Warehouse reshuffling: Insights and optimization

Warehouse reshuffling is a reorganization strategy that consists of repositioning items by moving them sequentially. This study investigates how to optimize warehouse reshuffling and quantifies the effect of common assumptions. A mathematical programming formulation for the general warehouse reshuffling problem, the complexity of the problem, several heuristics based on the problem structure, a formal proof delimitating instances where double-handling can be a productive move, and managerial insights on the performance of reshuffling policies in various environments are presented. Experimental results suggest that the proposed heuristics improve upon a benchmark heuristic by relaxing how cycles are handled and incorporating double-handling.     

Heuristics for the integration of crane productivity in the berth allocation problem

In this paper, the combined problem of berth allocation and crane assignment in container terminals is investigated. The proposed problem formulation includes important real world aspects such as the decrease of marginal productivity of quay cranes assigned to a vessel and the increase in handling time if vessels are not berthed at their desired position at the quay. To solve the problem a construction heuristic, local refinement procedures, and two meta-heuristics are presented. These methods perform well on a set of real world like instances. The results emphasize the important role of quay crane productivity in berth planning.     

Heuristics for quay crane scheduling at indented berth

This paper discusses the quay crane scheduling problem at indented berth, an extension to the current quay crane scheduling problem in the field of container terminal operation. A mixed integer programming model by considering the unique features of the quay crane scheduling problem at indented berth is formulated. For solution, decomposition heuristic framework is developed and enhanced by Tabu search. To evaluate the performance of the proposed heuristic framework, a comprehensive numerical test is carried out and its results show the good quality of the proposed heuristic framework.     

A robust optimization approach to the integrated berth allocation and quay crane assignment problem

This paper investigates the integrated berth allocation and quay crane assignment problem in container terminals. A deterministic model is formulated by considering the setup time of quay cranes. However, data uncertainties widely exist, and it may cause the deterministic solution to be infeasible. To handle the uncertainties, a robust optimization model is established. Furthermore, to control the level of conservativeness, another robust optimization model with the price constraints is proposed. A genetic algorithm and an insertion heuristic algorithm are suggested to obtain near optimal solutions. Computational experiments indicate that the presented models and algorithms are effective to solve the problems.     

Berth scheduling by customer service differentiation: A multi-objective approach

In this paper the discrete and dynamic berth allocation problem is formulated as a multi-objective combinatorial optimization problem where vessel service is differentiated upon based on priority agreements. A genetic algorithms based heuristic is developed to solve the resulting problem. A number of numerical experiments showed that the heuristic performed well in solving large, real life instances. The heuristic provided a complete set of solutions that enable terminal operators to evaluate various berth scheduling policies and select the schedule that improves operations and customer satisfaction. The proposed algorithm outperformed a state of the art metaheuristic and provided improved results when compared to the weighted approach.     

Models and algorithms for multi-crane oriented scheduling method in container terminals

“Multi-crane oriented” is a scheduling method that yard trailers can be shared by different quay cranes. In this paper, two models for this problem are developed. The first one is a model for an inter-ship-based sharing method. In this model, yard trailers can be shared by quay cranes of different ships. To solve the model, a two-phase Tabu search algorithm is designed. The second one is a model for a ship-based sharing method. In this model, yard trailers can only be shared by quay cranes of the same ship. Q-learning algorithm is designed to solve the model. Numerical tests show that the “multi-crane oriented” method can decrease the yard trailers’ travel distance, reduce the disequilibrium of different working lines, and thus improve the operation efficiency in container terminals.     

A milk collection problem with blending

A milk collection problem with blending is introduced. A firm collects milk from farms, and each farm produces one out of three possible qualities of milk. The revenue increases with quality, and there is a minimum requirement at the plant for each quality. Different qualities of milk can be blended in the trucks, reducing revenues, but also transportation costs, resulting in higher profit. A mixed integer-programming model, a new cut, and a branch-and-cut algorithm are proposed to solve medium-sized instances. A three-stage heuristic is designed for large instances. Computational experience for test instances and a large-sized real case is presented.     

