The two-flight low risk helicopter transportation problem under the split pickup scenario

Improved antlion optimization algorithm via tournament selection and its application to parallel machine scheduling

To solve problems that require far more memory than a single machine can supply, data can be swapped to disk in some manner, it can be compressed, and/or the memory of multiple parallel machines can be used to provide enough memory and storage space. Instead of implementing either functionality anew and specific for each application, or instead of relying on the operating system’s swapping algorithms (which are inflexible, not algorithm-aware, and often limited in their fixed storage capacity), our solution is a Large Virtual Machine (LVM) that transparently provides a large address space to applications and that is more flexible and efficient than operating system approaches.LVM is a virtual machine for Java that is designed to support large address spaces for billions of objects. It swaps objects out to disk, compresses objects where needed, and uses multiple parallel machines in a Distributed Shared Memory (DSM) setting. The latter is the main focus of this paper. Allocation and collection performance is similar to well-known JVMs if no swapping is needed. With swapping and clustering, we are able to create a list containing 1.2×108 elements far faster than other JVMs. LVM’s swapping is up to 10 times faster than OS-level swapping. A swap-aware GC algorithm helps by a factor of 3.KeywordsVirtual MachineHash TableGarbage CollectionAddress SpaceGarbage CollectorThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This paper deals with the capacitated single-stage production lot sizing and scheduling problem with multiple items, setup time, stochastic demand and unrelated parallel machines. A stochastic mixed-integer linear programming model is proposed to formulate the problem. Based on the uncertain constraints, the chance constrained programming approach is used to transform them into equivalent deterministic constraints and then obtain an optimal solution by deterministic mixed-integer linear programming model. Due to the complexity of problem, the mixed-integer programming (MIP) of the equivalence deterministic model of the capacitated single-stage production lot sizing problem with multiple items, setup time, stochastic demand and unrelated parallel machines is reformulated as a shortest path reformulation problem. The proposed algorithm is evaluated through a numerical example. Computational results show that the proposed method gives optimal or near optimal solution and have good-quality result for the test problem.

This paper presents a discrete artificial bee colony algorithm for a single machine earliness–tardiness scheduling problem. The objective of single machine earliness–tardiness scheduling problems is to find a job sequence that minimises the total sum of earliness–tardiness penalties. Artificial bee colony (ABC) algorithm is a swarm-based meta-heuristic, which mimics the foraging behaviour of honey bee swarms. In this study, several modifications to the original ABC algorithm are proposed for adapting the algorithm to efficiently solve combinatorial optimisation problems like single machine scheduling. In proposed study, instead of using a single search operator to generate neighbour solutions, random selection from an operator pool is employed. Moreover, novel crossover operators are presented and employed with several parent sets with different characteristics to enhance both exploration and exploitation behaviour of the proposed algorithm. The performance of the presented meta-heuristic is evaluated on several benchmark problems in detail and compared with the state-of-the-art algorithms. Computational results indicate that the algorithm can produce better solutions in terms of solution quality, robustness and computational time when compared to other algorithms.

In real world manufacturing systems there are many combinatorial optimization problems (COP) imposing on more complex issues, such as complex structure, nonlinear constraints, and multiple objectives to be handled simultaneously. Manufacturing scheduling is one of the important and complex COP models, where it can have a major impact on the productivity of a production process. Moreover, the COP models make the problem intractable to the traditional optimization techniques because most of scheduling problems fall into the class of NP-hard combinatorial problems. In order to develop effective and efficient solution algorithms that are in a sense good, i.e., whose computational time is small as within 3 min, or at least reasonable for NP-hard combinatorial problems met in practice, we have to consider: quality of solution, computational time and effectiveness of the nondominated solutions for multiobjective optimization problem (MOP). When solving any NP-hard problem, a genetic algorithm (GA) based on principles from evolution theory is most powerful metaheuristics. In this paper, we concern with the design of hybrid genetic algorithms (HGA) and multiobjective HGA (Mo-HGA) to solve manufacturing scheduling problems. Firstly we introduce typical models in manufacturing scheduling systems such as parallel machines scheduling (PMS), flexible job-shop scheduling problem (FJSP) and assembly line balancing (ALB) problem. Secondly to solve NP-hard COP models, we introduce design scheme of HGA combined with fuzzy logic controller (FLC) and multiobjective HGA (Mo-HGA) with several fitness assignment mechanisms. For demonstrating computational experiments by HGA and Mo-HGA, the effectiveness of the HGA for the HDD (hard disc device) and Mo-HGA for TFT-LCD (thin-film transistor-liquid crystal display) module assembly problems as a practical manufacturing model, respectively is demonstrated in the concatenated paper Part II.

