Search

We make a novel investigation of welfare costs associated with various suboptimal decisions made by retirees, both analytically and numerically. We utilize a unique framework that incorporates recursive utility with housing, and also encompasses expected utility and recursive utility without housing as special cases. Our findings indicate that under-investment in stocks incurs lower welfare costs compared to an equivalent over-investment. Suboptimal allocations in bond holdings result in higher costs than similar misallocations in stocks. Choosing not to participate in the stock market is less detrimental than avoiding the bond market. Should retirees opt to simplify their decision-making by investing solely in one type of asset, it is less costly for them to invest exclusively in bonds. Overconsumption of housing is less costly than an equivalent underconsumption. Suboptimal consumption imposes the highest welfare cost. Decisions regarding consumption, housing, and savings are found to be more crucial than the choice of how to distribute liquid savings between stocks and bonds. Additionally, recursive utility model better captures retirees' preference for bonds over stocks than expected utility model. Our results, which are consistent across various parameter settings, provide valuable insights for academics and policymakers aiming to enhance retiree welfare. [ABSTRACT FROM AUTHOR]

This paper addresses a two-stage assembly flowshop scheduling problem with the objective of minimising maximum tardiness where set-up times are considered as separate from processing times. The performance measure of maximum tardiness is important for some scheduling environments, and hence, it should be taken into account while making scheduling decisions for such environments. Given that the problem is strongly NP-hard, different algorithms have been proposed in the literature. The algorithm of Self-Adaptive Differential Evolution (SDE) performs as the best for the problem in the literature. We propose a new hybrid simulated annealing and insertion algorithm (SMI). The insertion step, in the SMI algorithm, strengthens the exploration step of the simulated annealing algorithm at the beginning and reinforces the exploitation step of the simulated annealing algorithm towards the end. Furthermore, we develop several dominance relations for the problem which are incorporated in the proposed SMI algorithm. We compare the performance of the proposed SMI algorithm with that of the best existing algorithm, SDE. The computational experiments indicate that the proposed SMI algorithm performs significantly better than the existing SDE algorithm. More specifically, under the same CPU time, the proposed SMI algorithm, on average, reduces the error of the best existing SDE algorithm over 90%, which indicates the superiority of the proposed SMI algorithm. [ABSTRACT FROM AUTHOR]

The two-machine flowshop scheduling problem to minimize total completion time with separate set-up times is addressed. Set-up times are modelled as uncertain within an interval where only the lower and upper bounds are known. Eighty-one different versions of a newly developed constructive algorithm are proposed. Computational experiments to evaluate the performance of the proposed algorithm are conducted in two stages. In the first stage, 81 versions of the algorithm are compared with each other and the top seven versions are selected. In the second stage, the performances of the top seven versions are compared with the performance of the best existing known algorithm for the deterministic set-up times solution in the literature. The computational results reveal that errors of the top seven (out of 81) versions of the algorithm are less than 0.005. All computational results are statistically verified. Therefore, the proposed algorithm has excellent performance. [ABSTRACT FROM AUTHOR]

A wide range of uncertainties exists in some real-world production environments which result in uncertain setup and/or processing times. Factors such as crew skills, shortages in equipment and resource breakdowns can be the sources of these uncertainties. This study considers a two-machine production flowshop scheduling problem where both setup and processing times are treated as uncertain variables. The objective is to minimise makespan which is an effective way of resource utilisation. There exists a dominance relation in the literature for the two-machine flowshop scheduling problem with uncertain setup and processing times. However, the dominance relation has not been evaluated. In this study, we evaluate the existing dominance relation. Moreover, a new dominance relation is established and shown to be more effective than the existing one. Furthermore, twenty-five implementations of a polynomial time algorithm are developed. Extensive computational experiments are conducted to evaluate the performance of the implementations of the algorithm. The computational experiments indicate that the overall gap (error) of the best implementation of the algorithm is less than 0.3% when compared to the optimal solution. Moreover, the performance of this implementation of the algorithm is the best one when compared to the remaining implementations for all the considered experimental environments. Additionally, the performance of this implementation of the algorithm is shown to be insensitive to the uncertainty in setup times. [ABSTRACT FROM AUTHOR]

The link between makespan and the profitability and competitiveness of a firm is addressed first. We then study the problem of minimising makespan in a two-machine flowshop with setup times. Jobs have random setup times that are bounded within certain intervals. The distributions of job setup times are not known. We propose a polynomial time algorithm that generalises Yoshida and Hitomi's algorithm. The algorithm uses a weighted average of lower and upper bounds for setup times. Different combinations of weights result in nine different versions of the algorithm. The computational results indicate that one of the versions, with equal weights given to the lower and upper bounds of setup times, performs much better than the others. Next, the performance of this best version is compared with that of the optimal solution, which is obtained by Yoshida and Hitomi's algorithm applied to the problem after setup times have been realised. Computational analysis shows that the overall average absolute error of the best algorithm is 0.03%, and this decreases in size as the number of jobs increases. The analysis also shows that the proposed best version yields robust results regardless of setup-time distributions and the range of setup times. [ABSTRACT FROM PUBLISHER]

The two-machine flowshop scheduling problem of minimising makespan is addressed where jobs have random processing times that are bounded within certain intervals. The probability distributions of job processing times within intervals are not known. The only known information about job processing times is the lower and upper bounds. The decision concerning the solution to the problem, i.e. finding a sequence, has to be made based on these bounds. Different heuristics using the bounds are proposed, and the proposed heuristics are compared based on randomly generated data. Computational analysis has shown that three of the proposed heuristics perform well with an overall average error of less than one percent. Moreover, for symmetric distributions, it is also shown that one of the heuristics, which applies Johnson's algorithm to the average of the lower and upper bounds, performs best with an overall average percentage error of 0.71. The obtained results are also shown to be consistent with recent results reported in the literature. [ABSTRACT FROM AUTHOR]