A new system partitioning method under performance and physical constraints for multi-chip modules

This paper discusses a new route optimization for inspecting printed circuit boards (PCBs). In inspections of PCBs, a number of PCBs arrayed on a plane are tested by a probe in some order or sequence. Since the length of an inspection route depends on the order or sequence that the probe visits and tests each of PCBs, the finding of best visiting order or sequence is quite important in order to reduce the PCB inspection time. Recently, due to the miniaturization of PCBs, the procedure of “alignment” has become necessary before the electrical test, which means that there is a precedence constraint between alignment and test. This paper models a PCB inspection route optimization problem with alignment operations as a class of pickup and delivery traveling salesman problem (PDTSP) which is a mixed 0–1 integer programming problem. An efficient heuristic algorithm is proposed to solve the problem with a practical computational time. The proposed algorithm is installed into real PCB inspection machines that have been widely used in the world. Cost reduction effects for PCB inspection factories are discussed.

Operations management in a complex manufacturing environment is practiced hierarchically: production planning and scheduling in sequence since the integrated approach is not efficient and practical. Production targets and allocation to the equipment are determined in the planning procedure by considering the only the critical factors, and the detailed scheduling is determined with more information needed to accomplish the production targets provided in the planning step. There is always, therefore, a gap between the planning and scheduling procedure, and some control parameters or factors are used to control those gaps. In this paper an approach connecting two procedures is suggested, which can be applied in the EDS (electrical die sorting) or the probe process in the semiconductor manufacturing. The EDS process requires very flexible operation management since the manufacturing processing time is relatively short and the supply of upstream fabrication and the demand of downstream assembly have to be met simultaneously. Daily planning and scheduling procedure are modeled to minimize the tester change-over within the daily target. Because of the relatively long setup change-over time, daily planning and scheduling have to be performed so that the time needed for the device and probe card change is minimized as possible. Mathematical programming is suggested for the problem, and the modified model is developed which can be solved in a practical computational time on a daily base. The scheduling heuristic with the planning data obtained from the suggested model, is designed, and their performance is evaluated through the computational experiment.

For mathematical programming (MP) to have greater impact as a decision tool, MP software systems must offer suitable support in terms of model communication and modelling techniques. In this paper, modelling techniques that allow logical restrictions to be modelled in integer programming terms are described, and their implications discussed. In addition, it is illustrated that many classes of non-linearities which are not variable separable may be, after suitable algebraic manipulation, put in a variable separable form. The methods of reformulating the fuzzy linear programming problem as a max-min problem is also introduced. It is shown that analysis of bounds plays a key role in the following four important contexts: model reduction, reformulation of logical restrictions as 0-1 mixed integer programmes, reformulation of non-linear programmes as variable separable programmes and reformulation of fuzzy linear programmes. It is observed that, as well as incorporating an interface between the modeller and the optimizer, there is a need to make available to the modeller software facilities which support the model reformulation techniques described here.

A Clique Partitioning Problem (CPP) finds an optimal partition of a given edge-weighted undirected graph, such that the sum of the weights is maximized. This general graph problem has a wide range of real-world applications, including correlation clustering, group technology, community detection, and coalition structure generation. Although a CPP is NP-hard, due to the recent advance of Integer Linear Programming (ILP) solvers, we can solve reasonably large problem instances by formulating a CPP as an ILP instance. The first ILP formulation was introduced by Grötschel and Wakabayashi (Mathematical Programming, 45(1-3), 59–96, 1989). Recently, Miyauchi et al. (2018) proposed a more concise ILP formulation that can significantly reduce transitivity constraints as compared to previously introduced models. In this paper, we introduce a series of concise ILP formulations that can reduce even more transitivity constraints. We theoretically evaluate the amount of reduction based on a simple model in which edge signs (positive/negative) are chosen independently. We show that the reduction can be up to 50% (dependent of the ratio of negative edges) and experimentally evaluate the amount of reduction and the performance of our proposed formulation using a variety of graph data sets. Experimental evaluations show that the reduction can exceed 50% (where edge signs can be correlated), and our formulation outperforms the existing state-of-the-art formulations both in terms of memory usage and computational time for most problem instances.

An Application of Interior Point Method to 0-1 Mixed Integer Programing Problems

We present an integer programming model for the ferry scheduling problem, improving existing models in various ways. In particular, our model has reduced size in terms of the number of variables and constraints compared to existing models by a factor of approximately O(n), where n being the number of ports. The model also handles efficiently load/unload time constraints, crew scheduling and passenger transfers. Experiments using real world data produced high quality solutions in 12 hours using CPLEX 12.4 with a performance guarantee of within 15% of optimality, on average. This establishes that using a general purpose integer programming solver is a viable alternative in solving the ferry scheduling problem of moderate size.

