Similar articles

Abstract: Efficient resource scheduling and allocation in radiological examination process (REP) execution is a key requirement to improve patient throughput and radiological resource utilization and to manage unexpected events that occur when resource scheduling and allocation decisions change due to clinical needs. In this paper, a tabu search based approach is presented to solve the resource scheduling and allocation problems in REP execution. The primary objective of the approach is to minimize a weighted sum of average examination flow time, average idle time of the resources, and delays. Unexpected events, i.e., emergent or absent examinations, are also considered. For certain parameter combinations, the optimal solution of radiological resource scheduling and allocation is found, while considering the limitations such as routing and resource constraints. Simulations in the application case are performed. Results show that the proposed approach makes efficient use of radiological resource capacity and improves the patient throughput in REP execution.

放射检查流程执行中基于禁忌搜索的资源分配

[1]Aickelin U, Dowsland KA, 2004. An indirect genetic algorithm for a nurse-scheduling problem. Comput Oper Res, 31(5):761-778.

[2]Bard JF, Purnomo HW, 2005. Hospital-wide reactive scheduling of nurses with preference considerations. IIE Trans, 37(7):589-608.

[3]Beck JC, Fox MS, 2000. Constraint-directed techniques for scheduling alternative activities. Artif Intell, 121(1-2):211-250.

[4]Błażewicz J, Pesch E, Sterna M, 2000. The disjunctive graph machine representation of the job shop scheduling problem. Eur J Oper Res, 127(2):317-331.

[5]Brah SA, Loo LL, 1999. Heuristics for scheduling in a flow shop with multiple processors. Eur J Oper Res, 113(1):113-122.

[6]Cheang B, Li H, Lim A, et al., 2003. Nurse rostering problems—a bibliographic survey. Eur J Oper Res, 151(3):447-460.

[7]Duftschmid G, Miksch S, Gall W, 2002. Verification of temporal scheduling constraints in clinical practice guidelines. Artif Intell Med, 25(2):93-121.

[8]Elmaghraby SE, 1993. Resource allocation via dynamic programming in activity networks. Eur J Oper Res, 64(2):199-215.

[9]Glover F, 1986. Future paths for integer programming and links to artificial intelligence. Comput Oper Res, 13(5):533-549.

[10]Huang ZX, van der Aalst WMP, Lu LD, et al., 2010. An adaptive work distribution mechanism based on reinforcement learning. Exp Syst Appl, 37(12):7533-7541.

[11]Huang ZX, Lu XD, Duan HL, 2011a. Mining association rules to support resource allocation in business process management. Exp Syst Appl, 38(8):9483-9490.

[12]Huang ZX, van der Aalst WMP, Lu XD, et al., 2011b. Reinforcement learning based resource allocation in business process management. Data Knowl Eng, 70(1):127-145.

[13]Kim SC, Horowitz I, Young KK, et al., 2000. Flexible bed allocation and performance in the intensive care unit. J Oper Manag, 18(4):427-443.

[14]Kubzin MA, Potts CN, Strusevich VA, 2009. Approximation results for flow shop scheduling problems with machine availability constraints. Comput Oper Res, 36(2):379-390.

[15]Marinagi CC, Spyropoulos CD, Papatheodorou C, et al., 2000. Continual planning and scheduling for managing patient tests in hospital laboratories. Artif Intell Med, 20(2):139-154.

[16]Mika M, Waligóa G, Węglarz J, 2008. Tabu search for multi-mode resource-constrained project scheduling with schedule-dependent setup times. Eur J Oper Res, 187(3):1238-1250.

[17]Negenman EG, 2001. Local search algorithms for the multiprocessor flow shop scheduling problem. Eur J Oper Res, 128(1):147-158.

[18]Oddi A, Cesta A, 2000. Toward interactive scheduling systems for managing medical resources. Artif Intell Med, 20(2):113-138.

[19]Roland B, di Martinelly C, Riane F, et al., 2010. Scheduling an operating theatre under human resource constraints. Comput Ind Eng, 58(2):212-220.

[20]Salimifard K, Wright M, 2001. Petri net-based modelling of workflow systems:an overview. Eur J Oper Res, 134(3):664-676.

[21]Spyropoulos CD, 2000. AI planning and scheduling in the medical hospital environment. Artif Intell Med, 20(2):101-111.

[22]Verhoeven MGA, 1998. Tabu search for resource-constrained scheduling. Eur J Oper Res, 106(2-3):266-276.

[23]Vermeulen IB, Bohte SM, Elkhuizen SG, et al., 2009. Adaptive resource allocation for efficient patient scheduling. Artif Intell Med, 46(1):67-80.

[24]Vilcot G, Billaut JC, 2008. A tabu search and a genetic algorithm for solving a bicriteria general job shop scheduling problem. Eur J Oper Res, 190(2):398-411.

[25]Zhang JY, Lu XD, Nie HC, et al., 2009. Radiology information system:a workflow-based approach. Int J Comput Assist Radiol Surg, 4(5):509-516.