Scheduling with an outsourcing option on both manufacturer and subcontractors

This paper addresses a single-stage scheduling problem with outsourcing allowed where each job can be either scheduled for in-house production or outsourced to one of the outside subcontractors available. The manufacturer has an unrelated parallel machine system, and each subcontractor has its own single machine. Subcontractors are capable to process all the jobs. Unlike most of past research, our study considers the joint scheduling of both in-house and outsourced jobs simultaneously. The objective is to minimize sum of the total weighted completion time and total outsourcing cost. An integer programming formulation is presented and then improved through an optimality property on job orders. A heuristic algorithm is also introduced to decompose the problem into smaller and easier subproblems and solve them to optimality.     

Logistics service scheduling with manufacturing provider selection in cloud manufacturing

In service-oriented manufacturing models, manufacturing resources in different enterprises are integrated and shared through network, cloud platforms, and logistics. On cloud manufacturing platforms, service providers offer on-demand manufacturing services to service demanders according to supply-demand matching results. As a special type of manufacturing services, logistics services provide transportation capabilities for production services and demanders. It is a critical issue to schedule logistics services efficiently, especially when manufacturer selections have been planned. This research focuses on the logistics scheduling problem in cloud manufacturing with pre-selected manufacturers. We analyze this optimization problem from aspects of tasks, production services, logistics services, and optimization objectives. Then a logistics scheduling method is proposed to reduce the average delivery time from manufacturers to customers. In the proposed method, the total time from start points of logistics to demanders is considered to reduce the average delivery time of all tasks. Based on four different scenarios, we build their scheduling models and run simulations to verify the effectiveness of the proposed method. Results show that the average task delivery time of the proposed method is shorter than three typical strategies.     

A survey on offline scheduling with rejection

In classical deterministic scheduling problems, it is assumed that all jobs have to be processed. However, in many practical cases, mostly in highly loaded make-to-order production systems, accepting all jobs may cause a delay in the completion of orders which in turn may lead to high inventory and tardiness costs. Thus, in such systems, the firm may wish to reject the processing of some jobs by either outsourcing them or rejecting them altogether. The field of scheduling with rejection provides schemes for coordinated sales and production decisions by grouping them into a single model. Since scheduling problems with rejection are very interesting both from a practical and a theoretical point of view, they have received a great deal of attention from researchers over the last decade. The purpose of this survey is to offer a unified framework for offline scheduling with rejection by presenting an up-to-date survey of the results in this field. Moreover, we highlight the close connection between scheduling with rejection and other fields of research such as scheduling with controllable processing times and scheduling with due date assignment, and include some new results which we obtained for open problems.     

Solving the integrated scheduling of production and rail transportation problem by Keshtel algorithm

Nowadays, scheduling of production cannot be done in isolation from scheduling of transportation since a coordinated solution to the integrated problem may improve the performance of the whole supply chain. In this paper, because of the widely used of rail transportation in supply chain, we develop the integrated scheduling of production and rail transportation. The problem is to determine both production schedule and rail transportation allocation of orders to optimize customer service at minimum total cost. In addition, we utilize some procedures and heuristics to encode the model in order to address it by two capable metaheuristics: Genetic algorithm (GA), and recently developed one, Keshtel algorithm (KA). Latter is firstly used for a mathematical model in supply chain literature. Besides, Taguchi experimental design method is utilized to set and estimate the proper values of the algorithms’ parameters to improve their performance. For the purpose of performance evaluation of the proposed algorithms, various problem sizes are employed and the computational results of the algorithms are compared with each other. Finally, we investigate the impacts of the rise in the problem size on the performance of our algorithms.     

Direct shipping logistic planning for a hub-and-spoke network with given discrete intershipment times

This paper proposes a heuristic procedure to solve the problem of scheduling and routing shipments in a hybrid hub‐and‐spoke network, when a given set of feasible discrete intershipment times is given. The heuristic procedure may be used to assist in the cooperative operational planning of a physical goods network between shippers and logistics service provider, or to assist shippers in making logistics outsourcing decisions. The objective is to minimise the transportation and inventory holding costs. It is shown through a set of problem instances that this heuristic procedure provides better solutions than existing economic order quantity‐based approaches. Computational results are presented and discussed.     

