Simultaneous solving of balancing and sequencing problems with station-dependent assembly times for mixed-model assembly lines

In spite of many studies, investigating balancing and sequencing problems in Mixed-Model Assembly Line (MMAL) individually, this paper solves them simultaneously aiming to minimize total utility work. A new Mixed-Integer Linear Programming (MILP) model is developed to provide the exact solution of the problem with station-dependent assembly times. Because of NP-hardness, a Simulated Annealing (SA) is applied and compared to the Co-evolutionary Genetic Algorithm (Co-GA) from the literature. To strengthen the search process, two main hypotheses, namely simultaneous search and feasible search, are developed contrasting Co-GA. Various parameters of SA are reviewed to calibrate the algorithm by means of Taguchi design of experiments. Numerical results statistically show the efficiency and effectiveness of the proposed SA in terms of both the quality of solution and the time of achieving the best solution. Finally, the contribution of each hypothesis in this superiority is analyzed.     

Fatigue behaviour and modelling of talc‐filled and short glass fibre reinforced thermoplastics,including temperature and mean stress effects

Effects of temperature and mean stress on fatigue behaviour of talc‐filled polypropylene (PP‐T) and short glass fibre reinforced polypropylene (PP‐G), polyamide‐66 (PA66), and a blend of polyphenylene ether and polystyrene (PPE/PS) were investigated. Load‐controlled fatigue tests were conducted under positive stress ratios (R = 0.1 and 0.3) and at several temperatures (T = 23, 85 and 120 °C). Larson–Miller parameter was used and a shift factor of Arrhenius type was developed to correlate fatigue data at various temperatures. Effect of mean stress on fatigue life was significant for some of the studied materials; however, for the PPE/PS blend no effect of mean stress was observed. Modified Goodman and Walker mean stress equations were evaluated for their ability to correlate mean stress data. A general fatigue life prediction model was also used to account for the effects of mean stress, temperature, anisotropy and frequency.     

Bi-objective parallel machines scheduling with sequence-dependent setup times using hybrid metaheuristics and weighted min–max technique

In the literature of multi-objective problem, there are different algorithms to solve different optimization problems. This paper presents a min–max multi-objective procedure for a dual-objective, namely make span, and sum of the earliness and tardiness of jobs in due window machine scheduling problems, simultaneously. In formulation of min–max method when this method is combined with the weighting method, the decision maker can have the flexibility of mixed use of weights and distance parameter to yield a set of Pareto-efficient solutions. This research extends the new hybrid metaheuristic (HMH) to solve parallel machines scheduling problems with sequence-dependent setup time that comprises three components: an initial population generation method based on an ant colony optimization (ACO), a simulated annealing (SA) as an evolutionary algorithm employs certain probability to avoid becoming trapped in a local optimum, and a variable neighborhood search (VNS) which involves three local search procedures to improve the population. In addition, two VNS-based HMHs, which are a combination of two methods, SA/VNS and ACO/VNS, are also proposed to solve the addressed scheduling problems. A design of experiments approach is employed to calibrate the parameters. The non-dominated sets obtained from HMH and two best existing bi-criteria scheduling algorithms are compared in terms of various indices and the computational results show that the proposed algorithm is capable of producing a number of high-quality Pareto optimal scheduling plans. Aside, an extensive computational experience is carried out to analyze the different parameters of the algorithm.     

A high performing metaheuristic for job shop scheduling with sequence-dependent setup times

This paper investigates scheduling job shop problems with sequence-dependent setup times under minimization of makespan. We develop an effective metaheuristic, simulated annealing with novel operators, to potentially solve the problem. Simulated annealing is a well-recognized algorithm and historically classified as a local-search-based metaheuristic. The performance of simulated annealing critically depends on its operators and parameters, in particular, its neighborhood search structure. In this paper, we propose an effective neighborhood search structure based on insertion neighborhoods as well as analyzing the behavior of simulated annealing with different types of operators and parameters by the means of Taguchi method. An experiment based on Taillard benchmark is conducted to evaluate the proposed algorithm against some effective algorithms existing in the literature. The results show that the proposed algorithm outperforms the other algorithms.     

