Formulation and solution of the non-linear,damped eigenvalue problem for skeletal systems

This paper presents and discusses an Arnoldi-based eigensolution technique for evaluating the complex natural frequencies and mode shapes from frequency dependent quadratic eigenproblems associated with vibration analysis of damped structures. The new solution technique is used in conjunction with a mixed finite element modelling procedure which utilizes both the polynomial and frequency dependent displacement fields in formulating the system matrices. This modelling provides the ability to represent a frequency dependent damping matrix in vibration analysis of skeletal systems. The eigensolution methodology presented here is based upon the ability to evaluate a specific set of parametrized curves for the non-linear eigenvalue problem at given values of the parameter. Numerical examples illustrate that this method, used in conjunction with a secant interpolation, accurately evaluates the complex natural frequencies and modes of the quadratic non-linear eigenproblem and verifies that the new eigensolution technique coupled with the mixed finite element modelling procedure is more accurate than the conventional finite element models.     

Application of ELSP solution techniques to the deterministic robotic scheduling problem

This paper studies the flexible deterministic robotic scheduling problem (FDRSP) where one tool changer (robot) is in charge of several machining operations and overlapping tool changes are not allowed. The relationship between the FDRSP and the economic lot scheduling problem (ELSP) is explored. The most promising solution techniques for the ELSP guaranteeing feasibility in advance are modified and applied to the FDRSP. It is concluded that the basic period approach performs the best in small problems, while the rounding to powers of two approach is superior for medium to large problems.     

The liquid crystal injection scheduling problem (LCISP)

In this paper, the liquid crystal injection scheduling problem (LCISP) involving the constraints on limited maximum waiting times, unequal ready times, and machine setup times is considered to form the batches with incompatible product families and to sequence those batches on identical parallel batch processing machines. The batch scheduling problem, LCISP, has many applications, especially in thin film transistor liquid crystal display (TFT-LCD) factories at the cell assembly stage. In the LCISP, the objective is to minimise the total machine workload without violating the limited maximum waiting time restriction. Furthermore, machine setup times that are sequence dependent for two consecutive batches classified into different product families on the same machine are also considered. Since the LCISP involves constraints on limited maximum waiting times and sequence dependent setup times, it is more difficult to solve than the classical parallel batch processing machine scheduling problem with incompatible product families. These restrictions mean that the existing methods cannot be applied into real-world factories directly. Therefore, this paper proposes a mixed integer programming model to solve the LCISP exactly. In addition, two efficient solution procedures which solve the LCISP are also presented.     

Comparing four metaheuristics for solving a constraint satisfaction problem for ship outfitting scheduling

This study compares the performance of four different metaheuristics for solving a constraint satisfaction scheduling problem of the outfitting process of shipbuilding. The ship outfitting process is often unorganised and chaotic due to the complex interactions between the stakeholders and the overall lack of sufficiently detailed planning. The examined methods are genetic algorithms (GA), simulated annealing (SA), genetic simulated annealing (GSA) and discrete particle swarm optimisation (PSO). Each of these methods relies on a list scheduling heuristic to transform the solution space into feasible schedules. Although the SA had the best performance for a medium-sized superstructure section, the GSA created the best schedules for engine room double-bottom sections, the most complex sections in terms of outfitting. The GA provided the best scalability in terms of computational time while only marginally sacrificing solution quality. The solution quality of the PSO was very poor in comparison with the other methods. All methods generated schedules with sufficiently high resource utilisation, approximately 95%. The findings from this work will be incorporated into a larger project with the aim of creating a tool which can automatically generate an outfitting planning for a vessel.     

A comparison of solution approaches for the fixed-sequence economic lot scheduling problem

The economic lot scheduling problem (ELSP) has received much attention recently. The general version of the problem has a non-convex objective function, so it is difficult to find truly optimal solutions. We examine the three most popular heuristic approaches to the fixed-sequence ELSP. Each approach imposes one or both of these simplifying constraints: the zero-switch constraint (production of a part is started only when its inventory is depleted) and the equal-lot constraint (the lot size of a given part is constant through time). We provide a formulation that clarifies the relationships between the general problem and the three constrained versions, and compare their performances in a computational study.     

