Industrial Instances Research Articles

Testing Hypothesis for New Class of Life Distribution Nbufr- t0

A new concept of ageing distribution, namely new better (worse) than used in failure rate at specific time 0 t (NBUFR0 t (NWUFR0 t )) are introduced. The problem is investigated how to prove that after a specified time 0 t of operation the failure rate of an item is greater than the corresponding failure rate of a new item. This problem occurs in various areas like for instance in industry, when designing a maintenance policy. A test statistics that based on the goodness of fit method are derived for testing exponentially versus the NBUFR0 t alternatives. The percentiles and powers of this test statistic are tabulated. The asymptotic efficiencies for some alternatives are derived. A medical data is taken as a practical application.

High level expression of a gene coding for pectate lyase in Bacillus subtilis 168

Pectate lyase degrade polygalacturonate of plant cell wall productes oligogalacturonates. In recent years, pectate lyase has attracted the considerable research interest because of their potential industrial application, for instance in the textile industry, paper making. Gene pel coding for pectate lyase from Bacillus subtilis VBS11 was subcloned and fused with α-amylase promoter from B. amyloliquefaciens (BApro). This construct was ligated into vector pMSE3 and transformated into the host strain BS168 for over expression under control of BApro promoter from B. amyloliquefaciens. rPel was producted in high level of 88,6 U/ml in Belisky minimal medium.

CROSS cyclic resource-constrained scheduling solver

Cyclic scheduling problems consist in ordering a set of activities executed indefinitely over time in a periodic fashion, subject to precedence and resource constraints. This class of problems has many applications in manufacturing, embedded systems and compiler design, production and chemical systems. This paper proposes a Constraint Programming approach for cyclic scheduling problems, based on modular arithmetic: in particular, we introduce a modular precedence constraint and a global cumulative constraint along with their filtering algorithms. We discuss two possible formulations. The first one (referred to as CROSS) models a pure cyclic scheduling problem and makes use of both our novel constraints. The second formulation (referred to as CROSS⁎) introduces a restrictive assumption to enable the use of classical resources constraints, but may incur a loss of solution quality. Many traditional approaches to cyclic scheduling operate by fixing the period value and then solving a linear problem in a generate-and-test fashion. Conversely, our technique is based on a non-linear model and tackles the problem as a whole: the period value is inferred from the scheduling decisions. Our approach has been tested on a number of non-trivial synthetic instances and on a set of realistic industrial instances. The method proved to effective in finding high quality solutions in a very short amount of time.

Finding optimal strategies in a multi-period multi-leader–follower Stackelberg game using an evolutionary algorithm

Stackelberg games are a classic example of bilevel optimization problems, which are often encountered in game theory and economics. These are complex problems with a hierarchical structure, where one optimization task is nested within the other. Despite a number of studies on handling bilevel optimization problems, these problems still remain a challenging territory, and existing methodologies are able to handle only simple problems with few variables under assumptions of continuity and differentiability. In this paper, we consider a special case of a multi-period multi-leader–follower Stackelberg competition model with non-linear cost and demand functions and discrete production variables. The model has potential applications, for instance in aircraft manufacturing industry, which is an oligopoly where a few giant firms enjoy a tremendous commitment power over the other smaller players. We solve cases with different number of leaders and followers, and show how the entrance or exit of a player affects the profits of the other players. In the presence of various model complexities, we use a computationally intensive nested evolutionary strategy to find an optimal solution for the model. The strategy is evaluated on a test-suite of bilevel problems, and it has been shown that the method is successful in handling difficult bilevel problems.

SAT-based MaxSAT algorithms

Many industrial optimization problems can be translated to MaxSAT. Although the general problem is NP hard, like SAT, many practical problems may be solved using modern MaxSAT solvers. In this paper we present several algorithms specially designed to deal with industrial or real problems. All of them are based on the idea of solving MaxSAT through successive calls to a SAT solver. We show that this SAT-based technique is efficient in solving industrial problems. In fact, all state-of-the-art MaxSAT solvers that perform well in industrial instances are based on this technique. In particular, our solvers won the 2009 partial MaxSAT and the 2011 weighted partial MaxSAT industrial categories of the MaxSAT evaluation. We prove the correctness of all our algorithms. We also present a complete experimental study comparing the performance of our algorithms with latest MaxSAT solvers.