Simultaneous control of vehicle routing and inventory for dynamic inbound supply chain

This paper investigates inbound logistics for an OEM (Original Equipment Manufacturing) manufacturer, who aims at short production time and JIT policy. In such a case, it can be argued that the inbound vehicle routing schedule should be combined with incoming parts inventory control. In this paper, we propose a simultaneous control method of combining vehicle scheduling and inventory control for such dynamic inbound logistics. For the transportation control, a vehicle routing system, in which delivery jobs are made with shipments of one supplier, is proposed to generate a vehicle routes plan by considering production start time, travel time, waiting time, and loading/unloading time. To evaluate the performance of the generated vehicle routing plan, a goal model is also developed by considering vehicle operating cost, stock level exceeding penalty, and transportation efficiency. A generated vehicle routing plan can be rejected when the stock level is over the capacity and an appropriate number of vehicles for its manufacturing environment can be determined. Using real data from an LCD firm, a simulation study is conducted. The simulation results indicate that the simultaneous control approach requires fewer vehicles than the existing system and shows better efficiency of transportation. This method can also be used to determine the appropriate incoming part inventory level or the number of vehicles required in dynamic inbound logistics.     

RETRACTED ARTICLE: Hierarchical evaluation algorithm of logistics carrying capacity based on transfer learning in multimedia environment

In order to measure and enhance the carrying capacity of regional logistics system scientifically and give full play to the efficiency of regional logistics system, based on the perspective of sustainable development, this paper analyzes the network topology structure of regional logistics system. Combining the component elements and operation flow of regional logistics system, we take link line, operation capacity and energy environment as constraint conditions, and construct the measurement model of regional logistics system carrying capacity. The integrated management of logistics requires systematic integration of various modes of transportation, such as highway transportation, water transportation, rail transportation and air transportation, as well as the network of key nodes and their network. Therefore, we need to solve the different transportation models and the nodal carrying capacity calculation methods that constitute the logistics system. Therefore, this paper analyzes the connotation, various transportation mode and port capacity related to the concept of research ideas, research tools and methods, the related calculation formula, traffic flow as the main line to construct different modes of transport carrying capacity between the inner link, and the bearing capacity formula of each transport mode into a unified unit, the transportation mode of coordinated development that is the basis of the overall optimization and decision of logistics system.

     

Pricing and production lot-size/scheduling with finite capacity for a deteriorating item over a finite horizon

Although the lately evolved manufacturing technologies such as enterprise resource planning (ERP) provide a unified platform for managing and integrating core business processes within a firm, the decision-making between marketing and production planning still remains rather disjoint. It is due in large parts to the inherent weaknesses of ERP such as the fixed and static parameter settings and uncapacitated assumption. To rectify these drawbacks, we propose a decision model that solves optimally the production lot-size/scheduling problem taking into account the dynamic aspects of customer's demand as well as the restriction of finite capacity in a plant. More specifically, we consider a single product that is subject to continuous decay, faces a price-dependent and time-varying demand, and time-varying deteriorating rate, production rate, and variable production cost, with the objective of maximizing the profit stream over multi-period planning horizon. We propose both coordinated and decentralized decision-making policies that drive the solution of the multivariate maximization problem. Both policies are formulated as dynamic programming models and solved by numerical search techniques. In our numerical experiments, the solution procedure is demonstrated, comparative study is conducted, and sensitivity analysis is carried out with respect to major parameters. The numerical result shows that the solution generated by the coordinated policy outperforms that by the decentralized policy in maximizing net profit and many other quantifiable measures such as minimizing inventory investment and storage capacity.Scope and purposeWe consider a manufacturing firm who produces and sells a single product that is subjected to continuous decay over a lifetime, faces a price-dependent and time-varying demand function, shortages are allowed and a completely backlogged, and has the objective of determining price and production lot-size/scheduling so as to maximize the total profit stream over multi-period planning horizon. We develop a tactical-level decision model that solves the production scheduling problem taking into account the dynamic nature of customer's demand which is partially controllable through pricing schemes. As analogous to the sales and operations planning, the proposed scheme can be used as a coordination center of the APS system within a generic enterprise resource planning framework which integrates and coordinates distinct functions within a firm.This paper differs from the existing works in several ways. First, we propose a dynamic version of the joint pricing and lot-size/scheduling problem taking into account the capacitated constraint. Second, several key factors being considered in the model, such as the demand rate, deteriorating rate, production rate, and variable production cost are assumed time-varying that reflect the dynamic nature of the market and the learning effect of the production system. A third difference between the past research and ours is that the price can be adjusted upward or downward in our model, making the proposed pricing policy more responsive to the structural change in demand or supply.     

