Modelling And Optimization For The Design Of Logistics Facilities And Operations In Terminal Yards

With the rapaid growth of the national economy, the continual expansion of the production scale and the rapaid development of e-commerce, the logistics volume of products and goods is significantly increasing, and thus it is urgent to make scientific planning and integrated management for the logistics. In this paper, the research on the modelling and optimization methods respectively for the design of facilities and the management of logistics operations is made. The scientific design for facilities will favor the centralized management, and thus improving service levels, reducing facility redundancy and the logistics cost. The fine management for the logistics operations of the existing facilities will improve the operation efficiency of the facilities, which will further reduce the logistics cost and enhance service levels.In this paper, taking the design of the facilities for a domestic resource-based city as background, the research on the facility location problem with capacity expansions of the existing facilities and the two-stage capacitated facility location problem is conducted in the way of modelling description, the design of Benders decomposition algorithm and some acceleration strategies of it; taking the logistics operations in container terminals as background, the research on the static reshuffling problem, dynamic stacking and reshuffling problem and the slot allocation problem of outbound containers in terminal yards is conducted in the way of modelling description, model improvement, algorithm design and animation simulation. The main contents of dissertation are summarized as follows:1) For the fault that the densities of the Pareto optimal cuts is low produced by the improved Benders decomposition algorithm to not restrict the solution space of the master problem effectively, a high density Pareto cut generation method which lifts the primal Benders cut to be closer to the convex hull constructed by the integer solutions of the master problem is proposed. The high density Pareto cut overcomes the weakness of the Pareto optimal cut which density is low, lifts the Pareto optimal cut, accelerate the solution speed of the master problem, and thus achieve the acceleration of the algorithm. This method is an optimization algorithm. At the same time, a fast approximate algorithm called TS&Benders hybrid algorithm is proposed.2) The facility location problem with the capacity expansions of the existing facilities is investigated. Compared with the ordinary facility location problem, the main feature of the problem is to consider the operation state of the existing facilities when selecting the establishment of new facilities, and the whole problem needs to find a tradeoff among opening, closing, expanding the existing facilities and establishing new facilities to minimize the total cost of the system. For this problem, a mixed-integer programming model is built, and Benders decomposition algorithm is applied. At the same time, three acceleration techniques of the Benders decomposition algorithm are proposed:(1) valid inequalities;(2) disaggregated cuts produced by separating the subproblem;(3) high density Pareto cut and a hybrid technique. The experimental results show that the combination of all the valid inequalities is the most effective to improve the lower bounds. The disaggregated cuts obtained by variable separation and facility separation have the best performance among all the types of the disaggregated cuts. The high density Pareto cut generation method has significant improvement for some instances where the number of iterations is relatively intensive and the CPU time is longer, and the hybrid technique has the best average performance. Compared to the standard Benders algorithm and optimization solver CPLEX, the improved Benders decomposition algorithm is advantageous in decreasing the total number of iterations and CPU time, especially for large-scale instances.3) TS&Benders hybrid algorithm is investigated based on the two-stage capacitated facility location problem. The two-stage capacitated facility location problem is to determine selecting which candidate facilities to open to minimize the sum of the the fixed setup costs and the transportation costs. To overcome the shortcoming that the solution speed of the master problem is slow, Tabu search algorithm is applied to solve the master problem to obtain an approximate solution to improve the speed of the Benders decomposition algorithm. The experimental results show that TS&Benders hybrid algorithm can obtain an approximate-optimal solution in a short time.4) The static reshuffling problem and the dymamic stacking and reshuffling problem in terminal yards are investigated. The static reshuffling problem is how to determine the storage locations of the reshuffled containers so that the total number of reshuffles is minimized when the configuration of a bay is given and all the containers are pickup according to their pickup priorities. For the static reshuffling problem, the existing model is improved and five polynomial-time heuristic algorithms and their extended versions are proposed to solve it, and their performance is analyzed from theory aspect. For the dynamic stacking and reshuffling problem, the proposed heuristic algorithms in the static situation and the heuristic algorithms based on the improved model are applied to the dynamic situation by some transformation, and a discrete-event simulation model is developed to simulate the different algorithms and display the animation. The experimental results show that the improved model can quickly obtain optimal solutions or feasible solutins compared with the existing model, and the performance of the five developed heuristics and their extended versions are superior to the existing heuristics and their extended versions, respectively.5) The slot allocation problem of outbound containers in terminal yard is investigated. The solt allocation problem of outbound containers is how to allocate the storage locations of outbound containers to improve the efficiency of loading when they are transported to the yard by external trucks. Based on the classification of outbound containers and the definitions of group, family and batch, remodel the slot allocation problem for outbound containers from a new perspective to achieve reducing the number of reshuffles when loading by controlling the input operations of logistics. The problem is formulated by a nonlinear integer programming model to balance the workloads among blocks, and the objective is to minimize the transportation distance of internal trucks and the number of confirmed reshuffles when loading. Through the equivalent transformation, convert the model to a linear model that can be directly solved by the standard optimization software CPLEX, and then verify the validity by a specific case.