Jose A. Ventura

Simulated annealing for parallel machine scheduling with earliness-tardiness penalties and sequence-dependent set-up times

Scheduling problems with earliness and tardiness penalties are commonly encountered in todays manufacturing environment due to the current emphasis on the just-in-time (JIT) production philosophy. The problem studied in this work is the parallel machine earliness-tardiness non-common due date sequence-dependent set-up time scheduling problem (PETNDDSP) for jobs with varying processing times, where the objective is to minimize the sum of the absolute deviations of job completion times from their corresponding due dates. The research presented provides a first step towards obtaining near optimal solutions for this problem using local search heuristics in the framework of a meta-heuristic technique known as simulated annealing (SA). The computational study shows that, using the SA methodology, significant improvements to the local search heuristic solutions can be achieved for problems of this type.

An application of genetic algorithms to lot-streaming flow shop scheduling

A Hybrid Genetic Algorithm (HGA) approach is proposed for a lot-streaming flow shop scheduling problem, in which a job (lot) is split into a number of smaller sublots so that successive operations can be overlapped. The objective is the minimization of the mean weighted absolute deviation of job completion times from due dates. This performance criterion has been shown to be non-regular and requires a search among schedules with intermittent idle times to find an optimal solution. For a given job sequence, a Linear Programming (LP) formulation is presented to obtain optimal sublot completion times. Objective function values of LP solutions are used to guide the HGAs search toward the best sequence. The performance of the HGA approach is compared with that of a pairwise interchange method.

Grouping parts and tools in flexible manufacturing systems production planning

SUMMARY Batch production in a flexible manufacturing environment is necessary not only to satisfy some technological constraints but also to achieve potential reductions in processing time, to reduce work-in-process and finished goods inventories, and to simplify the production planning process. Central to batch production is the problem of grouping part types and the required tools into families for simultaneous processing. This paper presents a practical and useful algorithm for grouping parts and tools in flexible manufacturing systems (FMSs).This problem is first formulated as a 0-1 linear integer program. A Lagrangian dual formulation is then developed to obtain an upper bound on the optimal objective function value. The Lagrangian dual program is further decomposed into a linear network subproblem and a set of knapsack subproblems. A subgradient algorithm with several enhancement strategies is employed to minimize the upper bound obtained from the dual problem. Computational results for medium and l...

Parallel machine scheduling with earliness-tardiness penalties and additional resource constraints

This research considers the problem of scheduling jobs on parallel machines with noncommon due dates and additional resource constraints. The objective is to minimize the total absolute deviation of job completion times about the corresponding due dates. All job processing times are assumed to be the same. This problem is motivated by restrictions that occur in the handling and processing of jobs in certain phases of semiconductor manufacturing and other production systems. We examine two special cases. For the first of these, the number of additional resource types and the resource requirements per job are arbitrary. The problem is formulated as a zero-one integer linear program and the Lagrangian relaxation approach is used to obtain tight lower bounds. In the second case, there exist one single type of additional resource and the resource requirements per job are zero or one. This problem is shown to be equivalent to the asymmetric assignment problem.

Minimizing the mean weighted absolute deviation from due dates in lot-streaming flow shop scheduling

Lot-streaming is the process of splitting a job (lot) into a number of smaller sublots so that successive operations can be overlapped in a multi-stage production system. This paper presents a procedure for minimizing the mean weighted absolute deviation from due dates when jobs are scheduled in a lot-streaming flow shop. This performance criterion has been shown to be non-regular and requires a search among schedules with inserted idle times to find an optimal solution. For a given job sequence, we present linear programming formulations to obtain optimal sublot completion times for cases where buffers between successive machines have limited or infinite capacities, and sublots have equal-size or are consistent. A no-wait flow shop problem is also considered. Sixteen pairwise interchange methods are considered to generate the best sequences. These algorithms are obtained by combining four rules to generate initial sequences with four neighborhood search mechanisms. Computational experiments are conducted on 140 test problems. The results show that the best solutions are obtained by the heuristic algorithm with a non-adjacent pairwise interchange method and the smallest overall slack time rule to generate the initial sequence.

