A multi-stage production inventory system,including service time

The basic objective in this paper is to bring down inventory carrying cost, in a multistage production inventory system, by incorporating the idea of service time in the model. The paper considers models with two types of information flow: the serial information flow, in which the inventories at each stage are measured against, orders placed by the following stage, and the parallel information flow, in which they are measured against customer orders materializing at the finished goods stage.

The optimization involved, considering the service times, smoothing of the production rate, etc., would serve to specify which points in the manufacturing process should be inventory points and which points should not. Also, it determines how big should the in-process inventories be for maximum operating efficiency.     

Allocation of external returns of different quality grades to multiple stages of a closed loop supply chain

A single period multistage closed loop supply chain (CLSC) is presented here considering simultaneous manufacturing of new products and remanufacturing of customer returned used products. The quantity of used products in reverse supply chain considered in the model is a fraction of the new products manufactured in the forward supply chain. Used products of known quantity are pulled from end customers as per quality grades. Products are assumed to be mechanical in nature and remanufacturer has to pay different acquisition prices for different quality grades of return. The groups of graded products based on the acquisition prices are thus sorted and sent to stages of CLSC earmarked for them as per demand for repairing/recycling of raw materials. After repairing/refurbishing and recycling at each stage of reverse supply chain the used parts/products become part of forward supply chain. In this paper, a nonlinear maximizing profitability function for CLSC has been formulated for the system with a price dependent demand for n-stages. The decision variables are selling price of product and percentage return of graded used products entering into different stages. A numerical example for a three stage model illustrates the method followed by managerial insight.     

The impact of information sharing on ordering policies to improve supply chain performances

Bullwhip effect represents the amplification and distortion of demand variability as moving upstream in a supply chain, causing excessive inventories, insufficient capacities and high operational costs. A growing body of literature recognizes ordering policies and the lack of coordination as two main causes of the bullwhip effect, suggesting different techniques of intervention. This paper investigates the impact of information sharing on ordering policies through a comparison between a traditional (R, S) policy and a coordination mechanism based on ordering policy (a combination of (R, D) and (R, S) policies). This policy relies on a slow, easy to implement, information sharing to overcome drawbacks of the effect, in which replenishment orders are divided into two parts; the first is to inform the upstream echelons about the actual customer demand and the second is to inform about the adjustment of the inventory position, smoothing at the same time the orders of the different levels of the supply chain. A simulation model for a multi-echelon supply chain quantifies the supply chain dynamics under these different policies, identifying how information sharing succeeds to achieve an acceptable performance in terms of both bullwhip effect and inventory variance.     

A queuing model on supply chain with the form postponement strategy

The form postponement (FP) strategy is an important strategy for manufacturing firms to utilize to achieve a quick response to customer needs while keeping low inventory levels of finished products. It is an important and difficult task to design a supply chain that uses FP strategy to mitigate the conflict between inventory level and service level. To this end, we develop a two-stage tandem queuing network to model the supply chain. The first stage is the manufacturing process of the undifferentiated semi-finished product, which is produced on a Make-To-Stock basis: the inventory is controlled by base-stock policy. The second stage is the customization process based on customers’ specified requirements. There are two types of order: ordinary order and special order. The former can be met by customizing from semi-finished product, while the latter must be entirely customized beginning from the first stage. The customer orders arrive according to a Poisson process. We first derive the inventory level and fill rate, and then present a total cost model. It turns out that the model is intractable due to the Poisson distribution in the objective function. To analytically solve the problem, we use normal distribution as an approximation of the Poisson distribution, which works well when the parameter of the Poisson distribution is quite large. Finally, some numerical experiments are conducted and managerial insights are offered based on the numerical results.     

