A control chart based approach to monitoring supply network dynamics using Kalman filtering

In today's global market, a critical issue for companies to survive the increasing competition is how to handle uncertainty in their supply network. In this paper, we explore the application of Kalman filtering to estimate the dynamic states in a supply network. Two state-space models are developed. The first one focuses on processing each individual order which includes both waiting time and value-added processing time. Considering the correlation of consecutive orders, the second one enhances the state-space model by employing autoregressive model of waiting time. To signal potential abnormal events, the estimates from the models are further used in control charts with control limits being updated at each monitoring stage according to the related estimation error. A supply network case example is studied and we conclude in the benchmark model (without using Kalman filtering) and the first state-space model, the data collected from the bottleneck stage turns out to be most valuable for increased accuracy in detecting tardy orders. The second state-space model consistently outperforms both the benchmark model and the first state-space model for robustly early detection of abnormalities at all stages, especially the stages before the bottleneck stage, of the system.     

A simulation-optimisation approach for supply chain network design under supply and demand uncertainties

We investigate a three-echelon stochastic supply chain network design problem. The problem requires selecting suppliers, determining warehouses locations and sizing, as well as the material flows. The objective is to minimise the total expected cost. An important feature of the investigated problem is that both the supply and the demand are uncertain. We solve this problem using a simulation-optimisation approach that is based on a novel hedging strategy that aims at capturing the randomness of the uncertain parameters. To determine the optimal hedging parameters, the search process is guided by particle swarm optimisation procedure. We present the results of extensive computational experiments that were conducted on a large set of instances and that provide evidence that the proposed hedging strategy constitutes an effective viable solution approach.     

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.     

Distributed supply chain network modelling and simulation: integration of agent-based distributed simulation and improved SCOR model

Agent-based distributed simulation is an efficient methodology for modelling and analysing such complex adaptive systems as dynamic supply chain networks. However, it lacks an acceptable generic standard. Supply chain operations reference (SCOR) model is a cross-functional framework widely accepted as an industry standard. It provides the standard processes, performance metrics, best practices and associated software functionalities for modelling, evaluating and improving supply chain networks. However, it is a static tool. Integration of agent-based distributed simulation and SCOR model can exploit their advantages to form a generic methodology for modelling and simulation of a wide range of supply chain networks. Therefore, this paper proposes a methodology for distributed supply chain network modelling and simulation by means of integration of agent-based distributed simulation and an improved SCOR model. The methodology contains two components: a hierarchical framework for modelling supply chain network based on the improved SCOR model and agent building blocks integrating the standard processes from the SCOR model. The hierarchical framework provides an approach for structure modelling in any level with different granularities based on the improved SCOR model, and allows rapidly mapping a supply chain network into the structure model of a multi-agent system; while agent building blocks are quite useful and convenient to fill the structure model to fulfil its function modelling. With the approach of structure modelling and function filling, not only can the process of agent-based supply chain network modelling be accelerated, but also the built models can be reused and expanded. Because the hierarchical framework is based on the conceptual framework of SCOR model and agent building blocks integrate the standard processes from SCOR model, the proposed methodology is more generic. In addition, the issues of sub-model synchronisation and data distribution management in the agent-based distributed simulation implementation are taken into consideration and the corresponding solutions for these issues are proposed. Finally, an example of a supply chain network is modelled and implemented to illustrate the proposed methodology and related solutions.     

A distributed approximation approach for solving the sustainable supply chain network design problem

This paper introduces a comprehensive Mixed Integer Linear Programming (MILP) model for a sustainable supply chain network design problem, and an efficient Distributed Approximation Approach (DAA) to solve it approximately. We study a multi-echelon, multi-product and multi-modal supply chain with different transportation modes. Besides relevant costs in the supply chain such as procurement, production and distribution cost, we also explicitly consider the environmental footprint, represented by carbon emissions and water consumption from production and transportation. The approximation approach is a decomposition-based method. First, the original problem is divided into a partner selection sub-problem and a transportation planning sub-problem. Then multiple filter mechanisms are used to remove potentially infeasible solutions, and an approximate value of the objective function is calculated for each of the remaining solutions to perform a further selection. The one with the lowest approximation is chosen to be applied with a branch-and-bound method. Finally, the algorithm is paralleled and implemented in Apache Spark distributed computing framework to further improve efficiency. Experimental results show that the proposed DAA can provide high quality solutions compared to the optimal solutions of the MILP model with mostly a negligible relative gap and solve large instances in much shorter time than CPLEX. Moreover, in our numerical study, we also compare the results of our model with another version of the model that does not take the environmental footprint into consideration. The results show that explicitly incorporating environmental footprint results in a substantial decrease of CO₂ emissions and water consumption at a negligible cost increase. This insight may be of interest to managers and other decision makers and policy makers.     

