Expected shortfall for the makespan in activity networks under imperfect information
- 34 Downloads
Abstract
This paper deals with the evaluation of the expected shortfall or the conditional value-at-risk for the makespan in scheduling problems represented as temporal networks under incomplete and uncertain information. We consider temporal activity network representations of scheduling problems affected by uncertainties related to the activity durations and we assume that for these uncertainties only incomplete or imperfect information is available. More precisely, for each activity only the interval for its integer valued duration is known to the scheduler. We address the evaluation of the expected shortfall associated to a feasible schedule discussing its importance in scheduling applications. We propose lower and upper bounds, heuristics to determine a fast computational estimation of the expected shortfall, and an exact method for a class of activity networks. The experimental results show that the proposed method can enable to use the expected shortfall as optimization criterion for wide classes of scheduling approaches considering risk-aversion in different practical contexts.
Keywords
Expected shortfall CVaR Project scheduling Makespan Uncertainty Activity networksNotes
References
- Artigues C, Leus R, Talla Nobibon F (2013) Robust optimization for resource-constrained project scheduling with uncertain activity durations. Flex Serv Manuf J 25:175–205CrossRefGoogle Scholar
- Atakan S, Bülbül K, Noyan N (2017) Minimizing value-at-risk in single-machine scheduling. Ann Oper Res 248(1–2):25–73MathSciNetzbMATHCrossRefGoogle Scholar
- Baker KR (2014) Setting optimal due dates in a basic safe-scheduling model. Comput Oper Res 41:109–114MathSciNetzbMATHCrossRefGoogle Scholar
- Baker K, Trietsch D (2009) Principles of sequencing and scheduling. Wiley, New YorkzbMATHCrossRefGoogle Scholar
- Bang-Jensen J, Gutin G (2008) Digraphs: theory, algorithms and applications, 2nd edn. Springer, LondonzbMATHGoogle Scholar
- Bein WM, Kamburowski J, Stallmann MFM (1992) Optimal reduction of two-terminal directed acyclic graphs. SIAM J Comput 21(6):1112–1129MathSciNetzbMATHCrossRefGoogle Scholar
- Bertsimas D, Lauprete GJ, Samarov A (2004) Shortfall as a risk measure: properties, optimization and applications. J Econ Dyn Control 28(7):1353–382MathSciNetzbMATHCrossRefGoogle Scholar
- Canon LC, Jeannot E (2010) Evaluation and optimization of the robustness of DAG schedules in heterogeneous environments. IEEE Trans Parallel Distrib Syst 21(4):532–546CrossRefGoogle Scholar
- Catalão JPS, Pousinho HMI, Contreras J (2012) Optimal hydro scheduling and offering strategies considering price uncertainty and risk management. Energy 37:237–244CrossRefGoogle Scholar
- Chanas S, Zieliński P (2002) The computational complexity of the critical problems in a network with interval activity times. Eur J Oper Res 136:541–550zbMATHCrossRefGoogle Scholar
- Chanas S, Dubois D, Zieliński P (2002) On the sure criticality of tasks in activity networks with imprecise durations. IEEE Trans Syst Man Cybern B Cybern 32(4):393–407CrossRefGoogle Scholar
- Chang Z, Song S, Zhang Y, Ding J-Y, Zhang R, Chiong R (2017) Distributionally robust single machine scheduling with risk aversion. Eur J Oper Res 256:261–274MathSciNetzbMATHCrossRefGoogle Scholar
- Crabill T, Maxwell W (1969) Single machine sequencing with random processing times and random due-dates. Nav Res Logist Q 16:549–555MathSciNetzbMATHCrossRefGoogle Scholar
