Robust scheduling in an advanced planning and scheduling environment

A new scheduling problem that appears in an advanced planning and scheduling (APS) environment is discussed. Under the precondition that all materials are available when needed, the problem is formulated as follows: min N i =1 ( D i - E i )/N subject to D i - C i S 0, for all i , where N is the number of customer orders arriving randomly at the shop during a certain period, D i is the estimated due-date for customer order i , E i is the due-date estimation time for customer order i and C i is the completion time for customer order i . Then, D i and C i are endogenous variables and E i is an exogenous variable. The ability to construct a flexible scheduling process that avoids the need to fix ongoing schedules is essential to all APS systems. Therefore, the concept of a due-date buffer is introduced which is expected to enable the production schedules for each customer order to be flexible at the beginning and gradually become fixed as the processing of the order progresses. A simulation-based scheduling algorithm using the concept of a due-date buffer is developed here and subsequently examined through a series of numerical experiments. The obtained computation results show that the performance of due-date buffers is outstanding with respect to complicated production processes having higher utilizations.     

Two-stage stochastic master production scheduling under demand uncertainty in a rolling planning environment

This paper proposes a scenario-based two-stage stochastic programming model with recourse for master production scheduling under demand uncertainty. We integrate the model into a hierarchical production planning and control system that is common in industrial practice. To reduce the problem of the disaggregation of the master production schedule, we use a relatively low aggregation level (compared to other work on stochastic programming for production planning). Consequently, we must consider many more scenarios to model demand uncertainty. Additionally, we modify standard modelling approaches for stochastic programming because they lead to the occurrence of many infeasible problems due to rolling planning horizons and interdependencies between master production scheduling and successive planning levels. To evaluate the performance of the proposed models, we generate a customer order arrival process, execute production planning in a rolling horizon environment and simulate the realisation of the planning results. In our experiments, the tardiness of customer orders can be nearly eliminated by the use of the proposed stochastic programming model at the cost of increasing inventory levels and using additional capacity.     

Designing the closed loop elements of a. material requirements planning system in a low volume,make-to-order company (with case study)

This paper describes how material requirements planning (MRP) can provide a working solution to job shop scheduling problems. In particular, the critical factors relating to resource and capacity management are examined within a low volume, make-to-order company. A rough cut capacity planning (RCCP) module is described which, when used with a detailed capacity management methodology, has enabled the collaborating company to achieve substantial savings in work-in-progress (WIP), a marked reduction in machining lead times and viable control over customer lead times. Finally, an MRP production methodology is described, which allows such concepts as unit cost justification and machine utilisation to be re-examined.     

Multi-objective master production scheduling in make-to-order manufacturing

In this paper a multi-objective, long-term production scheduling in make-to-order manufacturing is considered and a lexicographic approach with a hierarchy of integer programming formulations is proposed. The problem objective is to allocate customer orders with various due dates among planning periods with limited capacities to minimize the number of tardy orders as a primary optimality criterion. Then, the maximum level of the input and output inventory is minimized as a secondary criterion, and finally the aggregate production is leveled over the planning horizon as an auxiliary criterion. A close relation between minimizing the maximal inventory and the maximum earliness of customer orders is shown and used to simplify the inventory leveling problem. Numerical examples, modeled after a real-world make-to-order flexible flowshop in a high-tech industry, are provided and some computational results are reported. The paper indicates that the maximum earliness of customer orders is an important managerial decision variable, and its minimum value can be applied to control the inventory of purchased materials and finished products to maximize the customer service level and minimize production costs.     

