A hierarchic approach to production planning and scheduling of a flexible manufacturing system

The paper deals with the problem of improving the machine utilization of a flexible manufacturing cell. Limited tool magazine space of the machines turns out to be a relevant bottleneck. A hierarchic approach for this problem is proposed. At the upper level, sets of parts that can be concurrently processed (batches) are determined. At the lower levels, batches are sequenced, linked, and scheduled. Methods taken from the literature are used for the solution of the latter subproblems, and an original mixed integer programming model is formulated to determine batches. The proposed methods are discussed on the basis of computational experience carried out on real instances.     

A cutting plane approach for integrated planning and scheduling

In this paper we propose a branch-and-cut algorithm for solving an integrated production planning and scheduling problem in a parallel machine environment. The planning problem consists of assigning each job to a week over the planning horizon, whereas in the scheduling problem those jobs assigned to a given week have to be scheduled in a parallel machine environment such that all jobs are finished within the week. We solve this problem in two ways: (1) as a monolithic mathematical program and (2) using a hierarchical decomposition approach in which only the planning decisions are modeled explicitly, and the existence of a feasible schedule for each week is verified by using cutting planes. The two approaches are compared with extensive computational testing.     

A fuzzy mid-term single-fab production planning model

Production planning is a complicated task for a semiconductor fabrication plant because of the uncertainties in demand, product prices, cycle times, and product yields. Traditionally, mid-term production planning for a semiconductor fabrication plant is handled with MRP systems or optimized by solving LP or FLP problems. In this study, the philosophy of prioritizing demand classes with higher certainties as proposed by Leachman (1993) is applied to the FLP model of Chen and Wang (1998), and a new FLP model for planning the mid-term production of single wafer fabrication plant is constructed. Parameters in this model are given in the form of trapezoidal fuzzy numbers. Fuzzy comparison is adopted in dealing with the fuzzy objective function and expanding inequalities. The outputs are projected using Chen and Wang's fuzzy dynamic production function. The uncertain demand is classified and satisfied with four successively optimized FLP submodels according to their ascending uncertainties. Chen and Wang's example is adopted to demonstrate the proposed methodology and to make some comparisons. By moving more capacity to demand classes with higher certainties that are usually nearer and have larger discounted revenues, the proposed methodology achieves a higher value of the discounted cash flows than the two referenced models.     

Combined planning and scheduling in a divergent production system with co-production: A case study in the lumber industry

Many research initiatives carried out in production management consider process planning and operations scheduling as two separate and sequential functions. However, in certain contexts, the two functions must be better integrated. This is the case in divergent production systems with co-production (i.e. production of different products at the same time from a single product input) when alternative production processes are available. This paper studies such a context and focuses on the case of drying and finishing operations in a softwood lumber facility. The situation is addressed using a single model that simultaneously performs process planning and scheduling. We evaluate two alternative formulations. The first one is based on mixed integer programming (MIP) and the second on constraint programming (CP). We also propose a search procedure to improve the performance of the CP approach. Both approaches are compared with respect to their capacity to generate good solutions in short computation time.     

An approach for an integrated DSS for strategic planning

Although Decision Support Systems (DSS) have become widespread in recent years for operational control their use in strategic decision-making has only rarely been seen. This study investigates how DSS technology can be applied in the process of strategic planning. The requirements of Strategic Decision Support Systems (SDSS) are discussed and a conceptual frame for the construction of SDSS is developed. The authors emphasize the integration of both the planning instruments and the corresponding data flows. They present the StratConsult system - a PC-based prototype for supporting strategic sessions. Benefits and drawbacks of SDSS are explored and relevant trends for integrated computer-aided strategic DSS are outlined.     

A DSS for Production Planning

This paper presents a decision support system designed to aid the production planner in choosing the values of the principle parameters governing just-in-time manufacturing. The DSS uses a knowledge base composed of data from simulations of the production of the entire product line under systematically varied conditions. The planners may add data from actual production experience as it is obtained. The stochastic dominance outranking method of Martel and Zaras allows the identification of clusters of parameters that respect the preferences of the decision maker. When launching an order for a product, the decision maker may judge certain sets of parameters to be equivalent. A visual interactive simulation model will then allow their comparison according to three performance criteria.     