- Damelin S, Miller W (2011) The mathematics of signal processing. Cambridge University Press, CambridgezbMATHCrossRefGoogle Scholar
- Daniels RL, Kouvelis P (1995) Robust scheduling to hedge against processing time uncertainty in single-stage production. Manag Sci 41(2):363–376zbMATHCrossRefGoogle Scholar
- De P, Ghosh JB, Wells CE (1992) Expectation-variance analyss of job sequences under processing time uncertainty. Int J Prod Econ 28(3):289–297CrossRefGoogle Scholar
- Demeulemeester EL, Herroelen WS (2002) Project scheduling a research handbook. Kluwer Academic, DordrechtzbMATHGoogle Scholar
- De Reyck B, Demeulemeester E, Herroelen W (1999) Algorithms for scheduling projects with generalized precedence relations. In: Wȩglarz J (ed) Project scheduling, vol 14. International series in operations research and management science. Springer, BostonzbMATHCrossRefGoogle Scholar
- Elmaghraby SE (1977) Activity networks: project planning and control by network models. Wiley, New YorkzbMATHGoogle Scholar
- Elmaghraby SE (1990) Project bidding under deterministic and probabilistic activity durations. Eur J Oper Res 49:14–34zbMATHCrossRefGoogle Scholar
- Elmaghraby SE (2005) On the fallacy of averages in project risk management. Eur J Oper Res 165(2):307–313MathSciNetzbMATHCrossRefGoogle Scholar
- Fang C, Kolisch R, Wang L, Mu C (2015) An estimation of distribution algorithm and new computational results for the stochastic resource-constrained project scheduling problem. Flex Serv Manuf J 27(4):585–605CrossRefGoogle Scholar
- Framinan JM, Leisten R, Ruiz García R (2014) Manufacturing scheduling systems. An integrated view on models, methods and tools. Springer, LondonzbMATHCrossRefGoogle Scholar
- Fulkerson DR (1962) Expected critical path lengths in PERT networks. Oper Res 10:808–817zbMATHCrossRefGoogle Scholar
- García-González J, Parrilla E, Mateo A (2007) Risk-averse profit-based optimal scheduling of a hydro-chain in the day-ahead electricity market. Eur J Oper Res 181:1354–1369zbMATHCrossRefGoogle Scholar
- Hagstrom JN (1988) Computational complexity of PERT problems. Networks 18(2):139–147MathSciNetCrossRefGoogle Scholar
- Hall NG (2016) Research and teaching opportunities in project management. INFORMS Tutor Oper Res. https://doi.org/10.1287/educ.2016.0146
- Hartmann S, Kolisch R (2000) Experimental evaluation of state-of-the-art heuristics for resource constrained project scheduling. Eur J Oper Res 127(2):394–407zbMATHCrossRefGoogle Scholar
- Herroelen WS, Leus R (2005) Project scheduling under uncertainty: survey and research potentials. Eur J Oper Res 165(2):289–306zbMATHCrossRefGoogle Scholar
- Ivanescu CV, Fransoo JC, Bertrand JWM (2002) Makespan estimation and order acceptance in batch process industries when processing times are uncertain. OR Spectrum 24:467–495zbMATHCrossRefGoogle Scholar
- Kalinchenko K, Veremyev A, Boginski V, Jeffcoat DE, Uryasev S (2011) Robust connectivity issues in dynamic sensor networks for area surveillance under uncertainty. Pac J Optim 7:235–248MathSciNetzbMATHGoogle Scholar
- Kelley JE Jr (1961) Critical-path planning and scheduling: mathematical basis. Oper Res 9(3):296–320MathSciNetzbMATHCrossRefGoogle Scholar
- Kolisch R, Hartmann S (2006) Experimental investigation of heuristics for resource-constrained project scheduling: an update. Eur J Oper Res 174(1):23–37zbMATHCrossRefGoogle Scholar
- Kolisch R, Sprecher A (1996) PSPLIB—a project scheduling library. Eur J Oper Res 96:205–216zbMATHCrossRefGoogle Scholar
