Approximate analysis and optimization of batch ordering policies in capacitated supply chains

Devising manufacturing/distribution strategies for supply chains and determining their parameter values have been challenging problems. Linking production management to stock keeping processes improves the planning of the supply chain activities, including material management, culminating in improved customer service levels. In this study, we investigate a multi-echelon supply chain consisting of a supplier, a plant, a distribution center and a retailer. Material flow between stages is driven by reorder point/order quantity inventory control policies. We develop a model to analyze supply chain behavior using some key performance metrics such as the time averages of inventory and backorder levels, as well as customer service levels at each echelon. The model is validated against simulation, yielding good agreement of robust performance metrics. The metrics are then used within an optimization framework to design the supply chain so as to minimize expected total system costs. The outcome of the optimization framework specifies how to move inventory throughout the supply chain and how to set inventory control parameters, i.e., reorder levels and replenishment batch sizes.     

A transient stochastic simulation–optimization model for operational fuel planning in-theater

Army fuel planners are responsible for developing daily loading plans that specify which tankers to load, with what fuel, and where to send the loaded tankers. The tools used to accomplish this task are custom built spreadsheets which require large amounts of time and effort to use, update, and keep free of errors. This research presents a transient stochastic simulation–optimization model of the in-theater bulk fuel supply chain, where the simulation model is used to simulate the performance of the fuel supply chain under a particular fuel distribution policy and the optimization portion is used to update the policy so that it results in the performance desired by the Army fuel planner. The fuel distribution policy can then be used to derive the daily loading plan. Due to the multi-objective nature of the problem, the set of policies that form the efficient frontier are all candidate policies for the Army fuel planner to select from. Results of experimentation with a wide variety of supply chain scenarios indicate that, for a given supply chain scenario, the optimization portion of the model identifies a set of fuel distribution policies that address the objectives of the Army fuel planner. In addition, the simulation–optimization model comfortably solves the largest supply chain scenarios the Army fuel planner would reasonably be expected to encounter.     

Operational and financial effectiveness of e-collaboration tools in supply chain integration

This paper develops a comprehensive model of supply chain integration and uses it to analyze and assess the operational and financial effectiveness of different e-collaboration tools at various levels of supply chain integration. This model is also used to evaluate the importance of the sequence in which e-collaboration tools are adopted in supply chain integration. Computational results from a validated system dynamics simulation model with different implementation sequences of e-collaboration tools and different financial scenarios show that local financial constraints can also severely impact operational and financial performance of the entire supply chain.     

A Hybrid Taguchi–Immune approach to optimize an integrated supply chain design problem with multiple shipping

Supply chain network design is considered a strategic decision level problem that provides an optimal platform for the effective and efficient supply chain management. In this research, we have mathematically modeled an integrated supply chain design. To ensure high customer service levels, we propose the inclusion of multiple shipping/transportation options and distributed customer demands with fixed lead times into the supply chain distribution framework and formulated an integer-programming model for the five-tier supply chain design problem considered. The problem has been made additionally complex by including realistic assumptions of nonlinear transportation and inventory holding costs and the presence of economies of scale. In the light of aforementioned facts, this research proposes a novel solution methodology that amalgamates the features of Taguchi technique with Artificial Immune System (AIS) for the optimum or near optimum resolution of the problem at hand. The performance of the proposed solution methodology has been benchmarked against a set of test instances and the obtained results are compared against those obtained by Genetic Algorithm (GA), Hybrid Taguchi–Genetic Algorithm (HTGA) and AIS. Simulation results indicate that the proposed approach can not only search for optimal/near optimal solutions in large search spaces but also has good repeatability and convergence characteristics, thereby proving its superiority.     

Robust tests for the bullwhip effect in supply chains with stochastic dynamics

This paper analyzes the bullwhip effect in single-echelon supply chains driven by arbitrary customer demands and operated nondeterministically. The supply chain, with stochastic system parameters, is modeled as a Markovian jump linear system. The paper presents robust analytical conditions to diagnose the bullwhip effect and bound its magnitude. The tests are independent of the customer demand. Examples are given. Ordering policies that pass these tests, and thus avoid the bullwhip effect in random environments for arbitrary customer demands, are shown to exist. The paper also presents possible extensions to multi-echelon chains.     

