Carlos F. Bispo

Production planning and scheduling using a fuzzy decision system

In this paper short-range planning and scheduling problems are addressed using a nonclassical approach supported by fuzzy theory. The proposed methodology uses a hierarchical structure which includes three decision levels (higher, middle, lower), each responsible for a different production problem with a different time scale. The methodology approaches the tasks associated with each level using a heuristic formulation and solves the short-range planning and scheduling problems with a nonstationary policy. The higher decision level determines safety stock levels used to compensate for future resource failures. At the middle level, loading rates are computed. This is accomplished through a fuzzy controller that tends to minimize the error between the cumulative production and the cumulative demand while keeping the work in process below acceptable values. Finally, the lower level controls the flow of parts among the resources, using a modified version of the Yagers fuzzy decision method. This method has the ability to use several criteria to generate a decision. Simulation results reveal that the proposed system exhibits good performance, in terms of a high production percentage and a low WIP, under resource failures and demand variations. >

Managing Simple re-entrant Flow Lines: Theoretical Foundation and Experimental Results

We address several aspects related to managing re-entrant lines in a unified manner – capacity allocation, inventory management and production control. Our approach to study these systems is through simulation based optimization. Simulation offers the flexibility to model the complexities adequately while the gradient computation (via Infinitesimal Perturbation Analysis (IPA)) helps identify good solutions quickly. Our framework is a discrete time capacitated multiple product production-inventory system operating under a base stock policy. We analyze several different production and capacity allocation rules. We develop expressions for and validate the appropriate derivatives. These derivatives can then be used in an optimization tool which enables the determination of optimal parameters for the several policies proposed. We also present a summary of insights obtained through an extensive computational study.

Adaptive scheduling for high-volume shops

A conceptual algorithm for NP-hard scheduling problems that gives accurate reactive scheduling for high-volume shops is presented. The heuristic algorithm provides classical dispatch scheduling with an embedded optimization procedure. It delivers successive updates of the initially generated schedule by extending in an unconventional way the horizon of applicability. An adaptive mechanism is implemented. The successive schedule updates are generated using beam search. The conceptual algorithm is applied to problems where a periodic production pattern is assumed. Analysis of the algorithms behavior reveals that the performance measure converges to the calculated lower bounds and that the computational time can be polynomial, for each problem, in the number of parts to process. >

The single-server scheduling problem with convex costs

Being probably one of the oldest decision problems in queuing theory, the single-server scheduling problem continues to be a challenging one. The original formulations considered linear costs, and the resulting policy is puzzling in many ways. The main one is that, either for preemptive or nonpreemptive problems, it results in a priority ordering of the different classes of customers being served that is insensitive to the individual load each class imposes on the server and insensitive to the overall load the server experiences. This policy is known as the cμ-rule.We claim and show that for convex costs, the optimal policy depends on the individual loads. Therefore, there is a need for an alternative generalization of the cμ-rule. The main feature of our generalization consists on first-order differences of the single stage cost function, rather than on its derivatives. The resulting policy is able to reach near optimal performances and is a function of the individual loads.

An extended horizon scheduling algorithm for the job-shop problem

A real-time scheduling algorithm for a flexible manufacturing system environment is described. This algorithm uses a schedule space search which is characterized by a heuristic-oriented approach based on simulation. The algorithm can be seen as a look-ahead dispatching rule, because the simulation-based search gives a forecast of how good a schedule can be, before choosing the best one. The algorithm structure is based on the fact that a minimal set of parts can be defined, which is a common divisor of the total number of parts.<<ETX>>

Active Idleness Scheduler with Simulated Annealing

Abstract In this paper we address the problem of optimizing the Time Window Controller (TW-Controller). This controller was first introduced in 2001 as a supervising mechanism for distributed scheduling of multiclass queuing networks with the objective of stabilizing those networks. It was then also shown that the TW-Controller possesses the ability to improve performance of stable networks. We revise the controller and present a series of formal results concerning its main properties and features. Then, we propose to use Simulated Annealing on a simulation-based optimization approach and present numerical results that demonstrate the controllers ability to improve performance over any given scheduling policy.

An echelon inventory-based single-stage cost function for a two-station tandem system

We propose an alternative cost-accounting function for inventory control problems on a make-to-stock setting. Our proposal is based on observing that the traditional holding and backlog parameters introduce some odd short term distortions on the inventory state space. Our single-stage cost function accounts for echelon inventories and possesses a pair of cost parameters for each echelon inventory variable, depending on whether it is positive or negative. With the modified cost-accounting function, we study a twostation tandem system producing a single product, and investigate how it compares with the performances obtained with the usual single-stage cost function. The results available so far show that the optimal policies approach a multi-echelon base stock structure for each machine. Also, the service levels achieved are better under the modified function without increasing the levels of finished goods inventory.

Simulation based optimization package for periodic review inventory control

This paper presents a simulation based optimization package for periodic review Inventory Control, to study idling and non-idling policies. This package contains three different modules, a Simulink library for system design, an Infinitesimal Perturbation Analysis based simulator, and an optimization procedure based in the Davidon-Fletcher-Powell algorithm. We illustrate the usefulness of the package by means of presenting some preliminary numerical results for a three machine single product tandem flow line. The presented results allow us to extract some very interesting managerial insights and structural properties for multiple machine, multiple products inventory control. Namely, there are systems for which one can achieve significant performance gains by resorting to idling policies, and Local Base Stock policies may be better than Multi-Echelon Base Stock policies. Besides performance gains, by introducing idleness, we are also able to stabilize systems that under non-idling policies are unstable.

Smooth Priorities for Multi-Product Inventory Control

Abstract Whenever dealing with periodic review multi-products inventory control for capacitated machines, one of the main issues that has to be addressed concerns the dynamic capacity allocation. That is, how to assign capacity to the several competing products that require more than it is available. One typical approach is to assign priorities to the products, according to some degree of relative importance. Whereas priority based capacity allocation is attractive, due to its simplicity and for the fact that it makes sense in a significant number of settings, we contend this to be too unfair to lower priority products, given their access to production tends to be highly variable and uncertain. Departing from what we call strict priorities, we propose a priority based mechanism that improves on this drawback, termed smooth priorities. This new policy for multi-product, limited capacity production systems with stochastic demand is studied. Theoretical comparisons are made to the common policies of strict priorities and linear scaling. An optimizer based on Infinitesimal Perturbation Analysis, IPA, simulation is devised and results of practical comparison between smooth and strict priorities are presented. The structure of the cost function with smooth priorities is studied through function plots obtained from simulation and numerical results show consistent better performances than those achieved under strict priorities.

A CIM Approach to Scheduling and Material Handling: a case study

Many production plants have reached the state where important activities and processes have been automated, namely production planning and scheduling, machining, material handling and quality control. However, in general, these automated activities and processes have no dialogue in between them, behaving like isolated automation islands. The CIM approach to production management really addresses the problem of dialogue and synchronism between production activities.