On the efficient modeling and solution of the multi-mode resource-constrained project scheduling problem with generalized precedence relations

For variants of the single-mode resource-constrained project scheduling problem, state-of-the-art exact algorithms combine a Branch and Bound algorithm with principles from Constraint Programming and Boolean Satisfiability Solving. In our paper, we propose new exact approaches extending the above principles to the multi-mode RCPSP (MRCPSP) with generalized precedence relations (GPRs). More precisely, we implemented two constraint handlers cumulativemm and gprecedencemm for the optimization framework SCIP. With the latter, one can model renewable resource constraints and GPRs in the context of multi-mode activities, respectively. Moreover, they integrate domain propagation and explanation generation techniques for the above problem characteristics. We formulate three SCIP-models for the MRCPSP with GPRs, two without and one with our constraint handler gprecedencemm. Our computational results on instances from the literature with 30, 50 and 100 activities show that the addition of this constraint handler significantly strengthens the SCIP-model. Moreover, we outperform the state-of-the-art exact approach on instances with 50 activities when imposing time limits of 27 s. In addition, we close (find the optimal solution and prove its optimality for) 289 open instances and improve the best known makespan for 271 instances from the literature.     

Production scheduling with uncertain supply: a new solution to the open pit mining problem

The annual production scheduling of open pit mines determines an optimal sequence for annually extracting the mineralized material from the ground. The objective of the optimization process is usually to maximize the total Net Present Value (NPV) of the operation. Production scheduling is typically a Mixed Integer Programming (MIP) type problem containing uncertainty in the geologic input data and economic parameters involved. Major uncertainty affecting optimization is uncertainty in the mineralized materials (resource) available in the ground which constitutes an uncertain supply for mine production scheduling. A new optimization model is developed herein based on two-stage Stochastic Integer Programming (SIP) to integrate uncertain supply to optimization; past optimization methods assume certainty in the supply from the mineral resource. As input, the SIP model utilizes a set of multiple, stochastically simulated scenarios of the mineralized materials in the ground. This set of multiple, equally probable scenarios describes the uncertainty in the mineral resource available in the ground, and allows the proposed model to generate a single optimum production schedule. The method is applied for optimizing the annual production scheduling at a gold mine in Australia and benchmarked against a traditional scheduling method using the traditional single “average type” assessment of the mineral resource in the ground. In the case study presented herein, the schedule generated using the proposed SIP model resulted in approximately 10% higher NPV than the schedule derived from the traditional approach.     

A correction to the analytical solution of the mixed-mode bending (MMB) problem

This paper addresses the analytical solution of the mixed-mode bending (MMB) problem. The first published solutions used a load separation in pure mode I and mode II and were applied for a crack length less than the beam half-span, a ⩽ L. In later publications, the same mode separation was used in deriving the analytical solution for crack lengths bigger than the beam half-span, a > L. In this paper it is shown that this mode separation is not valid when a > L and in some cases may lead to very erroneous results. The correct mode separation and the corresponding analytical solutions, when a > L, are presented. Results, of force vs. displacement and force vs. crack length graphs, obtained using the existing formulation and the corrected formulation are compared. A finite element solution, which does not use mode separation, is also presented.     

Open shop scheduling problem with a multi-skills resource constraint: a genetic algorithm and an ant colony optimisation approach

The continuous evolution of manufacturing environments leads to a more efficient production process that controls an increasing number of parameters. Production resources usually represent an important constraint in a manufacturing activity, specially talking about the management of human resources and their skills. In order to study the impact of this subject, this paper considers an open shop scheduling problem based on a mechanical production workshop to minimise the total flow time including a multi-skill resource constraint. Then, we count with a number of workers that have a versatility to carry out different tasks, and according to their assignment a schedule is generated. In that way, we have formulated the problem as a linear as and a non-linear mathematical model which applies the classic scheduling constraints, adding some different resources constraints related to personnel staff competences and their availability to execute one task. In addition, we introduce a genetic algorithm and an ant colony optimisation (ACO) method to solve large size problems. Finally, the best method (ACO) has been used to solve a real industrial case that is presented at the end.     

Solving the integrated lot-sizing and job-shop scheduling problem

Production planning and scheduling are usually performed in a sequential manner, thus generating unfeasibility conflicts. Moreover, solving these problems in complex manufacturing systems (with several products sharing different resources) is very challenging in production management. This paper addresses the solution of multi-item multi-period multi-resource single-level lot-sizing and scheduling problems in general manufacturing systems with job-shop configurations. The mathematical formulation is a generalisation of the one used for the Capacitated Lot-Sizing Problem, including detailed capacity constraints for a fixed sequence of operations. The solution method combines a Lagrangian heuristic, determining a feasible production plan for a fixed sequence of operations, with a sequence improvement method which iteratively feeds the heuristic. Numerical results demonstrate that this approach is efficient and more appropriate than a standard solver for solving complex problems, regarding solution quality and computational requirements.     