A framework for designing a supply chain distribution network

Based on the analysis of the pertinent literature, we propose a framework to serve as a guide in designing a distribution network as a component of a supply chain (SC). It consists of three steps that are common to goods and services SCs: position of the decoupling point, structure of the network, and product delivery. For each step, we take into account the peculiarities of both goods and services separately in order to establish a list of available options and evaluate their suitability in relation to the relevant factors that characterise the products and the markets.     

A highly optimised tolerance-based approach for multi-stage,multi-product supply chain network design

In this paper we propose an algorithm called Highly Optimised Tolerance (HOT) for solving a multi-stage, multi-product supply chain network design problem. HOT is based on power law and control theory. The proposed approach takes its traits from the local incremental algorithm (LIA), which was initially employed to maximise the design parameter (i.e. yield), particularly in the percolation model. The LIA is somewhat analogous to the evolution by natural selection schema. The proposed methodology explores a wide search space and is computationally viable. The HOT algorithm tries to make the system more robust at each step of the optimisation. The objective of this paper is to reduce the total cost of supply chain distribution by selecting the optimum number of facilities in the network. To examine the effectiveness of the HOT algorithm we compare the results with those obtained by applying simulated annealing on a supply chain network design problem with different problem sizes and the same data sets.     

Integrated multi-site production-distribution planning in supply chain by hybrid modelling

Allocation of production volumes among multiple manufacturing sites and distribution of products among distribution channels involves many quantitative and qualitative variables and constraints. An integrated multi-product, multi-period, multi-site production-distribution planning subject to the production and distribution constraints, distribution system and local customers demand is considered in this paper. To do this, we took advantage of a hybrid mathematical-simulation model to solve the proposed problem. A mathematical model has been developed to solve the problem, aiming to decrease the costs of set up, production, inventory, distribution and transportation. Because of stochastic factors such as unexpected delays, queuing and machine failure, operation time provided by mathematical model cannot reflect dynamic characteristic of real-world systems and optimal solution of mathematical model is not acceptable in practice. Therefore, we took advantage of a hybrid mathematical-simulation approach to explore the behaviour of the real-world system. Through the computational experiments, we demonstrated that the number of iterations to converge hybrid procedure would lessen when we consider the production-distribution problem in an integrated manner. Also, supply chain overall costs will be reduced through the integration of production and distribution problems. Finally, the solution is a realistically optimal solution for the proposed integrated production-distribution planning problem.     

The design of Make-to-Order supply networks under uncertainties using simulation and optimisation

This paper addresses the problem of designing Make-to-Order (MTO) driven supply networks as is faced by producers of industrial goods. A major challenge in MTO network design is to estimate the operational performance of candidate networks. In particular, the stochastic and dynamic nature of order arrivals and fulfilment processes as well as the need to design a network that enables a timely delivery of ordered products complicate the decision-making. In this paper, a solution approach is presented where simulation is used for assessing the operational performance of candidate networks. The proposed simulation model captures multiple sources of uncertainties and incorporates fundamental control policies for reflecting the autonomous decision-making processes of operational planners. A Variable Neighbourhood Search (VNS) method is presented to guide the search for good network designs. Experiments are conducted on a set of multi-stage networks, where complex products are manufactured in MTO fashion and delivered to customers within a promised order lead time. The results show that our approach effectively produces supply networks that are able to cope with challenges arising from a strong customer orientation.     

