Sequencing on two-stage hybrid flowshops with uniform machines to minimize makespan

We study the problem of sequencing n simultaneously available jobs on a two-stage hybrid flowshop with uniform machines at each stage. The objective is to minimize the makespan. This problem belongs to the class of strongly NP-complete problems. We develop three heuristic procedures with time complexity of O ( n log n ). We derive the worst-case performance bound for heuristic 1 which ignores the dependence of the start time of the second stage on the completion time of the first stage. Heuristics 2 and 3 take such a dependence into account. Several lower bounds are derived, and the best among them serves as the benchmark for evaluation. Extensive computational experiments have been conducted to evaluate the performance of the proposed procedures. They demonstrate the efficacy of the first heuristic in the case of symmetric processing times. The last two heuristics are significantly better in the case of nonsymmetric processing times.     

The use of flowlines to simplify routing complexity in two-stage flowshops

Flexible manufacturing systems are often designed as flowshops supported by automated material handling devices that facilitate routing among any two processors of adjacent stages. This routing structure is complex, and results in excessive capital investment and costs of management. In this paper we propose a decomposition of two-stage flowshops into smaller independent flowlines that allow for unidirectional routing only. We solve optimally the problem of minimizing makespan on two parallel flowlines, by means of a Dynamic Programming algorithm (DP). Based on DP we develop lower bounds on the throughput performance of environments that consist of more than two flowlines. We present several heuristic algorithms and report their optimality gaps. Using these algorithms, we show that the decomposition of two stage flowshops with complicated routing into flowline-like designs with unidirectional routing is associated with minor losses in throughput performance, and hence significant savings in material handling costs.     

A note on performance guarantees for sequencing three-stage flexible flowshops with identical machines to minimize makespan

In this note a tight worst-case ratio bound is derived for a heuristic for the three-stage flexible flowshop makespan minimization problem. It is shown that the derived bound is the best available for the three-stage problem. Furthermore, the derived bound can be used to improve the best available worst-case ratio bound for the flexible flowshop makespan minimization problem with an arbitrary odd number of stages.     

A computational procedure for flexible beams with frictional contact constraints

This paper deals with the application of a parametric quadratic programming (PQP) method to the numerical solution of large-deflection beams involving frictional contact constraints. The flexibility of the structure is modelled by an intrinsic spatial beam theory which is approximated by transverse-shear deformable linear beam elements. The linear complementary problem (LCP) without the penalty function resulting from PQP is made part of a Newton-Raphson search. The tool for solving the complementary equations is Lemke's algorithm, in which frictional contact conditions are enforced and new contact surfaces are updated during iteration. Applying the resulting contact element, a more accurate approximation of the contact point can be guaranteed, and the contact force can be directly computed by the adjacent beam elements. Three numerical examples are analysed to show the effectiveness and validity of the method.     

A computational framework for viscoelastic analysis of flexible pavements under moving loads

A general quasi-static computational procedure is established to evaluate stresses and strains induced in the viscoelastic flexible pavement by moving traffic. The procedure is based on superposition principle and is computationally favourable, as it requires only reduced incremental problem to be solved numerically. The impact of traffic speed and density on the mechanical response of flexible pavement is examined numerically. Results relevant for two major modes of pavement??s distress, i.e. cracking and rutting, are reported. It is shown that the state-of-practice layered elastic analysis used in pavement design is unable to capture several important qualitative and quantitative aspects of pavements response.     

