Capacities-based supply chain network design considering demand uncertainty using two-stage stochastic programming

The design of a supply chain (SC) aims to minimize cost so the product can reach the customer at the cheapest cost with flexible demand. The demand of a product is variable with time and environment. Most of the researchers have considered investment cost, processing cost, and transportation cost as variable costs to minimize the cost while considering a constant demand. In actual practice, the demands are flexible. In this paper, a two-stage stochastic programming model has been proposed for a capacities-based network design of a supply chain for flexible demands while considering inventory carrying cost and missed opportunity cost in addition to the above-mentioned costs. It will enhance the logistic planning and seek the location network optimally. Furthermore, in the first stage, decision variables represent different nodes (facility locations of echelons) of the supply chain, with the assumption that they will be considered at the design stage before uncertain parameters are unveiled. On the other hand, decision variables related to the amount of products to be produced and stored in the nodes of the SC, the flows of materials among the entities of the network, and shortfalls and excess at the customer centers are considered as second-stage variables. The methodology has been illustrated by solving an example. It was found that the proposed model yields more feasible and advantageous results.     

一类两阶段生产系统生产计划与调度的集成优化

针对传统生产计划与调度制定中存在的不足,研究了一类两阶段生产系统的生产计划与调度集成优化问题。建立了能够反映生产计划与调度相互关联特点的离散双层规划模型,提出一种基于混合优化方法的分支定界解法以及有效缩减搜索空间的方法,并构造了有效下界。针对分支定界法的松弛问题,给出采用模拟退火算法与预估校正法交替迭代求解的混合优化方法。通过实验仿真,验证了模型与算法的有效性。     

Integrated model for production planning and scheduling in a supply chain using benchmarked genetic algorithm

Production planning and scheduling is one of the core functions in manufacturing systems. Furthermore, this task is drawing even more attention in supply chain environments as problems become harder and more complicated. Most of the traditional approaches to production planning and scheduling have adopted a multi-phased, hierarchical and decompositional approach. This traditional approach does not guarantee a feasible production schedule. And even when capacity constraints are satisfied, it may generate an expensive schedule. In order to overcome the limitations of the traditional approach, several previous studies tried to integrate the production planning and scheduling problems. However, these studies also have some limitations, due to their intrinsic characteristics and the method for incorporating the hierarchical product structure into the scheduling model. In this paper we present a new integrated model for production planning and scheduling for multi-item and multi-level production. Unlike previous lot sizing approaches, detailed scheduling constraints and practical planning criteria are incorporated into our model. We present a mathematical formulation, propose a heuristic solution procedure, and demonstrate the performance of our model by comparing the experimental results with those of a traditional approach and optimal solution.     

A two-stage supply chain with order cost reduction and credit period incentives for deteriorating items

In the proposed model, we consider the scenario of a supply chain with a single vendor and a single buyer for a single product, taking into consideration the effect of deterioration and credit period incentives. We also consider the situation in which the vendor and the buyer decide upon an investment in ordering cost reduction and coordinate their inventory policies to minimize their joint average annual cost. We study and analyze the benefits of order cost reduction and credit period incentives in a coordinated supply chain. The numerical examples with an exponential ordering cost function are used to evaluate the benefit of the proposed coordinated strategy.     

A project scheduling and staff assignment model considering learning effect

In a multi-project environment, we sometimes need to periodically schedule the tasks for each project and assign staff to the tasks. Such a decision-making problem has been studied in literature; however, learning effect of staff has not been considered in previous studies. This research formulates a mixed nonlinear program for project scheduling and staff allocation problems, which considers learning effect of staff. The objective function is to minimize outsourcing costs. A genetic algorithm (GA) is proposed to solve the problem. Experiments for solving various sizes of test problems has been carried out to validate the proposed GA.     

随机产量下的二级供应链契约协调

研究了由单个供应商与生产商构成的两级供应链中,当生产商为领导者且面对确定性产品需求,供应商是追随者且面对随机产量时的契约协调问题.考虑了乘法型和加法型两种随机产量情形,在集中控制模式下,研究了供应链系统的最优生产问题;在分散控制模式下,建立了由批发价契约和批发价与原材料缺货惩罚、剩余原材料收购策略联合形成的四种契约下的Stackelberg博弈模型.通过参数分析发现博弈结果能够协调乘法型随机产量供应链,并给出了相应契约参数的解析解,而对加法型随机产量,博弈结果不能达到供应链协调.通过算例进行了验证,并对这四种契约的特点进行了分析比较.     

