动态供应链与控制问题研究进展

首先,阐述了在新的信息网络,特别是无线射频识别技术(RFID)环境下,供应链系统动态情景问题;然后,讨论了动态供应链运作特性问题,并分析了动态供应链系统中经典控制、最优控制、模型预测控制、鲁棒控制等问题;最后,指出了动态供应链系统与控制进一步研究的几个问题.     

Decentralized and centralized model predictive control to reduce the bullwhip effect in supply chain management

Mitigating the bullwhip effect is one of crucial problems in supply chain management. In this research, centralized and decentralized model predictive control strategies are applied to control inventory positions and to reduce the bullwhip effect in a benchmark four-echelon supply chain. The supply chain under consideration is described by discrete dynamic models characterized by balance equations on product and information flows with an ordering policy serving as the control schemes. In the decentralized control strategy, a MPC-EPSAC (Extended Prediction Self-Adaptive Control) approach is used to predict the changes in the inventory position levels. A closed-form solution of an optimal ordering decision for each echelon is obtained by locally minimizing a cost function, which consists of the errors between predicted inventory position levels and their setpoints, and a weighting function that penalizes orders. The single model predictive controller used in centralized control strategy optimizes globally and finds an optimal ordering policy for each echelon. The controller relies on a linear discrete-time state-space model to predict system outputs. But the predictions are approached by either of two multi-step predictors depending on whether the states of the controller model are directly observed or not. The objective function takes a quadratic form and thus the resulting optimization problem can be solved via standard quadratic programming method. The comparisons on performances of the two MPC strategies are illustrated with a numerical example.     

Quantifying and mitigating the bullwhip effect in a benchmark supply chain system by an extended prediction self-adaptive control ordering policy

An undesired observation known as the bullwhip effect in supply chain management leads to excessive oscillations of the inventory and order levels. This paper presents how to quantify and mitigate the bullwhip effect by introducing model predictive control (MPC) strategy into the ordering policy for a benchmark supply chain system. Instead of quantifying the bullwhip effect with commonly used statistical measure, we derive equivalently the expression of bullwhip metric via control-theoretic approach by applying discrete Fourier transform and (inverse) z-transform when the demand signal is stationary stochastic. A four-echelon supply chain is formulated and its dynamical features are analyzed to give the discrete model. An extended prediction self-adaptive control (EPSAC) approach to the multi-step predictor is implemented in the development of MPC formulation. The closed-form solution to MPC problem is derived by minimizing a specified objective function. The transfer function for MPC ordering policy is then obtained graphically from an equivalent representation of this closed-form solution. A numerical simulation shows that MPC ordering policy outperforms the traditional ordering policies on reducing bullwhip effect.     

A review of supply chain complexity drivers

Studies on supply chain complexity mainly use the static and dynamic complexity distinction. While static complexity describes the structure of the supply chain, the number and the variety of its components and strengths of interactions between these; the dynamic complexity represents the uncertainty in the supply chain and involves the aspects of time and randomness. This distinction is also valid when classifying the drivers of supply chain complexity according to the way they are generated. Supply chain complexity drivers (e.g., number/variety of suppliers, number/variety of customers, number/variety of interactions, conflicting policies, demand amplification, differing/conflicting/non-synchronized decisions and actions, incompatible IT systems) play a significant and varying role in dealing with complexity of the different types of supply chains (e.g., food, chemical, electronics, automotive).     

Measuring supply chain resilience using a deterministic modeling approach

To achieve a competitive edge in an uncertain business environment where change is imperative, one of the significant challenges for an organization is to mitigate risk by creating resilient supply chains. This research proposes a model using graph theory which holistically considers all the major enablers of resilience and their interrelationships for analysis using an Interpretive Structural Modeling approach. The uniqueness of this model lies in its ability to quantify resilience by a single numerical index. The quantification of resilience will help organizations assess the effectiveness of various risk mitigation strategies. This will provide tools for managers to compare different supply chains while offering a deeper knowledge of how supply chain characteristics increase or decrease resilience and consequently affect supply chain risk exposure. Thus, the research supports organizations in measuring and analyzing supply chain resilience and facilitates supply chain decision-making. The proposed method could simplify the dynamic nature of environment for managing disruptions in a supply chain. This novel approach for determining the supply chain resilience index (SCRI) advocates the consideration of resilience aspects in supply chain design, thus giving a competitive advantage to achieve market share even during a disruption.     