Parallel-machine scheduling problems with sequence-dependent setup times using an ACO,SA and VNS hybrid algorithm

This paper proposes a hybrid metaheuristic for the minimization of makespan in scheduling problems with parallel machines and sequence-dependent setup times. The solution approach is robust, fast, and simply structured, and comprises three components: an initial population generation method based on an ant colony optimization (ACO), a simulated annealing (SA) for solution evolution, and a variable neighborhood search (VNS) which involves three local search procedures to improve the population. The hybridization of an ACO, SA with VNS, combining the advantages of these three individual components, is the key innovative aspect of the approach. Two algorithms of a hybrid VNS-based algorithm, SA/VNS and ACO/VNS, and the VNS algorithm presented previously are used to compare with the proposed hybrid algorithm to highlight its advantages in terms of generality and quality for large instances.     

An improved cluster formation process in wireless sensor network to decrease energy consumption

Wireless sensor network has special features and many applications, which have attracted attention of many scientists. High energy consumption of these networks, as a drawback, can be reduced by a hierarchical routing algorithm. The proposed algorithm is based on the Low Energy Adaptive Clustering Hierarchy (LEACH) and Quadrant Cluster based LEACH (Q-LEACH) protocols. To reduce energy consumption and provide a more appropriate coverage, the network was divided into several regions and clusters were formed within each region. In selecting the cluster head (CH) in each round, the amount of residual energy and the distance from the center of each node were calculated by the base station (including the location and residual energy of each node) for all living nodes in each region. In this regard, the node with the largest value had the highest priority to be selected as the CH in each network region. The base station calculates the CH due to the lack of energy constraints and is also responsible for informing it throughout the network, which reduces the load consumption and tasks of nodes in the network. The information transfer steps in this protocol are similar to the LEACH protocol stages. To better evaluate the results, the proposed method was implemented with LEACH LEACH-SWDN, and Q-LEACH protocols using MATLAB software. The results showed better performance of the proposed method in network lifetime, first node death time, and the last node death time.

     

The Assessment of Fracture Geometrical Properties on the Performance of Conventional In Situ Combustion

Abstract

The aim of the present work is to evaluate the effect of fractures geometrical properties such as orientation, density, location, and networking on the conventional fire flooding (CFF) process performance through simulation analysis. Combustion parameters of a fractured low-permeable carbonate heavy oil reservoir in Iran called Kuh-E-Mond (KEM); applied for simulation study and simulator has been validated with KEM combustion tube experimental data. The validated model was modified to study CFF in 3D semi-scaled combustion cells. Simulation results confirmed that CFF is more feasible in the case of densely fractured reservoirs such as those in the Middle East.     

Parallel merged multiplier-accumulator coprocessor optimized for digital filters

In an attempt to improve the speed of VLSI signal processing systems, a new architecture for a high-speed multiply-accumulate (MAC) unit optimized for digital filters is proposed. This unit is designed as a coprocessor for the LEON2 RISC processor [LEON2 Processor; 2005 [Online]. <http://www.gaisler.com/products/leon2/leon.html>]. In this work, four parallel MAC units with two dual-port coefficient register-files, a three-port general register-file and a control unit are included in the coprocessing block. With the existence of four parallel units, several SIMD format instructions have been added to LEON2 instruction set. Each MAC unit has two 16-bit inputs, 32-bit output register and a programmable round-saturate block. The MAC unit uses a new architecture which embeds the accumulate module within the partial products summation tree of the multiplier with minimum overhead. A central control unit controls inputs of the four MACs and loading of the output registers. Our experimental results demonstrate a high performance in implementation of digital filters at elevated speeds of up to 33 millions of input samples per second in a 0.18 μm technology.