A novel mathematical formulation for solving the dynamic and discrete berth allocation problem by using the Bee Colony Optimisation algorithm

AbstractBerth allocation is one of the crucial points for efficient management of ports. This problem is complex due to all possible combinations for assigning ships to available compatible berths. This paper focuses on solving the Berth Allocation Problem (BAP) by optimising port operations using an innovative model. The problem analysed in this work deals with the Discrete and Dynamic Berth Allocation Problem (DDBAP). We propose a novel mathematical formulation expressed as a Mixed Integer Linear Programming (MILP) for solving the DDBAP. Furthermore, we adapted a metaheuristic solution approach based on the Bee Colony Optimisation (BCO) for solving large-sized combinatorial BAPs. In order to assess the solution performance and efficiency of the proposed model, we introduce a new set of instances based on real data of the Livorno port (Italy), and a comparison between the BCO algorithm and CPLEX in solving the DDBAP is performed. Additionally, the application of the proposed model to a real berth scheduling (Livorno port data) and a comparison with the Ant Colony Optimisation (ACO) metaheuristic are carried out. Results highlight the feasibility of the proposed model and the effectiveness of BCO when compared to both CPLEX and ACO, achieving computation times that ensure a real-time application of the method

A green logistics solution for last-mile deliveries considering e-vans and e-cargo bikes

Abstract The environmental challenges and the initiatives for sustainable development in urban areas are mainly focused on eco-friendly transportation systems. Therefore, we introduce a new green logistics solution for last-mile deliveries considering synchronization between e-vans and e-cargo bikes, developed as a Two-Echelon Electric Vehicle Routing Problem with Time Windows and Partial Recharging (2E-EVRPTW-PR). The first echelon represents an urban zone, and the second echelon represents a restricted traffic zone (e.g., historical center) in which e-vans in the first and e-cargo bikes in the second echelon are used for customers' deliveries. The proposed 2E-EVRPTW-PR model aims to minimize the total costs in terms of travel costs, initial vehicles' investment costs, drivers' salary costs, and micro-depot cost. The effectiveness of the proposed solution has been demonstrated comparing two different cases, i.e., the EVRPTW-PR considering e-vans for the first case, and the 2E-EVRPTW-PR considering e-vans and e-cargo bikes for the second case. The comparison has been carried out on existing EVRPTW-PR instances for the first case, and on novel 2E-EVRPTW-PR instances for the second case, in which customers of initial EVRPTW-PR instances have been divided into two zones (urban and restricted traffic zones) by using Fuzzy C-mean clustering. Moreover, results encourage logistics companies to adopt zero-emission strategies for last-mile deliveries, especially in restricted traffic zones

A mathematical programming model for optimal fleet management of electric car-sharing systems with Vehicle-to-Grid operations

Electric car-sharing systems have attracted large attention in recent years as a new business model for achieving both economic and environmental benefits in urban areas. Among different types, the one considered in this paper is the so-called one-way car-sharing system whereby a user can begin and end a trip at any station of the system. At the same time, the Vehicle-to-Grid (V2G) concept is emerging as a possible innovative solution for smart power grid control. A management system that combines car-sharing system operations and V2G technology is a recent challenge for academia and industry. In this work, a mixed integer linear programming formulation is proposed to find the optimal management of electric vehicles in a one-way car-sharing system integrated with V2G technology. The proposed mathematical model allows finding the optimal start-of-day electric vehicles distribution that maximizes the total revenue obtained from system users and V2G profits through daily electric vehicles charging/discharging schedules. These schedules are based on mean daily users' electric vehicles requests and electricity prices. The model can be applied to evaluate the possible average daily profitability of V2G operations. In order to test the model performance, we applied it to a small-size test network and a real-size test network (the Delft network in the Netherlands). Under the model assumptions, the adoption of V2G technology allows to fully cover the daily charging costs due to users’ trips and to obtain V2G profits by taking advantage of electric vehicles unused time without significantly reducing the satisfied car-sharing system demand. Most of the energy purchased to charge the electric vehicles batteries is provided back to the grid during energy peak load demand, creating benefits also for energy providers.</p

