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.     

A metaheuristic approach to the reliable location routing problem under disruptions

This paper examines a reliable capacitated location–routing problem in which depots are randomly disrupted. Customers whose depots fail must be reinserted into the routes of surviving depots. We present a scenario-based mixed-integer programming model to optimize depot location, outbound delivery routing, and backup plans. We design a metaheuristic algorithm that is based on a maximum-likelihood sampling method, route-reallocation improvement, two-stage neighborhood search and simulated annealing. Numerical tests show that the heuristic is able to generate results that would keep operating costs and failure costs well balanced. Managerial insights on scenario identification, facility deployment and model simplification are drawn.     

A multi-objective approach to scheduling joint participation with variable space and time preferences and opportunities

Activity scheduling supports activity-based analysis in travel demand management and promotes a potentially popular traveler assistance service. A multi-objective approach is proposed to schedule joint participation of multiple individuals, in which the candidate space-time opportunities for joint participation are identified by a concept of time-varying network-based prisms, and optimal opportunities for joint participation are determined by the non-dominated sorting genetic algorithm-II (NSGA-II) with four objectives (i) minimizing cost for congestion charges, (ii) maximizing participation desirability of time-of-day, (iii) minimizing total travel distance and (iv) time in the trips of multiple individuals. A scenario of joint participation among four people is designed and implemented to demonstrate the feasibility of this approach. The results suggest that this approach has the ability to schedule activities within real situations.