Decision Support System for Green Real-Life Field Scheduling Problems

Abstract

A decision support system is designed in this paper for supporting the adoption of green logistics within scheduling problems, and applied to real-life services cases. In comparison to other green logistics models, this system deploys time-varying travel speeds instead of a constant speed, which is important for calculating the CO2 emission accurately. This system adopts widely used instantaneous emission models in literature which can predict second-by-second emissions. The factors influencing emissions in these models are vehicle types, vehicle load and traffic conditions. As vehicle types play an important role in computing the amount of emissions, engineers vehicles number plates are mapped to specified emission formulas. This feature currently is not offered by any commercial software. To visualise the emissions of a planned route, a Heat Map view is proposed. Furthermore, the differences between minimising CO2 emission compared to minimising travel time are discussed under different scenarios. The field scheduling problem is formulated as a vehicle routing and scheduling problem, which considers CO2 emissions in the objective function, heterogeneous fleet, time window constraints and skill matching constraints, different from the traditional time-dependent VSRP formulation. In the scheduler, this problem is solved by metaheuristic methods. Three different metaheuristics are compared. They are Tabu search algorithms with random neighbourhood generators and two variants of Variable Neighbourhood search algorithms: variable neighbourhood descent (VND) and reduced variable neighbourhood search (RVNS). Results suggest that RVNS is a good trade-off between solution qualities and computational time for industrial application.

A Review of Heuristics and Hybrid Methods for Green Vehicle Routing Problems considering Emissions

“…To deal with the GVRSP-Split, they provided a hybrid multi-start method (MS-ILS-SC) that combines the ILS heuristic with random VND (RVND) location search as the initial phase and an exact set covering (SC) model as the intensification phase. Zhou et al [91] presented a decision support system to assist the implementation of a green real-life field scheduling problem. +is system uses two instantaneous emissions models, for example, methodology for calculating transport emissions and energy consumption (MEET, Hickman et al [92]) and national atmospheric emissions inventory (NAEI, NAEI [93]) used in the literature, which can predict the emissions in each second.…”

“…[62] Leng et al [32] • RLCLRPRCC, Leng et al [32] Masmoudi et al [99] • HF-VRPS, Masmoudi et al [99] Sousa Matos et al [90] • GVRSP-split, Sousa Matos et al [90] Fang et al [63] • PRPSPD, Fang et al [63] Guo and Liu [58] • TD-PRP, Guo and Liu [58] Jabir et al [35] • MD-GVRP, Jabir et al [35] Kaabachi et al [36] • GMDVRPTW, Kaabachi et al [36] Liao [89] • Online VRP considers real-time demands, Liao [89] Yavuz and Çapar [24] • • MGVRP, Yavuz and Çapar [24] Zhou et al [91] • Green real-life field scheduling problem, Zhou et al [91] Gang et al [87] • GVRSP of free picking up and delivering customers for airlines ticketing company, Gang et al • CIRP under a mixed fleet of electric and conventional vehicles, Soysal et al [74]; GVRP, Soysal et al [25]; CumVRP-TW, Fernández et al [105]; GLRP, Dukkanci et al [31]; Biobjective PRP, Costa et al [61]; GSTDCVRP, Çimen and Soysal [113]; TD-PRP, Franceschetti et al [57]; F-GVRPSPDTW, Majidi et al [80]; GSTDCVRP, Soysal and Çimen [113]; MMPPRP-TW, Kumar et al [60]; GVRSP, Xiao and Konak [86] • VRPTW using time-varying data, Maden et al [96] Bredstrom et al [137] • VRPTW-SPFC, Ettazi et al [111]; HF-VRPS, Masmoudi et al [99] Iori et al [138] • 2L-MDCVRPB, Zhao et al [84] Li and Lim [139] • Green-PDPTW, Lu and Huang…”