An enhanced model for the integrated production and transportation problem in a multiple vehicles environment

Solving an integrated production and transportation problem (IPTP) is a very challenging task in semiconductor manufacturing with turnkey service. A wafer fabricator needs to coordinate with outsourcing factories in the processes including circuit probing testing, integrated circuit assembly, and final testing for buyers. The jobs are clustered by their product types, and they must be processed by groups of outsourcing factories in various stages in the manufacturing process. Furthermore, the job production cost depends on various product types and different outsourcing factories. Since the IPTP involves constraints on job clusters, job-cluster dependent production cost, factory setup cost, process capabilities, and transportation cost with multiple vehicles, it is very difficult to solve when the problem size becomes large. Therefore, heuristic tools may be necessary to solve the problem. In this paper, we first formulate the IPTP as a mixed integer linear programming problem to minimize the total production and transportation cost. An efficient genetic algorithm (GA) is proposed next to tackle the problem when it becomes too complicated. The objectives are to minimize total costs, where the costs include production cost and transportation cost, under the environment with backup capacities and multiple vehicles, and to determine an appropriate production and distribution plan. The results demonstrate that the proposed GA model is an effective and accurate tool.     

基于排队论的生产物流系统的仿真优化

研究生产过程中物流运输资源配置的问题。为了有效提高企业生产过程中物流效率并控制投资,依据排队理论,建立等待费用和配置资源费用的综合比较模型,从而选择最佳资源配置方案。通过对仓储物流系统的详细调研和数据采集,建立生产系统物流模型,运用Arena 7.0仿真软件进行仿真,从而设计和选择一个较好的策略。     

基于节点流的流程工业生产物流调度方法及应用

流程工业生产物流动态平衡调度是典型的多工序、多目标、多约束的多机并行调度问题,难以解析建模.为此,提出了节点流控制及切换的概念,建立了生产调度模型,并采用多目标分层求解调度模型.在此基础上,提出了流程工业生产的物流平衡调度方法.实际应用表明,所提出方法提高了物流动态调度的准确性和生产效率.     

Production scheduling with subcontracting: the subcontractor’s pricing game

This paper studies a two-stage game with a manufacturer and a subcontractor who are faced by a production scheduling problem. The manufacturer has a set of jobs to process, a subset of which can be subcontracted to the subcontractor to reduce the tardiness cost. In the game, the subcontractor makes the first decision to ask for a unit price of his machine time to be used by the manufacturer, and then the manufacturer follows to decide which jobs to be subcontracted to process and how the production scheduling is made. We analyze the game and derive how the subcontractor can optimize the unit price to maximize his profit. We then investigate the performance of such a simple contract from the viewpoint of coordination, and propose two other contracts that can achieve coordination between the two players.     

基于Windows服务的粮食物流短信服务平台的构建

针对粮食物流中的车辆调度管理和数据实时传输的问题,本文提出一种把短息技术SMS(Short Messaging Service,简称SMS)应用到粮食物流管理信息系统中的方法。本文采用Windows服务技术构建了一个粮食物流短信服务平台,给出了构建过程中的关键问题和设计思路,研究SMS在粮食物流管理信息系统中应用的关键技术,从而实现通过SMS对运粮车辆的调度管理、作业信息管理和实时监控。通过试点应用表明,该方法能够方便、有效、及时地对粮食物流信息进行采集、传输与管理。最后,总结了基于Windows服务的短信服务平台的特点及应用前景。     