考虑多提前期的厨房电器供应链网络库存系统鲁棒控制研究

在厨房电器供应链的管理实践中,市场、技术、客户需求等具有不确定,牛鞭效应也会影响供应链系统的稳定性,造成系统性的损失。为了提高厨房电器供应链的鲁棒性能,本文首先基于Takagi-Sugeno模糊控制系统,考虑采购、生产、销售等多种提前期以及客户需求的不确定性,构建一类厨房电器供应链网络动态模型;其次,根据不同周期下厨房电器供应链制造商的库存状况,对制造商的生产策略进行了设计;随后,提出了基于模糊鲁棒控制策略对多提前期、多种不确定因素对厨房电器供应链的影响;最后,基于厨房电器供应链的实际数据进行了仿真,结果显示本文提出的厨房电器供应链鲁棒策略是可行和有效的。     

A new multi-objective stochastic model for a forward/reverse logistic network design with responsiveness and quality level

Logistic network design has an important and strategic platform in an efficient and effective supply chain management, and usually involves multiple and conflicting goals, such as cost/profit, resource balance, customer responsiveness, quality, and the like. Besides, due to the implementation of government legislation, environmental concern, social responsibility and customer awareness, companies have been forced by customers not only to supply environmentally amicable products but also to be responsible for the returned products. Hence, this paper presents a stochastic multi-objective model for forward/reverse logistic network design under a uncertain environment including three echelons in forward direction (i.e., suppliers, plants, and distribution centers) and two echelons in backward direction (i.e., collection centers and disposal centers). We demonstrate a method to evaluate the systematic supply chain configuration maximizing the profit, customer responsiveness, and quality as objectives of the logistic network. The set of Pareto optimal solutions is obtained and also financial risk relevant to them is computed in order to show the tradeoff between objectives. The results give important insight for fostering the decision making process.     

考虑三级货损成本的冷链物流配送中心选址优化

为解决冷链物流配送中心的选址优化问题,依据易腐品完好率线性递减理论,提出了易腐品完好率与运输距离之间的分段递减函数关系,通过分析从供应点到配送中心运输过程,配送中心内部操作过程和从配送中心到需求点配送过程中所产生的三级货损实际情况,探究了本级货损对下级货损的叠加影响,以货物完好量作为客户的最终需求,构建了考虑三级货损成本的冷链物流配送中心选址优化模型,算例验证了所建模型的适用性和可行性.     

A fuzzy linear programming based approach for tactical supply chain planning in an uncertainty environment

This paper models supply chain (SC) uncertainties by fuzzy sets and develops a fuzzy linear programming model for tactical supply chain planning in a multi-echelon, multi-product, multi-level, multi-period supply chain network. In this approach, the demand, process and supply uncertainties are jointly considered. The aim is to centralize multi-node decisions simultaneously to achieve the best use of the available resources along the time horizon so that customer demands are met at a minimum cost. This proposal is tested by using data from a real automobile SC. The fuzzy model provides the decision maker (DM) with alternative decision plans with different degrees of satisfaction.     

An analytical method for cost analysis in multi-stage supply chains: A stochastic network model approach

This paper studies the cost distribution characteristics in multi-stage supply chain networks. Based on the graphical evaluation and review technique, we propose a novel stochastic network mathematical model for cost distribution analysis in multi-stage supply chain networks. Further, to investigate the effects of cost components, including the procurement costs, inventory costs, shortage costs, production costs and transportation costs of supply chain members, on the total supply chain operation cost, we propose the concept of cost sensitivity and provide corresponding algorithms based on the proposed stochastic network model. Then the model is extended to analyze the cost performance of supply chain robustness under different order compensation ability scenarios and the corresponding algorithms are developed. Simulation experiment shows the effectiveness and flexibility of the proposed model, and also promotes a better understanding of the model approach and its managerial implications in cost management of supply chains.     

