Planning the routing mix in FASs to minimize total transportation time

The routing mix problem in flexible assembly systems is considered. The problem consists of assigning the operations for each part to the machines, with the two objectives of balancing the machine workloads and minimizing the burden of the transportation system. These two objectives are sometimes conflicting, since the latter tends to support assigning operations to the same machine(s) as much as possible, and this may be bad for workload balancing. A linear programming problem is presented that, given a constraint on the workload of each machine, finds one solution that minimizes the overall time spent moving the parts from one machine to another. Since such a linear program may have an exponential number of variables, an efficient column generation technique to solve the problem is devised. The efficiency of the method is validated by experiments on a large number of random problems.     

Multiple-Objective Scheduling for the Hierarchical Control of Flexible Manufacturing Systems

This paper presents a hierarchical approach to scheduling flexible manufacturing systems (FMSs) that pursues multiple performance objectives and considers the process flexibility of incorporating alternative process plans and resources for the required operations. The scheduling problem is solved at two levels: the shop level and the manufacturing system level. The shop level controller employs a combined priority index developed in this research to rank shop production orders in meeting multiple scheduling objectives. To overcome dimensional complexity and keep a low level of work-in-process inventory, the shop controller first selects up to three production orders with the highest ranking as candidates and generates all possible release sequences for them, with or without multitasking. These sequences are conveyed to the manufacturing system controller, who then performs detailed scheduling of the machines in the FMS using a fixed priority heuristic for routing parts of multiple types while considering alternative process plans and resources for the operations. The FMS controller provides feedback to the shop controller with a set of suggested detailed schedules and projected order completion times. On receiving these results, the shop controller further evaluates each candidate schedule using a multiple-objective function and selects the best schedule for execution. This allows multiple performance objectives of an FMS to be achieved by the integrated hierarchical scheduling approach.     

Part feeding at high-variant mixed-model assembly lines

The part feeding problem at automotive assembly plants deals with the timely supply of parts to the designated stations at the assembly line. According to the just-in-time principle, buffer storages at the line are frequently refilled with parts retrieved from a central storage area. In the industrial application at hand, this is accomplished by means of an internal shuttle system which supplies the various stations with the needed parts based on a given assembly sequence. The main objective is to minimize the required number of shuttle drivers. To solve this in-house transportation problem, a heuristic solution procedure is developed which is based on the decomposition of the entire planning problem into two stages. First, transportation orders are derived from the given assembly sequence. In the second stage, these orders are assigned to tours of the shuttle system taking transportation capacity restrictions, due dates and tour scheduling constraints into account. Numerical results show that the proposed heuristic solves even large-sized problem instances in short computational time. Benchmark comparisons with Kanban systems reveal the superiority of the proposed predictive part feeding approach.     

A multicriteria decision making approach for estimating the number of clusters in a data set

Determining the number of clusters in a data set is an essential yet difficult step in cluster analysis. Since this task involves more than one criterion, it can be modeled as a multiple criteria decision making (MCDM) problem. This paper proposes a multiple criteria decision making (MCDM)-based approach to estimate the number of clusters for a given data set. In this approach, MCDM methods consider different numbers of clusters as alternatives and the outputs of any clustering algorithm on validity measures as criteria. The proposed method is examined by an experimental study using three MCDM methods, the well-known clustering algorithm-k-means, ten relative measures, and fifteen public-domain UCI machine learning data sets. The results show that MCDM methods work fairly well in estimating the number of clusters in the data and outperform the ten relative measures considered in the study.     

Exact and Metaheuristic Approaches for a Bi-Objective School Bus Scheduling Problem

As a class of hard combinatorial optimization problems, the school bus routing problem has received considerable attention in the last decades. For a multi-school system, given the bus trips for each school, the school bus scheduling problem aims at optimizing bus schedules to serve all the trips within the school time windows. In this paper, we propose two approaches for solving the bi-objective school bus scheduling problem: an exact method of mixed integer programming (MIP) and a metaheuristic method which combines simulated annealing with local search. We develop MIP formulations for homogenous and heterogeneous fleet problems respectively and solve the models by MIP solver CPLEX. The bus type-based formulation for heterogeneous fleet problem reduces the model complexity in terms of the number of decision variables and constraints. The metaheuristic method is a two-stage framework for minimizing the number of buses to be used as well as the total travel distance of buses. We evaluate the proposed MIP and the metaheuristic method on two benchmark datasets, showing that on both instances, our metaheuristic method significantly outperforms the respective state-of-the-art methods.     