A study on the performance of scheduling rules in buffer-constrained dynamic flowshops

Most studies on scheduling in manufacturing systems using dispatching rules deal with jobshops, while there are only few reports dealing with dynamic flowshops. It is known that the performance of many dispatching rules in dynamic jobshops is different from that in dynamic flowshops. Moreover, many research reports assume that there are no buffer constraints in the shop, and even those reports dealing with buffer-constrained shops present the evaluation of existing dispatching rules for unconstrained shops in the context of buffer constraints with the consideration of a limited number of objectives of scheduling. In this study, we deal with the problem of scheduling in dynamic flowshops with buffer constraints. With respect to different time-based objectives, the best dispatching rules for scheduling in unconstrained shops have been identified from the existing literature. In addition, two new dispatching rules specially designed for flowshops with buffer constraints are proposed. All dispatching rules under consideration are evaluated in dynamic flowshops with buffer constraints on the basis of an extensive simulation study covering different levels of buffer constraints, shop load or utilization, and missing operations in flowshops. The proposed rules are found to perform better than the existing dispatching rules in buffer-constrained flowshops with respect to many measures of performance.     

Scheduling distributed flowshops with flexible assembly and set-up time to minimise makespan

The scheduling problems under distributed production or flexible assembly settings have achieved increasing attention in recent years. This paper considers scheduling the integration of these two environments and proposes an original distributed flowshop scheduling problem with flexible assembly and set-up time. Distributed production stage is deployed several homogeneous flowshop factories that process the jobs to be assembled into final products in the flexible assembly stage. The objective is to find a schedule, including a production subschedule for jobs and an assembly subschedule for products, to minimise the makespan. Such a scheduling problem involves four successive decisions: assigning jobs to production factories, sequencing jobs at every factory, designating an assembly machine for each product and sequencing products on each assembly machine. The computational model is first established, and then a constructive heuristic (TPHS) and two hybrid metaheuristics (HVNS and HPSO) are proposed. Numerical experiments have been carried out and results validate the algorithmic feasibility and effectiveness. TPHS can obtain reasonable solutions in a shorter time, while metaheuristics can report better solutions using more yet acceptable time. HPSO is statistically comparable yet less robust compared with HVNS for small-scale instances. For the large-scale case, HPSO outperforms HVNS on both effectiveness and robustness.     

Evaluating artificial intelligence heuristics for a flexible Kanban system: simultaneous Kanban controlling and scheduling

This paper evaluates several artificial intelligence heuristics for a simultaneous Kanban controlling and scheduling on a flexible Kanban system. The objective of the problem is to minimise a total production cost that includes due date penalty, inventory, and machining costs. We show that the simultaneous Kanban controlling and scheduling is critical in minimising the total production cost (approximately 30% cost reduction over scheduling without a Kanban controlling). To identify the most effective search method for the simultaneous Kanban controlling and scheduling, we evaluated widely known artificial intelligence heuristics: genetic algorithm, simulated annealing, tabu search, and neighbourhood search. Computational results show that the tabu search performs the best in terms of solution quality. The tabu search also requires a much less computational time than the genetic algorithm and the simulated annealing. To further improve the solution quality and computational time for a simultaneous Kanban controlling and scheduling on a flexible Kanban system, we developed a two-stage tabu search. At the beginning of the tabu search process, an initial solution is constructed by utilising the customer due date information given by a decision support system. The two-stage tabu search performs better than the tabu search with a randomly generated initial solution in both the solution quality and computational time across all problem sizes. The difference in the solution quality is more pronounced at the early stages of the search.     

A computational approach for predicting the hydroelasticity of flexible structures based on the pressure Poisson equation

This work is concerned with the modeling of the interaction of fluid flow with flexible solid structures. The flow under consideration is governed by the Navier–Stokes equations for incompressible viscous fluids and modeled with low‐order velocity–pressure finite elements. The motion of the fluid domain is accounted for by the arbitrary Lagrangian–Eulerian formulation. The structure is represented by means of an appropriate standard finite element formulation. The spring smooth analogy is used to mesh control. The time integrating algorithm is based on the predictor–multi‐corrector algorithm. An important aspect of the present work is the introduction of a new monolithic approach based on the fluid pressure Poisson equation (PPE) to solve the hydroelasticity problem of an incompressible viscous fluid with an elastic body that is vibrating due to flow excitation. The PPE is derived to be consistent with the coupled system equation for the fluid–structure interaction (FSI). Based on this approach, an efficient monolithic method is adopted to simulate hydroelasticity between the flexible structure and the flow. The fluid pressure is implicitly derived to satisfy the incompressibility constraint, and the other unknown variables are explicitly derived. The coefficient matrix of the PPE for the FSI becomes symmetric and positive definite. To demonstrate the performance of the proposed approach, two working examples, a beam immersed in incompressible fluid and a guide vane of a Francis turbine passage, were used. The results show the validity of the proposed approach. Copyright © 2007 John Wiley & Sons, Ltd.     