Minimum risk criterion for uncertain production planning problems

This paper considers a new class of multi-product source and multi-period fuzzy random production planning problems with minimum risk and service levels where both the demands and the production costs are assumed to be uncertain and characterized as fuzzy random variables with known distributions. The proposed problems are formulated as a fuzzy random production planning (FRPP) model by maximizing the mean chance of the total costs less than a given allowable investment level. Because the exact value of the objective function for a given decision variable cannot be easily obtained, we adopt an approximation approach (AA) to evaluate the objective value and then discuss the convergence of the AA, including the convergence of the objective value, the convergence of the optimal solutions and the convergence of the optimal value. Since the approximating multi-product source multi-period FRPP model is neither linear nor convex, an approximation-based hybrid monkey algorithm (MA) which combines the AA, stochastic simulation (SS), neural network (NN) and MA is designed to solve the proposed model. Finally, numerical examples are provided to illustrate the effectiveness of the hybrid monkey algorithm.     

高校师资管理决策支持系统的研究与设计

该文根据我国高校的师资结构和师资管理的现状,以高校师资信息中存在的大量非确定性信息为基础,运用灰色系统理论分析了招生规模预测、师资需求量预测、师资拥有量预测的相关数学模型,并初步建立了高校师资管理决策支持系统,说明了系统的设计思路和总体结构。经验表明,系统具有较高的预测精度,基本满足了高校师资管理中的相关决策工作要求,增强了高校师资管理的主动性和科学性,提高了高校师资管理水平,效果良好。     

L-class enumeration algorithms for a discrete production planning problem with interval resource quantities

A discrete production planning problem which may be formulated as the multidimensional knapsack problem is considered, while resource quantities of the problem are supposed to be given as intervals. An approach for solving this problem based on using its relaxation set is suggested. Some L-class enumeration algorithms for the problem are described. Results of computational experiments are presented.     

A random search heuristic for a multi-objective production planning

Real world production planning is involved in optimizing different objectives while considering a spectrum of parameters, decision variables, and constraints of the corresponding cases. This comes from the fact that production managers desire to utilize from an ideal production plan by considering a number of objectives over a set of technological constraints. This paper presents a new multi-objective production planning model which is proved to be NP-Complete. So a random search heuristic is proposed to explore the feasible solution space with the hope of finding the best solution in a reasonable time while extracting a set of Pareto-optimal solutions. Then each Pareto-optimal solution is considered as an alternative production plan in the hand of production manager. Both the modeling and the solution processes are carried out for a real world problem and the results are reported briefly. Also, performance of the proposed problem-specific heuristic is verified by comparing it with a multi-objective genetic algorithm on a set randomly generated test data.     

Algorithms for propagating resource constraints in AI planning and scheduling: Existing approaches and new results

This paper summarizes the main existing approaches to propagate resource constraints in Constraint-Based scheduling and identifies some of their limitations for using them in an integrated planning and scheduling framework. We then describe two new algorithms to propagate resource constraints on discrete resources and reservoirs. Unlike most of the classical work in scheduling, our algorithms focus on the precedence relations between activities rather than on their absolute position in time. They are efficient even when the set of activities is not completely defined and when the time window of activities is large. These features explain why our algorithms are particularly suited for integrated planning and scheduling approaches. All our algorithms are illustrated with examples. Encouraging preliminary results are reported on pure scheduling problems as well as some possible extensions of our framework.     

Economic calibrated models for water allocation in agricultural production: A review

There is a growing need to assess the effects of policies and global changes on both water resources and agriculture. Agricultural programming models are adequate to study this topic as their functional forms and calibration capacities make them suitable to represent real agricultural production systems and explicit the link between water and production. We present a review of the research on agricultural economic programming models that represent water demand and allocation among farming activities for different problem settings. The different types of models are discussed and particularly the integration of water into the production function so as to reflect the agronomic response of yields to varying levels of water. Uncertainty and risk integration as well as spatial and temporal scale issues are reviewed as they are determining to model results in a science support to policy perspective. A research agenda for future research in this field is provided.     

A model for integrating process planning and production planning and control in machining processes

The goal of process planning is to propose the routing of a previously designed part and results in a sequence of operations and their parameters. It concerns and requires detailed information about the process. The goal of production planning, on the other hand, is to schedule, sequence and launch the orders introduced on the routing sheet into the job-shop according to the enterprise's strategic goal and the actual conditions of the production plant. The goals, information and decisions taken in process planning and production planning and control are often very different and, because of that, it is very difficult to integrate them.

