Finite capacity scheduling method for MRP with lot size restrictions

Materials requirements planning (MRP) is a widely used production scheduling technique in the manufacturing industry. Based on the projected demand and the corresponding product structure, MRP prescribes the periodic production quantities for every end-item, subassembly and component. The goal of MRP is to reduce the inventory cost while simultaneously ensuring that dependent demand relationships are met. In its basic form, the dependent demand explosion used by MRP to schedule production does not consider the availability of resources, consequently the schedule is often capacity infeasible. The MRP progressive capacity analyser (PCA) procedure in which finite capacity planning and lot sizing are performed concurrently with the MRP bill of material (BOM) explosion process is introduced. The PCA procedure is executed in four steps. It models the lot size multiple restriction and can be easily modified for other lot sizing rules. This method has been validated and tested for sensitivity to fluctuations in demand patterns and lot sizes. It has also been compared and shown to outperform a popular methodology used in practice.     

Material and capacity requirements planning with dynamic lead times

Traditional material requirements planning (MRP) does not consider the finite capacity of machines, and assumes fixed lead times. This paper develops an approach (material and capacity requirements planning; MCRP) to integrate capacity planning into MRP. To obtain a capacity feasible production plan, different measures for capacity adjustment such as alternative routeings, safety stock, lot splitting and lot summarisation, are discussed. Additionally, lead times are no longer assumed to be fixed. They are calculated dynamically with respect to machine capacity utilisation. A detailed example is presented to illustrate how the MCRP approach works successfully.     

Cyclic Assembly Schedules

Cyclic assembly schedules are schedules which follow a repeating production pattern. Each component is produced at times that are integer multiples of the base planning period of the finished product. These schedules have the managerial advantages of ease of control and planning, since future production follows a familiar, repeating pattern. Establishing and maintaining such a schedule requires a system to protect the schedule when bottlenecks occur. This paper discusses some of these issues and describes a simple method for calculating the optimal parameters of a cyclic schedule in a multistage assembly operation when production intervals are power-of-two multiples of a base period specified by management. The cycles are based on an Economic Order Quantity analysis, which is applicable if demand for the end items is fairly steady as in the case of a facility that supplies components to an assembly line.     

Material requirements planning in hierarchical production planning systems

Material requirements planning (MRP) is a way to develop the time phased material and other resource requirements to satisfy the needs of a master production schedule. Hierarchical production planning (HPP) is a framework for analysis and decision making in complex production environments. They are complementary approaches to the decision support system needs of multi-stage production systems. The hierarchical approach provides for efficient management review of proposed plans at corporate, plant, and shop levels while MRP provides a sound and detailed basis for understanding the implications of proposed plans and for executing the selected plan. The relationships between these two approaches are illustrated with an example application.     

Master production scheduling make-to-stock products: a framework for analysis

Excessive changes in MRP system schedules, commonly referred to as 'nervousness’, is frequently an obstacle in implementing an effective MRP based manufacturing planning and control system. This paper is concerned with the design of methods for freezing the master production schedule (MPS) as a way of controlling MPS stability under rolling planning conditions for make-to-stock products. It presents a framework for the design of MPS freezing methods and compares their performance when the design parameters of these methods are varied. Simulation experiments are reported that demonstrate important differences in performance considering criteria involving both the cost of lot-sizing the MPS and the stability of the master production schedule.     

Aggregate versus disaggregate production planning: a simulated experiment using LDR and MRP

Within the past several years, considerable research has been devoted to the aggregate production planning problem starting with the pioneering work of Holt el al, (1955) and the resulting Linear Decision Rule (LDR). However, researchers have also recognized that developing optimal aggregate production plans, per se, is not sufficient for solving real problems; these plans have to be disaggregated into specific schedules for specific products. Consequently, the thrust of current research is on the ‘disaggregation’ problem. Simultaneously a number of companies have been installing MRP systems with reports of significant improvements in inventory control, production planning, work force scheduling and production costs. This paper reports on an experiment which was designed and conducted to compare the effectiveness of the ‘aggregate-disaggregate’ and MRP approaches to production planning in a simulation environment. LDR was used as the optimal aggregate technique in the aggregate-disaggregate approach. The results appeared to favour the MRP approach     