Design of single assembly line for the delayed differentiation of product variants

Delayed Product Differentiation (DPD) can reduce the manufacturing complexities arising due to the proliferation of products variety. A new optimization model constructs the optimum layout of delayed differentiation assembly lines for a mix of products to be manufactured by the same system and optimizes the position of the differentiation points. This model employs a classification tool (Cladistics) used in biological analysis and modifies it for use in planning DPD assembly lines configurations in order to incorporate the assembly precedence constraints, required production rates of different product variants and existing production capacity of work stations. The optimum layout configuration ensures that the quantities required of different products are produced on the same line; while achieving balance, minimizing duplication of stations and maximizing the overall system utilization. The developed model has been applied to a group of automobile engine accessories normally assembled on different lines. The use of Cladistics to analyze product variants that are candidates for delayed assembly is an original approach for designing the assembly line layout and identifying the best differentiation points. It also helps rationalize the design of product variants and their features to further delay their assembly differentiation and achieve economy of scale without affecting their functionality.     

Metabolic engineering of carotenoid biosynthesis in Escherichia coli by ordered gene assembly in Bacillus subtilis

We attempted to optimize the production of zeaxanthin in Escherichia coli by reordering five biosynthetic genes in the natural carotenoid cluster of Pantoea ananatis. Newly designed operons for zeaxanthin production were constructed by the ordered gene assembly in Bacillus subtilis (OGAB) method, which can assemble multiple genes in one step using an intrinsic B. subtilis plasmid transformation system. The highest level of production of zeaxanthin in E. coli (820 microg/g [dry weight]) was observed in the transformant with a plasmid in which the gene order corresponds to the order of the zeaxanthin metabolic pathway (crtE-crtB-crtI-crtY-crtZ), among a series of plasmids with circularly permuted gene orders. Although two of five operons using intrinsic zeaxanthin promoters failed to assemble in B. subtilis, the full set of operons was obtained by repressing operon expression during OGAB assembly with a p(R) promoter-cI repressor system. This result suggests that repressing the expression of foreign genes in B. subtilis is important for their assembly by the OGAB method. For all tested operons, the abundance of mRNA decreased monotonically with the increasing distance of the gene from the promoter in E. coli, and this may influence the yield of zeaxanthin. Our results suggest that rearrangement of biosynthetic genes in the order of the metabolic pathway by the OGAB method could be a useful approach for metabolic engineering.     

Site-directed combinatorial construction of chimaeric genes: general method for optimizing assembly of gene fragments

Site-directed construction of chimaeric genes by in vitro recombination "mixes-and-matches" precise building blocks from multiple parent proteins, generating libraries of hybrids to be tested for structure-function relationships and/or screened for favorable properties and novel enzymatic activities. A direct annealing and ligation method can construct chimaeric genes without requiring sequence identity between parents, except for the short (approximately 3 nt) sequences of the fragment overhangs used for specific ligation. Careful planning of the assembly process is necessary, though, in order to ensure effective construction of desired fragment assemblies and to avoid undesired assemblies (e.g., repetition of fragments, fragments out of order). We develop algorithms for specific planned ligation of short overhangs (SPLISO) that efficiently explore possible assembly plans, varying the fragment overhangs and the order of ligation steps in the assembly pathway. While there is a combinatorial explosion in the number of possible assembly plans as the number of breakpoints and parent genes increases, we employ a dynamic programming approach to find globally optimal ones in low-order polynomial time (in practice, taking only seconds for basic assembly plans). We demonstrate the effectiveness of our algorithms in planning the assembly of hybrid libraries, under a variety of experimental options and restrictions, including flexibility in the position and amino acid sequence of breakpoints. Our method promises to enable more effective application of site-directed recombination to protein investigation and engineering.     