High uptime of petroleum plants, simple logistics, and good Health, Safety and Environment (HSE) conditions are all key factors for a successful petroleum industry. These issues need particular attention when facing new challenges with, e.g., production in more remote areas, but are also highly relevant for many current petroleum installations. Automation and remote supervision and control are both important areas which can address these challenges. To this end, robots are already contributing subsea, but also have the potential to contribute with significant benefits within both the offshore topside and onshore petroleum industry. Such robot technology, often termed offshore robotics, is currently not commercially available and a large scale joint effort is required in order to realize its inherent potential. Offshore robotics has been a topic of research and development for quite some years and relevant concepts and demonstrators have been developed. However, there are still significant challenges that need to be met in order to take advantage of the opportunities that robots can provide and realize commercial solutions. In this paper, we describe some of the important challenges to be met for enabling this inherent potential. There are both technical and organizational challenges, where the latter include, e.g., building acceptance for new technologies and responsibility issues in connection with remote operations. We also elaborate on key opportunities represented by use cases for robotics within the petroleum industry. A focus is on inspection, maintenance and emergency handling operations. The state of the art on offshore robotics is presented including the activity within this field at Statoil and Petrobras. Successful solutions to the use cases can lead to improved HSE (e.g., fewer people in exposed areas), simpler logistics (e.g., less helicopter transportation), and increased uptime (e.g., through early fault detection and intervention).

A bi-objective heuristic approach for green identical parallel machine scheduling

In this article, the Wolf Search algorithm was adapted to solve the problem a single machine with sequence-dependent setup times. To represent the solution of the problem, a permuted discrete coding scheme was proposed. The performance of the algorithm was compared with the Metaheuristic Viral System and Simulated Annealing in 35 instances with sizes between 15 and 150 jobs. From a Variance Analysis, it was found that the Wolf Search and Viral System algorithms are similar in their performance in terms of response quality, however, the Wolf Search algorithm has a low computational time compared to the Viral System algorithm. It is concluded that the Wolf Search algorithm can be used with great certainty to address the problem of production programming on a single machine with sequence-dependent enlistment times, for which it is proposed that it be used in other programming problems of combinatorial production or optimization.

The Framingham Heart Study has contributed importantly to understanding of the causes of coronary heart disease (CHD), stroke, and other cardiovascular diseases. Framingham research has helped define the quantitative and additive nature of these causes or, as they are now called, “cardiovascular risk factors.”1 The National Cholesterol Education Program (NCEP)2 3 has made extensive use of Framingham data in developing its strategy for preventing CHD by controlling high cholesterol levels. The NCEP guidelines2 3 adjust the intensity of cholesterol-lowering therapy with absolute risk as determined by summation of risk factors. The National High Blood Pressure Education Program (NHBPEP) has set forth a parallel approach for blood pressure control. In contrast to the NCEP,2 however, earlier NHBPEP reports issued through the Joint National Committee4 did not match the intensity of therapy to absolute risk for CHD. “Normalization” of blood pressure is the essential goal of therapy regardless of risk status. Blood pressure–lowering therapy is carried out as much for prevention of stroke and other cardiovascular complications as for reduction of CHD risk. Nonetheless, risk assessment could be important for making decisions about type and intensity of therapy for hypertension. Thus, the most recent Joint National Committee report5 gives more attention to risk stratification for adjustment of therapy for hypertension. Although Framingham data have already been influential in the development of national guidelines for risk factor management, the opportunity may exist for both cholesterol and blood pressure programs to draw more extensively from Framingham results when formulating improved risk assessment guidelines and recommending more specific strategies for risk factor modification. The American Heart Association has previously used Framingham risk factor data to prepare charts for estimating CHD risk. Framingham investigators of the National Heart, Lung, and Blood Institute prepared the original charts and have now revised …

This paper addresses the problem of scheduling n jobs on a single machine and on m identical parallel machines to minimize the completion time variance of jobs. This problem of scheduling jobs on parallel machines is motivated by a case study in an automobile ancillary unit. First, a heuristic to solve the single-machine scheduling problem is proposed. The parallel-machine scheduling problem is solved in two phases: job-allocation phase and job-sequencing phase. Two heuristics are proposed in the job-allocation phase, whereas in the job-scheduling phase, the single-machine scheduling approach is used. In this paper, both versions of parallel-machine scheduling problem (restricted and unrestricted) are considered. A good upper bound is obtained using a genetic algorithm, to evaluate the performance of the proposed heuristics for the parallel-machine scheduling problem. An extensive computation evaluation of the proposed heuristics is presented for both single-machine scheduling problem and the parallel-machine scheduling problem (especially considering the case study), along with the comparison of performances with the existing heuristics in the literature.