In this study, we propose a method for acoustic event diarization based on a feature of deep embedding and a clustering algorithm of integer linear programming. The deep embedding learned by deep auto-encoder network is used to represent the properties of different classes of acoustic events, and then the integer linear programming is adopted for merging audio segments belonging to the same class of acoustic events. Four kinds of TV/movie audios (21.5 h in total) are used as experimental data, including Sport, Situation comedy, Award ceremony, and Action movie. We compare the deep embedding with state-of-the-art features. Further, the clustering algorithm of integer linear programming is compared with other clustering algorithms adopted in previous works. Finally, the proposed method is compared to both supervised and unsupervised methods on four kinds of TV/movie audios. The results show that the proposed method is superior to other unsupervised methods based on agglomerative information bottleneck, Bayesian information criterion and spectral clustering, and is little inferior to the supervised method based on deep neural network in terms of acoustic event error.

An improved version of the augmented ε-constraint method (AUGMECON2) for finding the exact pareto set in multi-objective integer programming problems

Graph matching, set covering and partitioning problems are both theoretically and practically important in decision support systems. Numerous situations have been modeled as graph matching, set covering and partitioning problems. These situations include applications in the areas of task assignment, marriage problem, airline crew scheduling, truck deliveries or vehicle routing, political redistricting, etc. Relational database systems currently support these applications merely by providing some programming languages which are used to write the programs associated with the data. These programming languages are complete as opposed to existing query languages like SQL. These languages, since they are complete, have the usual complexity of a powerful language and lack the simplicity of query languages. This increases application development effort. Relational operators and the query language SQL are extended in this present work in order to enable these problems to be stated and solved directly by the relational database systems leading to benefits of improved data independence, increased productivity, and better performance. The relational operators are extended with six operators, i.e., match, maxmatch, cover, mincover, partition, and minpartition. Properties of these operators are described. Some of these operators may possibly take a very long time in finding a solution. Genetic algorithms are proposed. These algorithms are bounded by a polynomial time. These algorithms therefore enable DBMS to be capable of responding to queries involving proposed operators in a given time constraint. The database operations and query languages extended in this research provide a powerful tool for users as a database‐query tool which supports some decision support systems for information retrieval from a database.

An ad hoc network is an autonomous system of heterogeneous, mobile nodes that communicate with each other over wireless links. Routing protocols for these networks are inherently based on broadcasting control information and are therefore very bandwidth consuming. In order to limit the amount of routing information that has to be stored and maintained by the individual nodes, the technique of clustering is used. The network is partitioned into nonoverlapping sub networks, referred to as clusters, and one cluster node, the clusterhead, will take a leading role in the dissemination of control information. In this paper we approach the problem of finding an optimal partition that explicitly takes into account the heterogeneity of the network, as an integer linear programming (ILP) problem. In a second phase we have developed a new heuristic that approximates our ILP solution that is used in our clustering algorithm. It is shown that this heuristic tends to be more stable than existing clustering techniques that are solely based on ID number and/or connectivity and that do not take into account the heterogeneity of the network.

This paper presents a distributed server allocation model with preventive start-time optimization against a single server failure. The presented model preventively determines the assignment of servers to users under each failure pattern to minimize the largest maximum delay among all failure patterns. We formulate the proposed model as an integer linear programming (ILP) problem. We prove the NP-completeness of the considered problem. As the number of users and that of servers increase, the size of ILP problem increases; the computation time to solve the ILP problem becomes excessively large. We develop a heuristic approach that applies simulated annealing and the ILP approach in a hybrid manner to obtain the solution. Numerical results reveal that the developed heuristic approach reduces the computation time by 26% compared to the ILP approach while increasing the largest maximum delay by just 3.4% in average. It reduces the largest maximum delay compared with the start-time optimization model; it avoids the instability caused by the unnecessary disconnection permitted by the run-time optimization model.

Topology-aware wavelength partitioning for DWDM OBS networks: A novel approach for absolute QoS provisioning

Manufacturing has been incapable of keeping up with Moore's law without significantly increasing process variability and imposing massive geometric restrictions on design. This paper highlights the design impact of variability and geometric constraints - including traditional design rules and pattern-scale constraints - and describes our approach for evaluating and enforcing pattern-scale restrictions on design.

In this paper, we present a complete chip design method which incorporates a soft-macro resynthesis method in interaction with chip floorplanning for area and timing improvements. We develop a timing-driven design flow to exploit the interaction between HDL synthesis and physical design tasks. During each design iteration, we resynthesize soft macros with either a relaxed or a tightened timing constraint which is guided by the post-layout timing information. The goal is to produce area-efficient designs while satisfying the timing constraints. Experiments on a number of industrial designs have demonstrated that by effectively relaxing the timing constraint of the non-critical modules and tightening the timing constraint of the critical modules, a design can achieve 13% to 30% timing improvements with little to no increase in chip area.

The high cost of flight delays for airlines has motivated scientific research in air traffic flow management (ATFM). The majority of ATFM models in the literature are deterministic and do not take into account stochastic factors such as weather. In this paper, new stochastic programming models for ATFM are proposed. The models include as tactical control options: ground holding, airborne holding and rerouting. To solve the models, a new heuristic method that takes advantage of the problem structure is derived and illustrated. Computational results show that the heuristic method provides practical computation times. Furthermore, the value of the stochastic solution is up to 14% for cases where adverse weather affects a significant part of the network. This implies that using the proposed approach to make air traffic flow decisions can lead to tangible monetary benefits for airlines.