An extended multi-objective mixed integer programming for water resources management through possibility theory

In this study, we develop an extended multi-objective mixed integer programming (EMOMIP) approach for water resources management under uncertainty, in which the parameters are fuzzy random variables while the decision variables are interval variables. Furthermore, some alternatives are considered to retrieve the difference between the quantities of promised water-allocation targets and the actual allocated water. Then, the proposed EMOMIP for the problem is solved by a new method using fuzzy random chance-constrained programming based on the idea of possibility theory. This method can satisfy both optimistic and pessimistic decision makers simultaneously. Finally, a real example is given to explain the proposed method.     

Beyond make-or-buy: cross-company short-term capacity backup in semiconductor industry ecosystem

Since the uncertainty involved in demand forecast is increasingly amplified with the forecast lead-time, high-tech companies often suffer the risks of oversupply and shortage of capacity that will affect the profitability and growth. High-tech industries including semiconductor and TFT-LCD industries are capital-intensive, in which the capacity plan and corresponding capital investment decisions are critical due to demand fluctuation. Once the capacity is planned, the company may suffer the risks of either low capital-effectiveness due to low capacity utilization and capacity oversupply, or poor customer satisfaction caused by the capacity shortage. Most of the existing studies focused on solving the long-term capacity shortage issue through optimizing the capacity investment plan, or medium-term capacity plan to allocate demands among the wafer fabrication facilities (fabs) to balance the loading and product mix. Focusing on a real setting, this study proposed a systematic decision method to analyze short-term solutions of cross-company capacity backup between the companies in the semiconductor industry ecosystem. In particular, a game theory and decision tree analysis model was developed to support this decision. A case study was conducted with real data of semiconductor manufacturing companies in Taiwan for validation. The results have demonstrated practical viability of this approach. The approach suggested has been implemented in this company.     

Semiconductor fault detection and classification for yield enhancement and manufacturing intelligence

Process monitoring and profile analysis are crucial in detecting various abnormal events in semiconductor manufacturing, which consists of highly complex, interrelated, and lengthy wafer fabrication processes for yield enhancement and quality control. To address real requirements, this study aims to develop a framework for semiconductor fault detection and classification (FDC) to monitor and analyze wafer fabrication profile data from a large number of correlated process variables to eliminate the cause of the faults and thus reduce abnormal yield loss. Multi-way principal component analysis and data mining are used to construct the model to detect faults and to derive the rules for fault classification. An empirical study was conducted in a leading semiconductor company in Taiwan to validate the model. Use of the proposed framework can effectively detect abnormal wafers based on a controlled limit and the derived simple rules. The extracted information can be used to aid fault diagnosis and process recovery. The proposed solution has been implemented in the semiconductor company. This has simplified the monitoring process in the FDC system through the fewer key variables. The results demonstrate the practical viability of the proposed approach.     

Knapsack - TOPSIS Technique for Vertical Handover in Heterogeneous Wireless Network

In a heterogeneous wireless network, handover techniques are designed to facilitate anywhere/anytime service continuity for mobile users. Consistent best-possible access to a network with widely varying network characteristics requires seamless mobility management techniques. Hence, the vertical handover process imposes important technical challenges. Handover decisions are triggered for continuous connectivity of mobile terminals. However, bad network selection and overload conditions in the chosen network can cause fallout in the form of handover failure. In order to maintain the required Quality of Service during the handover process, decision algorithms should incorporate intelligent techniques. In this paper, a new and efficient vertical handover mechanism is implemented using a dynamic programming method from the operation research discipline. This dynamic programming approach, which is integrated with the Technique to Order Preference by Similarity to Ideal Solution (TOPSIS) method, provides the mobile user with the best handover decisions. Moreover, in this proposed handover algorithm a deterministic approach which divides the network into zones is incorporated into the network server in order to derive an optimal solution. The study revealed that this method is found to achieve better performance and QoS support to users and greatly reduce the handover failures when compared to the traditional TOPSIS method. The decision arrived at the zone gateway using this operational research analytical method (known as the dynamic programming knapsack approach together with Technique to Order Preference by Similarity to Ideal Solution) yields remarkably better results in terms of the network performance measures such as throughput and delay.     

