Abstract
This paper aims to evaluate the impact of customer allocation on the facility location in the multi-objective location problem for sustainable logistics. After a new practical multi-objective location model considering vehicle carbon emissions is introduced, the NSGA-II and SEAMO2 algorithms are employed to solve the model. Within the framework of each algorithm, three different allocation rules derived from the optimization of customer allocation based on distance, cost, and emissions are separately applied to perform the customer-to-facility assignment so as to evaluate their impacts. The results of extensive computational experiments show that the allocation rules have nearly no influence on the solution quality, and the allocation rule based on the distance has an absolute advantage of computation time. These findings will greatly help to simplify the location-allocation analysis in the multi-objective location problems.
Highlights
Location decision on logistics facilities plays a critical role in the strategic design of supply chain networks to ensure efficient and effective goods movement
The facility location problem (FLP) was formulated as a single objective model to minimize the total costs, which is usually expressed as the sum of shipping and opening costs
Both the non-dominated sorting genetic algorithm II (NSGA-II) and the simple evolutionary algorithm for multi-objective optimization 2 (SEAMO2) are effective in approximating the Pareto fronts of the proposed multi-objective sustainable location problem
Summary
Location decision on logistics facilities plays a critical role in the strategic design of supply chain networks to ensure efficient and effective goods movement. It determines where goods are stored, what quantity of goods is held in inventory, how goods are shipped from raw material sites to component fabrication or assembly plants, as well as how the finished products are delivered to customers. Instead of a single solution (often optimized on costs), the MOO techniques can offer the decision-maker a choice of tradeoff solutions It may be quite possible, for example, to greatly reduce greenhouse gas (GHG) emissions while incurring a slight increase in economic costs. Such compromise solutions can be missed by the traditional single-objective optimization
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