Integrating berth allocation and quay crane assignments

In this study, a dynamic allocation model using objective programming for berth allocation and quay crane assignments was preliminarily developed based on rolling-horizon approach. Afterwards, a hybrid parallel genetic algorithm (HPGA), which combined parallel genetic algorithm (PGA) and heuristic algorithm, was employed to resolve the proposed model. Furthermore, a simulation was conducted to evaluate the HPGA and to execute relevant gene repair techniques. Eventually, the numerical experiments on a specific container terminal were applied to illustrate the proposed models and algorithms. In so doing, the effectiveness of the proposed approach was verified.     

A heuristic algorithm for yard truck scheduling and storage allocation problems

The yard truck scheduling and the storage allocation are two important decision problems affecting the efficiency of container terminal operations. This paper proposes a novel approach that integrates these two problems into a whole. The objective is to minimize the weighted sum of total delay of requests and the total travel time of yard trucks. Due to the intractability of the proposed problem, a hybrid insertion algorithm is designed for effective problem solutions. Computational experiments are conducted to examine the key factors of the problem and the performance of the proposed heuristic algorithm.     

Scheduling technicians for planned maintenance of geographically distributed equipment

A real-world planned maintenance scheduling problem that exists at several business units within United Technologies Corporation (UTC) is addressed in this paper. The scheduling problem is formulated as a multiple tour maximum collection problem with time-dependent rewards and an adaptive memory tabu search heuristic is developed to solve it. The effectiveness of the proposed solution approach is examined using real-world problem instances supplied by UTC. Relevant upper bounds are derived for the application. Results of numerical experiments indicate that the proposed tabu search heuristic is able to obtain near optimal solutions for large-size (i.e., actual) problem instances in reasonable computation time.     

Arc routing problems to restore connectivity of a road network

After a disaster, restoring accessibility in the affected area is critical for response operations. We study two arc routing problems for clearing blocked roads. The first problem minimizes the time to reconnect the road network, while the second maximizes the total benefit gained by reconnecting network components within a time limit. For each problem, we develop a mixed integer programming formulation and two versions of a heuristic algorithm. We conduct computational experiments on Istanbul data and instances adapted from the literature. The heuristics achieve near-optimal or optimal solutions quickly in most of the tested instances.     

A novel modeling approach for the fleet deployment problem within a short-term planning horizon

This paper is concerned with model development for a short-term fleet deployment problem of liner shipping operations. We first present a mixed integer nonlinear programming model in which the optimal vessel speeds for different vessel types on different routes are interpreted as their realistic optimal travel times. We then linearize the proposed nonlinear model and obtain a mixed integer linear programming (MILP) model that can be efficiently solved by a standard mixed integer programming solver such as CPLEX. The MILP model determines the optimal route service frequency pattern and take into account the time window constraints of shipping services. Finally, we report our numerical results and performance of CPLEX on randomly generated instances.     

Optimal traffic calming: A mixed-integer bi-level programming model for locating sidewalks and crosswalks in a multimodal transportation network to maximize pedestrians’ safety and network usability

We study the effect that installing sidewalks and crosswalks, as traffic calming facilities, has on the safety and usability of a transportation network with automobile, public transit and walking as modes of transportation. A mathematical programming model is proposed for this problem whose objective is to minimize the safety hazard for pedestrians and the total transportation cost of the network. We utilize a customized greedy heuristic and a simulated annealing algorithm for solving the problem. The computational results indicate that installing sidewalks and crosswalks at proper locations can reduce the overall transportation cost and improve pedestrians’ safety.     

The Electric Traveling Salesman Problem with Time Windows

To minimize greenhouse gas emissions, the logistic field has seen an increasing usage of electric vehicles. The resulting distribution planning problems present new computational challenges.We address a problem, called Electric Traveling Salesman Problem with Time Windows. We propose a mixed integer linear formulation that can solve 20-customer instances in short computing times and a Three-Phase Heuristic algorithm based on General Variable Neighborhood Search and Dynamic Programming.Computational results show that the heuristic algorithm can find the optimal solution in most small-size instances within a tenth of a second and achieves goods solutions in instances with up to 200 customers.     

Hub location problems in transportation networks

In this paper we propose a 4-index formulation for the uncapacitated multiple allocation hub location problem tailored for urban transport and liner shipping network design. This formulation is very tight and most of the tractable instances for MIP solvers are optimally solvable at the root node. While the existing state-of-the-art MIP solvers fail to solve even small size instances of problem, our accelerated and efficient primal (Benders) decomposition solves larger ones. In addition, a very efficient greedy heuristic, proven to be capable of obtaining high quality solutions, is proposed. We also introduce fixed cost values for Australian Post (AP) dataset.     