On the representability of complete genomes by multiple competing finite-context (Markov) models

A finite-context (Markov) model of order k yields the probability distribution of the next symbol in a sequence of symbols, given the recent past up to depth k. Markov modeling has long been applied to DNA sequences, for example to find gene-coding regions. With the first studies came the discovery that DNA sequences are non-stationary: distinct regions require distinct model orders. Since then, Markov and hidden Markov models have been extensively used to describe the gene structure of prokaryotes and eukaryotes. However, to our knowledge, a comprehensive study about the potential of Markov models to describe complete genomes is still lacking. We address this gap in this paper. Our approach relies on (i) multiple competing Markov models of different orders (ii) careful programming techniques that allow orders as large as sixteen (iii) adequate inverted repeat handling (iv) probability estimates suited to the wide range of context depths used. To measure how well a model fits the data at a particular position in the sequence we use the negative logarithm of the probability estimate at that position. The measure yields information profiles of the sequence, which are of independent interest. The average over the entire sequence, which amounts to the average number of bits per base needed to describe the sequence, is used as a global performance measure. Our main conclusion is that, from the probabilistic or information theoretic point of view and according to this performance measure, multiple competing Markov models explain entire genomes almost as well or even better than state-of-the-art DNA compression methods, such as XM, which rely on very different statistical models. This is surprising, because Markov models are local (short-range), contrasting with the statistical models underlying other methods, where the extensive data repetitions in DNA sequences is explored, and therefore have a non-local character.     

A Three-Tiered Approach to Long Term Monitoring Program Optimization

Long term monitoring optimization (LTMO) has proved a valuable method for reducing costs, assuring proper remedial decisions are made, and streamlining data collection and management requirements over the life of a monitoring program. A three-tiered approach for LTMO has been developed that combines a qualitative evaluation with an evaluation of temporal trends in contaminant concentrations, and a spatial statistical analysis. The results of the three evaluations are combined to determine the degree to which a monitoring program addresses the monitoring program objectives, and a decision algorithm is applied to assess the optimal frequency of monitoring and spatial distribution of the components of the monitoring network. Ultimately, application of the three-tiered method can be used to identify potential modifications in sampling locations and sampling frequency that will optimally meet monitoring objectives. To date, the three-tiered approach has been applied to monitoring programs at 18 sites and has been used to identify a potential average reduction of over one-third of well sampling events per year. This paper discusses the three-tiered approach methodology, including data compilation and site screening, qualitative evaluation decision logic, temporal trend evaluation, and spatial statistical analysis, illustrated using the results of a case study site. Additionally, results of multiple applications of the three-tiered LTMO approach are summarized, and future work is discussed.     

Accounting for multiple ecosystem services in a simulation of land‐use decisions: Does it reduce tropical deforestation?

Conversion of tropical forests is among the primary causes of global environmental change. The loss of their important environmental services has prompted calls to integrate ecosystem services (ES) in addition to socio‐economic objectives in decision‐making. To test the effect of accounting for both ES and socio‐economic objectives in land‐use decisions, we develop a new dynamic approach to model deforestation scenarios for tropical mountain forests. We integrate multi‐objective optimization of land allocation with an innovative approach to consider uncertainty spaces for each objective. These uncertainty spaces account for potential variability among decision‐makers, who may have different expectations about the future. When optimizing only socio‐economic objectives, the model continues the past trend in deforestation (1975–2015) in the projected land‐use allocation (2015–2070). Based on indicators for biomass production, carbon storage, climate and water regulation, and soil quality, we show that considering multiple ES in addition to the socio‐economic objectives has heterogeneous effects on land‐use allocation. It saves some natural forest if the natural forest share is below 38%, and can stop deforestation once the natural forest share drops below 10%. For landscapes with high shares of forest (38%–80% in our study), accounting for multiple ES under high uncertainty of their indicators may, however, accelerate deforestation. For such multifunctional landscapes, two main effects prevail: (a) accelerated expansion of diversified non‐natural areas to elevate the levels of the indicators and (b) increased landscape diversification to maintain multiple ES, reducing the proportion of natural forest. Only when accounting for vascular plant species richness as an explicit objective in the optimization, deforestation was consistently reduced. Aiming for multifunctional landscapes may therefore conflict with the aim of reducing deforestation, which we can quantify here for the first time. Our findings are relevant for identifying types of landscapes where this conflict may arise and to better align respective policies.     