Segmentation of two-dimensional curve contours

Abstract Segmentation of digitized planar curves is one of the most important elements in early image processing, because a segmented image can describe the object profile in a compact form to facilitate higher level vision processing. In many applications, it is necessary to decompose an object boundary contour into several primitives, such as segments and curves. In this paper, a two-stage hybrid technique for the segmentation of two-dimensional (2D) curves is presented, in which the number of segments is assumed to be known. First, the boundary is iteratively approximated using a split-and-merge method. Next, an end-point adjustment procedure is applied to reach the best-fitting polygonal approximation. A computational comparison with two existing methods shows that the proposed technique is fast and accurate. An application of the new segmentation technique to industrial part inspection is also provided.

A neural network approach for defect identification and classification on leather fabric

In this paper, an automated vision system is presented to detect and classify surface defects on leather fabric. Visual defects in a gray-level image are located through thresholding and morphological processing, and their geometric information is immediately reported. Three input feature sets are proposed and tested to find the best set to characterize five types of defects: lines, holes, stains, wears, and knots. Two multilayered perceptron models with one and two hidden layers are tested for the classification of defects. If multiple line defects are identified on a given image as a result of classification, a line combination test is conducted to check if they are parts of larger line defects. Experimental results on 140 defect samples show that two-layered perceptrons are better than three-layered perceptrons for this problem. The classification results of this neural network approach are compared with those of a decision tree approach. The comparison shows that the neural network classifier provides better classification accuracy despite longer training times.

Segmentation of planar curves into straight-line segments and elliptical arcs

This paper presents a procedure for segmenting planar curves, mainly the projected boundary contours of machined parts, into straight-line segments and elliptical arcs. The break points are divided into two types: corners and smooth joins. The corners are detected by first applying adaptive smoothing to the tangent orientation along the curve, then taking the derivative of the smoothed tangent orientation, and finally locating the high spikes on the derivative. The smooth joins are first roughly located by a dynamic focusing fitting technique and then refined by an adjustment algorithm. The dynamic focusing fitting technique holds one end of a curve segment (which is bounded by a pair of adjacent corners) fixed and scans it from the other end until it focuses on a component segment which fits either a straight line or an elliptical arc. This component segment is identified and the process is repeated in the same manner for the rest of the curve. In the refining stage, each smooth join is adjusted to the left or to the right, point by point, until the measurement of goodness of fit for the curve segment is optimized. Tests of the procedure were performed with the boundary curves of three real object images.

Single machine scheduling with symmetric earliness and tardiness penalties

Abstract This research focuses on scheduling jobs with varying processing times and distinct due dates on a single machine subject to earliness and tardiness penalties. Hence, this work will find application in a just-in-time (JIT) production environment. The scheduling problem is formulated as a 0–1 linear integer program with three sets of constraints, where the objective is to minimize the sum of the absolute deviations between job completion times and their respective due dates. The first two sets of constraints are equivalent to the supply and demand constraints of an assignment problem. The third set, which represents the process time non-overlap constraints, is relaxed to form the Lagrangian dual problem. The dual problem is then solved using the subgradient algorithm. Efficient heuristics have also been developed in this work to yield initial primal feasible solutions and to convert primal infeasible solutions to feasibility. The computational results show that the relative deviation from optimality obtained by the subgradient algorithm is less than 3% for problem sizes varying from 10 to 100 jobs.

A Petri net model to determine optimal assembly sequences with assembly operation constraints

Abstract The assembly process in an automated assembly system is the execution of successive assembly operations in which each operation joins one component with another component to form a larger component. The selection of the assembly sequence of a product has a great effect on the efficiency of the assembly process. A systematic procedure is needed not only to generate all feasible assembly sequences but also to choose an optimal sequence. This paper describes a method for finding tight bounds on optimal sequences in an assembly system. A Petri net obtained from the AND/OR graph of a product can be formulated as a 0–1 integer linear program that minimizes the total assembly time or cost while satisfying three assembly operation constraints, namely, ease of component handling, ease of component joining, and tool changes. A Lagrangian dual formulation is then developed to obtain a lower bound. A dynamic programming algorithm provides a dual solution, and a subgradient optimization algorithm is used to maximize the lower bound obtained from the dual problem. The solution procedure is validated by determining the optimal assembly sequences of three products.