Optimal inventory control with sequential online auction in agriculture supply chain: an agent-based simulation optimisation approach

With the development of e-commerce, in agriculture supply chain, online auction is adopted as an inventory clearing tool. Comparing to mathematical models studying inventory control over online sequential auctions, our agent-based simulation model could systematically describe the complexities of bidders’ information interactions and behaviour preferences caused from financial and production perspectives, and by other supply chain members. In addition, we take into account the complex and dynamic market environment, which will impact the operation effect of auction policies. With identical auction items, the profit-maximising firm must decide auction lot-size, which is the number of units in each auction, minimum initial bid, and the time interval between auctions. To obtain the optimal solution, nested partitions framework and optimal expected opportunity cost algorithm are integrated to improve computation accuracy and efficiency. A case study based on real data is conducted to implement and validate the proposed approach. Furthermore, based on the model, the paper studies the sensitivities of the decision variables under different supply and demand scenarios.     

A strategic model for exact supply chain network design and its application to a global manufacturer

This paper presents a comprehensive model that captures significant strategic decisions involved in designing or redesigning high-performance supply chains from the perspective of the manufacturer. The problem considers deterministic demand by multiple clients, for multiple products, over the periods of a long-term horizon. The design decisions involve selection of suppliers, establishment or resizing of production facilities and distribution centres, possible subcontracting of related activities, and selection of transportation modes and routes. The problem is formulated by a Mixed Integer Linear Programming model. Its objective is to minimise the overall costs associated with procurement, production, inventory, warehousing, and transportation over the design horizon. Appropriate constraints model the complex relationships among the links of the supply chain. The proposed model has been applied to a large case study of a global manufacturing firm, providing valuable insights into the transformation of the firm’s current supply chain network, as well as into the potential of the proposed approach.     

Distributed simulation of virtual workshops for the multi-site scheduling feasibility evaluation

In this article, a distributed simulation tool for the feasibility evaluation of multi-site scheduling is proposed. The application areas concern supply chains (SCs) or networks of distributed workshops. The distributed simulation of workshops, called virtual workshops, generates various problems of causality and of tasks execution coordination. These problems are addressed in the proposed distributed architecture by the use of High Level Architecture protocol guaranteeing the synchronisation and the chronology of events occurring in the distributed simulations. An application to a simple case of the SC organising the flow between three workshops shows the effectiveness of the distributed simulation tool.     

一种基于HLA/RTI的卫星组网仿真系统

设计了一种基于HLA/RTI的卫星组网仿真系统,在该系统中卫星节点、星间链路、卫星轨道和通信节点路由被划分为功能独立的联邦成员.这种结构具有良好的可扩展性,可适用于不同的卫星星座结构,同时可以适应不同的路由策略和各类空间通信链路模型.最后通过统计卫星组网网管报文响应时间验证了该仿真系统的可行性.卫星组网仿真系统为卫星组网的其他研究提供了仿真验证平台.     

基于小波神经网络的蒸汽发生器水位辨识与控制仿真

提出一种基于小波神经网络的控制方法，对蒸汽发生器水位进行控制仿真．该方法利用小波神经网络作为控制系统的辨识器和控制器来构成控制系统．小波神经网络辨识器能更准确逼近非线性对象。小波神经网络控制器能自适应产生最佳的控制规律．仿真结果表明，该方案具有响应快、超调量小、较强抑制干扰能力等良好性能．     

Multi-level supply chain network design with routing

Recently, the multi-level and multi-facility industrial problem in supply chain management (SCM) has been widely investigated. One of the key issues, central to this problem in the current SCM research area is the interdependence among the location of facilities, the allocation of facilities, and the vehicle routing for the supply of raw materials and products. This paper studies the supply chain network design problem, which involves the location of facilities, allocation of facilities, and routing decisions. The proposed problem has some practical applications. For example, it is necessary for third party logistics (3PL) companies to manage the design of the network and to operate vehicle transportation. The purpose of this study is to determine the optimal location, allocation, and routing with minimum cost to the supply chain network. The study proposes two mixed integer programming models, one without routing and one with routing, and a heuristic algorithm based on LP-relaxation in order to solve the model with routing. The results show that a developed heuristic algorithm is able to find a good solution in a reasonable time.     