A project scheduling approach to production planning with feeding precedence relations

In Manufacturing-to-Order or Engineering-to-Order systems producing complex and highly customised items, each item has its own characteristics that are often tailored for a specific customer. Project scheduling approaches are suitable for production planning in such environments. However, when we consider the production of complex items, the distinct production operations are often aggregated into activities representing whole production phases. In such cases, the planning and scheduling problem works on the aggregate activities, considering that, in most cases, such activities also have to be manually executed. Moreover, simple finish-to-start precedence relations no longer correctly represent the real production process, but overlapping among activities should be allowed. In this paper, a project scheduling approach is proposed for production planning in Manufacturing-to-Order systems. The Variable Intensity formulation is used to allow the effort committed to the execution of activities to vary over time. Feeding precedences are developed to model generalised precedence relations when the execution mode of activities is not known a priori. Two mathematical formulations of these precedence relations are proposed. The formulations are applied both to randomly generated instances and to an industrial system producing machining centres and are compared in terms of computational efficiency.     

Computer-aided production management issues in the engineer-to-order production of complex capital goods explored using a simulation approach

This paper describes the exploration of manufacturing planning and control issues in the capital goods industry using a simulation approach. The companies produce products which have deep and complex product structures and are produced in low volume on an engineer- or make-to-order basis (ETO, MTO). The work reported here draws on the results of surveys of companies involved in the manufacture of capital goods which identified their characteristics of ETO and MTO capital goods companies and their strategic issues. The planning and control approaches adopted in the manufacturing facilities and the difficulties experienced in the application of computer-aided production management (CAPM) systems were also examined. The simulation model developed enables complex manufacturing systems to be modelled and was configured to represent a typical ETO/MTO facility using industrial data. A series of full factorial experiments were performed to explore a number of production management problems identified in surveys including capacity planning, assembly planning and scheduling strategies. Conclusions are drawn on the effects on performance and capacity of: applying minimum set-up and processing times for both major and minor activities; using different data update periods and assembly lead times; and adopting various scheduling and despatching approaches. These results are compared with those obtained by other workers who used survey techniques alone, and have implications for the manufacturers of capital goods.     

A new approach to finite scheduling

This paper discusses the general framework of a methodology for material planning and operations scheduling. The methodology is designed to individually schedule customer orders according to the relative priority values assigned to each order, based on various factors including but not limited to due-dates. Resource constraints are considered during the scheduling process to ensure the feasibility of the finite production schedule, while attempting to ensure a reduced production lead time. The methodology is exemplified through test problems compared with the normal approaches. The preliminary results are quite promising, although further mathematical proofs are required to establish the potential advantages of the method in a generic form.     

Fuzzy scheduling of a build-to-order supply chain

The overwhelming majority of the literature in the area of supply chain planning and scheduling considers the traditional make-to-stock (MTS) environment. However, manufacturers of assembled products such as cars, computers, furniture, etc. adopt the build-to-order supply chain (BOSC) to become agile in a mass customization process in order to meet diversified customer requirements. In this paper we propose an integrated production–distribution planning model for a multi-echelon, multi-plant and multi-product supply chain operating in a build-to-order (BTO) environment. The uncertainties associated with estimation of the various operational cost parameters are represented by fuzzy numbers. The BOSC scheduling model is thus constructed as a mixed-integer fuzzy programming (MIFP) problem with the goal of reducing the overall operating costs related to component fabrication, procurement, assembling, inspection, logistics and inventory, while improving customer satisfaction by allowing product customization and meeting delivery promise dates at each market outlet. An efficient compromise solution approach by transforming the problem into an auxiliary multi-objective linear programming model is also suggested.     

Revising the master production schedule in a HPP framework context

This paper investigates the stability of the master production schedule (MPS) in a multi-product batch chemical plant, a typical example of manufacturing plants in the process industry. The effects of demand pattern, replanning periodicity, setup costs and unit production cost on the performance of the MPS in a rolling horizon situation are examined. Adopting the hierarchical production planning framework, a two-level mathematical model is developed to conduct the study. A comprehensive simulation work and statistical analyses are reported in this work. The results of the study show that replanning periodicity significantly influences the scheduling instability and the impact of setup cost on scheduling instability is dependent on the demand pattern and the unit production cost. Moreover, the findings indicate that replanning frequency does not affect the total cost of the system if the cost structures are not extreme. Finally, the results show that cost structures affect the batching of orders (converting production quantities into an optimal number of batches to be processed).     