Improving production planning through finite-capacity MRP

Materials Requirement Planning (MRP) technique is widely employed by most manufacturing companies, even though field applications point out some weaknesses, including ignored production capacity constraints and fixed lead-times. These weaknesses often lead to infeasible production schedules, which trigger fluctuating workloads over time, significant adjustment effort and eventually unpredictably long lead times. This paper introduces a capacity-oriented MRP procedure that combines the traditional MRP procedure with an approach based on linear programming: in this way, requirement of lead times pre-determined a priori outside the MRP procedure is overcome. The new procedure is then applied to a real-life company and results highlight that feasible plans of orders are generated without requiring lead-times as input and without relevant computational burden.     

Applying the TOC thinking process in manufacturing: A case study

A case study is presented to illustrate the application of the Theory of Constraints thinking process logic tools in a manufacturing environment. The study firm performs design activities related to meeting future product requirements while concurrently meeting existing production schedules for the current design of the product. Current approaches to managing the firm's limited productive capacity do not allow for both design and production activities to occur simultaneously while meeting the customer's current product delivery schedule. Thus, despite their desire to satisfy their customer's future design requirements, management uses the majority of its production capacity to meet its customer's current product delivery schedule. This case study demonstrates how a team of employees used thinking process logic diagrams to document reality, identify a core conflict and problem, develop proposed changes to address the core problem, and create several detailed action plans to implement changes within the study organization. Initially, scenarios associated with some undesirable effects are used to understand how prevailing policies and behaviours result in less than desired production line performance. Then, a current reality tree is constructed to link the core problem or system constraint with the previously identified undesirable effects. Next, two major injections are developed to address the core problem in managing the production line as logically documented in a future reality tree. Finally, three transition trees are presented to guide the implementation of change at the study organization.     

Effective relational database approach to represent bills-of-materials

Today's production systems are under the pressure of mass customization where customer orders may result in an explosive number of product variations. Handling information related to materials and components becomes a complex issue with the increasing variability. This paper presents an effective approach to handle product information in a relational database environment for multi-product and multi-process production systems. The approach presented in this paper aims to reduce the efforts when defining products to the production systems for the purpose of generating bills-of-materials (BOMs) and executing material requirements planning (MRP). Two algorithms are presented in this paper. The first algorithm is to retrieve information from the relational database environment to generate the BOMs. The second algorithm calculates the material requirements for all products in a customer order list. Examples are chosen from textiles since complications related to the MRP issues are at a greater extent in this branch of the industry.     

Cycle time reduction by improved MRP-based production planning

The important practical problem of planning the production of large assemblies employing an MRP-based system is considered. The objective is to produce products on-time, with minimal cycle time and low work-in-process costs. The approach is based on the determination of accurate lead-time estimates and on the introduction and use of lead-time offsets in the solution methodology. An effective Lead-time Evaluation and Scheduling Algorithm (LETSA) is employed that can perform detailed backward scheduling of operations belonging to a large assembly on a given facility with an objective of minimizing the cycle time. A scaling procedure is used to account for capacity sharing effects by multiple products in a common facility. These scaled lead-time estimates are then employed by an MRP-based system to release work-orders on the shop-floor. The effectiveness of these lead-times and lead-time offsets are evaluated by simulating production using the MRP generated order release times and verifying on-time completion of the multiple assemblies in the common facility. Numerical experiments are presented to validate the performance of the approach. Optimized batch sizes for minimal work-in-process (WIP) costs can also be obtained using LETSA. Thus, the important objectives of minimizing cycle time for on-time delivery and minimizing schedule costs can be accomplished simultaneously.     

