Semi-online two-level supply chain scheduling problems

We consider two-level supply chain scheduling problems where customers release jobs to a manufacturer that has to process the jobs and deliver them to the customers. Processed jobs are grouped into batches, which are delivered to the customers as single shipments. The objective is to minimize the total cost which is the sum of the total flow time and the total delivery cost. Such problems have been considered in the off-line environment where future jobs are known, and in the online environment where at any time there is no information about future jobs. It is known that the best possible competitive ratio for an online algorithm is 2. We consider the problem in the semi-online environment, assuming that a lower bound P for all processing times is available a priori, and present a semi-online algorithm with competitive ratio \(\frac{2D}{D+P}\) where D is the cost of a delivery. Also, for the special case where all processing times are equal, we prove that the algorithm is \(1.045\sqrt{\frac{2-u}{u}}\)-competitive, where u is the density of the instance.     

基于供应链的协同生产调度研究发展现状与展望*

供应链的协同生产调度是确保供应链整体运作水平与效率的核心问题,阐述了供应链环境下协同生产调度的模型研究概况以及其关键技术,分别讨论multi-agent技术、协商机制、调度算法等技术研究现状,并分析了目前此类研究中存在的问题和不足。最后展望了供应链环境下协同生产调度问题进一步的研究方向。     

供应链环境下的柔性流水线调度问题*

针对供应链环境下的柔性流水线调度问题,对制造商接到的顾客订单,给出了在不改变已有工件调度的前提下,使用资源的可用时间区间尽早完工的实时调度算法,通过证明可知对于每个工件使用该算法都能够求得最短完工时间。最后给出了应用该算法求解的一个实际例子,并进行了仿真实验,仿真结果令人满意。     

Dynamic supply chain design with inventory

In this paper, we deal with a facility location problem where we build new facilities or close down already existing facilities at two different distribution levels over a given time horizon. In addition, we allow to carry over stock in warehouses between consecutive periods. Our model intends to minimize the total costs, including transportation and inventory holding costs for products as well as fixed and operating costs for facilities.     

带运输时间的无等待供应链在线调度问题研究

研究了供应链在线调度问题.该问题具有工件无等待、工序之间存在运输时间、加工时间介于一个区间等特点,制造商随时可能接到顾客订单,订单到达前,所有信息如订单数量、到达时间及加工时间等均未知.研究了在不改变已有工件调度的情况下,使用资源的可用时间区间最早完成临时订单的算法.计算机仿真表明,使用该算法求解大规模临时订单问题是十分有效的.     

Vehicle routing scheduling for cross-docking in the supply chain

One of the most important factors in implementing supply chain management is to efficiently control the physical flow of the supply chain. Due to its importance, many companies are trying to develop efficient methods to increase customer satisfaction and reduce costs. In various methods, cross-docking is considered a good method to reduce inventory and improve responsiveness to various customer demands. However, previous studies have dealt mostly with the conceptual advantages of cross-docking or actual issues from the strategic viewpoint. It is also necessary, however, to considering cross-docking from an operational viewpoint in order to find the optimal vehicle routing schedule. Thus, an integrated model considering both cross-docking and vehicle routing scheduling is treated in this study. Since this problem is known as NP-hard, a heuristic algorithm based on a tabu search algorithm is proposed. In the numerical example, our proposed algorithm found a good solution whose average percentage error was less than 5% within a reasonable amount of time.     

Integrated process planning and scheduling in a supply chain

This paper deals with the integration of process planning and scheduling, which is one of the most important functions in a supply chain to achieve high quality products at lower cost, lower inventory, and high level of performance. Solving the problem is essential for the generation of flexible process sequences with resource selection and for the decision of the operation schedules that can minimize makespan. We formulate a mixed integer programming model to solve this problem of integration. This model considers alternative resources: sequences and precedence constraints. To solve the model, we develop a new evolutionary search approach based on a topological sort. We use the topological sort to generate a set of feasible sequences in the model within a reasonable computing time. Since precedence constraints between operations are handled by the topological sort, the developed evolutionary search approach produces only feasible solutions. The experimental results using various sizes of problems provide a way to demonstrate the efficiency of the developed evolutionary search approach.     

