PRODUCTION-DISTRIBUTION PLANNING IN A LARGE SCALE COMMODITY PROCESSING NETWORK

This paper describes a methodology for production-distribution planning in a large scale commodity processing network. Based on earlier research efforts dealing with single-commodity and multi-commodity distribution system modeling and on production planning for a single-plant commodity processing facility, a mathematical programming methodology is developed for a multiplant soybean processing network. Application of the model leads to the specification of a production plan for a multi-period time horizon, while at the same time indicating the quantities of soybean meal and soybean oil to be supplied to various customers in various locales. Both sets of decisions are made under the general criterion of maximizing the net income produced by the soybean processing complex, subject to various production, inventory, capacity, supply and demand constraints. Test results from application of the model are presented and discussed.     

A Decision Support System for Production/ Distribution Planning in Continuous Manufacturing

This paper presents a decision support system (DSS) for managing production/distribution planning in a continuous manufacturing environment. The vendor has multiple plants and distribution centers (DCs). The trading partners have widely varying independent demand patterns. The DSS is designed for use in a multiproduct environment with overlapping raw materials and processing requirements. The production and distribution lead times at plants may span multiple planning periods. The impact of any manual override of a suggested solution can also be evaluated. The DSS is based on a linear programming model with a rolling horizon and was originally designed for a large process industry. Results of a pilot implementation using actual data are also presented, which show potential for significant savings for the company.     

A MODEL-BASED DECISION SUPPORT SYSTEM FOR SCHEDULING LUMBER DRYING OPERATIONS

Raw lumber must be dried to a specified level of moisture content before it can be used to make furniture. This paper deals with a model-based decision support system (DSS) for a local furniture manufacturing company to assist its management in scheduling lumber drying operations. In addition to buying ready-to-use dried lumber from vendors at a premium, the company processes raw lumber in house using two production process that require various lengths of processing time in predryers and dry-kilns. Given the demand for various types of dried lumber over a specified planning horizon, the processing times and costs for each production process, technological restrictions, and management policies, the problem of interest is to satisfy the demand at a minimum cost. The DSS incorporates the mathematical formulation of this problem, is user friendly, maintains model and data independence, and generates the necessary reports, including loading and unloading schedules for the equipment.     

A modified hierarchical production planning system integrated with MRP: A case study

In this paper, a hierarchical planning system is proposed which integrates aggregate capacity planning with MRP. This system is to be implemented in a metal box manufacturing company which multi-user MRP system covering manufacturing activities as well as procurement sales order processing and accounting systems. The hierarchical planning system includes a medium-range aggregate planning model adapted to the firm's requirements and strategies. The model consists of a mathematical formulation which covers labour capacity has already installed a constraints and includes certain cost estimations in the objective function. The planning horizon of the medium range planning is taken as twelve months in order to cover sales seasonality. The aggregate production quantities resulting from the optimized medium-range planning model are disaggregated according to procedures already found in the literature. Furthermore, the theoretical infeasibilities pertaining to the disaggregation procedures are also resolved in an heuristic manner. Using the latter modified disaggregation procedure, a feasible disaggregated plan is generated for the whole planning horizon. The proposed plan is compared with the current production policy of the firm and it is observed that the proposed plan leads to backorder reduction.     

受弹性时间段和价值波动双重影响的项目组合选择问题研究

许多现有的项目组合模型都认为项目集合可以在固定的计划期完成,但是在实际的项目执行过程中,决策者为了使得高收益的项目纳入项目集合中或者放弃风险高的项目来释放更多的资金,会选择对项目组合固定的计划期进行灵活性调整。同时,考虑到项目未来价值的不确定性,本文利用实物期权的二叉树模型给出了价值波动的公式,进而建立了同时受弹性时间段和价值波动影响的项目组合选择的新模型,对传统的计算项目价值的净现值法进行补充。通过GAMS/BARON对算例进行求解,验证了模型的合理性和有效性。结果表明：考虑弹性时间段和价值波动的影响会给更符合企业长期的发展战略,给企业带来更大的收益,该模型也为处理不确定情况下的项目组合问题提供了新思路和新方法。     

Interfacing aggregate plans and master production schedules via a rolling horizon feedback procedure

Aggregate planning and master scheduling are two important levels of a hierarchical production planning process. They serve as the front end of production and operations planning and control systems. It is imperative to have the front end well planned and coordinated. In this study, we demonstrate that a rolling horizon feedback procedure can be an effective coordination mechanism for interfacing aggregate planning and master scheduling. Each time the schedule is rolled ahead, the feedback of performance results from master scheduling provides the aggregate planning subsystem useful information for improving its planning activities. Our experiments show that there are at least three factors that make a rolling horizon procedure attractive: (i) the use of a horizon with a fractional portion of a seasonal cycle, (ii) high smoothing costs, and (iii) high setup costs. Using rolling horizons reduces the total aggregate costs in these situations relative to not using rolling horizons. The potential impacts of the rolling-horizon strategy are greater under some circumstances than others.     

