1. Introduction
The current market competition is turning from the competition between enterprises to the competition between supply chains because of the diversity and uncertainty of customer demand, the development of global economic integration, and decentralized network manufacturing. As one of the core components of the supply chain, the logistics system also needs to make corresponding adjustments and improvements. It should have a dynamic organizational structure and a dynamic, collaborative operation mode. The logistics network should have a high degree of integration of logistics processes to adapt to this realistic market environment.
Logistics management always decides on facility location, inventory strategy, and transportation issues around customer service objectives. Many enterprise decision-makers begin to realize that there is an interdependent relationship between the location of facilities (factories, distribution centers, and demand points), the inventory of goods, and the arrangement of vehicle distribution routes. Therefore, comprehensive optimization of logistics management activities should be carried out according to this interdependence. Based on the basic decision-making models in these three areas, some studies focused on the integration and optimization of logistics systems, such as location-routing problem (LRP), inventory-routing problem (IRP), location-inventory problem (LIP), and combined location-inventory-routing problem (CLIRP). CLIRP comprehensively considers facility location, inventory control, and vehicle routing. CLIRP refers to determining the best location of facilities, inventory strategy, and vehicle routing arrangement in the logistics system according to the customer’s demand information. The object of CLIRP is to minimize the sum of location costs, inventory costs, and transportation costs.
The global economy’s rapid development and population growth have adversely affected the earth’s ecological environment. The rapid development of urban distribution has also seriously impacted the urban environment. The carbon generated by logistics activities accounts for 5.5% of human activities and 5–15% of the total product life cycle emissions. In logistics activities, the carbon emissions from fossil fuels required for transportation and distribution account for more than 87% of the total. Previous studies have shown that by reasonably planning roads and distribution routes, carbon emissions in transportation can be reduced by 5% to ensure enterprises’ economic objectives. Therefore, the research on urban distribution in the operation of the low-carbon supply chain is of great significance in reducing carbon emissions. At the same time, how to let enterprises reduce the carbon emission of urban distribution by optimizing operations without increasing cost or increasing little cost is a challenging topic.
Demand management is essential for establishing and solving the CLIRP optimization model and algorithm. It is also the basis for enterprises’ planning and decision-making. Works on demand management always assume that the demand obeys a certain probability distribution. However, this method is difficult to fit the complex demand process affected by many factors. Some of the previous literature on CLIRP focus on integrated modeling of sustainable CLIRP or on solving multi-objective sustainable CLIRP problems with uncertain requirements. Most studies focus on the improvement and expansion of heuristic methods. The research on this problem is still not addressed at present. To fill this blank, this paper uses the multi-stage demand forecasting model to forecast garment products in different batches. Based on the multi-stage demand forecasting model, this paper comprehensively considers the trunk transportation and regional transportation costs, the fixed cost of the distribution center construction, the inventory holding cost, the shortage cost, and salvage in each region. The sustainable CLIRP model, which also considers carbon emission, is constructed with the minimum total logistics cost and low carbon emission as the objective function, and the global near-optimal solution is given by the hybrid heuristic algorithm combining tabu search algorithm (TS) with simulated annealing algorithm (SA) (HH-TS-SA).
The remainder of this paper is organized as follows.
Section 2 provides a literature review and discusses the contributions of this study.
Section 3 describes the model. The algorithm is established in
Section 4. A case study and comparison illustrate the results in
Section 5. Conclusions and managerial implications with future research directions are presented in
Section 6.
2. Literature Review
The performance of a logistics system largely depends on its logistics network planning and design. The unreasonable or outdated logistics network design will lead to improper facility location, inventory level setting, transportation mode, and route selection. As a result, the level of customer service needs to meet the requirements, which will result in the low contribution of logistics to enterprise profits compared with the due level. In recent years, the literature on the application of information technology to logistics system optimization has been increasing. The complexity of the combined location-inventory-routing problem is more in line with the actual characteristics of the current multi-objective logistics system with uncertain demand and has become a challenging research topic. The related literature in this section is all from the Web of Science Core Collection.
