An integrated model for the design of end-of-aisle order picking system and the determination of unit load sizes of AGVs

In this paper, we deal with an assembly line production system in which an automated guided vehicle (AGV) delivers parts to each workstation of assembly lines from a miniload automated storage/retrieval system (AS/RS). Each assembly production line is characterized by a sequence of workstations linked together for the production of a given type of product. We consider simultaneously the design problem of miniload AS/RS and the problem of determining a unit load size of the AGV. First, a non-linear mathematical model is formulated under a proposed AGV dispatching policy. And then, we determine an appropriate sequence of lines of the vehicle visits. Finally, based on the characteristics of the objective function and feasible region of the decision variables, a heuristic solution procedure is developed to find a near optimal solution. To examine the validity of the model, an example problem is solved.     

Multiobjective evolutionary algorithm for manufacturing scheduling problems: state-of-the-art survey

Scheduling is an important tool for a manufacturing system, where it can have a major impact on the productivity of a production process. In order to find an optimal solution to scheduling problems it gives rise to complex combinatorial optimization problems. Unfortunately, most of them fall into the class of NP-hard combinatorial problems. In this paper, we focus on the design of multiobjective evolutionary algorithms (MOEAs) to solve a variety of scheduling problems. Firstly, we introduce fitness assignment mechanism and performance measures for solving multiple objective optimization problems, and introduce evolutionary representations and hybrid evolutionary operations especially for the scheduling problems. Then we apply these EAs to the different types of scheduling problems, included job shop scheduling problem (JSP), flexible JSP, Automatic Guided Vehicle (AGV) dispatching in flexible manufacturing system (FMS), and integrated process planning and scheduling (IPPS). Through a variety of numerical experiments, we demonstrate the effectiveness of these Hybrid EAs (HEAs) in the widely applications of manufacturing scheduling problems. This paper also summarizes a classification of scheduling problems, and illustrates the design way of EAs for the different types of scheduling problems. It is useful to guide how to design an effective EA for the practical manufacturing scheduling problems. As known, these practical scheduling problems are very complex, and almost is a combination of different typical scheduling problems.     

Solving fuzzy shortest path problems by neural networks

In this paper, we introduce the neural networks for solving fuzzy shortest path problems. The penalization of the neural networks is realized after transforming into crisp shortest path model. The procedure and efficiency of this approach are shown with numerical simulations.     

Randomized constraint solvers: a comparative study

The complexity of constraints is a major obstacle for constraint-based software verification. Automatic constraint solvers are fundamentally incomplete: input constraints often build on some undecidable theory or some theory the solver does not support. This paper proposes and evaluates several randomized solvers to address this issue. We compared the effectiveness of a symbolic solver (CVC3), a random solver, two heuristic search solvers, and seven hybrid solvers (i.e. mix of random, symbolic, and heuristic solvers). We evaluated the solvers on a benchmark generated with a concolic execution of 9 subjects. The performance of each solver was measured by its precision, which is the fraction of constraints that the solver can find solution out of the total number of constraints that some solver can find solution. As expected, symbolic solving subsumes the other approaches for the 4 subjects that only generate decidable constraints. For the remaining 5 subjects, which contain undecidable constraints, the hybrid solvers achieved the highest precision (fraction of constraints that a solver can find a solution out of the total number of satisfiable constraints). We also observed that the solvers were complementary, which suggests that one should alternate their use in iterations of a concolic execution driver.     

A Network Architecture with High Availability for Real-time Premium Traffic over the Internet

Real-time traffic will be a predominant traffic type in the next generation networks, and networks with 100% reliability and availability will be required by real-time premium traffic. It is believed that QoS guarantees could be better provided by connection oriented networks such as Multi Protocol Label Switching (MPLS). These connection oriented networks are more vulnerable to network failure. Conventional path protection methods perform re-routing to cope with this. However, re-routing always causes packet losses and results in service outage. These losses are bursty in nature and highly degrade the QoS of the real-time premium traffic. Thus, 100% availability cannot be achieved by conventional methods. The novel path protection proposed in this paper recovers the bursty packet losses due to re-routing by using forward error correction (FEC) path. Therefore, it can provide network architecture with no service outage for such traffic. The numerical results show that the proposed method can achieve a very high availability for real-time premium traffic in future IP/MPLS networks.

Hybrid evolutionary method for obstacle location-allocation

In this paper we propose a hybrid evolutionary method for Obstacle Location-allocation problem. This problem can be described as a tri-level mixed integer programming problem. Since this problem is very complex and with many local solutions, no direct method is effective to solve it. Heuristic methods were proposed to it, but optimality is not guaranteed yet. Our hybrid evolutionary method adopts the main structure of Genetic Algorithms (GA) absorbing ideas from Evolutionary Strategy (ES) and combines with some traditional optimization techniques. In this way we can pursue global optimization maintaining a good efficiency of our method. A case study shows the effectiveness of this method.     

Linear programming software tools on UNIX System

Linear programming(LP) is one of the most widely used Operations Research/Management Science/Industrial Engineering techniques. Recently, multiple criteria decision making or multiple objective linear programming has been well established as a practical approach to seeking satisfactory solutions to real-world decision problems.

In this paper we develop software tools for solving various linear programming problems such as a traditional LP problem, bicriteria LP problem, and multi-criteria LP problem on UNIX system. In a phase for reading data of various LP problems, we define a BNF(Backus-Nauel form) of various LP problems and implement BNF rules by using the C programming language.

In a phase for computing various LP problems, we use efficient methods for solving LP problems, develop various software tools on UNIX system, and combine each LP tool corresponding to an user request in which the Shell programming is used.

We also demonstrate some real-world LP problems by using LP software tools developed here on an UNIX System. Sanyo MPS 020.     

Stopping cross-section and energy straggling of protons in SiC obtained by nuclear resonant reaction of protons with 12C and 28Si

In order to evaluate stopping cross-section and energy straggling of protons in compound material SiC and its constituents C and Si, resonant backscattering spectra have been measured using proton beams in an energy range 4.9–6.1 MeV per a 100 keV step. We have observed two sharp nuclear resonances at proton energies of 4.808 MeV by ¹²C and 4.879 MeV by ²⁸Si. By systematic analyses of the resonance peak profiles, i.e., energy shift of the peak position and broadening of the peak width, the values of the stopping cross-section and the energy straggling have been deduced to be compared with SRIM-2006 and Bohr’s prediction.