Transient inter-production scheduling based on Petri nets and constraint programming

In this article, we focus on the transient inter-production scheduling problem between two cyclic productions in the framework of flexible manufacturing systems. This problem is first formulated as a reachability problem in timed Petri nets (TPN), then solved using a methodology based on constraint programming. Our work is based on the controlled executions proposed by Chretienne to model the sequence of transition firing dates. Our methodology is based on a preliminary resolution of the state equation between initial and final states in the underlying non-TPN. Then, we choose a duration T _max corresponding to the maximal duration time of the scheduling. For each solution S of the state equation, we build a controlled execution from the sequence of firings in S. After the propagation of firing date constraints and reachability constraints in the TPN, we use constraint programming to enumerate the set of feasible controlled executions.     

The general lotsizing and scheduling problem

The GLSP (GeneralLotsizing andSchedulingProblem) addresses the problem of integrating lotsizing and scheduling of several products on a single, capacitated machine. Continuous lotsizes, meeting deterministic, dynamic demands, are determined and scheduled with the objective of minimizing inventory holding costs and sequence-dependent setup costs. As the schedule is independent of predefined time periods, the GLSP generalizes known models using restricted time structures. Three variants of a local search algorithm, based onthreshold accepting, are presented. Computational tests show the effectiveness of these heuristic approaches and are encouraging for further extensions of the basic model.     

On the solution of a minimum compliance topology optimisation problem by optimality criteria without a priori volume constraint specification

The paper presents a topology optimisation method based on optimality criteria for total potential energy maximisation with a volume constraint. The final volume of the optimal structural configuration has not to be specified a priori and is directly controlled by the stress, displacement or stiffness constraints defined at the design problem layout phase. The proposed method leads to the identification of well defined structures characterised by a small number of discrete elements with intermediate material properties within a limited number of iterations. The results obtained by solving several two dimensional benchmark problems are shown.     

Formulation and solution of hierarchical finite element equations

The paper presents a general hierarchical formulation applicable to both elliptic and hyperbolic problems. Static and eigenvalue linear elastic problems as well as convection–diffusion problems are studied. The hierarchical formulation is well suited for adaptive procedures. For the convection-diffusion problem the hierarchical approximation is made in time only. Different hierarchical functions are proposed for different types of problems. Both weighted residual and least-squares formulations are applied. A combination of these two gives a penalty method with a constraint equation corresponding to the least-squares method. A whole class of time integration formulae is obtained. These are all suitable for adaptive procedures owing to the hierarchical approximation in the time domain. If a linear discontinuous hierarchical base function is used in the Galerkin weak formulation, the method so obtained corresponds to the discontinuous Galerkin method in time and is especially suited for convection dominated problems. The streamline-diffusion method is found to be the aforementioned penalty method. This paper also examines the sequence of nested equation systems that results from a hierarchical finite element formulation. Properties of these systems arising from static problems are investigated. The paper presents some new possibilities for iterative solution of hierarchic element equations, and different procedures are compared in a numerical example. Finally, a simple ID convection-diffusion problem clearly shows that the proposed hierarchical formulation in time gives a stable and accurate solution even for convection dominated flow.     

Revisiting the mixed-model multi-level just-in-time scheduling problem

Morabito and Kraus (1994) propose a modification to the mathematical model of the mixed-model, multi-level just-in-time (JIT) scheduling problem introduced in Miltenburg and Sinnamon (1989). In this note we relate their proposal to the goals of JIT production and emphasize the importance of using the weights in the original formulation to express management priorities.     

An optimal solution of energy scheduling problem based on chance-constraint programming model using Interval-valued neutrosophic constraints

Optimization and Engineering - Neutrosophic sets have been commenced as a generalization of crisp, fuzzy and intuitionistic fuzzy sets to depict vague, incompatible and deficient information about...     

A tabu search algorithm with solution space partition and repairing procedure for cyclic robotic cell scheduling problem

This paper proposes a tabu search (TS) algorithm to solve an NP-hard cyclic robotic scheduling problem. The objective is to find a cyclic robot schedule that maximises the throughput. We first formulate the problem as a linear program, provided that the robot move sequence is given, and reduce the problem to searching for an optimal robot move sequence. We find that the solution space can be divided into some specific subspaces by the maximal number of works-in-process. Then, we propose a TS algorithm to synchronously perform local searches in each subspace. To speed up our algorithm, dominated subspaces are eliminated by lower and upper bounds of the cycle time during the iterations. In the TS, a constructive heuristic is developed to generate initial solutions for each subspace and a repairing procedure is proposed to maintain the feasibility of the solutions generated in the initialisation stage and the neighbours search process. Computational comparison both on benchmark instances and randomly generated instances indicates that our algorithm is efficient for the cyclic robotic scheduling problem.