改进的哈里斯鹰算法求解农产品供应链调度

针对农产品流通体系的效率低、流通链条协同效率不高、紧急情况下食品供给慢等问题,通过将农产品供应链调度问题建模成混合流水车间调度问题。结合禁忌搜索算法中禁忌表机制,离散化实数编码,提出了一种改进的哈里斯鹰算法来求解农产品供应链调度问题。该方法相比较原始的哈里斯鹰算法,降低了算法陷入局部最优的可能,进一步提高了算法的求解精度。实验结果表明：相比较对比算法,改进的哈里斯鹰算法在提出的农产品供应链调度问题模型上取得了更好的效果。     

求解敏捷供应链调度优化问题的混合遗传算法

针对敏捷供应链调度决策中,需求的时间、数量约束和供应商生产能力、可用调度时段约束造成系统优化的复杂性,设计结合贪婪算法的混合遗传算法进行求解。算法以供应链系统库存成本和运输成本为适应度函数,以包含企业信息、部件信息和调度时段信息的时段编码作为遗传编码,以线性次序交叉LOX算子和逆序变异INV算子进行交叉和变异操作,在解码过程中结合贪婪算法进行调度决策和适应度计算,保证算法在满足约束条件的基础上快速收敛到系统Pareto最优解,通过算例验证算法的有效性。     

供应链动态合作广告策略

研究产品信誉受广告投入水平影响的供应链合作广告问题,建立具有广告投入水平抑制作用的产品信誉动态模型.通过哈密顿-雅可比-贝尔曼方程分别得到制造商和零售商在分散决策和集中决策下的最优广告策略以及在分散决策下制造商的最优合作广告参与率,比较发现,集中决策下制造商和零售商的最优广告投入水平高于分散决策下的相应值.设计了双边补贴策略来协调供应链.数值仿真实验验证了所得结论的正确性.     

A genetic approach to two-phase optimization of dynamic supply chain scheduling

In today’s competitive environment, agility and leanness have become two crucial strategic concerns for many manufacturing firms in their efforts to broaden market share. Recently, the build-to-order (BTO) manufacturing strategy is becoming a popular operation strategy to achieve both in a mass-scale customization process. BTO system combines the characteristics of make-to-order strategy with a forecast driven make-to-stock strategy. As a means to improve customer responsiveness, customized products are assembled according to specific orders while standard components are pre-manufactured based on short-term forecasts. Planning of the two subsystems using a two-phase sequential approach offers both operational and modeling incentives. In this paper, we formulate a two-phase mixed integer linear programming (MILP) model for material procurement, components fabrication, product assembly and distribution scheduling of a BTO supply chain system. In the proposed approach, the entire problem is first decomposed into two subsystems and evaluated sequentially. The first phase deals with assembling and distribution scheduling of customizable products, while the second phase addresses fabrication and procurement planning of components and raw-materials. The objective of both models is to minimize the aggregate costs associated with each subsystem, while meeting customer service requirements. The search space for the first phase problem involves a complex landscape with too many candidate solutions. A genetic algorithm based solution procedure is proposed to solve the sub-problem efficiently.     

