有多种运输方式的供应链排序问题

本文考虑了由一个制造商和多个客户组成的供应链系统。每个客户有一个订单交给制造商加工,每个订单都有一个强制交货期。工厂采用承诺到货时间的发货方式,目标是在满足客户强制交货期的情况下,合理的安排订单的加工顺序,以极小化总的运输费用。本文考虑了多种情况,分别给出了相应的算法。     

带交货期的工件族生产与配送的排序问题

本文考虑了多个客户订购不同种类的工件,工件生产完后需要运输到客户的单机供应链排序问题.由于工件属于不同的种类,在加工不同种类工件前要有一个准备时间.每个客户分布在不同位置,客户的每个工件都有一个交货期,工件是分批配送的,每一批配送需要花费一定的时间及费用.考虑了两个与交货期有关的目标函数,分别给出了它们的最优算法.     

具有多个制造商和分批配送的同类机排序问题

考虑了同类机环境下多个工件加工和配送的排序问题.有多个制造商分布在不同位置,每个制造商处有一台机器可以加工工件.不同的机器对应着不同的加工速度和加工费用.工件生产完后需要运输到客户处,每一批配送需要花费一定的时间和费用.研究了排序理论中主要的3个目标函数,分析了问题的复杂性,对于这些问题给出了它们的最优算法.     

单机带有可拒绝的供应链排序问题

考虑了单机上带有工件拒绝的供应链排序问题.有多个客户分布在不同区域,每个客户都有一定数量的工件需要在一台机器上进行加工.制造商可以拒绝加工一些工件,但要支付相应的拒绝费用.工件生产完后需要运输到相应的客户处,每一批配送需要花费一定的时间和费用.我们研究了排序理论中主要的几个目标函数,构建了单机情况下的具体模型,分析了问题的复杂性,对具体的问题给出了它们的最优算法.     

不确定条件下双目标组批生产的交货期设置研究

交货期是调度方法的函数,因而具有不确定性.研究变批量、变批次、变生产能力下,单阶段、双目标有条件相容组批的交货期设置问题,将它转化为订单投放策略和调度模式研究.建立了一个基于目标的双目标订单投放策略数学模型.采用目标序列优先方法进行双目标求解,用两种调度模式求出区间值,进行最优交货期逼近.模式1:松弛掉产品加工约束条件,基于负荷考虑、给出离散生产模式下订单完工率最大的订单排序算法,算法综合考虑了任务紧急程度、可调度性、重要度和流程时间最短四个方面,得到区间的一个端点.模式2是有条件相容的启发式组批调度算法,即通过聚类计算将订单安排问题转化为多队列调度问题,将新来订单的投放转化为某个队列的插单和批量分割问题,不同队列中批的投产顺序由批中优先级最高的订单决定,并在能力约束下进行批量分割计算,得到区间的另一个端点,结合流程可靠性求出区间.实例证明,模式2的交货期设置小,订单完工率和生产率高.     

加工时间离散可控的分批配送排序问题

研究了工件的加工时间是离散可控的,并且工件加工完后需要分批配送到客户的单机排序问题.一个客户在初始时刻将一批工件交给一个制造商进行加工.每个工件有多种加工模式,分配给每个工件的加工资源越多,则其加工时间越短.工件生产完后需要分批配送到客户处,每一批需要花费一定的时间和费用.研究了排序理论中主要的四个目标函数,构建了单机情况下的具体模型,分析了问题的复杂性,对具体的问题给出了它们的最优算法.     

具有学习效应的三层供应链排序问题

研究了具有学习效应的三层供应链排序问题. 多个客户分布在不同位置,每个客户都有订 单需要制造商进行生产. 制造商需要针对每一个不同订单的客户从不同的地方进购对应的原材料进行生产,生产完工后需要利用有限的车辆将工件运输到相应客户处. 要求每辆运输车装载尽可 能多的货物才开始运输. 利用动态规划算法研究了最大流程时间、总流程时间以及最大延迟三个目标函数.     