Integrating theory of constraints and particle swarm optimization in order planning and scheduling for machine tool production

Order planning and scheduling has become a significant challenge in machine tool enterprises, who want to meet various demands of different customers and make full use of existing resources in enterprises simultaneously. Based on the Theory of Constraints, a three-stage order planning and scheduling solution is proposed to optimize the whole system performance with bottleneck resources' capability as the constraints. After the identification of bottleneck resources, multicriteria priority sequencing is made with order per-contribution rate, order delivery urgency, and customer importance as the evaluation criteria, and the evaluation result deduced from the ideal point function can decide the production mode of all orders and products. Then, a PSO-based multiobjective optimization model is set up with minimizing bottleneck machines' makespan and minimizing total products' tardiness as the two objectives. Finally, the proposed solution is applied in one machine tool enterprise by integrating into Baosight MES (Manufacturing Execution System) system. In addition, some comparisons are carried out to evaluate the proposed PSO optimization method. The comparison with actual report shows that PSO can satisfy enterprise's needs better than before; the comparisons with genetic algorithm and ant colony optimization algorithms indicate that PSO is more effective than the others because of its faster convergence rate.     

基于交货期窗口约束的多级供应链批量调度问题研究

为解决三级供应网络中供应商和制造商的加工任务顺序优化和合理性问题,建立了交货期窗口约束下供应商与制造商协同决策的批量调度模型.以最小化供应链的物流和交付总成本为目标,综合考虑了供应商与制造商的库存成本、运输成本和未按期交付的惩罚成本.提出了采用两级遗传算法和两级编码的模型求解算法.通过一个仿真算例,验证了调度模型和求解策略的可行性.     

面向用户多品种定制的分布式供应链调度

针对云制造面向分布式供应链环境中的多品种定制问题,提出一种多目标生产运输优化调度模型和基于改进混合蛙跳算法的求解方法。该模型考虑供应链中的制造/供应两阶段,以及不同企业间的运输时间和成本,得到成本最小、时间最短、生产空闲率最小、订单延迟最小的调度方案。同时,该算法重新定义原有算法的核心操作算子以适应离散调度问题求解,并结合模拟退火和极值优化算法的特性,进一步改进离散域的混合蛙跳算法的局部搜索性能。通过项目调度标准测试集验证了该改进算法的性能,并以6个品种定制需求为案例,验证了所提模型与算法的有效性。     

订单型企业多目标生产计划的制定及其调度

针对制定订单式小批量生产计划问题,提出了一种使用动态随机投入产出函数来制定多目标生产计划的方法。针对生产调度问题,提出了联合使用最长加工时间优先(LPT)与遗传算法(GA)的混合遗传算法(HGA)来求解混合流水线的调度,并给出了一种新的编码方法,选择了相应的交叉和变异方法。研究结果表明,该计划制造方法能较好地满足订单型企业的随机性要求,而且生产计划编制效率高。该编码方法在保证染色体合法性的同时也保证了算法本身的随机性。某轧辊厂的实际案例分析结果也验证了所提出的订单型企业多目标生产计划的制定及其调度方法的可行性。     

供应链中基于不同运输主体的批量模型比较研究

在单位产品运输成本对批量敏感时,为了使订货商的最佳订货批量与供应商的最佳生产批量趋同,建立了供应链中不同主体在产品运输时,供、需双方在分散决策和集中决策情形下的最佳批量模型。研究表明,首先,在供应商负责产品运输时,运输能力柔性越强,其最佳生产批量越小,从而与订货商要求的短周期、小批量的订货更趋近;其次,订货商负责产品运输时,其最佳订货批量与供应商的最佳生产批量更趋一致,从而供、需方可以在双赢的前提下使整个供应链的利益更优。     

JIT approach to integrating production order scheduling and production activity control

Two of the most desirable features in integrating production order scheduling and production activity control is sustaining a finite workcentre capacity assumption for accurate production order scheduling, and efficiently performing a what-if sensitivity analysis to handle the dynamic uncertainties in production activity control. A finite workcentre capacity assumption leads to too many constraints that tends to slow down the computation process, and a what-if analysis tends to put too much strain on the design of the database system for production activity control. With a finite workcentre capacity and what-if sensitivity analysis capability as the basis, a holistic approach towards production planning and control is emphasized in this paper by simplifying the system based on JIT principles such as shallow levels in bill of material, a pull type backward planning and a clear identification of the type of operational environment for which the designed system is readily applicable, and data integrity by tight integration of the various modules in production planning and control. As an illustration, a system is designed and has been successfully applied to a rapidly changing made-to-order printed circuit board manufacturing environment.     