A Three-Tiered Approach to Long Term Monitoring Program Optimization

Long term monitoring optimization (LTMO) has proved a valuable method for reducing costs, assuring proper remedial decisions are made, and streamlining data collection and management requirements over the life of a monitoring program. A three-tiered approach for LTMO has been developed that combines a qualitative evaluation with an evaluation of temporal trends in contaminant concentrations, and a spatial statistical analysis. The results of the three evaluations are combined to determine the degree to which a monitoring program addresses the monitoring program objectives, and a decision algorithm is applied to assess the optimal frequency of monitoring and spatial distribution of the components of the monitoring network. Ultimately, application of the three-tiered method can be used to identify potential modifications in sampling locations and sampling frequency that will optimally meet monitoring objectives. To date, the three-tiered approach has been applied to monitoring programs at 18 sites and has been used to identify a potential average reduction of over one-third of well sampling events per year. This paper discusses the three-tiered approach methodology, including data compilation and site screening, qualitative evaluation decision logic, temporal trend evaluation, and spatial statistical analysis, illustrated using the results of a case study site. Additionally, results of multiple applications of the three-tiered LTMO approach are summarized, and future work is discussed.     

Clustering of main orthologs for multiple genomes

The identification of orthologous genes shared by multiple genomes is critical for both functional and evolutionary studies in comparative genomics. While it is usually done by sequence similarity search and reconciled tree construction in practice, recently a new combinatorial approach and high-throughput system MSOAR for ortholog identification between closely related genomes based on genome rearrangement and gene duplication has been proposed in Fu et al. MSOAR assumes that orthologous genes correspond to each other in the most parsimonious evolutionary scenario, minimizing the number of genome rearrangement and (postspeciation) gene duplication events. However, the parsimony approach used by MSOAR limits it to pairwise genome comparisons. In this paper, we extend MSOAR to multiple (closely related) genomes and propose an ortholog clustering method, called MultiMSOAR, to infer main orthologs in multiple genomes. As a preliminary experiment, we apply MultiMSOAR to rat, mouse, and human genomes, and validate our results using gene annotations and gene function classifications in the public databases. We further compare our results to the ortholog clusters predicted by MultiParanoid, which is an extension of the well-known program InParanoid for pairwise genome comparisons. The comparison reveals that MultiMSOAR gives more detailed and accurate orthology information, since it can effectively distinguish main orthologs from inparalogs.     

Root anatomy, morphology, and longevity among root orders in Vaccinium corymbosum (Ericaceae)

Understanding root processes at the whole-plant or ecosystem scales requires an accounting of the range of functions within a root system. Studying root traits based on their branching order can be a powerful approach to understanding this complex system. The current study examined the highly branched root system of the ericoid plant, Vaccinium corymbosum L. (highbush blueberry) by classifying its root orders with a modified version of the morphometric approach similar to that used in hydrology for stream classification. Root anatomy provided valuable insight into variation in root function across orders. The more permanent portion of the root system occurred in 4th- and higher-order roots. Roots in these orders had radial growth; the lowest specific root length, N:C ratios, and mycorrhizal colonization; the highest tissue density and vessel number; and the coarsest root diameter. The ephemeral portion of the root system was mainly in the first three root orders. First- and 2nd-order roots were nearly anatomically identical, with similar mycorrhizal colonization and diameter, and also, despite being extremely fine, median lifespans were not very short (115-120 d; estimated with minirhizotrons). Our research underscores the value of examining root traits by root order and its implications to understanding belowground processes.     

Lipid classification, structures and tools

The study of lipids has developed into a research field of increasing importance as their multiple biological roles in cell biology, physiology and pathology are becoming better understood. The Lipid Metabolites and Pathways Strategy (LIPID MAPS) consortium is actively involved in an integrated approach for the detection, quantitation and pathway reconstruction of lipids and related genes and proteins at a systems-biology level. A key component of this approach is a bioinformatics infrastructure involving a clearly defined classification of lipids, a state-of-the-art database system for molecular species and experimental data and a suite of user-friendly tools to assist lipidomics researchers. Herein, we discuss a number of recent developments by the LIPID MAPS bioinformatics core in pursuit of these objectives.