An Effective Memetic Algorithm with Population Management for the Split Delivery Vehicle Routing Problem

Recent Developments in Modeling and Solving the Split Delivery Vehicle Routing Problem

“…In particular, they apply scatter search to obtain a low-cost feasible solution that uses the minimum number of routes (i.e., vehicles). This objective function is slightly different from the one minimized in Archetti et al [2,3], Boudia et al [5], and Chen et al [7]. There may be a number of low-cost solutions using more than the minimum number of vehicles.…”

“…Tabu Search. Archetti et al [2] formulate a mixed-integer program (MIP) for the SDVRP in which the quantity delivered on a route cannot exceed a value k (they call this Ho and Haugland [16] 2004 Tabu search Time windows Mitra [21] 2005 Cheapest-insertion Backhauls Archetti et al [2] 2006 Tabu search Lee et al [19] 2006 Dynamic program, shortest path Exact algorithm Boudia et al [5] 2007 Memetic algorithm Chen et al [7] 2007 MIP, record-to-record travel Jin et al [17] 2007 LP with valid inequalities Exact algorithm Mitra [22] 2007 Cluster and route Backhauls Mota et al [23] 2007 Scatter search Tavakkoli-Moghaddam et al [28] 2007 Simulated annealing Heterogeneous fleet Thangiah et al [29] 2007 First insertion, local search Real-time events Archetti et al [3] 2008 IP route optimization Jin et al [18] 2008 Column generation Bounds generation Liu et al [20] 2008 Greedy heuristic, bin-packing Fixed route Nowak et al [24] 2008 Local search, Clarke-Wright Pickups problem the k-SDVRP). The authors develop a tabu search algorithm (called SPLITABU) for solving the k-SDVRP.…”

“…Boudia et al [5] solve the SDVRP using a memetic algorithm with population adjustment (MA|PM) described in Sörensen and Sevaux [27]. The authors create an initial population of VRP solutions with no splits using the Clarke and Wright [10] algorithm and the sweep method of Gillett and Miller [14].…”

“…Boudia et al [5] apply their MA|PM algorithm to the 49 problems used by Archetti et al [2] and compare results to SPLITABU-DT. They find that one run of MA|PM improves the SPLITABU-DT solution (average of five runs) in 37 problems and appears to be faster (although the machines are slightly different).…”

An Iterated Local Search for the Split Delivery Vehicle Routing Problem

“…Boudia et al [8] point out that the using of dynamic programming increases the running time by 30% without significant improvement than the one found by a greedy heuristic.In this work, we propose a greedy iterated local search (ILS) algorithm in which the insertion move in local search phase follows the simple principle that always do the best insertion, considering the possible splitting of the demand. In order to extend the search space effectively, without deteriorating the quality of the restart solution, a multi-restart perturbation method is designed, which is derived from the idea of Dong et al [9].…”