混装线投产序列和工位任务的协同调度机理

柔性汽车混装线需根据订单和生产需求调整当月工位任务规划和车型投产序列.通过分析汽车行业装配生产现状,提出高效而精准的调度目标,指出面向车型投产序列规划和工位任务规划的协同调度思想;说明针对不同目标采用的投产序列和工位规划方法,陈述了协同调度的策略框架,并用实际事例递进演示了协同调度机理.实验 证明协同调度能达到混装生产作业的高效精准过程控制的目的.     

Agile production planning and control with advance notification to change schedule

An agile production planning and control system (APPCS) is proposed that allows the agile rescheduling upon unexpected events. It executes schedule-based planning in the sense that scheduling is tightly connected with net-requirements planning and capacity requirements planning. APPCS does precise scheduling with so-called advanced planning system for the set of demands that are accepted in the planning cycle. It produces a feasible production plan, using production data. After releasing the production or purchase orders, notification to change the schedule may arrive from suppliers or customers in advance of due date. Based on the notification, APPCS reschedules in accordance with the then situation. Safety lead time in purchase orders and safety stock in raw material are used in APPCS to absorb the effects caused by sudden changes. As an example of implementation, it is applied to a fictitious but typical factory with a variety of products. The simulation result shows that planning with notification is effective, and that notification has the similar effect with safety lead time.     

Prediction of manufacturing resource requirements from customer demands in mass-customisation production

Mass-customisation production is a new manufacturing approach to produce customised products based on requirements of individual customers while maintaining the quality and efficiency of mass production. Due to the large variations of customised products, the traditional methods for planning manufacturing resources based on volumes of mass produced products are not effective for mass-customization production. In this research, a new manufacturing resource planning method is developed by studying the relations between customer demands and manufacturing resource requirements based on the true data from a mass-customisation production company—Gienow Windows and Doors. In this research, first the relations between the customer demands, modeled by sales data at levels of whole company, sales branches, and markets in sales branches, and the manufacturing resource requirements, modeled by labour requirements of different production lines are studied. Fuzzy pattern clustering method is employed for classifying the resource requirements into patterns to further understand the relations. Based on this study, linear regression and neural network are used to model the linear and non-linear relations between customer demands and manufacturing resource requirements, and to predict the manufacturing resource requirements from available customer demands. A manufacturing resource planning system was developed to demonstrate the effectiveness of this introduced approach.     

A capacity-oriented hierarchical approach to single-item and small-batch production planning using project-scheduling methods

Most production planning and control (PPC) systems used in practice have an essential weakness in that they do not support hierarchical planning with feedback and do not observe resource constraints at all production levels. Also, PPC systems often do not deal with particular types of production, for example, low-volume production. We propose a capacity-oriented hierarchical approach to single-item and small-batch-production planning for make-to-order production. In particular, the planning stages of capacitated master production scheduling, multi-level lot sizing, temporal and capacity planning, and shop floor scheduling are discussed, where the degree of aggregation of products and resources decreases from stage to stage. It turns out that the optimization problems arising at most stages can be modelled as resourceconstrained project scheduling problems.     

Lagrangean relaxation approach to joint optimization for production planning and scheduling of synchronous assembly lines

This paper focuses on simultaneous optimisation of production planning and scheduling problem over a time period for synchronous assembly lines. Differing from traditional top-down approaches, a mixed integer programming model which jointly considers production planning and detailed scheduling constraints is formulated, and a Lagrangian relaxation method is developed for the proposed model, whereby the integrated problem is decomposed into planning, batch sequencing, tardiness and earliness sub-problems. The scheduling sub-problem is modelled as a time-dependent travelling salesman problem, which is solved using a dynasearch algorithm. A proposition of Lagrangian multipliers is established to accelerate the convergence speed of the proposed algorithm. The average direction strategy is employed to solve the Lagrangian dual problem. Test results demonstrate that the proposed model and algorithm are effective and efficient.     