Evaluation of Process Capability Using Fuzzy Inference System

In many industrial instances product quality depends on a multitude of dependent characteristics and as a consequence, attention on capability indices shifts from univariate domain to multivariate domain. In this research fuzzy inference system is used to determine the process capability index. Fuzzy sets can represent imprecise quantities as well as linguistic terms. Fuzzy inference system (FIS) is a method, based on the fuzzy theory, which maps the input values to the output values. The mapping mechanism is based on some set of rules, a list of if-then statements. In this research Mamdani fuzzy inference system is used to derive the overall output process capability when subjected to six crisp input and one output. This paper deals with a novel approach to evaluating process capability based on readily available information using fuzzy inference system.

Single Vs Hierarchical Population-Based Memetic Algorithm for Sat-Encoded Industrial Problems:A Statistical Comparison

In this work, a hierarchical population -based memetic algorithm for solving the satisfiability problem is presented. The approach suggests looking at the evolution as a hierarchical process evolving from a coarse population where the basic unit of a gene is composed of cluster of variables that represent the problem to a fine population where each gene represents asingle variable. The optimization process is carried out by letting the converged population at a child level serve as the initial population to the parent level. A benchmark composed of industrial instances is used to compare the effectiveness of the hier archical approach against its single-level counterpart.

Satisfiability in composition-nominative logics

Abstract Composition-nominative logics are algebra-based logics of partial predicates constructed in a semantic-syntactic style on the methodological basis, which is common with programming. They can be considered as generalizations of traditional logics on classes of partial predicates that do not have fixed arity. In this paper we present and investigate algorithms for solving the satisfiability problem in various classes of composition-nominative logics. We consider the satisfiability problem for logics of the propositional, renominative, and quantifier levels and prove the reduction of the problem to the satisfiability problem for classical logics. The method developed in the paper enables us to leverage existent state-of-the-art satisfiability checking procedures for solving the satisfiability problem in composition-nominative logics, which could be crucial for handling industrial instances coming from domains such as program analysis and verification. The reduction proposed in the paper requires extension of logic language and logic models with an infinite number of unessential variables and with a predicate of equality to a constant.

Multi-product sequencing and lot-sizing under uncertainties: A memetic algorithm

The paper deals with a stochastic multi-product sequencing and lot-sizing problem for a line that produces items in lots. Two types of uncertainties are considered: random lead time induced by machine breakdowns and random yield to take into account part rejects. In addition, sequence dependent setup times are also included. This study focuses on maximizing the probability of producing a required quantity of items of each type for a given finite planning horizon. A decomposition approach is used to separate sequencing and lot-sizing algorithms. Previous works have shown that the sequencing sub-problem can be solved efficiently, but the lot-sizing sub-problem remains difficult. In this paper, a memetic algorithm is proposed for this second sub-problem. Computational results show that the algorithms developed can be efficiently used for large scale industrial instances.

A discrete time exact solution approach for a complex hybrid flow-shop scheduling problem with limited-wait constraints

We study a real-world complex hybrid flow-shop scheduling problem arising from a bio-process industry. There are a variety of constraints to be taken into account, in particular zero intermediate capacity and limited waiting time between processing stages. We propose an exact solution approach for this optimization problem, based on a discrete time representation and a mixed-integer linear programming formulation. The proposed solution algorithm makes use of a new family of valid inequalities exploiting the fact that a limited waiting time is imposed on jobs between two successive production stages. The results of our computational experiments confirm that the proposed method produces good feasible schedules for industrial instances.

Temporal and spatial Lagrangean decompositions in multi-site, multi-period production planning problems with sequence-dependent changeovers

We address in this paper the optimization of a multi-site, multi-period, and multi-product planning problem with sequence-dependent changeovers, which is modeled as a mixed-integer linear programming (MILP) problem. Industrial instances of this problem require the planning of a number of production and distribution sites over a time span of several months. Temporal and spatial Lagrangean decomposition schemes can be useful for solving these types of large-scale production planning problems. In this paper we present a theoretical result on the relative size of the duality gap of the two decomposition alternatives. We also propose a methodology for exploiting the economic interpretation of the Lagrange multipliers to speed the convergence of numerical algorithms for solving the temporal and spatial Lagrangean duals. The proposed methods are applied to the multi-site multi-period planning problem in order to illustrate their computational effectiveness.

