Journal of Quality Engineering and Production Optimization

Journal of Quality Engineering and Production Optimization

A bi-objective model for civil court scheduling problem with pragmatic procedures and factors under uncertainty

Document Type : Research Paper

Authors
1 Department of Industrial Engineering, QA.C., Islamic Azad University, Qazvin, Iran
2 Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabataba’i University, Tehran, Iran
Abstract
This study introduces a novel framework for planning and scheduling the handling of litigation cases in a judicial system. For this purpose, an unprecedented, bi-objective optimization model is proposed for scheduling family and civil cases in different courts, including primary, review, and supreme. In this regard, a broad range of pragmatic procedures and factors are considered, including different courts, the structuring of the process of judicial cases, the diversity of lawsuits and their miscellaneous cases, specialized chambers in courts, judges with different specializations, the frequency of reviewing cases in courts, filing objections, priority of cases, and permissible waiting time for handling cases and issuing their verdicts. More importantly, a sharing mechanism is embedded in the scheduling procedure to balance the workload across the chambers of the primary court, given the high volume and variety of cases. Furthermore, since the uncertainty in case handling times is an undeniable concern in planning such problems, the set-induced robust optimization approach is applied to address it. Finally, a real case study of Iran's judicial system is examined to illustrate the applicability and validation of the proposed model. The results showed that the considered sharing mechanism can reduce excess service capacity by 28%.
Keywords