考虑释放时间的单机JIT调度问题

本文主要研究二级供应链中的生产-库存-直接配送协同调度问题,其中存在一个制造商和多个零售商, 制造商根据订单进行生产, 然后将产品配送给零售商。该类问题可以抽象为考虑释放时间的单机JIT调度问题。借助于禁忌搜索算法, 本文提出了求解问题的CTA-TS算法, 并通过大量的实验数据与已有算法进行比较,说明了本文提出算法的有效性。     

基于时间敏感需求及随机完工期的承诺交货期决策

在新的经济形势和高新技术指引下,产品更新换代的速度加快,基于时间的竞争成为供应链竞争的焦点.在制造商为核心的供应链中,产品需求除了与价格有关外,与承诺交货期也有一定关联,且部分客户愿意为快速交货而支付更高价格.当需求与时间及价格具有敏感性且实际完工期服从一定的随机分布时,建立利润最大化及服务水平约束的承诺交货期决策模型,并对模型进行讨论及优化分析.通过算例验证了模型的有效性,通过参数敏感性分析得出的结论是:当客户服务水平达到一定阈值时,最优承诺交货期将发生改变;价格及交货期敏感系数不影响承诺交货期,但影响产品需求及最终利润;最优承诺交货期与单位提早完工成本是反向变动的关系而与单位延迟完工成本是正向变动的关系;随着完工期均值及标准差的不断增大,最优承诺交货期呈上升趋势,利润、市场需求及价格不断下降.     

具有可变配送费用和固定配送时刻的单机排序问题

研究了单机环境下生产与配送的协同排序问题.有多个工件需要在一台机器上进行加工,加工完的工件需要分批配送到一个客户.每批工件只能在固定的几个配送时刻出发,不同的配送时刻对应着不同的配送费用.我们的目标是找到生产与配送的协同排序,极小化排序的时间费用与配送费用的加权和.研究了排序理论中主要的四个目标函数,构建了单机情况下的具体模型,分析了问题的复杂性,对于配送费用单调非增的情况给出了它们的最优算法.     

Production and availability policies through the Markov Decision Process and myopic methods for contractual and selective orders

In this paper, we consider a supply chain with one manufacturer, one retailer, and some online customers. In addition to supplying the retailer, manufacturers may selectively take orders from individuals online. Through the Markov Decision Process, we explore the optimal production and availability policy for a manufacturer to determine whether to produce one more unit of products and whether to indicate “in stock” or “out of stock” on website. We measure the benefits and influences of adding online customers with and without the retailer’s inventory information sharing. We also simulate the production and availability policy via a myopic method, which can be implemented easily in the real world. Prediction of simple switching functions for the production and availability is proposed. We find the information sharing, production capacity and unit profit from online orders are the primary factors influencing manufacturer profits and optimal policy. The manufacturer might reserve 50% production capacity for contractual orders from the retailer and devote the remaining capacity to selective orders from spontaneous online customers.     

A decision support system for order acceptance/rejection in hybrid MTS/MTO production systems

In this paper, we present a novel decision support system for order acceptance/rejection in a hybrid Make-to-Stock/Make-to-Order production environment. The proposed decision support system is comprised of five steps. At the first step, the customers are prioritized based on a fuzzy TOPSIS method. Rough-cut capacity and rough-cut inventory are calculated in the second step and in case of unavailability in capacity and materials, some undesirable orders are rejected. Also, proper decisions are made about non-rejected orders. At the next step, prices and delivery dates of the non-rejected orders are determined by running a mixed-integer mathematical programming model. At the fourth step, a set of guidelines are proposed to help the organization negotiate over price and due date with the customers. In the next step, if the customer accepts the offered price and delivery date, the order is accepted and later considered in the production schedule of the shop floor, otherwise the order is rejected. Finally, numerical experiments are conducted to show the tractability of the applied mathematical programming model.     

Minimizing Labor Requirements in a Periodic Vehicle Loading Problem

In this paper, we address a logistics problem that a manufacturer of auto parts in the north of Spain described to the authors. The manufacturer stores products in its warehouse until customers retrieve them. The customers and the manufacturer agree upon an order pickup frequency. The problem is to find the best pickup schedule, which consists of the days and times during the day that each customer is expected to retrieve his/her order. For a given planning horizon, the optimization problem is to minimize the labor requirements to load the vehicles that the customers use to pick up their orders. Heuristically, we approach this situation as a decision problem in two levels. At the first level, customers are assigned to a calendar, consisting of a set of days with the required frequency during the planning horizon. Then, for each day, the decision at the second level is to assign each customer to a time slot. The busiest time slot determines the labor requirement for a given day. Therefore, once customers have been assigned to particular days in the planning horizon, the second-level decision is a multiprocessor scheduling problem, where each time slot is the equivalent of a processor, and where the objective is to minimize the makespan. A metaheuristic procedure is developed for the problem of minimizing labor requirements in this periodic vehicle-loading problem and artificial as well as real data are used to assess its performance.     