敏捷供应链静态调度模型及其贪婪算法

针对确定性市场需求下的敏捷供应链(ASC)调度具有批量和时间双重约束的问题,根据供应层级关系建立多级供应链结构模型(SCSM),在此基础上设计调度问题的线性规划(LP)模型及其两阶段求解算法：第一阶段的最短响应时间调度贪婪算法判定供应链系统对需求批量和时间约束满足与否;第二阶段的精益调度贪婪算法求解以最小化供应链库存成本为目标的最优调度方案。最后通过算例验证了模型和算法的实用性和有效性。     

基于异类多种群蚁群算法的MC供应链分布式调度研究

分析了目前大规模定制（MC）供应链调度现有调度方法的不足,针对MC供应链调度具有分布式和计算规模大的特点,提出了异类多种群蚁群算法,并设计了MC分布式调度优化的模型。异类多种群蚁群算法由多类不同特性蚁群构成,不同类型蚁群具有不同特质,并优势互补,彼此间具有潜在的合作性。将多个进行寻优的异类蚁群分散到供应链的不同计算节点上多个蚁群进行协同工作,蚁群间既独立也协作,充分发挥并行高效的特点,满足大规模定制供应链调度的要求。此模型算法可根据多订单特点,充分利用供应链上分布的计算资源,对调度规模大而且复杂的供应商选择及企业间合作时序进行寻优,确定优化调度方案。实验结果证明,该算法模型具有较好的有效性、稳定性和订单适应能力。     

Assessing the efficiency of supply chain scheduling algorithms using data envelopment analysis

Where different supply chain planning algorithms are used, generally similar results may pose some challenges on the differentiating powers of evaluating different production schedules because of the increasing complexity of a supply chain network structure. For the comparison purpose, performance evaluation of different supply chain planning algorithms aims to use different supply chains models with different demands, capacities, and commonality through efficiency perspective by using a modified network rational data envelopment analysis (DEA) model. The proposed DEA model has the abilities: (1) to treat only undesirable outputs that exist without normal output, and the situation where input and output are both zero by introducing two new parameters to denote the maximum inventory and amount of delayed demands of a given node in a given time period; and (2) to evaluate the effect of the undesirable outputs/inputs on efficiency with assumption that they leave the system at the end of the current time period and re-enter the system at the beginning of the next time period. To prove the effectiveness of this DEA model, eighteen scenarios with different demands, capacities, and multiple periods are compared. In addition, this study tests the DEA model on a wafer testing/probing operation of a leading global semiconductor manufacturing and testing company in Taiwan by internal supply chain perspective. Results show that the DEA model proposed in this study can be used to assess the efficiency of a real-world operation with undesirable outputs/inputs, such as inventory and delayed demands.     

Applicability of optimal control theory to adaptive supply chain planning and scheduling

Decisions in supply chain planning and control are interconnected and depend on tackling uncertainties and dynamics. From this perspective, control theory (CT) is an interesting research avenue for the supply chain management (SCM). In this paper, the applicability of optimal CT to SCM is investigated. Our analysis is based on the fundamentals of control and systems theory and experimental modeling. The paper describes important issues and perspectives that delineate dynamics in supply chains, identifies and systemizes different streams in application of CT to production, logistics, and SCM in the period from 1960 to 2011. It derives some classifications, performs a critical analysis, and discusses further researches. Some drawbacks and missing links in the literature are pointed out. Several crucial application areas of control theory to SCM are discussed. Subsequently, optimal program control, challenges and advantages of its application in the SCM are addressed. It is shown how optimal program control can be applied to adaptive supply chain planning. In addition, it is concluded that with the help of CT, robustness, adaptability, and resilience of supply chains can be investigated in their consistency with operations planning and execution control within a conceptually and mathematically integrated framework. However, although SCs resemble control systems, they have some peculiarities which do not allow a direct application of CT methods. In this setting, further development of interdisciplinary approaches to supply chain optimization is argued. An extended co-operation between control and supply chain experts may have the potential to introduce more realism to the dynamic planning and models and improve real-time supply chain control policies.     

