Priority scheduling policies for repair shops

Abstract

The use of repair as an alternative to the replacement of products is a growing trend in many industries, especially those employing expensive assets. Repair shop environments are characterized by a greater degree of uncertainty than traditional job or assembly shop environments, and this introduces unique managerial complications. In this study, scheduling policies are examined in the repair shop environment where no end-item spares are available or where the spares stocking decision is deferred until the minimum obtainable¯owtimes for enditems are established. Previous studies of scheduling policies have focused on the case where spares are allowed and have not considered all of the sources of variation in the repair environment. The model developed in this study incorporates these sources of uncertainty, as well as other factors likely to in¯uence repair shop performance. The results show that the variability in repair shops is su ciently higher than in traditional job or assembly shops to warrant di erent scheduling policies than those previously reported. The choice of scheduling policy to provide minimum¯owtimes and RMS tardiness is operating-environment speci® c, and clear guidelines are presented for the manager in a repair shop environment.

Maintenance spare parts planning and control: a framework for control and agenda for future research

“…Also Caggiano et al (2006) provide a model to allocate repair capacity in real time to different repair jobs based on current inventory levels. Priority schedules and use of flexible capacity in the form of overtime are discussed by Guide Jr et al (2000) and Hausman and Scudder (1982).…”

Green supply‐chain management: A state‐of‐the‐art literature review

“…Simple enumeration techniques (Inderfurth et al 2001; van der Laan and Salomón 1997; van der Laan et al 1996a, 1999b) are sometimes used for solution. Software tools such as EDS R-Log (Nagel and Meyer 1999), EDIT (Johnson and Wang 1995) and MATLAB (Guide et al 2005) have also been used.Simulation(Ashayeri et al 1996; Srivastava 1997a,b, 1998;Guide et al 1996 Guide et al ,b,c, 1999bGuide et al , 2000b; Haas and Murphy 2002; Hirsch et al 1998; Khoo et al 2001; Linton and Johnson 2000; Marx-Gomez et al 2002; Vlachos and Tagaras 2001) is commonly used for scenario generation and analysis. Srivastava and Srivastava (2006) use system dynamics simulation for estimating end-ofthe-life returns.…”

A color petri net based scheduling model for remanufacturing system with stochastic process routing

“…For the implementation of the simulation, the input data is populated as follows. c) The due date for each machine tools are randomly generated by a uniform distribution, Due(l) ∈ U [1,3]. d) The processing cost is $30 per hour.…”

“…One of their works presented in [3] examined the performance of five priority dispatching rules (i.e., First come first served, shortest processing time, earliest due date, lowest level in bill of materials (BOM), and highest level in BOM) in a repair shop. The work was further extended to analyze the performance of static priority rules in order to minimize total weighted average sojourn time (TWAST) for a stylized two-product and three-component remanufacturing system [4].…”