Aircraft line maintenance scheduling and optimisation

With the advent of new technologies and more modern aircraft, many of the maintenance jobs traditionally scheduled for periodic block checks can now be performed in the ‘‘line maintenance” environment, i.e., during layovers between scheduled flights of an aircraft. This flexibility can be exploited to reduce maintenance costs and improve fleet utilisation of an airline. In this paper we introduce and study the Line Maintenance Scheduling Problem (LMSP). The LMSP assigns jobs to available maintenance opportunities, defined by aircraft routes, and sets the starting time for each job. Its objective is to minimise the deviation from this schedule with respect to given due dates for each task, without exceeding resource capacity at the airports at any moment. We formulate the LMSP as a mixed integer programming problem, and describe and compare two solution approaches for this problem: an integrated exact solution algorithm, which solves job assignment and timetabling simultaneously, and a sequential, heuristic approach. We tested our algorithms on a set of instances inspired on data provided by an industry partner. Our experiments show the applicability of both approaches on realistic settings: the exact approach was able to find the optimal solution for all instances, in less than 10 min on average. Our analysis also shows with an example that line maintenance can be more efficient when capacity is spatially spread, even if the total capacity is reduced.     

A novel heuristic approach for solving aircraft landing problem with single runway

Nowadays, airlines administrations are more willing to utilize optimization tools to control air traffic due to considerable increases in volume of air transports. A challenging problem in the field of air traffic is how to optimally schedule landing time of aircrafts and assign them to different runways such that early and late landing costs are minimized. This problem is called aircraft landing problem (ALP). This paper proposes a novel decomposition based heuristic by solving two sub-problems for the ALP with single runway. In the first sub-problem, we apply the adaptive large neighborhood search (ALNS) algorithm to find a sequence of aircrafts. The solution found in the first sub-problem will be sent to the second sub-problem, to check for the feasibility of the solution using CPLEX solver. A set of benchmark problem are taken from the OR library for the purpose of comparison with other existing approaches. The computational results exhibit that the proposed algorithm is capable of finding the best known optimal solution for all the instances.     

A dynamic approach for aircraft assignment and maintenance scheduling by airlines

The problems of assigning planes to flights and of fleet maintenance operations scheduling are considered in this paper. While recent approaches make use of artificial intelligence techniques running on main frame computers to solve combinatorial optimization problems for nominal operations, a dynamic approach is proposed here to face on-line operation conditions. The proposed solution mixes a Dynamic Programming approach (to cope with the fleet assignment problem) and a heuristic technique (to solve the embedded maintenance schedule problem). When applied to a medium charter airline, this approach shows acceptability characteristics for operational staffs, while providing efficient solutions. The proposed solution scheme can be considered as the basis for the development of an on-line decision support system for fleet operations management within airlines.     

Stochastic fleet deployment models for public bicycle rental systems

This paper presents two stochastic bike deployment (SBD) models that determine the optimal number of bicycles allocated to each station in a leisure-oriented public bicycle rental system with stochastic demands. The SBD models represent the stochastic demands using a set of scenarios with given probabilities. A multilayer bike-flow time-space network is constructed for developing the models, where each layer corresponds to a given demand scenario and effectively describes bicycle flows in the spatial and temporal dimensions. As a result, the models are formulated as the integer multi-commodity network flow problem, which is characterized as NP-hard. We propose a heuristic to efficiently obtain good quality solutions for large-size model instances. Test instances are generated using real data from a bicycle rental system in Taiwan to evaluate the performance of the models and the solution algorithm. The test results show that the models can help the system operator of a public bicycle system make effective fleet deployment decisions.     

The impact of truck arrival information on container terminal rehandling

This paper uses simulation to evaluate the use of truck arrival information to reduce container rehandles during the import container retrieval process by improving terminal operations. A variety of scenarios with different levels of truck information and various container bay configurations are modeled to explore how the information quality and bay configuration affect the magnitude of benefit. The results demonstrate that a complete arrival sequence is not required to substantially reduce rehandles, significant benefit can be obtained under small amounts of information, the benefits grow with the bay size, and that updating information in real time significantly lowers information requirements.