A fuzzy technique for supply chain network design with quantity discounts

This paper proposes a hierarchical technique for Supply Chain Network (SCN) efficiency maximisation under uncertainty composed of three steps. The first step extends a previous fuzzy cross-efficiency Data Envelopment Analysis approach, originally intended for suppliers’ selection, in order to evaluate and rank all the actors in each SCN stage under conflicting nondeterministic criteria. Afterwards, a fuzzy linear integer programming model is stated and solved for each pair of subsequent SCN stages to determine the quantities required from each stakeholder to maximise the overall SCN efficiency while satisfying the estimated demand and respecting the nodes capacity. Finally, a heuristics is applied to limit the exchange of small quantities in the SCN, in which the trade is not economically convenient according to quantity discounts. An illustrative example from the literature shows the technique effectiveness.     

基于HLA的火控雷达网融合控制系统仿真研究

为提高火控雷达的战场生存能力,研究通过组网技术降低雷达信号截获概率的方法。根据设定作战场景,介绍火控雷达网的工作过程,给出融合控制系统的结构及动态执行过程,设计基于高层体系结构的火控雷达网融合控制系统仿真软件,以此作为平台验证火控雷达组网理论的正确性。最后,通过RTI平台进行仿真实验,结果表明:仿真系统可以有效模拟火控雷达网的数据融合处理和雷达参数控制,组网系统能够延长火控雷达被敌方侦察系统的截获时间。     

Revealing interfaces of supply chain resilience and sustainability: a simulation study

Dynamics of structures and processes is one of the underlying challenges in supply chain management, where multiple dimensions of economic efficiency, risk management and sustainability are interconnected. One of the substantiated issues in supply chain dynamics is resilience. Resilience has a number of intersections with supply chain sustainability. This paper aims at analysing disruption propagation in the supply chain with consideration of sustainability factors in order to design resilient supply chain structure in regard to ripple effect mitigation and sustainability increase. Ripple effect in the supply chain occurs if a disruption at a supplier cannot be localised and cascades downstream impacting supply chain performance. This simulation-based study helps to identify what sustainability factors mitigate the ripple effect in the supply chain and what sustainability factors enhance this effect. The results indicate that (i) sustainable single sourcing enhances the ripple effect; (ii) facility fortification at major employers in regions mitigates the ripple effect and enhances sustainability; and (iii) a reduction in storage facilities in the supply chain downstream of a disruption-risky facility increases sustainability but causes the ripple effect.     

Structural-aware simulation analysis of supply chain resilience

Supply chain resilience (SCRES) refers to the ability of a supply chain (SC) to both resist disruptions and recover its operational capability after disruptions. This paper presents a simulation model that includes network structural properties in the analysis of SCRES. This simulation model extends an existing graph model to consider operational behaviours in order to capture disruption-recovery dynamics. Through structural analysis of a supply chain network (SCN), mitigation strategies are designed to build redundancy, while contingency strategies are developed to prioritise recovery of the affected SCN. SCRES indexes are proposed by sampling SC performance measures of disruption for each plant and aggregating the measures based on the criticality of the plants in the SCN. The applicability of this simulation model is demonstrated in a real-world case study of different disruption scenarios. The application of mitigation and contingency strategies is shown to both improve recovery and reduce the total costs associated with disruptions. Through such simulation-based analysis, firms can gain insight into the SCRES of their existing SCNs and identify suitable strategies to improve SCRES by considering recovery time and costs.     

A method for designing power supply chain networks accounting for failure scenarios and preventive maintenance

A power grid is vulnerable and failures are inevitable. Failures decrease the power supply with an adverse effect on meeting the demand for electricity. Therefore, there is a need for a method to design power grid networks that result in the least possible disruption to the power supply when a failure occurs. In the literature, the focus has been on the design of the generation system without considering the transmission system or failures in the transmission system. Since power grids are integrated generation and transmission systems, each system will affect the other, so both generation and transmission systems need to be considered, as they are in this article. Methods developed for the structural modelling and analysis of supply chains are shown to be useful. The focus in this article is on describing a method using the supply chain construct for designing power grids that are relatively insensitive to failure in the integrated generation and transmission system. The efficacy of the method is illustrated using data from the Tehran Regional Electric Company. One of the findings is that targeted failures have a higher impact on decreasing the performance of the power grid than random failures. However, the focus is on the method rather than the results per se.