A cost-based module mining method for the assemble-to-order strategy

The assemble-to-order (ATO) strategy is one of the most popular operations management approaches to achieve mass customized products while maintaining lower costs. In the ATO system, manufacturers keep inventory at the component and module level, and postpone product differentiation until the final stage of production. However, most research on modularity assumes that modules are already known in advance. In fact, in the ATO system, the determination of which components should be pre-assembled as modules mainly depends on the types and volumes of products ordered by customers. That is, module composition and volume should be derived dynamically from the product database based on updated customer orders. To bridge this gap, a two-stage cost-based module mining method for the assemble-to-order strategy is proposed. The first stage determines which sets of components can be formed (pre-assembled) as modules based on a list of customer orders. In the second stage, a cost-based selection approach is developed to evaluate the total cost of each module implementation project generated from the set of feasible modules. The module implementation project with the lowest cost is thus found and suggested to production managers.     

A Model Predictive Control framework for robust management of multi-product,multi-echelon demand networks

Model Predictive Control (MPC) is presented as a robust, flexible decision framework for dynamically managing inventories and meeting customer requirements in demand networks (a.k.a. supply chains). As a control-oriented framework, an MPC-based planning scheme has the advantage that it can be tuned to provide acceptable performance in the presence of significant uncertainty, forecast error, and constraints on inventory levels, production and shipping capacity. The translation of the supply chain problem into a formulation amenable to MPC implementation is initially developed for a single-product, two-node example. Insights gained from this problem are used to develop a partially decentralized MPC implementation for a six-node, two-product, three-echelon demand network problem developed by Intel Corporation that consists of interconnected assembly/test, warehouse, and retailer entities. Results demonstrating the effectiveness of this Model Predictive Control solution under conditions of demand forecast error, constraints on capacity, shipping and release, and discrepancies between actual and reported production throughput times (i.e. plant-model mismatch) are presented. The Intel demand network problem is furthermore used to evaluate the relative merits of various information sharing strategies between controllers in the network. Both the two-node and Intel problems show the potential of Model Predictive Control as an integral component of a hierarchical, enterprise-wide planning tool that functions on a real-time basis, supports varying levels of information sharing and centralization/decentralization, and relies on combined feedback–feedforward control action to enhance the performance and robustness of demand networks. These capabilities ultimately mitigate the “bullwhip effect” in the supply chain while reducing safety stocks to profitable levels and improving customer satisfaction.     

Controller design and reduction of bullwhip for a model supply chain system using z-transform analysis

In this work, a discrete time series model of a supply chain system is derived using material balances and information flow. Transfer functions for each unit in the supply chain are obtained by z-transform. The entire chain can be modeled by combining these transfer functions into a close loop transfer function for the network. The model proves to be very useful in revealing the dynamics characteristic of the system. The system can be viewed as a linear discrete system with lead time and operating constraints. The stability of the system can be analyzed using the characteristic equation. Controllers are designed using frequency analysis. The bullwhip effect, i.e. magnification of amplitudes of demand perturbations from the tail to upstream levels of the supply chain, is a very important phenomenon for supply chain systems. We proved that intuitive operation of a supply chain system with demand forecasting will cause bullwhip. Moreover, lead time alone would not cause bullwhip. It does so only when accompanied by demand forecasting. Furthermore, we show that by implementing a proportional intergral or a cascade inventory position control and properly synthesizing the controller parameters, we can effectively suppress the bullwhip effect. Moreover, the cascade control structure is superior in meeting customer demand due to its better tracking of long term trends of customer demand.     

Combination of genetic algorithm with Lagrange multipliers for lot-size determination in multi-stage production scheduling problems

In this paper a meta-heuristic approach for lot-size determination problems in a complex multi-stage production scheduling problems with production capacity constraint has been developed. This type of problem has multiple products with sequential production processes which are manufactured in different periods to meet customer’s demand. By determining the decision variables, machinery production capacity and customer’s demand, an integer linear program with the objective function of minimization of total costs of set-up, inventory and production is has been provided. In the first step, the original problem is converted to several individual problems using a heuristic approach based on the limited resource Lagrange multiplier. Thus, each individual problem can be solved using one of the easier methods. In the second step, through combining the genetic algorithm with one of the neighborhood search techniques, a new approach has been developed for the individual problems. In the third step, to obtain a better result, resource leveling is performed for the smaller problems using a heuristic algorithm. Using this method, each product’s lot-size is determined through several steps. We have verified our results through several empirical experiments.     