- Lai T-C, Sotskov YN (1999) Sequencing with uncertain numerical data for makespan minimisation. J Oper Res Soc 50:230–243zbMATHCrossRefGoogle Scholar
- Larsen R, Pranzo M (2019) A framework for dynamic rescheduling problems. Int J Prod Res 57(1):16–33CrossRefGoogle Scholar
- Lawrence SR, Sewell EC (1997) Heuristic, optimal, static, and dynamic schedules when processing times are uncertain. J Oper Manag 15:71–82CrossRefGoogle Scholar
- Li Z, Ierapetritou M (2008) Process scheduling under uncertainty: review and challenges. Comput Chem Eng 32:715–727CrossRefGoogle Scholar
- Luh PB, Chen D, Thakur LS (1999) An effective approach for job-shop scheduling with uncertain processing requirements. IEEE Trans Robot Autom 15(2):715–727CrossRefGoogle Scholar
- Meloni C, Pacciarelli D, Pranzo M (2004) A rollout metaheuristic for job shop scheduling problems. Ann Oper Res 131(1–4):215–235MathSciNetzbMATHCrossRefGoogle Scholar
- Pinedo M (2001) Scheduling: theory, algorithms, and systems, 2nd edn. Prentice Hall, Upper Saddle, NJGoogle Scholar
- Pranzo M, Meloni C, Pacciarelli D (2003) A new class of greedy heuristics for job shop scheduling problems. Lect Notes Comput Sci 2647:223–236MathSciNetzbMATHCrossRefGoogle Scholar
- Pranzo M, Pacciarelli D (2016) An iterated greedy metaheuristic for the blocking job shop scheduling problem. J Heurist 22(4):587–611CrossRefGoogle Scholar
- Ramponi FA, Campi MC (2017) Expected shortfall: heuristics and certificates. Eur J Oper Res 267(3):1003–1013MathSciNetzbMATHCrossRefGoogle Scholar
- Rockafellar RT, Uryasev S (2000) Optimization of conditional value-at-risk. J Risk 2(3):21–41CrossRefGoogle Scholar
- Rockafeller RT (2007) Coherent approaches to risk in optimization under uncertainty. INFORMS Tutor Oper Res. https://doi.org/10.1287/educ.1073.0032
- Rothkopf MH (1966) Scheduling with random service times. Manag Sci 12(9):707–713MathSciNetzbMATHCrossRefGoogle Scholar
- Sabuncuoglu I, Bayiz M (2000) Analysis of reactive scheduling problems in a job shop environment. Eur J Oper Res 126:567–586zbMATHCrossRefGoogle Scholar
- Sarin SC, Nagarajan B, Liao L (2010) Stochastic scheduling: expectation-variance analysis of a schedule. Cambridge University Press, New YorkzbMATHCrossRefGoogle Scholar
- Sarin SC, Sherali HD, Liao L (2014) Minimizing conditional-value-at-risk for stochastic scheduling problems. J Sched 17:5–15MathSciNetzbMATHCrossRefGoogle Scholar
- Słowiński R, Hapke M (eds) (2010) Scheduling under fuzziness. Physica-Verlag, HeidelbergzbMATHGoogle Scholar
- Szelke E, Kerr RM (1994) Knowledge-based reactive scheduling. Prod Plan Control 5(2):124–145CrossRefGoogle Scholar
- Tao L, Wu DD, Liu S, Dolgui A (2018) Optimal due date quoting for a risk-averse decision-maker under CVaR. Int J Prod Res 56(5):1934–1959CrossRefGoogle Scholar
- Urgo M, Váncza J (2018) A branch-and-bound approach for the single machine maximum lateness stochastic scheduling problem to minimize the value-at-risk. Flex Serv Manuf J. https://doi.org/10.1007/s10696-018-9316-z CrossRefGoogle Scholar
- Vieira GE, Herrmann JW, Lin E (2003) Rescheduling manufacturing systems: a framework of strategies, policies and methods. J Sched 6:39–62MathSciNetzbMATHCrossRefGoogle Scholar
- Wiesemann W (2012) Optimization of temporal networks under uncertainty. Springer, HeidelbergCrossRefGoogle Scholar
- Wu CW, Brown KN, Beck JC (2009) Scheduling with uncertain durations: modeling \(\beta\)-robust scheduling with constraints. Comput Oper Res 36:2348–2356MathSciNetzbMATHCrossRefGoogle Scholar