A Multi-Objective Genetic Algorithm for Solving Assembly Line Balancing Problem

In this paper, a multi-objective genetic agorithm to solve assembly line balancing problems is proposed. The performance criteria considered are the number of workstations, the line efficiency, the smoothness index before trade and transfer, and the smoothness index after trade and transfer. The developed genetic algorithm is compared with six popular heuristic algorithms, namely, ranked positional weight, Kilbridge and Wester, Moodie and Young, Hoffmann precedence matrix, immediate update first fit, and rank and assign heuristic methods. For comparative evaluation, 20 networks are collected from open literature, and are used with five different cycle times. All the six heuristics and the genetic algorithm are coded in C++ language. It is found that the proposed genetic algorithm performs better in all the performance measures than the heuristics. However, the execution time for the GA is longer, because the GA searches for global optimal solutions with more iterations.     

A comparative evaluation of assembly line balancing Heuristics

A comparative evaluation is presented of six popular assembly line balancing heuristics, namely, ranked positional weight, Kilbridge and Wester, Moodie and Young, Hoffman precedence matrix, immediate update first fit, and rank and assign heuristic. The evaluation criteria used are the number of excess stations given, line efficiency, smoothness index and CPU time. The trade and transfer phase of the Moodie and Young method is applied to all heuristics, then the results are compared based on the evaluation criteria. Twenty problems each with 5 different cycle times are used for the comparative evaluation of heuristics. All six heuristics are coded in C++ language. Among the six considered heuristics the Hoffmann enumeration procedure performs best; but, the execution time for the Hoffmann procedure is longer because this procedure enumerates all the feasible alternative sets of tasks for the stations. An erratum to this article is available at .     

Simultaneously optimizing the independent thermoelectric properties in (Ti,Zr,Hf)(Co,Ni)Sb alloy by in situ forming InSb nanoinclusions

We report an induction-melting spark-plasma-sintering synthesis process of the nanocomposite material composed of (TiZrHf)(CoNi)Sb coarse grains and in situ formed InSb nanoinclusions that occur primarily on the grain boundaries. We were able to qualitatively control the amount of InSb nanoinclusions by varying the In and Sb contents in the starting materials. The effects of the nanoinclusion formation and the matrix–nanoinclusion boundaries on the thermoelectric properties have been studied and correlated. In particular, the nanoinclusion-induced electron injection and electron filtering mechanisms helped to simultaneously decrease the resistivity, enhance the Seebeck coefficient and reduce the thermal conductivity of the nanocomposite. A figure of merit of ZT ～ 0.5 was attained at 820 K for the sample containing 1 at.% InSb nanoinclusions, which is a 160% improvement over the sample containing no nanoinclusions. The experimental results are discussed in the context of the effective medium model formerly proposed by Bergman and Fel.     

A TSP-GA multi-objective algorithm for flow-shop scheduling

A multi-objective evolutionary search algorithm using a travelling salesman algorithm and genetic algorithm for flow-shop scheduling is proposed in this paper. The initial sequence is obtained by solving the TSP. The initial population of the genetic algorithm is created with the help of a neighbourhood creation scheme known as a random insertion perturbation scheme, which uses the sequence obtained from TSP. The proposed algorithm uses a weighted sum of multiple objectives as a fitness function. The weights are randomly generated for each generation to enable a multi-directional search. The performance measures considered include minimising makespan, mean flow time and machine idle time. The performance of the proposed algorithm is demonstrated by applying it to benchmark problems available in the OR-Library.     

Flow control valves for analytical microfluidic chips without mechanical parts based on thermally responsive monolithic polymers

Monolithic plugs of poly(N-isopropylacrylamide) cross-linked with 5% methylenebisacrylamide have been prepared by photoinitiated polymerization within the channel of a microfluidic device. The volume change associated with the polymer phase transition at its lower critical solution temperature of 32 degrees C allows both the rapid swelling and the deswelling of the monoliths enabling the polymer to close or open the channel as it functions as a nonmechanical valve. Thermoelectric elements capable of changing the temperature of the system between 17 and 57 degrees C were used to actuate the valve. Flow through the device was monitored by fluorescence measurements via the laser-triggered photobleaching of a dye contained in the liquid phase. Photobleaching occurs quickly once the flow is stopped, and the time required to open and close the valve was 3.5 and 5.0 s, respectively. No changes in function were observed even after 120 open-close cycles. Although the 2-mm-long valve was prepared from a polymerization mixture consisting of only a 5% aqueous solution of monomers, it resists pressures of up to 1.38 MPa (200 psi) without observable structural damage.