An arc flow formulation to the multitrip production,inventory, distribution,and routing problem with time windows

The multitrip production, inventory, distribution, and routing problem with time windows (MPIDRPTW) is an integrated problem that combines a production and distribution problem, a multitrip vehicle routing problem, and an inventory routing problem. In the MPIDRPTW, a set of customers, which have a time-varying demand during a finite planning horizon, is served by a single production facility. The distribution is accomplished by a fleet of homogeneous vehicles that deliver the customer orders within their specific time windows. Production management has to be done according to the inventories at the facility and at the customers. An exact arc flow model based on a graph is proposed to solve the MPIDRPTW, where the nodes represent instants of time. The main goal of the problem is to minimize the costs associated with the entire system. The proposed approach was implemented and a set of experimental tests were conducted based on a set of adapted instances from the literature.     

阔大货物装载加固方案多目标优化模型

随着运输技术和需求的发展,阔大货物装载加固方案仅仅由承运单位和方案制定单位来确定已经不能满足铁路货物运输的发展,需要在确定货物装载加固方案时将客户需求考虑进去。基于层次分析法建立阔大货物装载加固方案多目标优化模型,首次将货主的客户需求纳入装载加固方案优化目标中,充分考虑方案制定者、方案使用者、客户3方面的需求。优化模型确定运输安全、运输时间、运输费用、客户满意度4个评价指标,给出4个一级目标的权重及运输安全的二级目标的权重。算例分析表明,对同一件阔大货物的3个装载加固方案进行多目标评价,针对不同的客户需求,可以得出较优方案。     

Adaptive logistic controller for integrated design of distributed supply chains

Usually each manufacturing stage in a supply chain makes its own decision regarding quantity and timing of parts it purchases from its suppliers, thereby controlling its inventory position and overall supply chain dynamics. Such decisions, although good for each individual stage, can adversely affect the overall performance of the supply chain. This can be viewed as distributed control of inventory, in which each controller is making autonomous decisions based on local objectives. Because these controllers do not have information about inventory position or order quantities at other stages, the safety stock tends to be higher, leading to higher inventories and cost. This also causes demand amplification and the bullwhip effect. This paper presents a distributed feedback control algorithm, called the Adaptive Logistics Controller (ALC), which simultaneously decides the order quantities for each stage of the supply chain to minimize the total WIP in the entire supply chain for a given demand. In this approach, simulation is used to provide the feedback to the ALC controller, leading to an iterative numerical computational approach. Computational experiments compared with the traditional centralized (Q,r)$(Q,r)$ policy model show that the order quantities calculated by the distributed ALC are much superior in terms of total overall WIP and hence result in lesser total costs for the entire supply chain.     

Admission control and scheduling in simple series parallel networksusing fuzzy logic

Two outstanding problems of admission control and scheduling in networks with three and two workstations, respectively, are solved using fuzzy logic. Neither problem has been tackled up until now analytically, whereas the fuzzy approach provides computational solutions. In the first case, we have one workstation with two parallel ones. A reward is earned whenever the first stage accepts a customer and a holding cost is incurred by a customer in queue in the second stage. The class of customer to be next served by the first stage is dynamically selected so as to maximize an average benefit over an infinite horizon. In the second case, there are two parallel servers and three arrival processes generated by independent Poisson streams. Each server has its own queue and receives customers from its own arrival stream. A third arrival stream consists of customers with resource demand on both servers. Each customer pays a holding cost per unit time in the system. Again, the scheduling policy is specified which minimizes the average cost. The fuzzy models are new in this context and tackle computationally problems for which we have not analytical solutions     