Understanding supply chain dynamics: A chaos perspective

Variability in orders or inventories in supply chain systems is generally thought to be caused by exogenous random factors such as uncertainties in customer demand or lead time. Studies have shown, however, that orders or inventories may exhibit significant variability even if customer demand and lead time are deterministic. In this paper, we investigate how this class of variability, chaos, may occur in a multi-level supply chain and offer insights into how to manage relevant supply chain factors to eliminate or reduce system chaos. The supply chain is characterized by the classical beer distribution model with some modifications. We observe the supply chain dynamics under the influence of various factors: demand pattern, ordering policy, demand-information sharing, and lead time. Through proper decision-region formation, the effect of various factors on system chaos is investigated using a factorial design. The degree of system chaos is quantified using the Lyapunov exponent across all levels of the supply chain. This study shows that, to reduce the degree of chaos in the supply chain system, the adjustment parameters for both inventory and supply line discrepancies should be more comparable in magnitude. Counter-intuitively, in certain decision regions, sharing demand information can do more harm than good. Similar to the bullwhip effect observed previously in demand, we discover the phenomenon of “chaos-amplification” in inventory across supply chain levels.     

A fuzzy-based decision support model for monitoring on-time delivery performance: A textile industry case study

This paper investigates uncertainties in complex supply chain situations and proposes a fuzzy-based decision support model for determining the chance of meeting on-time delivery in a complex supply chain environment. It integrates fuzzy logic principles and unitary structure-based supply chain model and enables addressing uncertainties associated with key inputs of on-time delivery performance for effective decision making process. The proposed pragmatic model deals with the fuzziness of the key inputs including, variations in demand forecasting, materials shortages and distribution lead time, and combines a fuzzy reasoning approach for monitoring on-time delivery of finished products. In systematically dealing with the uncertainties of complex supply chains, this model supports the minimizing of business losses that result from penalties and customer dissatisfaction, and the consequent reduced market share. Application of the proposed model is illustrated using a textile industry case study.     

Clustering model and algorithm for production inventory and distribution problem

One of the most challenging problem for optimizing supply chain is the coordination of production and distribution decision. This paper considers a customer clustering problem for a large scale production, inventory and distribution problem (PIDRP) where the demands at customers need to be satisfied in each period in the planning horizon with limited production and transportation capacity. The clustering aspect of the distribution problem is an important component for reducing complexity of the dispatch operation especially when the number of customers is large.     

基于系统动力学的供应链库存策略仿真研究

针对一个动态、多级的供应链库存系统,应用系统动力学的方法,建立了供应链(s,S)库存策略下的物流成本模型,并通过动态仿真,分析了库存策略的变动对于供应链库存系统各级成员间库存供需的动态行为,提出了(s,S)策略下的供应链库存系统的有效管理方法.     

Collaborative production planning of supply chain under price and demand uncertainty

This research is motivated by an automobile manufacturing supply chain network. It involves a multi-echelon production system with material supply, component fabrication, manufacturing, and final product distribution activities. We address the production planning issue by considering bill of materials and the trade-offs between inventories, production costs and customer service level. Due to its complexity, an integrated solution framework which combines scatter evolutionary algorithm, fuzzy programming and stochastic chance-constrained programming are combined to jointly take up the issue. We conduct a computational study to evaluate the model. Numerical results using the proposed algorithm confirm the advantage of the integrated planning approach. Compared with other solution methodologies, the supply chain profits from the proposed approach consistently outperform, in some cases up to 13% better. The impacts of uncertainty in demand, material price, and other parameters on the performance of the supply chain are studied through sensitivity analysis. We found the proposed model is effective in developing robust production plans under various market conditions.     