Joint lot-sizing and scheduling for multi-stage multi-product flow shops

Here we consider a multi-product flow shop under capacity constraints and develop an efficient heuristic procedure to determine joint lot sizes, production sequence and production schedule. We treat setup costs as fixed in the short run and thus independent of the number of setups. Loss of production capacity due to setup times is explicitly accounted for and the transfer of portions of a production lot between stages is permitted. The procedure is based on identifying the bottleneck workcentre and synchronizing production schedules at all other workcentres with the bottleneck workcentre such that product throughput requirements are met with minimal inventory costs. A compact procedure for constructing the Gantt charts is also presented. Conversion of the lot si2es and transfer batches into an information control system with kanbans is also presented.     

A framework for modelling setup carryover in the capacitated lot sizing problem

This paper addresses the single-level capacitated lot sizing problem (CLSP) with setup carryover. Specifically, we consider a class of production planning problems in which multiple products can be produced within a time period and significant setup times are incurred when changing from one product to another. Hence, there might be instances where developing a feasible schedule becomes possible only if setups are carried over from one period to another. We develop a modelling framework to formulate the CLSP with setup times and setup carryovers. We then extend the modelling framework to include multiple machines and tool requirements planning. The need for such a model that integrates both planning and lot sizing decisions is motivated by the existence of a similar problem in a paper mill. We apply the modelling framework to solve optimally, an instance of the paper mill's problem.     

Lot sizing in capacitated production planning and control systems

Current production planning and control (PPC) systems often separate material requirements from capacity planning. As a result, practitioners often complain about the infeasibility of production schedules regarding capacity, which causes long and unpredictable lead times and poor customers service. This paper describes a hierarchically structured PPC system that explicitly considers production capacity at each stage of the planning process. The impact of the certainty of demand data on the integration of lot sizing and sequencing decisions is discussed. A decision model for lot sizing applicable to changing demand data is proposed. It distinguishes between resources that are critical or uncritical with respect to batching decisions. Several currently available solution procedures are discussed and compared that support lot sizing decisions in multi-level production systems subject to multiple capacity constraints, setup times and dynamic demand rates.     

Evaluating the effects of capacity constraints and demand patterns on supply chain replenishment strategies

This research considers inventory replenishment in a stochastic, multi-echelon supply chain involving both production and distribution functions. Simulation is used to compare distribution/material requirements planning (DRP/MRP), re-order point (ROP) and Kanban (KBN) replenishment strategies. Additional experimental factors include the demand pattern and the existence of manufacturing capacity constraints. Trade-off curves between inventory and delivery performance are generated. Statistical techniques, including analysis of variance (ANOVA), are then used to compare the areas under the trade-off curves and determine the relative dominance among the replenishment strategies. The methodology is used to identify both main and interaction effects. With seasonal demand, DRP/MRP performance is found to be best, followed by ROP and KBN, respectively. Without seasonal demand, the relative performance ranking depends on the presence of capacity constraints. Without capacity constraints, ROP performs best, followed by DRP/MRP and KBN. With capacity constraints, the ranking is reversed. This difference in behaviour can be explained using queuing analysis.     

MRP performance effects due to lot size and planned lead time settings

The effects of lot size and planned lead time settings are evaluated in a shop producing assembled products with common components. Material requirements planning (MRP) logic is used for production planning. A framework for the analysis of delays within the production system is presented. These delays are evaluated using spreadsheet-based MRP software linked to a simulation package that emulates shopfloor activity. Results show that by selecting the proper lot sizes and then using the planned leadtimes to control delivery performance, the required inventory levels can be minimized.     