Approximating the Pareto optimal set using a reduced set of objective functions

Real-world applications of multi-objective optimization often involve numerous objective functions. But while such problems are in general computationally intractable, it is seldom necessary to determine the Pareto optimal set exactly. A significantly smaller computational burden thus motivates the loss of precision if the size of the loss can be estimated. We describe a method for finding an optimal reduction of the set of objectives yielding a smaller problem whose Pareto optimal set w.r.t. a discrete subset of the decision space is as close as possible to that of the original set of objectives. Utilizing a new characterization of Pareto optimality and presuming a finite decision space, we derive a program whose solution represents an optimal reduction. We also propose an approximate, computationally less demanding formulation which utilizes correlations between the objectives and separates into two parts. Numerical results from an industrial instance concerning the configuration of heavy-duty trucks are also reported, demonstrating the usefulness of the method developed. The results show that multi-objective optimization problems can be significantly simplified with an induced error which can be measured.

An adaptive directional importance sampling method for structural reliability

In this paper, an adaptive directional importance sampling (ADIS) method is presented. The algorithm is based on a directional simulation scheme in which the most important directions are sampled exact and the others by means of a response surface approach. These most important directions are determined by a β -sphere enclosing the most important part(s) of the limit state. The β -sphere and response surface are constantly updated during sampling with information that becomes available from the exact evaluations making the scheme adaptive. Various widely used test problems, representing a broad range of complex limit states that can occur in practice, of which several that pose potential problems to stochastic methods in general, demonstrate the high efficiency, accuracy and robustness of the method. As such, the ADIS method is of particular interest in applications with a low probability of failure and medium number (up to about 40) of stochastic variables, for instance in aircraft and nuclear industry.

An efficient heuristic for medium-term planning in shampoo production

This paper presents a single machine problem which occurs in shampoo production at medium-term planning phase. The considered production plant is linked to subsidiary companies which are themselves linked to final customers. The aim is to answer subsidiary companies requests by keeping their stocks in a window defined by their safety stock and maximum inventory levels. After an introduction, we present a formal definition of the problem. Next, we present a two-phase heuristic algorithm: the first phase is based on a greedy algorithm and the second phase on the Goldberg and Tarjan algorithm for the minimum cost flow problem. Experimental testings close to industrial instances show that the heuristic performs very efficiently.

Reducing the size of resolution proofs in linear time

DPLL-based SAT solvers progress by implicitly applying binary resolution. The resolution proofs that they generate are used, after the SAT solver’s run has terminated, for various purposes. Most notable uses in formal verification are: extracting an unsatisfiable core, extracting an interpolant, and detecting clauses that can be reused in an incremental satisfiability setting (the latter uses the proof only implicitly, during the run of the SAT solver). Making the resolution proof smaller can benefit all of these goals: it can lead to smaller cores, smaller interpolants, and smaller clauses that are propagated to the next SAT instance in an incremental setting. We suggest two methods that are linear in the size of the proof for doing so. Our first technique, called Recycle-Units uses each learned constant (unit clause) (x) for simplifying resolution steps in which x was the pivot, prior to when it was learned. Our second technique, called simplifies proofs in which there are several nodes in the resolution graph, one of which dominates the others, that correspond to the same pivot. Our experiments with industrial instances show that these simplifications reduce the core by ≈5% and the proof by ≈13%. It reduces the core less than competing methods such as run- till- fix, but whereas our algorithms are linear in the size of the proof, the latter and other competing techniques are all exponential as they are based on SAT runs. If we consider the size of the proof (the resolution graph) as being polynomial in the number of variables (it is not necessarily the case in general), this gives our method an exponential time reduction comparing to existing tools for small core extraction. Our experiments show that this result is evident in practice more so for the second method: rarely it takes more than a few seconds, even when competing tools time out, and hence it can be used as a cheap proof post-processing procedure.

Regulation of Nanomaterials under present and future Chemicals legislation Analysis and regulative options