Supervisory control of a multi-echelon supply chain: A modular Petri net approach for inter-organizational control

Petri nets (PNs) are frequently utilized to model system dynamics due to their ability to handle concurrencies and sequential dependence. In this paper, a portion of the supply chain operations reference (SCOR) model has been extracted and modeled using PNs for the purpose of exerting supervisory control upon a multi-echelon supply chain (SC). The activities of source, make and deliver, inherent in the SCOR model form the basis of the representation of the PN model for each echelon considered in the SC model. Two control nets are utilized: one above the base model of each echelon to exert local constraints and an enterprise level supply chain manager (SCM). The local constraints are at the tactical and operational levels while the SCM enforces additional constraints consisting of long term planning goals at the strategic level. Place invariants are used to create the supervisors. Performance measures of the total SC are formulated to determine the effectiveness of any partnership. An efficient method for finding the current state of the system is developed which is used to determine the performance measures of each echelon. This paper presents a modular approach to the overall structure and PN modeling for a SC system. It is intended to extend the use of supervisory control from a shop-floor level to an inter-organizational facility and enterprise level.     

Simulation-based optimization of process control policies for inventory management in supply chains

A simulation-based optimization framework involving simultaneous perturbation stochastic approximation (SPSA) is presented as a means for optimally specifying parameters of internal model control (IMC) and model predictive control (MPC)-based decision policies for inventory management in supply chains under conditions involving supply and demand uncertainty. The effective use of the SPSA technique serves to enhance the performance and functionality of this class of decision algorithms and is illustrated with case studies involving the simultaneous optimization of controller tuning parameters and safety stock levels for supply chain networks inspired from semiconductor manufacturing. The results of the case studies demonstrate that safety stock levels can be significantly reduced and financial benefits achieved while maintaining satisfactory operating performance in the supply chain.     

A control model for object virtualization in supply chain management

Due to the emergence of the Internet of Things, supply chain control can increasingly be based on virtual objects instead of on the direct observation of physical objects. Object virtualization allows the decoupling of control activities from the handling and observing of physical products and resources. Moreover, virtual objects can be enriched with information that goes beyond human observation. This will allow for more advanced control capabilities, e.g. concerning tracking and tracing, quality monitoring and supply chain (re)planning. This paper proposes a control model for object virtualization in supply chain management, which is based on a multiple case study in the Dutch floriculture. It includes a typology of distinct mechanisms for object virtualization, which discerns reference objects and future projections next to the representation of real physical objects. The control model helps to define feasible redesign options for the virtualization of supply chain control. It is also of value as a basis to define the requirements for information systems that enable these redesign options.     

串行供应链最优控制优化模型

最优控制策略的确定是供应链成败的关键因素之一,为此对串行供应链库存控制策略进行研究．首先提出了基于非线性整数规划与推拉控制通用模型相结合的串行供应链库存控制优化模型;然后利用遗传算法与仿真分析相结合的方法确定最优控制策略．实例仿真验证了该方法的有效性．     

Dynamic mobile RFID-based supply chain control and management system in construction

Construction project control attempts to effectively obtain real-time information and enhance dynamic control and management via information sharing and analysis from involved participants of the projects to reduce construction conflicts and project delay. However, extending the construction project control system to job sites is considered inefficient since construction sites are unconventional practice. Integrating promising information technologies such as radio frequency identification (RFID) technology, mobile devices-PDA and web portals can help improve the effectiveness and convenience of information flow in construction supply chain control systems. Radio frequency identification is appropriate for various construction applications, and provides cost savings through increased speed and accuracy of data entry. This study demonstrates the effectiveness of a RFID-based supply chain management application called the mobile construction RFID-based dynamic supply chain management (M-ConRDSCM) system in construction projects, demonstrating that it responds efficiently and enhances the information flow among offices and sites in a construction supply chain environment. The M-ConRDSCM system is then applied to a selected case study involving a High-Tech factory building in Taiwan to verify the proposed methodology and demonstrate the effectiveness of information sharing of project control in the construction phase. The advantage of the M-ConRDSCM system lies not only in improving work efficiency for on-site engineers, but also in providing dynamic operation control and management to enable project participants to control the whole project. Moreover, this study presents a generic system architecture and its implementation.     

