On the cost of misperceived travel time variability

Recent studies show that traveler’s scheduling preferences compose a willingness-to-pay function directly corresponding to aggregate measurement of travel time variability under some assumptions. This property makes valuation on travel time variability transferable from context to context, which is ideal for extensive policy evaluation. However, if respondents do not exactly maximizing expected utility as assumed, such transferability might not hold because two types of potential errors: (i) scheduling preference elicited from stated preference experiment involving risk might be biased due to misspecification and (ii) ignoring the cost of misperceiving travel time distribution might result in undervaluation. To find out to what extent these errors matter, we reformulate a general scheduling model under rank-dependent utility theory, and derive reduced-form expected cost functions of choosing suboptimal departure time under two special cases. We estimate these two models and calculate the empirical cost due to misperceived travel time variability. We find that (i) travelers are mostly pessimistic and thus tend to choose departure time too earlier to bring optimal cost, (ii) scheduling preference elicited from stated choice method could be quite biased if probability weight- ing is not considered and (iii) the extra cost of misperceiving travel time distribution contributes trivial amount to the discrepancy between scheduling model and its reduced form

Jackson's rule for one-machine scheduling : making a good heuristic better

Includes bibliographical references (leaves 21-22).by Leslie A. Hall and David B. Shmoys

A survey of scheduling problems with setup times or costs

Author name used in this publication: C. T. NgAuthor name used in this publication: T. C. E. Cheng2007-2008 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Solving no-wait two-stage flexible flow shop scheduling problem with unrelated parallel machines and rework time by the adjusted discrete Multi Objective Invasive Weed Optimization and fuzzy dominance approach

Purpose: Adjusted discrete Multi-Objective Invasive Weed Optimization (DMOIWO) algorithm, which uses fuzzy dominant approach for ordering, has been proposed to solve No-wait two-stage flexible flow shop scheduling problem. Design/methodology/approach: No-wait two-stage flexible flow shop scheduling problem by considering sequence-dependent setup times and probable rework in both stations, different ready times for all jobs and rework times for both stations as well as unrelated parallel machines with regards to the simultaneous minimization of maximum job completion time and average latency functions have been investigated in a multi-objective manner. In this study, the parameter setting has been carried out using Taguchi Method based on the quality indicator for beater performance of the algorithm. Findings: The results of this algorithm have been compared with those of conventional, multi-objective algorithms to show the better performance of the proposed algorithm. The results clearly indicated the greater performance of the proposed algorithm. Originality/value: This study provides an efficient method for solving multi objective no-wait two-stage flexible flow shop scheduling problem by considering sequence-dependent setup times, probable rework in both stations, different ready times for all jobs, rework times for both stations and unrelated parallel machines which are the real constraints.Peer Reviewe