Nanotechnology has already entered our everyday life. It finds application in a large number of industrial areas, for instance in the automobile industry, in energy and environmental technology, mechanical engineering, the chemicals and pharmaceuticals industry, in medicine, cosmetics and the food industry. Nanoscale titanium dioxide in sunscreen products, for example, provides UV protection, car tyres contain – not only recently – nanoscale carbon black, and many scratchproof, antireflection, non-stick and de-misting surfaces are manufactured with the help of nanomaterials. What distinguishes nanomaterials from previously used substances and processes is, above all, their large and active surface in proportion to their volume. The small particle size can result in modified chemical properties and functionalities compared to conventional substance in a non-nanoscale form, which can range from varied melting and boiling points to greater hardness, magnetism and catalytic effects. Nanotechnology is regarded as a key technology of the 21st century. Considerable economic expectations are attached to its further development. Due to its low consumption of resources and high energy efficiency, nanotechnology also offers potential ecological relief that should be exploited. At the same time, little is presently known about risks to human health and the environment associated with nanotechnology. The modified properties of nanoscale substances can lead to different risk assessment compared to conventional materials. Early knowledge in this respect has been available for some time. As far as titanium dioxide is concerned, the suspicion has been confirmed: This material, which has been manufactured and used as white pigment for many years, was regarded as unproblematic before its appearance in this small particle size, since tests carried out with non-nanoscale particles were negative. Results of tests on titanium dioxide in the nanoscale form showed, however, that these particles could have ecotoxic effects. In view of this conflict between expected benefits and potential risks, the question arises as to which legal requirements nanotechnology is subject to. In the spring of 2006 the Federal Environmental Agency commissioned a legal appraisal of the present framework of environmental legislation with regard to nanotechnologies and the drawing up of proposals for initial action should regulatory gaps be identified. The main focus of this analysis was chemicals law, and its findings are presented in this article.

Cache Conscious Data Structures for Boolean Satisfiability Solvers

Current SAT solvers are well engineered and highly efficient, and significant research effort has been put into creating data structures that can produce maximal efficiency for the core propagation engine within SAT solvers. However, there is still substantial room for improvement. As the disparity between CPU speeds and cache sizes have increased, cache conscious data structures and algorithms have become very important. They are even more important in the context of parallel SAT solving, as issues like cache contention and main memory contention can dramatically slow down a parallel SAT solver. We present a series of data structure and algorithmic modifications that are able to increase the core propagation speed of MiniSat 2.0 by an average of 80% on a set of medium sized industrial instances, and increase the speed of a parallelized version of MiniSat running with 8 threads by 140% on those same instances.

Graph approach for optimal design of transfer machine with rotary table

A line balancing problem for transfer machines with rotary tables is considered. This type of machine is used in mass production. A part is sequentially machined on m working positions and is moved from one position to the next using a rotary table. The operations are grouped into blocks, where the operations of the same block are simultaneously performed by one piece of equipment with several tools (a multi-spindle head). All multi-spindle heads of a machine are simultaneously activated. Therefore, all operations of the machine are executed in parallel on m parts mounted on the rotary table. The line balancing problem consists of partitioning the set of all operations into sub-sets in order to minimize the number of working positions and the total number of spindle heads while satisfying all the constraints (precedence, compatibility for spindle heads, etc.). The method proposed in this paper is based on transforming this line balancing problem into a search for a constrained shortest path. An algorithm for simultaneously generating a graph and finding a constrained shortest path is developed. Some dominance rules for reducing the graph size are provided. An industrial example is presented in detail and experimental results on other industrial instances are reported.

Solving modern mixed-size placement instances

Physical design of modern systems-on-chip is extremely challenging. Such digital integrated circuits often contain tens of millions of logic gates, intellectual property blocks, embedded memories and custom register-transfer level (RTL) blocks. At current and future technology nodes, their power and performance are impacted, more than ever, by the placement of their modules. However, our experiments show that traditional techniques for placement and floorplanning, and existing academic tools cannot reliably solve the placement task. To study this problem, we identify particularly difficult industrial instances and reproduce the failures of existing tools by modifying pre-existing benchmark instances. Furthermore, we propose algorithms that facilitate placement of these difficult instances. Empirically, our techniques consistently produce legal placements, and on instances where comparison is possible, reduce wirelength by 13% over Capo 9.4 and 31% over PATOMA 1.0—the pre-existing tools that most frequently produce legal placements in our experiments.

Modelling and solving a multimode and multisite industrial problem: Lagrangean relaxation and heuristic approaches

In this paper, we tackle the problem of scheduling multisite and multimode activities in order to minimise the project duration that meets the constraints imposed by limited resources. The industrial system is composed of many production sites, and each site has its own resources called local resources, and every site can receive resources called global resources. The objective is to support the repartition of all the lots of pieces on different sites in a cooperative way, and to assign resources. The system reduced to only one site highlights the well-known problem of the Multimode Resource-Constrained Project Scheduling Problem (MRCPSP). We propose a mathematical programming approach based on competitive feasible solutions and strong lower bounds, within quite reasonable computation times. The basic ingredients of this approach are the Lagrangean relaxation. We also propose a method based on stochastic descent with restart to compute large-sized industrial instances. The experiments show that this technique gives solutions close to the optimum. This seems to indicate that these resolution methods are good candidates for real industrial problems.