FREQUENCY OF REPLANNING IN A ROLLING HORIZON MASTER PRODUCTION SCHEDULE FOR A PROCESS INDUSTRY ENVIRONMENT: A CASE STUDY

This study addresses the problem of replanning frequency for a rolling horizon master production schedule (MPS) in a process industry environment under demand certainty. The major contribution of this paper is the demonstration of how the appropriate replanning frequency for a MPS can be determined under the conditions of minimum batch-size production restrictions in a rolling planning horizon setting. In addition, the problem environment for this study is an actual MPS operation that includes features such as multiple production lines, multiple products, capacity constraints, minimum inventory requirements, and multiple goals. Actual data from a paint company are used to determine the appropriate replanning frequency for a rolling horizon MPS. Results indicate that a 2-month replanning frequency was the best at this firm because of the significant cost savings it provided when compared to actual company performance and the other replanning intervals.     

A BRANCH AND BOUND PROCEDURE FOR SETUP PROBLEMS IN FLEXIBLE MANUFACTURING SYSTEMS

We address a medium- to short-term production planning problem in a flexible manufacturing environment. First we present a single-machine, mixed integer programming model for part type selection and lot-sizing problems over a T-period planning horizon. Demand for part types changes dynamically through the periods. The objective is to meet the demand for part types during the periods they are demanded. Available machine time and tool magazine capacities are the system constraints in our models. We next extend on the single- machine model to include multiple machines. In addition to part type selection and lotsizing decisions, the extended model also addresses the machine-loading decision. We present exact branch and bound procedures based on linear programming relaxations for the two models. We also report the results of our computational experiments.     

The Production of Several Items in a Single Facility with Linearly Changing Demand Rates

In this paper we extend the ELSP model to allow for linearly changing demand rates over a fixed planning horizon. This extension of the ELSP research provides a model that can be used in coordinating the production and marketing planning activities in a firm. The model allows the user to evaluate the impact of changes in product demand on production costs and customer service. We solve the model using a standard nonlinear programming package (MINOS) and show through examples based on actual production data how the model can be used to support coordinated production and marketing planning.     

有限时域内部分缺货的变质品生产-定价策略研究

研究有限时域内对变质产品同时安排生产计划和制定定价策略的问题。有限时域被划分成同等长度的多个周期,产品以固定的生产率间歇生产,并以固定的速率发生变质。需求同时依赖于时间和产品价格,在每个周期内都允许缺货且短缺量部分延迟订购。目的是要寻求一个使有限时域内系统的平均利润最大化的生产计划和定价策略。在算例中,分别讨论了需求随时间增大和减小两种不同的需求模式,采用Box复合算法,通过数值计算求得相应的最优生产计划和定价策略。分析表明：对于成长期或销售季初的产品,系统应采用“小批量多批次”的生产方式;而对于衰退期或销售季末的产品,应采用“大批量小批次”的生产方式。此外,不同类型的产品在不同需求模式下的定价策略有所不同：对于有限时域较短或变质率较高的产品,处于衰退期或销售季末的定价要低于成长期或销售季初的定价;相反,对于有限时域相对较长或变质率较低的产品,在衰退期或销售季末的最优定价要高于在成长期或销售季初的定价。     

Horizons for strategic planning

In this paper we present a normative model for setting time horizons for planning. Provided that certain conditions are met, we can state that only part of the future is relevant for present strategic planning, and a study of the future beyond that time horizons is a waste of resources.Following the introduction the impact of predetermined planning horizon on planning is discussed. Next, the model is presented: the problem is defined, developed and solved. The discussion of the model is designed to incorporate—and to show its implication on—existing views and methods for setting time horizons for strategic planning. Thus, simplifying assumptions which facilitate the mathematical solution of the problem, are discussed and relaxed in order to show how realistic situations are illuminated by the model.     

Hierarchical production planning for multi-product lines in the beverage industry¶

Multi-commodity production and distribution scheduling is one of the most complex and crucial problems facing many manufacturing companies. For a major European manufacturer specialising in bottling juices and drinks, we have designed and developed a hierarchical decomposition approach to the solution of the multi-commodity production planning problem. In this paper we focus our attention on the coarsest decomposition level, called multi-commodity aggregate production planning (MCAP). It concerns the choice of the best feasible production plan for a set of products (commodities) over an extended time horizon so as to meet forecast aggregate demands throughout the horizon. At this level, the problem constraints include hard constraints (such as production lines having a maximum capacity and products having short life-times), and soft constraints (budgetary concerns.) The objective is to determine the production plan that covers each period's demands as best as possible, while minimizing all relevant costs. Our method for solving MCAP produces optimal plans in negligible times in commodity PC workstations.     

