finally, some conclusions and suggestions for further research works are presented.     

Control of dead-time systems using derivative free local search guided population based incremental learning algorithms

This paper introduces two improved forms of population based incremental learning (PBIL) algorithm applied to proportional integral derivative (PID) controller and Smith predictor design. Derivative free optimization methods, namely simplex derivative pattern search (SDPS) and implicit filtering (IMF) are used to intensify search mechanism in PBIL algorithm with improved convergence than that of the original PBIL. Although the idea of combining local methods and global methods is not new, this paper focuses application of hybrid heuristics to the vast field of control design especially, control of systems having dead-time. The effectiveness of the controller schemes arrived using the developed algorithms namely simplex derivative pattern search guided population based incremental learning (SDPS-PBIL) and implicit filtering guided population based incremental learning (IMF-PBIL) are demonstrated using unit step set point response for a class of dead-time systems. The results are compared with some existing methods of controller tuning.     

LMI-based robust voltage tracking control for a class of renewable energy systems

This paper introduces a method of robust voltage tracking control for a class of renewable energy systems, which uses a linear matrix inequality (LMI)-based approach. A remodeling method is introduced where the nonlinear model of the renewable energy system is represented as a nonlinear combination of linear subsystems with state-dependent parameters (the so-called linear state-dependent parameter system model, LSDPM). Next, the remodeling method is applied to a boost converter, a photovoltaic (PV) system, and a wind energy conversion system as examples. A robust output voltage tracking approach is proposed based on the LSDPM. Using Lyapunov’s direct method, controller stability analyses are expressed as LMI formulations. Powerful numerical toolboxes then solve for the controller gains. The advantages of this approach include: i) the generalized remodeling technique is applicable to a wide variety of renewable energy systems; ii) it creates a unified LMI-based design framework for multiple control objectives; iii) it provides numerically solved control gains; and iv) it provides robust tracking control for the multiple objectives of renewable energy systems. Using an LMI-based robust voltage tracking controller is an accurate and effective method of control for many types of renewable energy systems. Finally, practical DSP-based experiments are carried out using the buck converter as an example. The satisfactory results of these experiments verify the theoretical derivations for this control method.     

针对阶跃形式失控的基于门限自回归模型的SPC-EPC集成研

SPC-EPC集成是一种控制和提升产品质量的有效方法,目前在传统SPC EPC集成的研究中通常使用线性时间序列模型来描述过程的动态自相关关系,但线性模型难以对更加复杂的非线性自相关关系进行有效描述．针对这一问题,提出了使用一类非线性时间序列模型,即门限自回归模型(TAR)来描述系统的动态自相关关系,并依此建立最小均方误差控制器,并进一步建立SPC EPC集成控制体系．针对在生产过程中常见的以阶跃形式存在的过程失控,首先通过例子研究了控制器在单独使用以及集成控制方法下的控制效果并且与线性控制器相应的结果进行了对比,之后通过模拟研究进一步验证和分析了这一集成控制方法的控制效果．结果表明,基于非线性时间序列的集成SPC EPC控制方法可以针对含阶跃形式失控的复杂的非线性自相关过程进行有效的控制．     

Service levels,system cost and stability of production–inventory control systems

Production-inventory control system models have been analysed in the literature either in terms of their stability against demand fluctuations or in terms of their service level and cost performance under uncertain demand. This article analyses the production-inventory system performance in terms of service level (i.e. order fill rate) and average system costs, under stable settings of the control parameters. The classical automatic pipeline variable inventory and order-based production control system has been modified by explicitly modelling safety stock to help achieve higher services levels in the face of random demand. The stability of the system is affected by the control parameters: fractional rates of adjustment of work-in-process and inventory. However, the service level and average cost are affected by the control parameters as well as the smoothing factor in demand forecasting. This article puts forward five propositions which give light to general system performance based on the parameters selection. Intensive simulation experiments have also been carried out to reveal the performance variations within the stable region, leading to further insights on the system behaviour. The managerial insights which can assist proper tuning of systems to help achieve the desired performances have been discussed.     

基于需求依赖库存的变质性商品最优库存-配送模型

超市型销售终端中商品展示水平的“选择效应”和“广告效应”,使得商品需求在很大程度上依赖其库存展示水平。针对这一实际情形,假设销售商在此类终端销售某一变质性商品,其中需求线性依赖于该商品当前库存水平,当库存水平下降到一定量时,立即从配送中心运送商品对超市库存进行补充。以单位平均利润最大化为目标构造相应的库存-配送模型,使用最优化理论讨论了模型最优解的存在性及唯一性,并提供寻求模型整体最优解的算法,以此求出最优的库存与配送方案。最后给出了数值例子,分析了模型参数变化对系统最优策略和利润的影响。     

Implementation of delayed differentiation in batch process industries: a standardization problem

Postponement consists in holding some activities in a supply chain until customer orders are received, these activities being differentiation manufacturing process or distribution activities. The delayed differentiation consists in maintaining products in an undifferentiated state as long as possible during the manufacturing process. This approach permits firms to be more responsive and other advantages that are often mentioned are risk-pooling and lead-time uncertainty reduction. Industrial applications of postponement mainly concern automotive or computers industries (large assembly systems). This paper studies the implementation of delayed differentiation in batch process industries. First, the addition of an intermediate stock is proposed between the two main stages of a process, which leads to an important reduction of the lead-time, but the inventory cost can be prohibitive. Reducing this stock gives rise to a standardization problem. Second, a standardization of the component types in the intermediate stock is performed to make this stock economically profitable. A 01 linear-programming model of the standardization problem is proposed. This model takes into account costly batch resources: standard components are chosen in order to optimize the use of batch resources. An industrial application of this approach is presented (an aluminium-conversion industry). In this case, a drastic reduction of components in the intermediate stock is performed since the number of component is reduced from about 100 to eight standard components.