Zizhen Zhang

A memetic algorithm for the multiperiod vehicle routing problem with profit

In this paper, we extend upon current research in the vehicle routing problem whereby labour regulations affect planning horizons, and therefore, profitability. We call this extension the multiperiod vehicle routing problem with profit (mVRPP). The goal is to determine routes for a set of vehicles that maximizes profitability from visited locations, based on the conditions that vehicles can only travel during stipulated working hours within each period in a given planning horizon and that the vehicles are only required to return to the depot at the end of the last period. We propose an effective memetic algorithm with a giant-tour representation to solve the mVRPP. To efficiently evaluate a chromosome, we develop a greedy procedure to partition a given giant-tour into individual routes, and prove that the resultant partition is optimal. We evaluate the effectiveness of our memetic algorithm with extensive experiments based on a set of modified benchmark instances. The results indicate that our approach generates high-quality solutions that are reasonably close to the best known solutions or proven optima, and significantly better than the solutions obtained using heuristics employed by professional schedulers.

The single vehicle routing problem with toll-by-weight scheme: A branch-and-bound approach

Expressways in China make use of the toll-by-weight scheme, in which expressway tolls are collected based on the weight and traveling distance of the vehicle. Most vehicle routing models assume that the cost of traversing each edge is equivalent to edge length or some constant; as a result, such models cannot be practically applied to the Chinese expressway transportation system. This study addresses a new single vehicle routing problem that takes the vehicle’s (laden and unladen) weight into account. To solve this problem exactly, we provide a branch-and-bound algorithm with a provably valid lower bound measure, along with five dominance checkers for additional pruning. We analyze our algorithm using instances generated from standard TSP test cases, as well as two new sets of test cases based on real expressway information from the Gansu and Jiangxi provinces in China. The algorithm can be applied to any toll scheme in which the toll per unit distance monotonically increases with weight, even if the toll function is non-linear.

A tabu search algorithm for the multi-period inspector scheduling problem

This paper introduces a multi-period inspector scheduling problem (MPISP), which is a new variant of the multi-trip vehicle routing problem with time windows (VRPTW). In the MPISP, each inspector is scheduled to perform a route in a given multi-period planning horizon. At the end of each period, each inspector is not required to return to the depot but has to stay at one of the vertices for recuperation. If the remaining time of the current period is insufficient for an inspector to travel from his/her current vertex A to a certain vertex B, he/she can choose either waiting at vertex A until the start of the next period or traveling to a vertex C that is closer to vertex B. Therefore, the shortest transit time between any vertex pair is affected by the length of the period and the departure time. We first describe an approach of computing the shortest transit time between any pair of vertices with an arbitrary departure time. To solve the MPISP, we then propose several local search operators adapted from classical operators for the VRPTW and integrate them into a tabu search framework. In addition, we present a constrained knapsack model that is able to produce an upper bound for the problem. Finally, we evaluate the effectiveness of our algorithm with extensive experiments based on a set of test instances. Our computational results indicate that our approach generates high-quality solutions.

The freight consolidation and containerization problem

In today’s global free market, third-party logistics providers (3PLs) are becoming increasingly important. This paper studies a problem faced by a 3PL operating a warehouse in Shanghai, China, under contract with a major manufacturer of children’s clothing based in the United States. At the warehouse, the 3PL receives textile parcel shipments from the suppliers located in China; each shipment is destined for different retail stores located across the United Sates. These shipments must be consolidated and loaded into containers of varying sizes and costs, and then sent along shipping routes to different destination ports. An express company, such as UPS and FedEx, unloads the shipments from the containers at the destination ports and distributes them to their corresponding stores or retailers by parcel delivery. The objective is to find an allocation that minimizes the total container transportation and parcel delivery costs. We formulate the problem into an integer programming model, and also propose a memetic algorithm approach to solve the problem practically. A demonstration of a good solution to this problem was a decisive factor in the awarding of the contract to the 3PL in question.