The integrated production–inventory–distribution–routing problem

The integration of production and distribution decisions presents a challenging problem for manufacturers trying to optimize their supply chain. At the planning level, the immediate goal is to coordinate production, inventory, and delivery to meet customer demand so that the corresponding costs are minimized. Achieving this goal provides the foundations for streamlining the logistics network and for integrating other operational and financial components of the system. In this paper, a model is presented that includes a single production facility, a set of customers with time varying demand, a finite planning horizon, and a fleet of vehicles for making the deliveries. Demand can be satisfied from either inventory held at the customer sites or from daily product distribution. In the most restrictive case, a vehicle routing problem must be solved for each time period. The decision to visit a customer on a particular day could be to restock inventory, meet that day’s demand or both. In a less restrictive case, the routing component of the model is replaced with an allocation component only. A procedure centering on reactive tabu search is developed for solving the full problem. After a solution is found, path relinking is applied to improve the results. A novel feature of the methodology is the use of an allocation model in the form of a mixed integer program to find good feasible solutions that serve as starting points for the tabu search. Lower bounds on the optimum are obtained by solving a modified version of the allocation model. Computational testing on a set of 90 benchmark instances with up to 200 customers and 20 time periods demonstrates the effectiveness of the approach. In all cases, improvements ranging from 10–20% were realized when compared to those obtained from an existing greedy randomized adaptive search procedure (GRASP). This often came at a three- to five-fold increase in runtime, however.     

两阶段混合优化算法求解模糊需求下多时间窗车辆路径问题

针对现实中广泛存在的一类模糊需求下多时间窗车辆路径问题(vehicle routing problem with multiple time windows under fuzzy demand,VRPMTW_FD),即车辆配送前客户需求模糊但车辆到达客户后其需求变为确定的多时间窗车辆路径问题(vehicle rout...     

Determination and exchange of supply information for co-operation in complex production networks

Today's manufacturing industry is characterised by strong interdependencies between companies operating in globally distributed production networks. The operation of such value-added chains has been enabled by recent developments in information and communication technologies (ICT) and computer networking. To gain competitive advantages and efficiency improvements such as reduced inventory and higher delivery reliability, companies are introducing information exchange systems that communicate demand to suppliers and production progress information to customers in the network. This article proposes a system that supports co-operation in complex production networks by enabling companies to determine and exchange supply information with their customers. The requirements for such a system are analysed and it is embedded in a framework of supply chain management business processes. The system facilitates the determination and exchange of meaningful, reliable and up-to-date order status information from the supplier to the customer. Based on comparing the progress of an internal production order with a pre-defined milestone model for each product, the status of the customer order is determined and—in case of lateness—communicated to the customer together with an early warning. To demonstrate the developed supply information concepts and processes, the business process is implemented as a pilot system and evaluated by the user companies participating in the 5th Framework IST project Co-OPERATE.     

Manufacturing production plan optimization in three-stage supply chains under Bass model market effects

The Bass model is a very successful parametric approach to forecast the diffusion process of new products. In recent years, applications of the Bass model have been extended to other operational research fields such as managing customer demands, controlling inventory levels, optimizing advertisement strategies, and so forth. This study attempts to establish an application for optimizing manufacturers’ production plans in a three-stage supply chain under the Bass model’s effects on the market. The supply chain structure considered in this research is similar to other common supply chains comprised of three stages, namely retailer, distributor and manufacturer. The retailer stage has to handle customer demands following the Bass diffusion process. Market parameters and essential information are assumed to be available and ready for access. Each stage is expected to determine its inventory policy rationally. That is, each stage will attempt to maximize its own profits. These decisions will back-propagate their effects to upper stages. This study adopts a dynamic programming approach to determine the inventory policies of each stage so as to optimize manufacturers’ production plans.     