Modeling and analysis of a supply–assembly–store chain

We consider a supply–assembly–store chain with produce-to-stock strategy, which comprises a set of component suppliers, a mixed-model assembly line with a constantly moving conveyor linking a set of workstations in series, and a set of product storehouses. Each supplier provides components of a specified family, which are assembled at a corresponding workstation. Units belonging to different models of products are sequentially fed onto the conveyor, and pass through the workstations to generate finished products. Each storehouse stores finished products belonging to a specific model for satisfying customer demands. The suppliers deliver components according to a just-in-time supply policy with stochastic leadtimes. Customer demands for a particular model of products arrive at the corresponding storehouse according to a Poisson stream. The paper conducts a modeling and performance analysis in the design stage of the system in the sense of “long-term-behavior”. A rolling technique is constructed for analyzing stationary probability distributions of the numbers of components. A two-dimensional Markov chain with infinite states is introduced for analyzing stationary probability distributions of inventories of finished products. Based on these distributions, performance measures of the system, such as work-in-process of components, inventory amounts of finished products, as well as service levels for customers, can be easily obtained. Managerial insights are obtained from both analytical and numerical results.     

Optimal models for a multi-stage supply chain system controlled by kanban under just-in-time philosophy

This research studies a multi-stage supply chain system that operates under a JIT (just-in-time) delivery policy. Kanbans play an important role in the information and material flows in a supply chain system. Thus, a kanban mechanism is employed to assist in linking different production processes in a supply chain system to implement the scope of JIT philosophy. For a multi-stage supply chain system, a mixed-integer nonlinear programming (MINLP) problem is formulated from the perspective of JIT delivery policy where a kanban may reflect to a transporter such as a truck or a fork-lifter. The number of kanbans, the batch size, the number of batches and the total quantity over one period are determined optimally. It is solved optimally by branch and bound method. A greedy heuristic to avoid the large computational time in branch-and-bound algorithm is developed for solving a large MINLP. Coupled with plant-wide efforts for cost control and management commitment, a logistic system for controlling the production as well as the supply chain is built, which results in minimizing the total cost of the supply chain system. The results show that the improvements in reduction of inventory, wasted labor and customer service in a supply chain are significantly accomplished through the kanban mechanism.     

The determination of optimal partial fill policy for an inventory system with lumpy demand items

This paper presents a mathematical model developed for the synthesis of optimal replenishment policies for items that experience lumpy demands. In order to avoid disrupting the inventory system, a cutoff point of w units is introduced such that the system would only satisfy routinely customer orders with transaction sizes less than or equal to w units. For customer orders with transaction sizes larger than w units, the system would only supply the cutoff amount (w units). The excess units would be refused. The control discipline is the (s, S) inventory policy with continuous review, and the nature of the customer orders is approximated by a discrete stuttering Poisson distribution. The optimal values of the control parameters, w, s and S, are determined. The theoretical results obtained are illustrated with a numerical example.     

A robust optimization approach to closed-loop supply chain network design under uncertainty

The concern about significant changes in the business environment (such as customer demands and transportation costs) has spurred an interest in designing scalable and robust supply chains. This paper proposes a robust optimization model for handling the inherent uncertainty of input data in a closed-loop supply chain network design problem. First, a deterministic mixed-integer linear programming model is developed for designing a closed-loop supply chain network. Then, the robust counterpart of the proposed mixed-integer linear programming model is presented by using the recent extensions in robust optimization theory. Finally, to assess the robustness of the solutions obtained by the novel robust optimization model, they are compared to those generated by the deterministic mixed-integer linear programming model in a number of realizations under different test problems.     

Managerial and economic impacts of reducing delivery variance in the supply chain

This paper addresses the managerial and economic impacts of improving delivery performance in a serial supply chain when delivery performance is evaluated with respect to a delivery window. Building on contemporary management theories that advocate variance reduction as the critical step in improving the overall performance of a system, we model the variance of delivery time to the final customer as a function of the investment to reduce delivery variance and the costs associated with untimely delivery (expected earliness and lateness). A logarithmic investment function is used and the model solution involves the minimization of a convex–concave total cost function. A numerical example is provided to illustrate the model and the solution procedure. The model presented provides guidelines for determining the optimal level of financial investment for reducing delivery variance. The managerial implications as well as the economic aspects of delivery variance reduction in supply chain management are discussed.