A memetic algorithm for the capacitated m-ring-star problem

The Capacitated m-Ring-Star Problem (CmRSP) models a network topology design problem in the telecommunications industry. In this paper, we propose to solve this problem using a memetic algorithm that includes a crossover operation, a mutation operation, a local search involving three neighborhood operators, and a population selection strategy that maintains population diversity. Our approach generates the best known solutions for 131 out of 138 benchmark instances, improving on the previous best solutions for 24 of them, and exhibits more advantages on large benchmark instances when compared with the best existing approach. Additionally, all existing algorithms for this problem in literature assume that the underlying graph of the problem instance satisfies the triangle inequality rule; our approach does not require this assumption. We also generated a new set of 36 larger test instances based on a digital data service network price structure to serve as a new benchmark data set for future researchers.

A feasibility-based heuristic for the container pre-marshalling problem

This paper addresses the container pre-marshalling problem (CPMP) which rearranges containers inside a storage bay to a desired layout. By far, target-driven algorithms have relatively good performance among all algorithms; they have two key components: first, containers are rearranged to their desired slots one by one in a certain order; and second, rearranging one container is completed by a sequence of movements. Our paper improves the performance of the target-driven algorithm from both aspects. The proposed heuristic determines the order of container rearrangements by the concepts of state feasibility, container stability, dead-end avoidance and tier-protection proposed in this paper. In addition, we improve the efficiency of performing container rearrangements by discriminating different task types. Computational experiments showcase that the performance of the proposed heuristic is considerable.

Branch and bound algorithm for a single vehicle routing problem with toll-by-weight scheme

Most of previous studies on vehicle routing problems assume that traversal cost of each edge is simply equivalent to a constant number. Unfortunately, the models of this kind can not be applied in China because toll per kilometer of Chinese expressways varies with vehicles weight. Motivated by rapidly increasing market of expressway transportation in China, we address a new and special vehicle routing problem that takes a single vehicle and its weight into account. To solve this problem practically, we provide a branch-and-bound algorithm with a welldesigned lower bound. This algorithm can deal with any toll scheme in which toll per unit distance monotonically increases with weight. Computational results show that test instances with up to 42 vertices can be solved in reasonable computing time.

The Stowage Stack Minimization Problem with Zero Rehandle Constraint

The stowage stack minimization problem with zero rehandle constraint SSMP-ZR aims to find a minimum number of stacks to accommodate all the containers in a multi-port voyage without occurring container rehandles. In this paper, we first give the integer models of the SSMP-ZR with uncapacitated and capacitated stack height. Next, heuristic algorithms are proposed to construct solutions to the SSMP-ZR. The theoretical performance guarantee of the algorithms is then discussed. To evaluate the actual performance of the algorithms, we conduct experiments on a set of instances with practical size. The results demonstrate that our heuristic approaches can generate very promising solutions compared with the random loading solutions and integer programming solutions by CPLEX.

A Branch-and-Bound Algorithm for the Talent Scheduling Problem

The talent scheduling problem is a simplified version of the real-world film shooting problem, which aims to determine a shooting sequence so as to minimize the total cost of the actors involved. We devise a branch-and-bound algorithm to solve the problem. A novel lower bound function is employed to help eliminate the non-promising search nodes. Extensive experiments over the benchmark instances suggest that our branch-and-bound algorithm performs better than the currently best exact algorithm for the talent scheduling problem.

An enhanced branch-and-bound algorithm for the talent scheduling problem

The talent scheduling problem is a simplified version of the real-world film shooting problem, which aims to determine a shooting sequence so as to minimize the total cost of the actors involved. In this article, we first formulate the problem as an integer linear programming model. Next, we devise a branch-and-bound algorithm to solve the problem. The branch-and-bound algorithm is enhanced by several accelerating techniques, including preprocessing, dominance rules and caching search states. Extensive experiments over two sets of benchmark instances suggest that our algorithm is superior to the current best exact algorithm. Finally, the impacts of different parameter settings, algorithm components and instance generation distributions are disclosed by some additional experiments.