Production inventory with service time and interruptions

This paper considers an (s,S) production inventory system with positive service time, with time for producing each item following Erlang distribution. Customers arrive according to a Poisson process. A customer who arrives when there is no inventory in the system is considered lost. On the other hand, a customer who finds a busy server with at least one inventory in the system joins a queue of infinite capacity. When the inventory level falls to s, production process is switched on, and it is switched off when the inventory level reaches back to S. Service time to each customer also follows an Erlang distribution. The service of a customer may be interrupted, where the time for such a phenomenon follows an exponential distribution, whenever it occurs. An interrupted service, after repair, resumes from where it was stopped. The correction/repair time follows an exponential distribution. We assume that the service of a single customer may encounter any number of interruptions and that the customer being served waits there until his service is completed. Moreover, at a time the server is subject to at most one interruption. We also assume that no inventory is lost due to a service interruption. Like the service process, the production process also is subject to interruptions, where the duration to an interruption follows an exponential distribution. However, in contrast to the service interruption, in the case of interruption to production process, we assume that the item being processed is lost because of interruption. That is, the production process, on being interrupted, restarts from the beginning, after repair. The repair time of an interrupted production process follows exponential distribution. Few of the last service phases are assumed to be protected in the sense that the service will not be interrupted while being in these phases. The same is assumed for the production process also.

The model is analysed as a level-independent quasi-birth–death process. We apply a novel method to obtain an explicit expression for the necessary and sufficient condition for the stability of the system under study. This method works even if we assume general phase-type distributions for the production as well as the service processes, and hence can be used to characterise the stability of inventory systems where the assumption of disallowing the customers to join the system, when there is a shortage of inventory has been made. Under stability, we apply matrix analytic methods to compute the system state distribution. In consequence to that, several system performance measures have been derived, and their dependence on the system parameters has been studied numerically.     

Exploring the interaction of inventory policies across the supply chain: An agent-based approach

The Bullwhip Effect, which refers to the increasing variability of orders traveling upstream the supply chain, has shown to be a severe problem for many industries. The inventory policy of the various nodes is an important contributory factor to this phenomenon, and hence it significantly impacts on their financial performance. This fact has led to a large amount of research on replenishment and forecasting methods aimed at exploring their suitability depending on a range of environmental factors, e.g. the demand pattern and the lead time. This research work approaches this issue by seeing the whole picture of the supply chain. We study the interaction between four widely used inventory models in five different contexts depending on the customer demand variability and the safety stock. We show that the concurrence of distinct inventory models in the supply chain, which is a common situation in practice, may alleviate the generation of inefficiencies derived from the Bullwhip Effect. In this sense, we demonstrate that the performance of each policy depends not only upon the external environment but also upon the position within the system and upon the decisions of the other nodes. The experiments have been carried out via an agent-based system whose agents simulate the behavior of the different supply chain actors. This technique proves to offer a powerful and risk-free approach for business exploration and transformation.     

Dynamic programming model for multi-stage single-product Kanban-controlled serial production line

The executive concern of this paper is how to control and synchronize the flow of materials in Kanban controlled serial production line so as to build a dynamic material-flow system that successfully meets customer demand Just-In-Time. The proposed approach should yield a consistent integrated control policy with a feasible level of Work-In-Process and a feasible corresponding operational cost. The production line is described as queuing network, and then a Dynamic Programming (DP) algorithm is used to solve the network by decomposing it into several numbers of single-stage sub-production lines. Backward computations of DP are done recursively with synchronization mechanism, in the since that the solution of one sub-production line is used as an input to the previous one. A performance measure is then developed to determine and to compare the values of production parameters. Numerical examples are used to demonstrate the computations of different system parameters, the results are validated by discrete events simulation using ProModel software version 6.0, the performance measure coincided with the results of the model with very small error (0.044). As a result the number of Kanbans that are needed to deliver the batches from upstream stage to the downstream stage is determined in such a way that keeps the stages synchronized with the external customer demand.     

System dynamics of supply chain network organization structure

Information technology is providing manufacturers with additional flexibility with regard to their supply chain network choices. Our research studies supply chain network organization structures categorized by the organic and mechanistic management control structures. The structural impacts on cost and fill rate performance are studied in two-echelon and two-supply-chain network organization models under different market coordination conditions using system dynamic simulations. Our results show significant effects of demand and network structural factors, and their interactions, on these measures. As demand becomes dynamic, the cooperative interaction model, where supply chains cooperate to satisfy customer demand, is found to have better system performance than the competitive supply chain model. The analysis also suggests that increasing the responsiveness at the downstream plant is particularly important to the overall system performance improvement.