Simulation-based performance assessment of master planning approaches in semiconductor manufacturing

Semiconductor manufacturing is confronted with a large number of products whose mix is changing over time, heterogeneous fabrication processes, re-entrant flows of material, and different sources of environmental and system uncertainty. In this context, the mid-term production planning approach, i.e., master planning, typically does not capture the entire complexity of the shop-floor. It deals with an aggregated representation of the production system. There is a need for evaluating the planning algorithm in use while taking the execution level into account. Therefore, we introduce in this paper a simulation-based framework that allows for modeling the behavior of the market demand and the production system. An appropriate performance assessment methodology is proposed. The performance of two heuristic approaches for master planning in semiconductor manufacturing, a genetic algorithm and a rule-based assignment procedure, is evaluated within a rolling horizon setting while considering demand and execution uncertainty. A reduced discrete-event simulation model is used to mimic a one-stage network of wafer fabrication facilities. The results of simulation experiments are discussed.     

DYNAMIC LOT SIZING FOR A SINGLE-FACILITY MULTIPRODUCT PROBLEM IN A ROLLING-HORIZON ENVIRONMENT

This paper considers a production planning model for a single-facility multiproduct problem where backlogging is not allowed. A planning-horizon theorem is derived. From that theorem, a forward algorithm for finding an optimal solution over a finite horizon and a procedure for selecting the first-period production in a rolling-horizon environment are developed. Computational results from a set of simulation experiments designed to investigate the cost effectiveness of the procedure demonstrate its effectiveness.     

A disaggregation problem for a multiplant,multiproduct scheduling application

Abstract

This paper discusses an integer programming technique for planning production when there are multiple plants involved in the production of a number of products and the plants are relatively close. This allows for the closing of one plant during a planning period with a shift of the mobile resources, mainly labour, to other plants. The model is introduced and then an example problem is given. Finally, there is a discussion of why this method improves upon other techniques proposed to solve this problem. Some actual case studies are cited.     

An aggregate production planning framework for the evaluation of volume flexibility

Flexibility in manufacturing has been identified as one of the key factors to success in the marketplace. Many types of flexibility have been identified in the literature among which volume flexibility is one of the most important. Volume flexibility of a manufacturing system is defined as its ability to be operated profitably at different overall output levels. Volume flexibility permits a manufacturing system to adjust production upwards or downwards within wide limits. In this paper, we develop an aggregate production planning model for volumeflexible production systems. The model can be used with a Monte Carlo simulation to evaluate the optimal level of investment in volume flexibility for a firm operating under a given set of market conditions. In addition, the model can be used to develop some conclusions about the relationship between the value of volume flexibility and the cost of holding inventory, the cost of shortage, forecast accuracy, and the length of the planning horizon.     

A multistage approach for production and workforce planning in long-cycle product environments

Aggregate production planning (APP) has been studied extensively for the past two decades. The APP problem, also called production and workforce scheduling, is to determine the optimal workforce and production level in each period of the planning horizon in order to satisfy demand forecasts for these periods. The advantages of the APP are low cost of data collection and computational cost of the running model; the accuracy of data; and, effective managerial understanding of the results. If the product of concern takes longer than one period, it is called a long-cycle product. Examples of long-cycle products are aircraft, ships, buildings and special machines. A detailed model incorporating dynamic productivity and long-cycle products considerations is presented to solve the problem of production and workforce planning. Using a multistage production system approach, a search technique is developed to solve this class of problems where the objective function is linear and some of the constraint coefficients are dynamically nonlinear. The model provides a better solution than an aggregate production planning model, often used to solve these problems.     

多车型确定性动态车辆调配问题

详细地描述了多车型确定性动态车辆调配问题.建立了问题的线性规划模型,鉴于线性模型的缺点,构造一个线性函数来近似目标函数中未来时段部分,从而建立起问题的时空分解模型,把问题从时间和空间上分解为多个单时段单节点问题,并根据单时段单节点问题的特点设计简单的排序求解方法.最后给出了问题的完整求解过程,从而使问题能够得到有效解决.     

A goal programming approach to production planning for flexible manufacturing systems

The increased use of flexible manufacturing systems (FMS) reduces production time variability as the fixed processing time of automated systems replaces the variable processing time of human resources in production systems. This reduction in the variability permits the application of deterministic, constrained optimization models that recognize the conflicting multi-objective nature of resource utilization in an FMS environment. One such multi-objective optimization model is linear goal programming. Unlike most linear programming models, linear goal programming is designed to handle conflicting multi-objective problems. A goal programming model is developed a and presented in this paper as an aid in FMS planning. An application is also presented to illustrate the informational benefits of using the proposed model.     

The use of demand information in selecting production plans and schedules

The strategic problem of selecting a production plan for a given planning horizon is usually treated as independent of the tactical problem of scheduling the production plan. This paper approaches both selecting the production plan and scheduling it as one problem. The problem is formulated as a zero-one integer program. The formulation accommodates many real-life considerations. The integer program is solved using a branch and bound procedure which provides the optimal production plan and schedule as well as the importance indices of the orders, a concept which is introduced and used in this study to rank the available orders within the planning horizon according to their importance to the firm. The integer program and the search procedure can be used as a decision supporting tool to respond to any changes in the demand information, capacity of the firm, or its operating strategy, and it guarantees the selection of feasible production plan(s) and optimal schedules.