At present, works on sustainable CLIRP problems still need to be completed, and most focus on the flexible use of heuristic methods and the choice of different strategies. In terms of the flexible application of heuristic methods, Renaud et al. [
1] use the tabu search heuristic algorithm to discuss three well-known routing problems: the periodic vehicle routing problem, the periodic traveling salesman problem, and the multi-depot vehicle routing problem with capacity and distance constraints. Tuzun and Burke [
2] propose a two-stage tabu search method that can significantly improve the current heuristic method for solving LRP problems. Lin and Lin [
3] propose a heuristic algorithm using the tabu search and simulated annealing algorithm to solve a CLIRP problem. However, they divide the CLIRP problem into two sub-problems: facility location-route and inventory control, and assume that the demand is uniformly distributed. Different from Lin, this paper uses the Holt-Winters model for demand forecasting. Benjaafar et al. [
4] consider the problem of allocating demand that originates from multiple sources among multiple inventory locations. Mehrjerdi et al. [
5] consider a capacitated location-routing problem with fuzzy demands and propose a greedy clustering method, including the stochastic simulation, to solve the problem. Nekooghadirli et al. [
6] present a novel bi-objective location-routing-inventory model that considers a multi-period and multi-product system. Due to the NP-hardness of the given problem, the paper applies four multi-objective meta-heuristic algorithms, namely multi-objective imperialist competitive algorithm (MOICA), multi-objective parallel simulated annealing (MOPSA), non-dominated sorting genetic algorithm II (NSGA-II) and Pareto archive evolution strategy (PAES) to solve it. Unlike solving a bi-objective integrated LRI problem, this paper takes the minimum cost as the objective function and considers one product and a multi-period supply chain system. Saragih et al. [
7] develop a heuristic method for the location-inventory-routing problem in a three-echelon supply chain system where inventory decisions are made in the three involved entities. Karakostas et al. [
8] investigate a mixed integer programming formulation for the location-inventory-routing problem with distribution outsourcing (LIRPDO) and present a General Variable Neighborhood Search (GVNS)-based metaheuristic algorithm for solving it. The numerical analysis illustrates that the algorithm enhances the performance of the improvement phase in the GVNS component of the proposed solution method. Wang [
9] studied the impact of partner selection on the value of information sharing in a three-echelon supply chain system with one capacitated make-to-stock manufacturer and two retailers. Wang assumed the demand followed a normal distribution and applied a search annealing algorithm (SA) to solve the model. To fit the complex demand process, this paper uses demand forecasting based on original data. Ji et al. [
10] study a combined location-routing-inventory system (CLRIS) in a three-echelon supply chain network with environmental considerations. Chavez et al. [
11] propose a multi-objective model for the design of agricultural waste-based biofuel production with an integrated formulation of location, inventory, and routing decisions, and a two-phase heuristic method is utilized to solve the formulated model. Shang et al. [
12] study a classic location-inventory-routing problem with both deterministic and uncertain demands. A robust optimization model is proposed, and applying the strong duality theorem can transform it into a tractable linear equivalent formulation. However, it cannot solve the problem on a large scale. In this paper, a heuristic algorithm is designed to solve this problem.