Many firms have to deal with the problems of scheduling and transportation allocation. The problems of assembly scheduling mainly focus on how to arrange orders in proper sequence on the assembly line with the purpose of minimizing the maximum completion time before they are flown to their destinations. Transportation allocation problems arise in how to assign processed orders to transport modes in order to minimize penalties such as earliness and tardiness. The two problems are usually separately discussed due to their complexity. This paper simultaneously deals with these two problems for firms with multiple identical parallel machines. We formulate this problem as a mixed integer programming model. The problem belongs to the class of NP-complete combinatorial optimization problems. This paper develops a hybrid genetic algorithm to obtain a compromised solution within a reasonable CPU time. We evaluate the performance of the presented heuristic with the well-known GAMS/CPLEX software. The presented approach is shown to perform well compared with well-known commercial software.

Understanding the relationships between objectives in a multiobjective optimisation problem is important for developing tailored and efficient solving techniques. In particular, when tackling combinatorial optimisation problems with many objectives that arise in real-world logistic scenarios, better support for the decision maker can be achieved through better understanding of the often complex fitness landscape. This paper makes a contribution in this direction by presenting a technique that allows a visualisation and analysis of the local and global relationships between objectives in optimisation problems with many objectives. The proposed technique uses four steps: first the global pairwise relationships are analysed using the Kendall correlation method; then the ranges of the values found on the given Pareto front are estimated and assessed; next these ranges are used to plot a map using Gray code, similar to Karnaugh maps, that has the ability to highlight the trade-offs between multiple objectives; and finally local relationships are identified using scatter-plots. Experiments are presented for three different combinatorial optimisation problems: multiobjective multidimensional knapsack problem, multiobjective nurse scheduling problem and multiobjective vehicle routing problem with time windows. Results show that the proposed technique helps in the gaining of insights into the problem difficulty arising from the relationships between objectives.

This chapter presents a newly defined novel combinatorial optimisation problem, namely, the General Combinatorial Optimisation Problem (GCOP), whose decision variables are a set of elementary algorithm components. The combinations of these algorithm components, i.e. solutions of GCOP, thus represent different search algorithms. The objective of GCOP is to find the optimal combinations of algorithm components for solving optimisation problems. Solving the GCOP is thus equivalent to automatically designing the best search algorithms for optimisation problems. The definition of the GCOP is presented with a new taxonomy which categorises relevant literature on automated algorithm design into three lines of research, namely, automated algorithm configuration, selection and composition. Based on the decision space under consideration, the algorithm design itself is defined as an optimisation problem. Relevant literature is briefly reviewed, motivating a new line of exciting and challenging directions on the emerging research of automated algorithm design.

This paper considers two types of stochastic group scheduling problems, one involves a single machine, and the other involves an m -machine flow shop. In both problems, jobs are classified into groups. The jobs of the same group are to be processed one after another, and a sequence independent setup time is incurred between each two consecutively scheduled groups. The job processing times are independent random variables and group setup times are functions of the job processing times. The objective is to find both a group sequence and a job sequence that minimizes the expected total flow time. An optimal group sequence and an optimal job sequence are obtained by processing jobs in increasing stochastic order of their mean processing times and group setup times for a single machine problem. A precedence relationship is derived for the m -machine flow shop problem.

Deficient intake of micronutrients involved in one-carbon metabolism (eg, choline, methionine, vitamin B12 and folic acid) leads to hepatocellular carcinoma (HCC) development in rodents, but is under-investigated in humans. We investigated the association between one-carbon metabolism-related micronutrient intake and HCC risk in a prospective cohort of 494 860 participants with 16 years of follow-up in the NIH-AARP study. Dietary intakes and supplement use were ascertained at baseline using a food-frequency questionnaire. Total intake (diet plus supplements) of the following one-carbon metabolism-related micronutrients were calculated: folate, methionine and vitamins B2 (riboflavin), B3 (niacin), B6 and B12 . These micronutrients were examined both individually and simultaneously, with adjustment for covariates. Cox proportional hazard models were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs). Over the 16-year follow-up period, 647 incident HCC cases were diagnosed. When examined individually, higher total vitamin B3 intake was associated with a lower HCC risk (HRQ5 vs Q1 = 0.60; 95% CI = 0.42-0.85; Ptrend = .008), and the association remained significant when all six micronutrients were examined simultaneously (HRQ5 vs Q1 = 0.32; 95% CI = 0.18-0.55; Ptrend < .0001). Among participants with >3 years of follow-up, higher total vitamin B3 intake was again associated with lower risk (HRQ5 vs Q1 = 0.37; 95% CI = 0.20-0.68; Ptrend = .001), whereas higher total vitamin B6 intake was associated with higher risk (HRQ5 vs Q1 = 2.04; 95% CI = 1.02-4.07; Ptrend = .04). Restricted cubic spline analyses showed a dose-response inverse association between total vitamin B3 intake and HCC risk, and dose-response positive association between total vitamin B6 intake and HCC risk. The study suggests that higher vitamin B3 intake is associated with lower HCC risk, whereas higher vitamin B6 intake is associated with increased risk.