Explicit/multi-parametric model predictive control (MPC) of linear discrete-time systems by dynamic and multi-parametric programming

This work presents a new algorithm for solving the explicit/multi-parametric model predictive control (or mp-MPC) problem for linear, time-invariant discrete-time systems, based on dynamic programming and multi-parametric programming techniques. The algorithm features two key steps: (i) a dynamic programming step, in which the mp-MPC problem is decomposed into a set of smaller subproblems in which only the current control, state variables, and constraints are considered, and (ii) a multi-parametric programming step, in which each subproblem is solved as a convex multi-parametric programming problem, to derive the control variables as an explicit function of the states. The key feature of the proposed method is that it overcomes potential limitations of previous methods for solving multi-parametric programming problems with dynamic programming, such as the need for global optimization for each subproblem of the dynamic programming step.     

Formulation and analysis of dynamic supply chain of backfill in construction waste management using agent-based modeling

Backfill is the excavated material from earthworks, which constitutes over 50% of the construction wastes in Hong Kong. This paper considers a supply chain that consists of construction sites, landfills and commercial sources in which operators seek cooperation to maximize backfill reuse and improve waste recovery efficiency. Unlike the ordinary material supply chain in manufacturing industries, the supply chain for backfill involves many dynamic processes, which increases the complexity of analyzing and solving the logistic issue. Therefore, this study attempts to identify an appropriate methodology to analyze the dynamic supply chain, for facilitating the backfill reuse. A centralized optimization model and a distributed agent-based model are proposed and implemented in comparing their performances. The centralized optimization model can obtain a global optimum but requires sharing of complete information from all supply chain entities, resulting in barriers for implementation. In addition, whenever the backfill supply chain changes, the centralized optimization model needs to reconfigure the network structure and recompute the optimum. The distributed agent-based model focuses on task distribution and cooperation between business entities in the backfill supply chain. In the agent-based model, decision making and communication between construction sites, landfills, and commercial sources are emulated by a number of autonomous agents. They perform together through a negotiation algorithm for optimizing the supply chain configuration that reduces the backfill shipment cost. A comparative study indicates that the agent-based model is more capable of studying the dynamic backfill supply chain due to its decentralization of optimization and fast reaction to unexpected disturbances.     

Review of supply chain performance measurement systems: 1998–2015

In today’s competitive business environment, supply chain performance is one of the most critical issues in various industries. It is argued that supply chain performance measurement is fundamental to efficient supply chain management. Over the past two decades, several frameworks and systems have been developed to meet this need. This study reviews the literature in the field of supply chain performance measurement and assembles an overview of those systems, approaches, techniques and criteria. For this purpose, 83 of 374 related articles from 1998 to 2015 were selected for final review using the Scopus and ISI databases. Findings disclose that performance measurement in supply chain contexts is still a fruitful area of research. The study also provides an overview of the performance measures employed in supply chain systems. These findings present a solid basis for future academic and practitioner work in the field of supply chain performance measurement.     