Integrated Production Planning and Scheduling on Automobile Assembly Lines

We address the closely related problems of production planning and scheduling on mixed model automobile assembly lines. We propose an integrated solution, in which a production plan that is feasible with respect to aggregate capacity constraints is developed and then a sequence that is feasible with respect to this plan is sought. We propose three tabu-search-based algorithms that explore the solution spaces for both problems to different degrees to find a combination of a production plan and schedule that are feasible and that approximately optimize the objective function (involving the overproduction and underproduction of finished automobiles, the set-up cost, the idle times of work-cells on the line, the makespan and the load deviations among work-cells). Simulation is used to evaluate alternative schedules. Stochastic extensions are proposed and the complexities of these algorithms are discussed. Example runs comparing the algorithms are presented for deterministic cases, stochastic cases, types of automobiles, buffer sizes and number of work-cells. The results show that an embedded tabu search algorithm is suitable for solving small scale problems, an alternate tabu search algorithm for the medium scale and a serial tabu search algorithm for the large scale.     

Flow shop rescheduling under different types of disruption

Almost all manufacturing facilities need to use production planning and scheduling systems to increase productivity and to reduce production costs. Real-life production operations are subject to a large number of unexpected disruptions that may invalidate the original schedules. In these cases, rescheduling is essential to minimise the impact on the performance of the system. In this work we consider flow shop layouts that have seldom been studied in the rescheduling literature. We generate and employ three types of disruption that interrupt the original schedules simultaneously. We develop rescheduling algorithms to finally accomplish the twofold objective of establishing a standard framework on the one hand, and proposing rescheduling methods that seek a good trade-off between schedule quality and stability on the other.     

Optimization-based manufacturing scheduling with multiple resources, setup requirements, and transfer lots

The increasing demand for on-time delivery of products and low production cost is forcing manufacturers to seek effective schedules to coordinate machines and operators so as to reduce costs associated with labor, setup, inventory, and unhappy customers. This paper presents the modeling and resolution of a job shop scheduling system for J. M. Products Inc., whose manufacturing is characterized by the need to simultaneously consider machines and operators, machines requiring significant setup times, operators of different capabilities, and lots dividable into transfer lots. These characteristics are typical for many manufacturers, difficult to handle, and have not been adequately addressed in the literature. In our study, an integer optimization formulation with a separable structure is developed where both machines and operators are modeled as resources with finite capacities. Setups are explicitly considered following our previous work with additional penalties on excessive setups. By analyzing transfer lot dynamics, transfer lots are modeled by using linear inequalities. The objective is to maximize on-time delivery of products, reduce inventory, and reduce the number of setups. By relaxing resource capacity constraints and portions of precedence constraints, the problem is decomposed into smaller subproblems that are effectively solved by using a novel dynamic programming procedure. The multipliers are updated using the recently developed surrogate subgradient method. A heuristic is then used to obtain a feasible schedule based on subproblem solutions. Numerical testing shows that the method generates high quality schedules in a timely fashion.     

Scheduling of die casting operations including high-mix low-volume and line-type production

This article addresses the scheduling problem of a real die casting shop. The problem is of practical importance and yet complicated, especially for a modern casting environment where a variety of cast products made of different alloys are simultaneously manufactured in relatively small lot sizes. As a simple and robust scheduling methodology, a Linear Programming (LP) model is proposed so as to determine the quantity of each product in a casting shift. The solution of the LP model maximises the average efficiency of melting furnaces, i.e., the percentage use of molten alloys throughout the shifts. Our model can represent a most general casting environment to the extent that some die casting machines carry out frequent in-process die exchanges for flexible manufacturing. At the same time, we employ line-type casting as well as a combination die with multi-cavities which can cast dissimilar shapes concurrently. In the high-mix low-volume manufacturing world, the proposed LP model can assist the die casting industry to strengthen its competence by providing an optimal schedule that satisfies practical constraints on casting processes.     

A note on 'deliberately slowing down output in a family production context'

In this paper, we consider the economic lot scheduling problem with controllable production rates and imperfect quality. The problem is to schedule the production of several different items in the same facility on a repetitive basis. The facility is such that only one item can be produced at a time and the demand rate for each item is constant over an infinite planning horizon. We focus on the case where quality is imperfect and yield rates decrease with increased production rates and lot sizes. We show that the resulting lot sizes under the proposed extension are smaller. Also, we show that the production rates for some products can be considerably different from the case of perfect quality.