A mathematical programming model for optimal fleet management of electric car-sharing systems with Vehicle-to-Grid operations

Electric car-sharing systems have attracted large attention in recent years as a new business model for achieving both economic and environmental benefits in urban areas. Among different types, the one considered in this paper is the so-called one-way car-sharing system whereby a user can begin and end a trip at any station of the system. At the same time, the Vehicle-to-Grid (V2G) concept is emerging as a possible innovative solution for smart power grid control. A management system that combines car-sharing system operations and V2G technology is a recent challenge for academia and industry. In this work, a mixed integer linear programming formulation is proposed to find the optimal management of electric vehicles in a one-way car-sharing system integrated with V2G technology. The proposed mathematical model allows finding the optimal start-of-day electric vehicles distribution that maximizes the total revenue obtained from system users and V2G profits through daily electric vehicles charging/discharging schedules. These schedules are based on mean daily users' electric vehicles requests and electricity prices. The model can be applied to evaluate the possible average daily profitability of V2G operations. In order to test the model performance, we applied it to a small-size test network and a real-size test network (the Delft network in the Netherlands). Under the model assumptions, the adoption of V2G technology allows to fully cover the daily charging costs due to users’ trips and to obtain V2G profits by taking advantage of electric vehicles unused time without significantly reducing the satisfied car-sharing system demand. Most of the energy purchased to charge the electric vehicles batteries is provided back to the grid during energy peak load demand, creating benefits also for energy providers.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Discrete Mathematics and OptimizationTransport and Plannin

Long-term effects of high-efficiency on-line haemodiafiltration on uraemic toxicity. A multicentre prospective randomized study.

Haemodiafiltration (HDF) may improve survival of chronic dialysis patients. This prospective, multicentre randomized cross-over study evaluated the effects of long-term on-line HDF on the levels of solutes of different molecular weight markers or causative agents of the most common metabolic derangements in uraemia. Methods. Sixty-nine patients from eight Italian centres were randomly assigned to two 6-month treatment sequences: A-B and B-A [A, low-flux haemodialysis (HD) and B, on-line HDF]. Comparative evaluation of basal levels of small, medium-sized and protein-bound solutes at the end of the two treatment periods and analysis of parameters dependence during the interventions were performed. Results. On-line HDF showed greater efficiency than low-flux HD in removing small solutes (eKt/Vurea 1.60 ± 0.31 versus 1.44 ± 0.26, P < 0.0001) and in reducing basal levels of beta2-microglobulin (22.2 ± 7.8 versus 33.5±11.8 mg/L, P < 0.0001), total homocysteine (15.4±5.0 versus 18.7±8.2 μmol/L, P = 0. 003), phosphate (4.6±1.3 versus 5.0±1.4 mg/dL, P = 0.008) and, remarkably, of intact parathyroid hormone (202±154 versus 228±176 pg/mL, P = 0.03). Moreover, in on-line HDF, lower levels of C-reactive protein (5.5±5.5 versus 6.7±6.1 mg/L, P = 0.03) and triglycerides (148±77 versus 167±87 mg/dL, P = 0.008) and increased HDL cholesterol (49.2±12.7 versus 44.7±12.4 mg/dL, P = <0.0001) were observed. The asymmetric dimethylarginine level was not significantly affected (0.97±0.4 versus 0.84±0.37 μmol/L). Erythropoietin and phosphate binders' doses could be reduced. Conclusions. On-line high-efficiency HDF resulted in enhanced removal and lower basal levels of small, medium-sized and protein-bound solutes, which are markers or causative agents of uraemic pathologies, mainly inflammation, secondary hyperparathyroidism and dyslipidaemia. This may contribute to reducing uraemic complications and possibly to improving patient surviva