The objective of this work is to develop a model that can be applied in the future to the development of an integrated process planning and scheduling tool using an integrated definition (IDEF) methodology to design an activity model, which integrates process and production planning in metal removal processes. An activity model will be used to develop a system that allows the user to plan the process and the production at the same time in collaborative engineering work. To design the activity model, a wide range of parts were evaluated and processed in an actual job-shop factory. Several activities were developed in detail to be tested in real cases, and an example of one of them is introduced in this article.     

A multi-attribute decision model for setting production planning parameters

Production and inventory-related decisions, which significantly influence each other and sometimes involve multiple attributes, trade-off assessment and uncertainties, serve a key role in the performance of make-to-stock (mts) manufacturing systems that are controlled by a constant work in process (conwip) order release policy. To benefit from established production planning methods, a crucial task in this context is to define suitable production parameter settings for a given planning horizon. To address this problem, we present a multi-attribute decision model to determine appropriate settings for the planning parameters, namely, cycle time, throughput rate, holding cost and stockout cost. The proposed model uses discrete event simulation to evaluate the performance of a conwip/mts manufacturing system in relation to the work in process and finished goods inventory. Analysis of variance (ANOVA) and a Kruskal-Wallis test are conducted to verify the significant effect on the analyzed parameters. The compromise solution that is recommended for the conwip/mts problem is obtained by considering a multi-attribute expected utility function that is representative of a decision maker’s preferences and risk attitude regarding the probability distribution of the simulation outputs. In contrast with preview studies on planning parameter setting, the result compensates the low performance of one of the attributes as a result of the high performance of another attribute, based on the axiomatic structure of MAUT.Based on the real data of a multi-product assembly line, a numerical application is employed to visualize the steps of this decision model and to demonstrate its usefulness in practical issues.     

A bimodal scheme for multi-stage production and inventory control

This study considers a multi-stage multi-item production plant with its supply chain and customer environment. The production, supply and inventory plan is optimized on a dual-mode basis, under two different information patterns. The short-term plan relies on firm orders received from customers. On the contrary, the long-term plan is based on predicted demands represented by random sequences. In this study, the role of the long-term plan is mainly to impose a final condition set to the short-term plan.     

On the importance of sequencing decisions in production planning and scheduling

We discuss the traditional hierarchical approach to production planning and scheduling, emphasizing the fact that scheduling constraints are often either ignored or considered in a very crude way. In particular, we underline that the way scheduling is carried out is crucial for the capacity constraints on the lot sizes. Usual methods to handle capacity in theory or in practice are reviewed. Finally, we present an approach that tries to overcome these drawbacks by capturing the shop–floor capacity through scheduling considerations.     

A multi-plant production planning model considering non- repeated setup and aperiodic shipment

A new model for multi-plant production planning is developed. As the important actual features of some manufacturers, non-repeated setup and aperiodic shipment are appropriately introduced into the multi-plant production planning model and the corresponding constraints are accurately linearized. The new model is also applicable in the case of periodic shipment or backorder prohibition. Its effectiveness is examined by an instance which simulates many real characteristics. The experimental results indicate that the new model achieves the optimal profit. The sensitivity of unit setup cost and unit shipment cost is analyzed. The significance of backorder and the limitation of shipment at a time are discussed in detail.     

A long range production/resource planning system using linear programming

In January 1985, a project was initiated to optimize and update the entire Production Planning function, including long-range and short-range scheduling, Material Requirements Planning, and a Production/Resource Planning System for the Molding and Assembly area. This paper addresses the latter.

The Molding and Assembly area at Johnson Wax molds and assembles caps and their components for our products. We were looking for a long range strategic plan for the area. The natural solution was optimization in the form of linear programming.

The models were set up on Control Data's Cybernet Computer using PDS/Magen (for matrix generation) and APEX IV (for problem solving). This was accessed from an IBM-XT via a modem. (The models have since been moved to our inhouse IBM 3083 Mainframe and use Haverly Systems' OMNI and MPSX-370.) The project involved gathering information on the Molding and Assembly area, developing and testing a prototype, developing the data base and data tables for all caps concerned, expanding the prototype and developing the program to handle all caps, preparing output reports (using FOCUS Information Management System), and final implementation.