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 hybrid multi-objective approach to capacitated facility location with flexible store allocation for green logistics modeling

We propose an efficient evolutionary multi-objective optimization approach to the capacitated facility location–allocation problem (CFLP) for solving large instances that considers flexibility at the allocation level, where financial costs and CO₂ emissions are considered simultaneously. Our approach utilizes suitably adapted Lagrangian Relaxation models for dealing with costs and CO₂ emissions at the allocation level, within a multi-objective evolutionary framework at the location level. Thus our method assesses the robustness of each location solution with respect to our two objectives for customer allocation. We extend our exploration of selected solutions by considering a range of trade-offs for customer allocation.     

A multi-objective sustainable load planning model for intermodal transportation networks with a real-life application

Growing importance of intermodal transportation necessitates modeling and solving load planning problems by taking into account various complex decisions simultaneously like transportation mode/service type selection, load allocation, and outsourcing. This paper presents a mixed-integer mathematical programming model for a multi-objective, multi-mode and multi-period sustainable load planning problem by considering import/export load flows to satisfy transport demands of customers and many other related issues. Several multiple objective optimization procedures are utilized in order to handle conflicting objectives simultaneously under crisp and fuzzy decision making environments. A real-life case study is also performed to present application and usefulness of the proposed model.     

A heuristic approach for solving an integrated gate reassignment and taxi scheduling problem

Capacity limitation of airport ground operation is one of the major limiting factors in air traffic operation. The congestion on the gate and taxiway causes severe delay and propagate effect on the flight schedule. This paper considers the problem of integrated gate reassignment and taxiway scheduling, in which complex constraints related to runway restriction, gate allocation and taxiway conflict are all incorporated when determining the schedule. To solve this problem, we propose a novel heuristic approach. First, all possible aircraft schedules are enumerated by disretizing the waiting time along the path. Then, the cost is evaluated for each schedule and the conflict detection is conducted to generate constraint sets. Finally, we propose a set partition model, in which each decision variable denotes a candidate schedule that takes into account the possible constraints when generated. This method is compared with a sequential method that solves gate reassignment and taxiway scheduling problem separately. Computational results highlight the strength of our method.     

A spatiotemporal partitioning approach for large-scale vehicle routing problems with time windows

For VRP with time windows (VRPTW) solved by conventional cluster-first and route-second approach, temporal information is usually considered with vehicle routing but ignored in the process of clustering. We propose an alternative approach based on spatiotemporal partitioning to solving a large-scale VRPTW, considering jointly the temporal and spatial information for vehicle routing. A spatiotemporal representation for the VRPTW is presented that measures the spatiotemporal distance between two customers. The resulting formulation is then solved by a genetic algorithm developed for k-medoid clustering of large-scale customers based on the spatiotemporal distance. The proposed approach showed promise in handling large scale networks.     

The heterogeneous green vehicle routing and scheduling problem with time-varying traffic congestion

The green vehicle routing and scheduling problem (GVRSP) aims to minimize green-house gas emissions in logistics systems through better planning of deliveries/pickups made by a fleet of vehicles. We define a new mixed integer liner programming (MIP) model which considers heterogeneous vehicles, time-varying traffic congestion, customer/vehicle time window constraints, the impact of vehicle loads on emissions, and vehicle capacity/range constraints in the GVRSP. The proposed model allows vehicles to stop on arcs, which is shown to reduce emissions up to additional 8% on simulated data. A hybrid algorithm of MIP and iterated neighborhood search is proposed to solve the problem.     

An exact solution approach for vehicle routing and scheduling problems with soft time windows

A new column generation based exact optimization approach for the vehicle routing and scheduling problem with semi soft time windows (VRPSSTW) is presented. Elementary shortest path problem with resource constraints and late arrival penalties is solved as a subproblem, which rises from the Dantzig–Wolfe decomposition method. Exact solutions of VRPSSTW and hard time windows variant are compared on Solomon’s benchmark instances as well as on an instance based on Tokyo road network. It was found that the VRPSSTW solution results in fewer routes thus overall costs are reduced and late arrival penalties contribute only a small fraction to total cost.     

An approximation approach to a trade-off among efficiency,efficacy, and balance for relief pre-positioning in disaster management

This work develops a multi-objective, two-stage stochastic, non-linear, and mixed-integer mathematical model for relief pre-positioning in disaster management. Improved imbalance and efficacy measures are incorporated into the model based on a new utility level of the delivered relief commodities. This model considers the usage possibility of a set of alternative routes for each of the applied transportation modes and consequently improves the network reliability. An integrated separable programming-augmented ε-constraint approach is proposed to address the problem. The best Pareto-optimal solution is selected by PROMETHEE-II. The theoretical improvements of the presented approach are validated by experiments and a real case study.     

A robust mathematical model and heuristic algorithms for integrated aircraft routing and scheduling,with consideration of fleet assignment problem

One of the most important airline's products is to determine the aircraft routing and scheduling and fleet assignment. The key input data of this problem is the traffic forecasting and allocation that forecasts traffic on each flight leg. The complexity of this problem is to define the connecting flights when passengers should change the aircraft to reach the final destination. Moreover, as there exists various types of uncertainties during the flights, finding a solution which is able to absorb these uncertainties is invaluable. In this paper, a new robust mixed integer mathematical model for the integrated aircraft routing and scheduling, with consideration of fleet assignment problem is proposed. Then to find good solutions for large-scale problems in a rational amount of time, a heuristic algorithm based on the Simulated Annealing (SA) is introduced. In addition, some examples are randomly generated and the proposed heuristic algorithm is validated by comparing the results with the optimum solutions. The effects of robust vs non-robust solutions are examined, and finally, a hybrid algorithm is generated which results in more effective solution in comparison with SA, and Particle Swarm Optimization (PSO).     

A robust optimization approach for the road network daily maintenance routing problem with uncertain service time

This paper studies the robust optimization approach for the routing problem encountered in daily maintenance operations of a road network. The uncertainty of service time is considered. The robust optimization approach yields routes that minimize total cost while being less sensitive to substantial deviations of service times. A robust optimization model is developed and solved by the branch-and-cut method. In computational experiments, the behavior of the robust solutions and their performance are analyzed using Monte Carlo simulation. The robust optimization model is also compared with a classic chance-constrained programming model. The experimental analysis provides managerial insights for decision makers to determine an appropriate routing strategy.     

Multi-objective decision support to enhance environmental sustainability in maritime shipping: A review and future directions

This paper aims to examine the potential of multi-objective optimization (MOO) as a decision support to improving sustainability in maritime shipping. We focus on environmental sustainability and the trade-offs involved with economic and operational objectives. Through a systematic approach, we review the literature on environmental sustainability, decision support and multi-objective optimization in maritime shipping. We identify the gaps and directions for future research. It is expected that the next generation of decision support systems for maritime transport will exploit the theoretical development in MOO to facilitate informed decision making in maritime supply chains considering environmental sustainability and the competing objectives.     

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.