Regarding the choice of different strategies, Wendy et al. [
13] propose the improvement cycle strategy of the integrated inventory transportation system for multiple products by developing an integrated inventory transportation system with an improved periodic inspection and ordering strategy and a traveling salesman component. Bertazzi et al. [
14] studied the continuous and discrete transportation strategies of several products based on the single cycle problem from one starting point to one destination. Veenstra et al. [
15] introduce a simultaneous facility location and vehicle routing problem that arises in healthcare logistics. Huang et al. [
16] introduce an electric logistics vehicle routing problem, including the locations of multi-type stations, under diversified service strategies combining customer self-pickup and door-to-door delivery. Alkaabneh et al. [
17] consider the strategy of inventory routing in the context of perishable products and find the near-optimal replenishment scheduling and vehicle routes by applying the greedy random adaptive search procedure (GRASP). Achamrach et al. [
18] study a multi-product, multi-vehicle inventory sharing routing problem in a two-level supply chain where a company manufactures products and sells them through its point-of-sale network and design a two-phase algorithm that combines a genetic algorithm and simulated annealing. Schenekemberg et al. [
19] introduce the two-echelon production-routing problem and propose a novel exact algorithm by employing parallel computing techniques, to combine local search procedures within a traditional B&C scheme. Liu et al. [
20] propose an integrated model of location-inventory-routing for perishable products, considering the factors of carbon emissions and product freshness. Wang and Wang [
21] consider the joint replenishment and delivery schedule of the one-warehouse-retailer system with stochastic demand.
Considering the allocation demand problem with multiple demand sources in multiple inventory locations, Escalona et al. [
22] analyze the design of a location-inventory model for fast-moving items able to provide differentiated service levels in terms of product availability for two demand classes (high and low priority) using a critical level policy. They show that the benefit of using a critical-level policy configuring a distribution network is greater when the holding cost per unit and unit time is high. Darvish et al. [
23] consider a production-distribution system that deals with location, production, inventory, and distribution decisions. The objective is to minimize total costs while satisfying demands within a delivery window. Rekik et al. [
24] are concerned with analyzing and modeling the effects of judgmental adjustments to replenishment order quantities, and the results show the analytical equivalence of adjusting an order quantity and deriving an entirely new one in light of a necessary update of the estimated demand distribution. Zheng et al. [
25] investigate the integrated optimization of location, inventory, and routing in supply chain network design problems and propose an exact algorithm based on the Generalized Benders Decomposition (GBD) method to solve the model. Unlike assuming demand follows a certain distribution, this paper uses original data to forecast the demand, making the demand more accurate. Han et al. [
26] consider an integrated production, inventory, and outbound distribution scheduling model that arises in a three-stage supply chain and develop a pseudo-polynomial or polynomial-time solution algorithm to solve the problem. This paper extends it into a multi-stage based on demand forecasting. Xu et al. [
27] propose new models, integrating with customer behavior data analysis, to optimize collection and delivery points for online retailers. Chan et al. [
28] develop a mixed integer linear programming model for an intelligent food logistics system and propose a modified multi-objective particle swarm optimization algorithm to solve the model. Wu et al. [
29] propose a supervised learning-driven (SLD) heuristic to solve the capacitated facility location and production planning (CFLPP) problem. Chavez et al. [
30] study a multi-objective model for the design of agricultural waste-based biofuel production with an integrated formulation of location, inventory, and routing decisions.
Table 1 provides a classification of the related literature and shows the differences between this paper and the previous studies. As
Table 1 reveals, the above literature on sustainable CLIRP is still at the level of the solution by using an optimal algorithm, while there is scarce literature on integrated modeling of sustainable CLIRP or on solving multi-objective sustainable CLIRP problems with uncertain requirements. Most studies focus on the improvement and expansion of heuristic methods. Demand management is essential for establishing and solving sustainable CLIRP optimization models and algorithms. However, research on this problem still needs to be completed. Works on demand management assume that the demand obeys a certain probability distribution. However, this method is difficult to fit the complex demand process affected by many factors. Therefore, the establishment of a sustainable CLIRP model, which is based on demand forecasting, and the development of an algorithm for solving specific problems have more theoretical and practical significance.
According to the review above, CLRIP can be decomposed into two subproblems: depot location-allocation problem and routing and inventory problem. Each subproblem can be solved by a hybrid heuristic combining tabu search (TS) with simulated annealing (SA) sharing the same tabu list, where TS and SA are two well-known methods to solve combinatorial problems such as CLRIP. The reasons for choosing the hybrid heuristic approach are: (1) to share the same tabu list between TS and SA to avoid search cycling and improve search efficiency and (2) to improve search effectiveness.