Two new meta-heuristics for a bi-objective supply chain scheduling problem in flow-shop environment

In this study, an integrated multi-objective production-distribution flow-shop scheduling problem will be taken into consideration with respect to two objective functions. The first objective function aims to minimize total weighted tardiness and make-span and the second objective function aims to minimize the summation of total weighted earliness, total weighted number of tardy jobs, inventory costs and total delivery costs. Firstly, a mathematical model is proposed for this problem. After that, two new meta-heuristic algorithms are developed in order to solve the problem. The first algorithm (HCMOPSO), is a multi-objective particle swarm optimization combined with a heuristic mutation operator, Gaussian membership function and a chaotic sequence and the second algorithm (HBNSGA-II), is a non-dominated sorting genetic algorithm II with a heuristic criterion for generation of initial population and a heuristic crossover operator. The proposed HCMOPSO and HBNSGA-II are tested and compared with a Non-dominated Sorting Genetic Algorithm II (NSGA-II), a Multi-Objective Particle Swarm Optimization (MOPSO) and two state-of-the-art algorithms from recent researches, by means of several comparing criteria. The computational experiments demonstrate the outperformance of the proposed HCMOPSO and HBNSGA-II.     

Dynamic ordering and pricing for a perishable goods supply chain

The paper studies the combined problem of pricing and ordering for a perishable product supply chain with one supplier and one retailer in a finite horizon. The lifetime of the product is two periods and demand in each period is random and price-sensitive. In each period, the supplier determines first a wholesale price and then the retailer decides an order quantity and retail prices. We show that the optimal pricing strategy for the non-fresh product depends only on its inventory, and the optimal pricing strategy and the optimal order quantity for the fresh product depend only on the wholesale price and they have a constant relation. Moreover, the game between the retailer and the supplier for finite horizon is equivalent to a one period game with only one order. Thus, the optimal policies are identical at each period. For the additive and multiplicative demands, we further obtain equations to compute the optimal strategies. All of above results are extended into the infinite horizon case and longer lifetime products. Finally, a numerical analysis is given.     

Variable neighborhood search for the inventory routing and scheduling problem in a supply chain

The inventory, routing and scheduling decisions are three major driving factors for supply chain performance. Since they are related to one another in a supply chain, they should be determined simultaneously to improve the decision quality. In the past, the inventory policy, vehicle routing and vehicle scheduling are determined sequentially and separately. Hence, the total cost (inventory, routing and vehicle costs) would increase. In this paper, an integrated model for the inventory routing and scheduling problem (IRSP) is proposed. Since searching for the optimal solution for this model is a non-polynomial (NP) problem, a metaheuristic, variable neighborhood search (VNS), is proposed. The proposed method was compared with other existing methods. The experimental results indicate that the proposed method is better than other methods in terms of average cost per day.     

Investigating imperfect process and demand effects on inspection scheduling and supply chain replenishment policy

The challenge of supply chain management (SCM) is to have a supervision in managing operations that is critical to coordinate with partners and have high quality product. Global companies are now striving to engage in production improvement and product design efficiency to provide a good support for competition in any parts of the world. Recently, the development and improved capability of production and information technology have changed the ways in which companies and manufacturers operate. With the assist of advanced production philosophy and collaborative design and manufacturing (CDM), the quality of product and manufacturing has been notably enhanced. However, a traditional single-stage inventory model considers a case in which depletion of inventory is merely caused by a constant rare. In practice, there is an inventory loss due to deterioration and imperfect production. The purpose of this paper is to investigate the effects of stock- and warranty-dependent demand, and take the relationship among imperfect, warranty policy and inspection scheduling into consideration. An integrated two-stage production inventory deteriorating model for replenishment policy and inspection plan is developed utilizing time-weighted inventory approach. A numerical example is presented to demonstrate the theory. The results show that fixed selling rate, the holding cost and the unit inspection cost are the critical factors in applying the deteriorating inventory model.     

基于蚁群算法的MC供应链调度优化研究

为解决大规模定制模式下客户订单分离点后的动态供应链调度问题，提出了包括供应商选择及企业合作时序安排的优化调度模型，设计了基于蚁群算法的求解过程。通过多组数据实验及结果比较分析，对模型算法的有效性、稳定性进行了验证。