Scheduling and route optimisation for labour cost reduction in facility custodial maintenance

Abstract

Purpose The purpose of this paper is to present an optimised scheduling system for facility mangers and custodians. Experience-driven systems currently in use can result in poor ratings for facility maintenance metrics such as overtime hours, utilisation difference and labour costs. Design/methodology/approach The cleaning schedule and custodian work assignments defined by the manager are simulated for the entire year. Clustering and routing algorithms assign work to custodians equally and find optimal cleaning routes. The manager may use the resulting feedback to iteratively find a suitable schedule which lowers costs. Findings Data were collected at a large university building in consultation with facility management and custodians. Results indicate a significant reduction in overtime hours, improvement in utilisation difference and a lowering of labour costs. Research limitations/implications The methodology was validated at a single building in the facility. Variable selection and optimisation model design will benefit from a comprehensive case study which spans the entire facility. Practical implications The methodology may easily be integrated with existing facility maintenance software, adding to it features such as a manager scheduling interface with feedback on critical cleaning metrics and a custodian user interface which highlights room visitation routes and task times. Originality/value This study acts on the need for facility cleaning labour cost management highlighted in literature. It achieves its goals using a novel combination of scheduling, simulation and optimisation. It is designed to empower key decision-makers, i.e. facility managers and custodians, with better information.

The Model of Vehicle and Route Selection for Energy Saving

“…The linear mathematical system of road transport infrastructure, presented by Andy Chow and Li [12], is based on traffic flow and concentration ratio and development in space and time. Optimal use of transport operations, namely planning of transport services, diversion of vehicles, control of transport, and selection of optimal routes improves customer service, increases the use of transport assets, reduces transport costs and time, and increases the satisfaction of transport workers [13][14][15].…”

“…Mode choice [3,5,11,14,23,44], this paper Carrier selection [4,5,30,35,39,49], this paper Environmental use concerns [1,5,10,26,35,44,47], this paper Energy use concerns [5,6,23,26,31,33,37,38,41,44], this paper Vehicle routing [7,8,[12][13][14][15]24,26,29,30,44], this paper Supply chain networking [5,9,11,14,16,17,23,26,29,32,33,…”

“…Route planning [2,5,15,42], this paper Transportation capacity [5,9,12,23,24,30,45] Economies of scale and scope [5,23,30,34,36,45,48], this paper Minimization the total distance of all the vehicle [9,12,28,30], this paper Minimize the total number of vehicle [12,28,30], this paper Service the customers as punctually as possible [1,8,9,42] Sensitivity to demand [7,9,11,12,16,18,26,27,30,41] Just-in-time [9,17] Cost reduction [3,6,9,11,15,16,24,25,27,…”

“…Multi-objective optimization problem [6][7][8]16,19,26,27,32,33,49,51], this paper Non-dominated sorting genetic algirithm II (NSGA-II) [8] Case study [5,6,9,11,12,14,15,25,26], this paper System dynamic approach [2,10,19,41,49], this paper Simulation model [2,3,5,8,9,17] Finite-element-method (FEM) [3,12] Math model [10][11][12][13]16,18,24,43,48], this paper Linear model [2,6,12,19,20,24,30,32,[46]…”

Conceptual frameworks for the drivers and barriers of integrated sustainable solid waste management

“…Many researchers have successfully attempted to use scientific operations research techniques such as multi-criteria decision analysis (Pradhan et al , 2017; Chatzouridis and Komilis, 2012; Islam and Rahman, 2012; Liu and He, 2012) and qualitative empirical research (Meadows, 2008) to address the planning issues in solid waste management. Economic optimization (Anderson, 1968), dynamic mixed integer programming model (Baetz and Neebe, 1994; Chi and Huang, 1998), fuzzy mathematical program (Chang et al , 1997; Huang et al , 1994), static non-linear programming model (Sundberg et al , 1994), multi-period and multi-regional model (Everett and Modak, 1996), optimization model (Berger and Savard et al , 1999; Tanskanen, 2000), etc., are also to be mentioned among the other few.…”