A hybrid scheduling approach for automated flowshops with material handling and time constraints

Flowshop scheduling problems have been extensively studied by several authors using different approaches. A typical flowshop process consists of successive manufacturing stages arranged in a single production line where different jobs have to be processed following a predefined production recipe. In this work, the scheduling of a complex flowshop process involving automated wet-etch station from semiconductor manufacturing systems requires a proper synchronisation of processing and transport operations, due to stringent storage policies and fixed transfer times between stages. Robust hybrid solution strategies based on mixed integer linear programming formulations and heuristic-based approaches, such as aggregation and decomposition methods, are proposed and illustrated on industrial-scale problems. The results show significant improvements in solution quality coupled with a reduced computational effort compared to other existing methodologies.     

A computational approach for peptidomic analysis in taxonomic study of amphibian species

Peptides in the skin secretion of frogs have been studied for some time now because they frequently possess important biological activity such as antibiotic, antimicrobial, or anticancer properties. In this paper, we present a computational approach for measuring the degree of similarity between the entire peptide complement of the skin secretion of specimens from the same or different species. The first step in the analysis is the generation of a mass spectral profile from an experimental high-performance liquid chromatography/electrosparay ionization analysis of the sample. An "overlap" between the mass spectral profiles of different specimens is then proposed as a measure of their similarity. Analysis of specimens from three species of the genus Litoria, viz., L. Aurea, L. Caerulea, and L. Infrafrenata, and Rana Capito of genus Rana shows that the degree of similarity is highest between specimens from the same species, lower for specimens from different species of the same genus, and lowest between specimens from different genera. This indicates that comparison of skin peptide profiles (i.e., mass spectral profiles of skin secretion) is potentially a useful aid in the taxonomic study of amphibian species.     

Heuristics for the integer one-dimensional cutting stock problem: A computational study

In this paper the problem of generating integer solutions to the standard one-dimensional cutting stock problem is treated. In particular, we study a specific class of heuristic approaches that have been proposed in the literature, and some straightforward variants. These methods are compared with respect to solution quality and computing time. Our evaluation is based on having solved 4,000 randomly generated test problems. Not only will it be shown that two methods are clearly superior to the others but also that they solve almost any instance of the standard one-dimensional cutting stock problem to an optimum.     

A computational study of LP-based heuristic algorithms for two-dimensional guillotine cutting stock problems

In this paper we develop and compare several heuristic methods for solving the general two-dimensional cutting stock problem. We follow the Gilmore-Gomory column generation scheme in which at each iteration a new cutting pattern is obtained as the solution of a subproblem on one stock sheet. For solving this subproblem, in addition to classical dynamic programming, we have developed three heuristic procedures of increasing complexity, based on GRASP and Tabu Search techniques, producing solutions differing in quality and in time requirements. In order to obtain integer solutions from the fractional solutions of the Gilmore-Gomory process, we compare three rounding procedures, rounding up, truncated branch and bound and the solution of a residual problem. We have coded and tested all the combinations of algorithms and rounding procedures. The computational results obtained on a set of randomly generated test problems show their relative efficiency and allow the potential user to choose from among them, according to the available computing time. Rceived: January 9, 2001 / Accepted: December 10, 2001     

Bottleneck-based heuristics to minimize tardy jobs in a flexible flow line with unrelated parallel machines