A study on inventory replenishment policies in a two-echelon supply chain system

Inventory control plays an important role in supply chain management. Properly controlled inventory can satisfy customers’ demands, smooth the production plans, and reduce the operation costs; yet failing to budget the inventory expenses may lead to serious consequences. The bullwhip effect, observed in many supply chain management cases, causes excessive inventory due to information distortion, i.e. the order amount is exaggerated while a minor demand variation occurs, and the information amplified dramatically as the supply chain moves to the upstream. In this paper, one of the main causes of bullwhip effect, order batching, is considered. A simplified two-echelon supply chain system, with one supplier and one retailer that can choose different replenishment policies, is used as a demonstration. Two types of inventory replenishment methods are considered: the traditional methods (the event-triggered and the time-triggered ordering policies), and the statistical process control (SPC) based replenishment method. The results show that the latter outperforms the traditional method in the categories of inventory variation, and in the number of backlog when the fill-rate of the prior model is set to be 99%. This research provides a different approach to inventory cost-down other than the common methods like: information sharing, order batch cutting, and lead time reduction. By choosing a suitable replenishment policy, the number of backorder and the cost of inventory can be reduced.     

Modeling supplier selection and the use of option contracts for global supply chain design

As supply chains become more and more dependent on the efficient movement of materials among facilities that are geographically dispersed there is more opportunity for disruption. One of the common disruptions is the loss of production capability at supplier sites. We formulate a two-stage stochastic program and a solution procedure to optimize supplier selection to hedge against these disruptions. This model allows for the effective quantitative exploration of the trade-off between cost and risks to support improved decision-making in global supply chain design. A realistic case study is explored.     

Towards a framework of critical success factors for implementing supply chain information systems

Supply chain information systems (SCISs) have emerged as the core of successful management in supply chains. However, the difficulties of SCIS implementations have been widely cited in the literature. Research on the critical success factors (CSFs) for SCIS implementation is rather scarce and fragmented. Therefore, the objective of this paper is to compile a framework of CSFs for implementing SCISs. Based on 10 key articles focusing on ERP implementations, we have built a list of critical success factors as a starting point for the SCIS literature search. Thereafter, based on 21 SCIS articles, CSFs for supply chain information system implementation have been defined. The analysis showed that some CSFs have been ignored and important supply chain characteristics have been overlooked. Moreover, it is not always easy for project managers to know how to implement and apply CSFs in practice.     

Analysis of the effectiveness of preventive and deterrent piracy control strategies: Agent-based modeling approach

We use agent-based modeling approach to analyze the impact of various digital piracy control strategies on consumers, retailers, record labels, and artists. We model heterogeneous agent behavior, motives, and interactions to examine the consequences in terms of aggregate system behavior. Using a multi-agent programmable modeling environment (Netlogo), several experiments were conducted to test the simulation model and develop managerial insights. We show that an educational strategy is more effective when consumers are resistant to anti-piracy efforts and budgets for combating piracy are small. Furthermore, value-added service and low-price strategies should be used to encourage legitimate purchases since legal and educational strategies alone deter piracy but do not provide consumers’ incentives to purchase legitimate products. Therefore, effectiveness of piracy control strategies can be improved by combining a legal or an educational strategy with a value-added or a low-price strategy. We also find that the profit-maximizing strategies are different for different players in the supply chain. While the record label prefers a low-cost strategy, it is optimal for the whole supply chain to use combined legal or educational strategy with a value-added strategy. Therefore, there is potential for all parties in the supply chain being better off if the record label and the retailer cooperate in combating piracy.     

A location–inventory supply chain problem: Reformulation and piecewise linearization

In this paper, we study a two-echelon inventory management problem with multiple warehouses and retailers. The problem is a natural extension to the well-known one-warehouse multi-retailer inventory problem. The problem is formulated as a mixed integer non-linear program such that its continuous relaxation is non-convex. We propose an equivalent formulation with fewer non-linear terms in the objective function so that the continuous relaxation of the new model is a convex optimization problem. We use piecewise linearization to transform the resulting MINLP to a mixed integer program and we solve it using CPLEX. Through numerical experiments, we compare the solutions obtained by solving the new formulation using CPLEX with two previously published Lagrangian relaxation based heuristics to solve the original mixed integer non-linear program. We demonstrate that the new approach is capable of providing almost the same solutions without the need of using specialized algorithms. This important contribution further implies that additional variants of the problem, such as multiple products, capacitated warehouses and routing, can be added to result in a problem that will again be solvable by commercial optimization software, while the respective Lagrangian heuristics will fail to solve such variants or extended problems.