Annual audit planning is a multi-criteria decision-making problem faced by internal audit departments of all organizations. Due to the constrained audit resources, the planning process primarily involves the analysis and evaluation of complex factors for selecting auditable units that maximize the full potential of internal audit. Previous research on internal audit planning only focused on the goal of risk minimization and applied ranking methods to prioritize alternatives. In order to enable internal audit activities to add more value to the organization, the integrated risk-based internal audit planning is proposed to assist audit department in achieving multiple objectives in addition to risk management. Meanwhile, a multi-stage framework is proposed to support the development of such value-added internal audit plan. The new framework integrates the risk assessment of auditable units with the selection of audit activities and resource allocation through a combined analytic hierarchy process (AHP), fuzzy comprehensive evaluation (FCE) and weighted multi-choice goal programming (WMCGP) approach. The model considers both qualitative and quantitative decision criteria. A real-life case study of the development of an integrated risk-based annual audit plan is presented, and sensitivity analysis is performed to illustrate the validity of the proposed approach. The results indicate that the proposed framework is a useful tool for internal audit planning and the implications of the study can be extended to various selection and allocation problems.

This paper proposes a matching-and-pricing mechanism for a drivers’ demand-reporting problem in parking-sharing programs in which owners share their private parking slots with drivers. We generate a driver-slot matching solution by a centralized assignment procedure according to the demand and supply information reported by drivers and owners, respectively, and determine truth-telling pricing by the Vickrey-Clark-Grove mechanism. We show that under the assumption that drivers do not know with certainty whether other drivers will show up to compete for the parking slots, the mechanism proposed in this paper induces drivers to truthfully report their private information of the travel plans and guarantees three other desirable properties: participation of drivers and slot owners, optimal system efficiency, and balance of the system’s budget. We further extend these results to two dynamic situations. Finally, the results of the numerical experiments based on real-world data demonstrate the performance of the mechanism. History: This paper has been accepted for the Service Science Special Issue on Innovation in Transportation-Enabled Urban Services. Funding: This work was partially funded by the National Natural Science Foundation of China [Grants 71972026, 72192805, and 71971044], the Major Program of National Social Science Foundation of China [Grant 20&ZD084], and the Leading Talent Program of Guangdong Province [Grant 2016LJ06D703]. Supplemental Material: The online supplement is available at https://doi.org/10.1287/serv.2022.0303 .

Hoist scheduling in automated electroplating lines has been extensively studied in a static environment. However, practical electroplating lines are subject to diversified unforeseen disruptions that require frequent rescheduling to maintain or optimize system performance. This paper addresses a hoist scheduling problem, where randomly arriving jobs need to be inserted into an existing schedule without changing the sequence of hoist moves already scheduled. The objective is to minimize the total completion time of all the jobs in the existing schedule and a newly inserted job. We develop a polynomial-time heuristic that adjusts the starting times of the existing hoist moves to a limited extent but does not bring about a severe disturbance of the existing hoist moves. We compare our algorithm with two existing approaches with different rescheduling policies (i.e., partial and zero adjustment of the existing schedule). We empirically analyze the productivity and the stability of the schedules generated by the three approaches. Computational results demonstrate that our algorithm can generate more productive and stable schedules than the two existing approaches.