Two Machine Scheduling under Disruptions with Transportation Considerations

Effective logistics scheduling requires synchronization of manufacturing and delivery to optimize customer service at minimum total cost. In this paper, we study a new scheduling problem that arises in a disruption environment. Such a problem occurs when a disruption unexpectedly happens, and consequently, some machines become unavailable for certain periods. Jobs that are assigned to the disrupted machines and have not yet been processed can either be moved to other available machines for processing, which may involve additional transportation time and cost, or can be processed by the same machine after the disruption. Our goal is to reschedule jobs so that an objective function, including the original cost function, and possibly transportation costs and disruption cost caused by deviating from the originally planned completion times, is minimized. In this paper, we focus on the two-machine case to demonstrate some major properties, and hope that these properties can provide insights for solving other general problems, such as multiple (more than two) machine scheduling and machine scheduling in other configurations (job shop or flow shop) under disruption. We study problems with different related costs. In each problem, we either provide a polynomial algorithm to solve the problem optimally, or show its NP-hardness. If the problem is NP-hard in the ordinary sense, we also present a pseudo-polynomial algorithm to solve the problem optimally. This research is supported in part by Hong Kong RGC grant HKUST 6145/03E and in part by NSF Grant DMI-0300156.     

Coordinated scheduling of production and logistics for large-scale closed-loop manufacturing using Benders decomposition optimization

This paper studies coordinated scheduling of production and logistics for a large-scale closed-loop manufacturing system by integrating its manufacturing and recycling process. In addition to the forward manufacturing process, different recycling units in reverse recycling process are also studied. A decentralized network is designed to formulate the coordinated scheduling problem as a mixed integer programming model with both binary and integer variables. As the problem for closed-loop manufacturing is large-scale and computational-consuming in nature, the model is divided into integer variable sub-models and complex binary variable sub-models for preprocessing and reprocessing respectively. An iterative solution approach by Benders decomposition is developed to accelerate the solving efficiency in large-scale case by updating custom constraints. A case study is conducted to investigate the managerial implications of the decentralized network for the closed-loop manufacturing system. Computational experiments demonstrate the validity and efficiency of the proposed iterative solution approach for the large-scale scenarios.     

A survey of challenges in modelling and decision-making for discrete event logistics systems

In this paper, we consider discrete event logistics systems (DELS). DELS are networks of resources through which material flow. They have been the subject of a large body of analytic research. A huge variety of specific models exists that generally require application by model and/or solution experts to answer narrowly-defined logistics questions about inventory, sourcing, scheduling, routing, etc. It has, however, proven difficult to integrate these models in any comprehensive way into information systems for logistics because of the lack of conceptual alignment between the models produced by researchers and the information systems deployed in practice with which they should be integrated. In this paper, we systematically identify several challenges for DELS research. We analyse the root of the difficulties for applying academic research results in DELS, and indicate some potential future research directions.     

带尺寸约束的二机流水车间生产运输协调博弈调度问题

研究二机流水车间生产运输协调调度问题,当工件在第1台机器加工完成后,由1台带有容量限制的运输车分批次运输到第2台机器加工,运输过程考虑工件尺寸约束,目标函数为最小化最大完工时间.考虑到源于不同客户的工件对机器及运输设备的竞争,以工件为博弈方,工件在生产运输过程中等待时间为策略,各工件完工时间为收益,建立非合作博弈模型.通过将问题转化为马尔可夫决策过程,设计线性逼近值函数的Q-learning算法求解纳什均衡调度.实验结果表明Q-learning算法求得的纳什均衡调度具有较好的全局最优性,从而能够在满足客户的利益下,提高企业的生产效率,实现客户与企业的双赢.     

单台批处理机生产运输协调合作博弈调度问题

研究单台批处理机生产与生产前运输的协调调度问题,目标函数为最小化与完成时间相关的生产总成本.以工件为博弈方,以联盟的最大成本节省为特征函数,将调度问题转换为合作博弈模型.针对相同运输时间与加工时间的情形,证明该合作博弈具有非空核,beta规则可得一个核分配.针对一般问题,设计Q-learning算法求解联盟最优调度,并利用beta规则对节省的成本进行分配.数值算例验证了合作博弈模型的可行性以及Q-learning算法与beta规则对节省成本分配的有效性.