钢铁工业三级供应链协调生产计划研究

为了研究矿山、选矿厂和炼铁厂所构成的钢铁工业三级供应链中的协调生产计划问题，建立了相应的混合整数非线性规划模型。该模型的特点是：①集成3个环节的生产计划；②集成生产与运输计划。针对该模型的特殊结构，提出了一种将原问题转化为等价的多个线性规划问题，然后使用Excel标准线性规划程序求解的策略。最后，通过案例说明了模型的有效性，并探讨了进一步研究的方向。     

考虑运输-生产-库存集成的精益供应链模型

研究了考虑运输、生产、库存集成的精益供应链模型.模型从成本的角度集成了供应链的生产、供应和分销环节,并且将采购与分销运输进行合并.将运输路径长度,运输频次作为决策变量,建立了一个多变量的非线性规划模型.首先采用改进蚁群算法求出最优运输路径和路径长度,然后再求出最优运输频次和运输批量.最后结合案例验证模型,结果表明模型简单、实用,对生产实践具有较好的指导意义.     

A two-stage stochastic programming project scheduling approach to production planning

In engineering-to-order (ETO) or manufacturing-to-order (MTO) systems producing highly customized items, the high level of customization, together with long flow times, forces the production plan to be defined before information on items customization, and details on the manufacturing activities are completely disclosed. Due to the partial available information, the production plan must provide a robust schedule of the activities and of the resources utilization, trying to incorporate a certain degree of anticipation of uncertainty. This paper proposes a two-stage stochastic programming project scheduling approach to support production planning in ETO/MTO system. The approach provides a baseline production plan together with a set of revisions of the plan to react to the occurrence of uncertain events. A scenario-based approach is used to model the changes affecting the characteristics of the activities to be processed. The proposed approach is tested on random-generated instances and on a real manufacturing system producing machining centers.     

跨国供应链环境下的批量订货模型

针对当前供应链批量订货模型研究的不足,提出了跨国供应链环境下的非线性混合整数规划批量订货模型。与以往研究不同,除了传统的决策变量——订货次数外,模型的决策变量还包括转移价格和运输成本分配因子,目标函数为跨国供应链税后利润最大化。由于模型属于NP难题,求解不便,根据问题的具体特点将其转化成为若干个线性规划模型,然后利用MATLAB的优化工具箱进行了数据分析,计算结果表明,转让价格和运输费用分配的调整对跨国供应链最优订货批量及税后利润具有显著影响。     

考虑消费者退货决策的两阶段电子商务供应链制造商回购策略

为了优化两阶段电子商务供应链,避免由于信息不对称导致的供应链效率低下,研究了一类在生命周期中采用C2B-B2C两阶段模式进行销售的电子商务供应链模型,分别对第一阶段消费者根据零售商退货补偿价格制定的退货决策、第一阶段消费者退货决策对第二阶段零售商的最优订货量与最优利润的影响进行了分析,发现C2B-B2C电子商务供应链两阶段具有不同的零售商的最优退货价格。对零售商最优退货价格的分析发现,制造商能够通过回购和对退货进行再制造来调节零售商最优退货价格,实现两阶段的协调。在此基础上对不同类型的制造商回购合同进行分析,结果表明,差异化回购合同能够更有效地协调两阶段供应链;另外,在产品非易逝且消费者愿意接受再制造产品的前提下,制造商提供再制造的差异化回购合同能够更好地增进供应链的整体利润。     

Optimum coverage of uncertainties in a supply chain with an order size constraint

Supply chain (SC) uncertainties are present in both the demand and supply sides of the chain. If these uncertainties are not countered appropriately, the customer service level (CSL) and customer satisfaction will decrease, and this, in turn, will lead to a loss of sales volume and SC profitability. Maintaining an appropriate level of safety stock as a counteracting solution to SC uncertainties can boost the CSL. In traditional decision making, the retailer determines the CSL by determining its own safety stock level. However, the CSL affects the sales volume and profitability of the upstream members. This paper investigates the joint decision making on CSL in a two-stage SC with a constraint on the retailer's order size to analyze the benefits of coordination and cooperation when determining the CSL. The results of this study show that CSL coordination can significantly increase the profitability of the SC.     

Flexibility of scheduling tools for order production problems

A fundamental issue in scheduling tools for order production systems is the trade-off between the feasibility of a solution with respect to a set of dynamic local constraints and the evaluation of a schedule with respect to different global optimality criteria. CIM managers clamour for more flexible and easy to handle tools than those provided by classical scheduling theory. Decision Support Systems (DSS) have been proved to be valuable tools for manipulating schedules and for managing information on the production process. This paper surveys previous work on scheduling in production systems, and presents a methodology for facing production scheduling problems. New design criteria and interactive models for effectively supporting the methodology in flexible and dynamic production environments are discussed.