A lexicographic approach to bi-objective scheduling of single-period orders in make-to-order manufacturing

This paper presents a lexicographic approach and integer programming formulations for a dual-objective, long-term production scheduling in make-to-order manufacturing environment. The problem objective is to assign single-period customer orders for various product types to planning periods to complete all the orders with minimum number of tardy orders as a primary criterion and to level the aggregate production or the total capacity utilization over a planning horizon as a secondary criterion. Each order must be completed during one planning period. The basic integer programming formulation has been strengthened by the addition of some cutting constraints derived by relating the demand on required capacity to available capacity for each subset of orders with the same due date. The approach has been applied to optimize production schedules in a flexible flowshop made up of several processing stages in series, with identical, parallel machines, and an output buffer of limited capacity for holding completed products before delivery to the customers. Numerical examples modeled after a real-world make-to-order flexible assembly line in the electronics industry are provided and some computational results are reported.     

The one-dimensional cutting stock problem with due dates

The one-dimensional cutting stock problem is the problem of cutting stock material into shorter lengths, in order to meet demand for these shorter lengths while minimizing waste. In industrial cutting operations, it may also be necessary to fill the orders for these shorter lengths before a given due date. We propose new optimization models and solution procedures which solve the cutting stock problem when orders have due dates. We evaluate our approach using data from a large manufacturer of reinforcement steel and show that we are able to solve industrial-size problems, while also addressing common cutting considerations such as aggregation of orders, multiple stock lengths and cutting different types of material on the same machine. In addition, we evaluate operational performance in terms of resulting waste and tardiness of orders using our model in a rolling horizon framework.     

Real-time order tracking for supply systems with multiple transportation stages

This paper presents a stochastic model that evaluates the value of real-time shipment tracking information for supply systems that consist of a retailer, a manufacturer, and multiple stages of transportation. The retailer aggregates demand for a single product from end customers and places orders on the manufacturer. Orders received by the manufacturer may take several time periods before they are fulfilled. Shipments dispatched by the manufacturer move through multiple stages before they reach the retailer, where each stage represents a physical location or a step in the replenishment process. The lead time for a new order depends on the number of unshipped orders at the manufacturer’s site and the number and location of all shipments in transportation. The analytic model uses real-time information on the number of orders unfulfilled at the manufacturer’s site, as well as the location of shipments to the retailer, to determine the ordering policy that minimizes the long-run average cost for the retailer. It is shown that the long-run average cost is lower with real-time tracking information, and that the cost savings are substantial for a number of situations. The model also provides some guidelines for operating this supply system under various scenarios. Numerical examples demonstrate that when there is a lack of information it is better for the retailer to order every time period, but with full information on the status in the supply system it is not always necessary for the retailer to order every time period to lower the long-run average cost.     

Minimizing the weighted number of tardy jobs with due date assignment and capacity-constrained deliveries for multiple customers in supply chains

In this paper, an integrated due date assignment and production and batch delivery scheduling problem for make-to-order production system and multiple customers is addressed. Consider a supply chain scheduling problem in which n orders (jobs) have to be scheduled on a single machine and delivered to K customers or to other machines for further processing in batches. A common due date is assigned to all the jobs of each customer and the number of jobs in delivery batches is constrained by the batch size. The objective is to minimize the sum of the total weighted number of tardy jobs, the total due date assignment costs and the total batch delivery costs. The problem is NP-hard. We formulate the problem as an Integer Programming (IP) model. Also, in this paper, a Heuristic Algorithm (HA) and a Branch and Bound (B&B) method for solving this problem are presented. Computational tests are used to demonstrate the efficiency of the developed methods.     

同类机下的供应链排序及转包策略

研究了一类工件排序与转包关联的模型,即工件既可以在制造商的同类机上加工,也可以较高费用转包给某个承包商加工.需要确定被转包的工件集,以及未转包工件的加工顺序,使得工件加工与转包费用在工件最大完工时间满足限制条件下达到极小.证明了该问题的NP困难性,用数学规划方法构造多项式时间近似算法,并分析算法性能比.     

The final order problem for repairable spare parts under condemnation

We consider a manufacturer of complex machines that offers service contracts to her customers, committing herself to repair failed spare parts throughout a fixed service period. The suppliers of spare parts often discontinue the production of some parts as technology advances and ask the manufacturer to place a final order. We address the problem of determining final orders for such spare parts. The parts that we consider are repairable, but they are subject to the risk of condemnation. We build a transient Markovian model to represent the problem for a repairable spare part with a certain repair probability and repair lead time and we present some approximations that allow for further real-life characteristics to be included. Furthermore, an approximate model that can be computed more efficiently is presented, and the sensitivity of the results obtained with respect to the problem parameters is discussed, helping us develop several managerial insights.