This paper develops new bottleneck-based heuristics with machine selection rules to solve the flexible flow line problem with unrelated parallel machines in each stage and a bottleneck stage in the flow line. The objective is to minimize the number of tardy jobs in the problem. The heuristics consist of three steps: (1) identifying the bottleneck stage; (2) scheduling jobs at the bottleneck stage and the upstream stages ahead of the bottleneck stage; (3) using dispatching rules to schedule jobs at the downstream stages behind the bottleneck stage. A new approach is developed to find the arrival times of the jobs at the bottleneck stage, and two decision rules are developed to schedule the jobs on the bottleneck stage. This new approach neatly overcomes the difficulty of determining feasible arrival times of jobs at the bottleneck stage. In order to evaluate the performance of the proposed heuristics, six well-known dispatching rules are examined for comparison purposes. Six factors are used to design 729 production scenarios, and ten test problems are generated for each scenario. Computational results show that the proposed heuristics significantly outperform all the well-known dispatching rules. An analysis of the experimental factors is also performed and several interesting insights into the heuristics are discovered.     

A derived heuristics based multi-objective optimization procedure for micro-grid scheduling

With the availability of different types of power generators to be used in an electric micro-grid system, their operation scheduling as the load demand changes with time becomes an important task. Besides satisfying load balance constraints and the generator's rated power, several other practicalities, such as limited availability of grid power and restricted ramping of power output from generators, must all be considered during the operation scheduling process, which makes it difficult to decide whether the optimization results are accurate and satisfactory. In solving such complex practical problems, heuristics-based customized optimization algorithms are suggested. However, due to nonlinear and complex interactions of variables, it is difficult to come up with heuristics in such problems off-hand. In this article, a two-step strategy is proposed in which the first task deciphers important heuristics about the problem and the second task utilizes the derived heuristics to solve the original problem in a computationally fast manner. Specifically, the specific operation scheduling is considered from a two-objective (cost and emission) point of view. The first task develops basic and advanced level knowledge bases offline from a series of prior demand-wise optimization runs and then the second task utilizes them to modify optimized solutions in an application scenario. Results on island and grid connected modes and several pragmatic formulations of the micro-grid operation scheduling problem clearly indicate the merit of the proposed two-step procedure.     

A two-stage method for O-glycosylation site prediction

Correctly predicting the site of O-glycosylation will greatly benefit the search and design of new specific and efficient GalNAc-transferase inhibitors. In this article, the site of O-glycosylation was studied using the correlation-based feature subset (CfsSubset) selection method combined with a wrapper method. Twenty-three important biochemical features were found based on a jackknife test from original data set containing 4779 features. By using the AdaBoost method with the twenty-three selected features, the prediction model yields an accuracy rate of 88.1% for the jackknife test and 87.5% for an independent set test, with increased accuracy over the original dataset by 8.5% and 10.42%, respectively. It is expected that our feature selection scheme can be referred to as a useful assistant technique for finding effective competitive inhibitors of GalNAc-transferase. An online predictor based on this research is available at http://chemdata.shu.edu.cn/gal_p/.     

A two-stage sampling plan for bogey tests

When accelerated life tests can be applied to simulate the normal product operating conditions, engineers usually terminate the test upon successfully running to a multiple of a prespecified bogey without any failure to demonstrate a required minimum reliability level. This testing philosophy is called ‘bogey test’ (or extended test) in the automotive industry and is a subset of type I censored tests in standard reliability literature. Sometimes engineers encounter the difficulty of using this reliability demonstration approach when incidents do occur during the test. The incident might be a legitimate failure or a withdrawal caused by external forces such as a broken fixture of a non-functional power supply. This paper derives a two-stage sampling plan for possible back-up solution on planning and running a bogey test with possible occurrence of incidents. A Weibull distribution with a given shape parameter is assumed for the underlying life characteristic.