Research On Low-carbon Inventory Routing Problem

Under economic globalization,in order to reduce costs and to be more competitive,decision makers begin to plan logistics activities by considering all activities as a whole.In this circumstance,the combinatorial optimization problems of the supply chain arise.Inventory management and vehicle scheduling are two key activities in the supply chain system,which account for a large proportion of the logistics costs.However,there is a tradeoff between inventory cost and transportation cost.When one of them decreases,the other will increase.Therefore,it is necessary to optimize them together,which will help enterprises improve operational efficiencyand save costs.In recent years,the quick development of vendor-managed inventory model has made the integration of inventory management and vehicle scheduling possible.Based on the traditionalmulti-period inventory routing problem(MIRP),this paper has studiedtwo extendedproblems: fuel consumptionconsidered MIRP and MIRP under carbon emission policies.For each problem,we construct mathematical models and develop algorithms.Meanwhile,numerical tests are performed to show the effectiveness of proposed algorithms and to analyze the effects of different parameters on results.A multi-period inventory routing problem considering fuel consumption is firststudied in this thesis.In the traditional MIRP,travel distance is considered as the only measurement of vehicles' variable transportation cost;however,in fact,it is the cost of fuel consumption,not the distance,which is the greater concern to transportation companies.Fuel consumption cost is determinedby fuel consumption rate(affected by load),distance and unit fuel price.We present a mathematical model to quantitatively characterize the fuel consumption considered MIRP.Several valid inequalities are added to strengthen the model.A branch-and-cutalgorithm is developed to solve the model and shows good performance.Computational tests indicate that the new model will affect route direction,route order and inventory strategy.Compared to the traditional MIRP,in most cases the fuel consumption considered MIRP will help enterprises save energy without increasing travel distance and inventory cost.Second,MIRP under carbon emission regulations is studied.We first present the generic MIRP model,and then investigate the impacts of the four carbon emission regulations on the MIRP.A hybrid genetic algorithm using binary matrix representation is proposed to find near-optimal solutions for these problems.Numerical tests are performed to show the effectiveness ofthe proposed algorithm,and a few managerial insights are observed from parameter sensitive analyses.Results demonstrate thatthe reaction to carbon caps varies significantly due to different industries and that rigid emission limits will put heavy burdens on companies whose products are high-valued,perishable or quickly updated.We also find that a tighter carbon cap sometimes paradoxically leads to a higher carbon emission level under some policies.Meanwhile,unit fuel price and carbon price will influence the supply chain system's costs and emissions.More importantly,higher price does not always result in better environmental benefits.