A Hyper Heuristic Algorithm to Solve the Low-Carbon Location Routing Problem

Abstract

This paper proposes a low-carbon location routing problem (LCLRP) model with simultaneous delivery and pick up, time windows, and heterogeneous fleets to reduce the logistics cost and carbon emissions and improve customer satisfaction. The correctness of the model is tested by a simple example of CPLEX (optimization software for mathematical programming). To solve this problem, a hyper-heuristic algorithm is designed based on a secondary exponential smoothing strategy and adaptive receiving mechanism. The algorithm can achieve fast convergence and is highly robust. This case study analyzes the impact of depot distribution and cost, heterogeneous fleets (HF), and customer distribution and time windows on logistics costs, carbon emissions, and customer satisfaction. The experimental results show that the proposed model can reduce logistics costs by 1.72%, carbon emissions by 11.23%, and vehicle travel distance by 9.69%, and show that the proposed model has guiding significance for reducing logistics costs.

Location‐routing problem: a classification of recent research

“…The environmental issue of logistics received much attention from the LRP research community recently. The impact of logistics activities to the surrounding environment is addressed in several works either by the minimization of CO 2 emission incurred (e.g., Govindan et al, 2014;Tang et al, 2016;Chen et al, 2018;Leng et al, 2018;Rabbani et al, 2018a;Leng et al, 2019aLeng et al, , 2019bLu et al, 2019b) as shown in 9.05% articles, or the minimization of fuel consumption (Akararungruangkul and Kaewman, 2018;Dukkanci et al, 2019;Khalafi and Zarei, 2019;Zhang et al, 2019b) related to the transportation cost of the system. The environmental issue discussed is not only restricted to the emission impact but also the negative risk that may be incurred from the decisions within an LRP model.…”

A novel bi-objective model of cold chain logistics considering location-routing decision and environmental effects

“…worst, the reason is that the estimation of FCCE depends on plentiful parameters which may change over traveling time. For a comprehensive view of the models and factors, the reader is referred to the surveys [21] and papers [20,[28][29][30][31].…”

A Hyper-Heuristic Algorithm for Time-Dependent Green Location Routing Problem With Time Windows

“…There are many selection strategies (SA) include the simple random (SR) sampling, choice function (CF), genetic algorithm (GA) [45], [46], Tabu Search (TS) [47], [48], and quantum evolutionary algorithm (QEA) [25] strategies. SA falls into two categories: deterministic acceptance, which accepts the resultant solution based on the fitness or special rules; and non-deterministic acceptance, which accepts the resultant solution based on a threshold or probability [49].…”