Allaei, A.S., Vahdani, B., Gholami, H.R. & Alinezhad, A. (2024). Assessing cross-docking performance using a novel network DEA model regarding sustainability and undesirable factors under uncertainty. International Journal of Systems Science: Operations & Logistics, 11(1), p.2436182.
Alexandra, H.I. & Dan, M. (2016). Uses of the Balanced Scorecard System in the Strategic Planning and the Improvement of the Judiciary Functioning. Annals of 'Constantin Brancusi'University of Targu-Jiu. Economy Series/Analele Universităţii'Constantin Brâncuşi'din Târgu-Jiu Seria Economie, (3).
Azaria, S., Ronen, B. & Shamir, N. (2023). Justice in time: A theory of constraints approach.
Ben-Tal, A. and Nemirovski, A. (2000). Robust solutions of linear programming problems contaminated with uncertain data. Mathematical programming88(3), pp.411-424.
Bertsimas, D., Litvinov, E., Sun, X.A., Zhao, J. & Zheng, T. (2012). Adaptive robust optimization for the security constrained unit commitment problem. IEEE transactions on power systems, 28(1), pp.52-63.
Bray, R.L., Coviello, D., Ichino, A. & Persico, N. (2016). Multitasking, multiarmed bandits, and the Italian judiciary. Manufacturing & Service Operations Management18(4), pp.545-558.
Castelliano, C., Grajzl, P., Alves, A. & Watanabe, E. (2021). Adjudication forums, specialization, and case duration: Evidence from brazilian federal courts. Justice System Journal42(1), pp.50-77.
Castelliano, C., Grajzl, P. & Watanabe, E. (2023). Multidomain judging and administration of justice: evidence from a major emerging-market jurisdiction. International Review of Administrative Sciences89(2), pp.577-594.
de Oliveira Gomes, A., de Aquino Guimaraes, T. & Akutsu, L. (2016). The relationship between judicial staff and court performance: Evidence from Brazilian State Courts. In IJCA (Vol. 8, p. 12).
Ehrgott, M. & Wiecek, M.M. (2005). Multiobjective programming. Multiple criteria decision analysis: State of the art surveys78, pp.667-708.
Falavigna, G. & Ippoliti, R. (2023). Data envelopment analysis to investigate the Italian legal system and its reform. Journal of Public Affairs23(4), p.e2877.
Gheisariha, E., Etebari, F., Vahdani, B. & Tavakkoli-Moghaddam, R. (2023). A holistic, integrated supply-production–distribution problem in the dairy industry under uncertain supply and demand. Computers & Industrial Engineering, 181, p.109296.
Giacalone, M., Nissi, E. & Cusatelli, C. (2020). Dynamic efficiency evaluation of Italian judicial system using DEA based Malmquist productivity indexes. Socio-Economic Planning Sciences72, p.100952.
Gomes, A.O., Guimaraes, T.A. & Akutsu, L. (2017). Court caseload management: the role of judges and administrative assistants. Revista de Administração Contemporânea21(05), pp.648-665.
Grajzl, P. & Silwal, S. (2020). Multi-court judging and judicial productivity in a career judiciary: evidence from Nepal. International Review of Law and Economics61, p.105888.
Gupta, M. & Bolia, N.B. (2024). Factors affecting efficient discharge of judicial functions: Insights from Indian courts. Socio-Economic Planning Sciences91, p.101755.
Gunawan, M.M. & Fathoni, M.Y. (2023). The establishment of simple lawsuit rules in business disputes in Indonesia: an challenge to achieve fair legal certainty. Journal of Law, Environmental and Justice1(1), pp.19-35.
Jain, D., Borah, M.D. & Biswas, A. (2023). Bayesian optimization based score fusion of linguistic approaches for improving legal document summarization. Knowledge-Based Systems264, p.110336.
Ko, J., Nazarian, E., Nam, Y. & Guo, Y. (2015). Integrated redistricting, location-allocation and service sharing with intra-district service transfer to reduce demand overload and its disparity. Computers, Environment and Urban Systems54, pp.132-143.
Kwak, N.K., Kuzdrall, P.J. & Schniederjans, M.J. (1984). Felony case scheduling policies and continuances: A simulation study. Socio-Economic Planning Sciences18(1), pp.37-43.
Lashgari, Z., Vahdani, B., Gholami, H.R. & Amiri, M. (2025). A bi-objective scheduling-sharing optimization model for criminal court planning under uncertainty. Journal of the Operational Research Society, pp.1-18.
Mavrotas, G. (2009). Effective implementation of the ε-constraint method in multi-objective mathematical programming problems. Applied mathematics and computation, 213(2), pp.455-465.
Mavrotas, G. & Florios, K. (2013). An improved version of the augmented ε-constraint method (AUGMECON2) for finding the exact pareto set in multi-objective integer programming problems. Applied Mathematics and Computation219(18), pp.9652-9669.
Oji, A., Pour Erfan, I. & Askari, H. (2023). Comparative study of divisible lawsuits and separation of lawsuits in the civil procedure of Iran and France. Strategic Studies of Jurisprudence and Law5(special issue), pp.717-734.
Olyaeei, G., Taheri, S. & Fadaei, H. (2020). The Preferred Model of Additional Lawsuit in Iranian Law. Civil Law Knowledge9(2), pp.127-138.
Pereira, S.P.M., Correia, P.M.A.R., Da Palma, P.J., Pitacho, L. & Lunardi, F.C. (2022). The conceptual model of role stress and job burnout in judges: The moderating role of career calling. Laws11(3), p.42.
Rashedi, M., Vahdani, B., Etebari, F. & Gholami, H.R. (2025). Design of a water desalination and distribution network addressing challenges of sharing, trading, and fairness under uncertainty. Desalination, 598, p.118420.
Riccio, L.J. (1974). Model for Court Resource Planning. Just. Sys. J.1, p.49.
Sadeghlou, A.Y. & Mohammadi, K. (2020). The Study Of Suspicious Or Possible Lawsuits In Interpleaders In The Iranian Code Of Civil Procedure. Journal of Exploratory Studies in Law and Management7(4), pp.173-187.
Schniederjans, M.J. & Hollcroft, E. (2005). A multi-criteria modeling approach to jury selection. Socio-Economic Planning Sciences39(1), pp.81-102.
Spurr, S.J. (1997). The duration of litigation. Law & Policy19(3), pp.285-316.
Seppälä, P., Kerkkänen, A., Pekkanen, P. & Pirttilä, T. (2013). Applying the operations management–approach to reduce process delays in justice courts. International Journal of Business Excellence, 6(2), pp.131-147
Wongsinlatam, W. & Buchitchon, S. (2018, July). The comparison between dragonflies algorithm and fireflies algorithm for court case administration: a mixed integer linear programming. In Journal of Physics: Conference Series (Vol. 1061, p. 012005). IOP Publishing.
Zhang, Y., Feng, Y. & Rong, G. (2016). New robust optimization approach induced by flexible uncertainty set: Optimization under continuous uncertainty. Industrial & Engineering Chemistry Research56(1), pp.270-287.