Applied column generation-based approach to solve supply chain scheduling problems

More and more enterprises have chosen to adopt a made-to-order business model in order to satisfy diverse and rapidly changing customer demand. In such a business model, enterprises are devoted to reducing inventory levels in order to upgrade the competitiveness of the products. However, reductions in inventory levels and short lead times force the operation between production and distribution to cooperate closely, thus increasing the practicability of integrating the production and distribution stages. The complexity of supply chain scheduling problems (integrated production and distribution scheduling) is known to be NP-hard. To address the issues above, an efficient algorithm to solve the supply chain scheduling problem is needed. This paper studies a supply chain scheduling problem in which the production stage is modelled by an identical parallel machine scheduling problem and the distribution stage is modelled by a capacitated vehicle routing problem. Given a set of customer orders (jobs), the problem is to find a supply chain schedule such that the weighted summation of total job weighted completion time and total job delivering cost are minimised. The studied problem was first formulated as an integer programme and then solved by using column generation techniques in conjunction with a branch-and-bound approach to optimality. The results of the computational experiments indicate that the proposed approach can solve the test problems to optimality. Moreover, the average gap between the optimal solutions and the lower bounds is no more than 1.32% for these test problems.     

Development and challenges of planning and scheduling for petroleum and petrochemical production

Production planning and scheduling are becoming the core of production management, which support the decision of a petrochemical company. The optimization of production planning and scheduling is attempted by every refinery because it gains additional profit and stabilizes the daily production. The optimization problem considered in industry and academic research is of different levels of realism and complexity, thus increasing the gap. Operation research with mathematical programming is a conventional approach used to address the planning and scheduling problem. Additionally, modeling the processes, objectives, and constraints and developing the optimization algorithms are significant for industry and research. This paper introduces the perspective of production planning and scheduling from the development viewpoint.     

Synchronisation of production scheduling and shipment in an assembly flowshop

This paper studies a synchronised scheduling problem of production simultaneity and shipment punctuality in a two-stage assembly flowshop system. Production simultaneity seeks to ensure all products belonging to a same customer order are simultaneously completed (at least as close as possible). Shipment punctuality attempts to satisfy orders’ individual shipment due dates. We provide two criteria, i.e. mean longest waiting duration and mean earliness and tardiness, for measuring production simultaneity and shipment punctuality, respectively. A synchronised scheduling model is developed by balancing the two criteria using linear weighted sum method. A modified genetic algorithm (GA) is then proposed for solving this model. Numerical studies demonstrate the effectiveness of the proposed approach. The results indicate that considering production simultaneity can remarkably reduce finished products inventory. A prioritised weight combination interval for production simultaneity and shipment punctuality has been suggested. Production simultaneity is affected by the production system configuration, especially in peak seasons.     

The economic lot and delivery scheduling problem: common cycle, rework, and variable production rate

The economic lot and delivery scheduling problem is to simultaneously determine the production sequence of several assembly components at a supplier and the delivery interval of those components to the customer. The customer, an assembly facility, is assumed to use the components at a constant rate. The objective is to find the production sequence and delivery interval that minimize the holding, setup, and transportation cost for the supply chain. Previous solutions to the problem assume a constant production rate for each component and that all components are of acceptable quality. These assumptions ignore volume flexibility and quality cost. Volume flexibility permits a system to adjust the production rate upwards or downwards within wide limits. Also, component quality may deteriorate with larger lot sizes and decreased unit production times. In this paper, we develop an algorithm for solving the economic lot and delivery scheduling problem for a supplier using a volume flexible production system where component quality depends on both lot sizes and unit production times. We test the performance of the algorithm and illustrate the models with numerical examples.     

物料需求计划不稳定性的模拟研究

介绍物料需求计划不稳定性的基本概念和模拟研究方法。在不确定性需求的流动式计划环境下,研究冻结参数和计划算法在不同生产条件下对物料需求计划不稳定性的影响。通过设计模拟实验和大量模拟计算及统计分析表现：费用结构、预测模式、冻结比例、计划周期和计划算法对物料需求计划不稳定性有较大影响,且交互作用显著。研究结果对减小物料需求计划的不稳定性有一定指导意义。