Job scheduling methods for reducing waiting time variance

This paper extends carrier assignment models used in winner determination auctions for transportation procurement to include shipper non-price objectives and carrier transit point costs. The models are unlike traditional carrier assignment models which incorporate only carrier lane bids, and different from combinatorial auction models which focus on packets of lanes without considering transit point costs. We develop solutions, including metaheuristics, for the new models and through computational experimentation show that the algorithms work well and can be easily implemented.

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Carrier assignment models in transportation procurement

This paper addresses an n-job, single-machine scheduling problem with an objective to minimize flow-time variance, a non-regular performance measure. A spreadsheet-based genetic algorithm (GA) approach is presented to solve the problem. A domain-independent general-purpose GA is used, which is an add-in to the spreadsheet software. The paper demonstrates an adaptation of the proprietary GA software to the problem of variance minimization. To test the performance of the proposed approach, test problems are taken from already published literature. The proposed approach is found to perform better than the previous GA, simulated annealing (SA) and Tabu search (TS) approaches for variance minimization in that it produces an optimal solution for all the test problems. Empirical analysis was carried out to study the effect of GA parameters, namely, the crossover rate, mutation rate, and population size.

Minimizing flow-time variance in a single-machine system using genetic algorithms

Hierarchical minimization of completion time variance and makespan in jobshops

https://isiarticles.com/bundles/Article/pre/pdf/20084.pdf

G. Viswanathkumar

Papers

A branch and bound algorithm to minimize completion time variance on a single processor