Algorithm design for the fleet sizing problem in grocery retail distribution

From the classical Vehicle Routing Problem (VRP) and other extensions as well, our research will focus on constraints within grocery retail distribution. Usually they are tackled in the literature as VRP with multiple compartments and multiple trip VRP. Furthermore, we will cope with time windows and other extensions. In addition, our problem will be stated and a solution proposal will be sketched, both considering heuristic and exact approaches

Sustainable maritime crude oil transportation: a split pickup and split delivery problem with time windows

This paper studies a novel sustainable vessel routing problem modeling considering the multi-compartment, split pickup and split delivery, and time windows concepts. In the presented problem, oil tankers transport crude oil from supply ports to demand ports around the globe. The objective is to find ship routes, as well as port arrival and departure times, in a way that minimizes transportation costs. As a second objective, we considered the sustainability aspect by minimizing the vessel energy efficiency operational indicator. Multiple products are transported by a heterogeneous fleet of tankers. Small realistic test instances are solved with the exact method

Optimization Approach for Multi-Period Fuel Replenishment

This paper proposes mathematical models and solution approaches for solving the multi-period fuel replenishment planning problem. The model aims to search for a set of routes, determining the quantity of several petroleum products to be loaded on individual vehicle compartments, and specifying the quantity to be discharged to customer tanks over a given planning horizon in which multiple constraints are satisfied. The objective function is to minimize the transportation unit cost, equal to the total transportation cost divided by the sum of replenished quantity. As the model size grows exponentially when the number of customers, vehicles, and time period increases, an exact algorithm is not feasible. Hence, in this study, we propose two heuristic approaches: two-phase method (2PM) and three-phase method (3PM). The 2PM is primarily designed for solving small problems whereas the 3PM adopts a similar approach but has the ability to solve larger problems. The proposed solutions were tested using a real-life scenario and randomly generated test instance. The results showed that our solution outperforms the solution constructed by experienced planners and also proved that considering multiple periods when devising the fuel replenishment plan, gives superior results in comparison to single periods

A multiple type bike repositioning problem

This paper investigates a new static bicycle repositioning problem in which multiple types of bikes are considered. Some types of bikes that are in short supply at a station can be substituted by other types, whereas some types of bikes can occupy the spaces of other types in the vehicle during repositioning. These activities provide two new strategies, substitution and occupancy, which are examined in this paper. The problem is formulated as a mixed-integer linear programming problem to minimize the total cost, which consists of the route travel cost, penalties due to unmet demand, and penalties associated with the substitution and occupancy strategies. A combined hybrid genetic algorithm is proposed to solve this problem. This solution algorithm consists of (i) a modified version of a hybrid genetic search with adaptive diversity control to determine routing decisions and (ii) a proposed greedy heuristic to determine the loading and unloading instructions at each visited station and the substitution and occupancy strategies. The results show that the proposed method can provide high-quality solutions with short computing times. Using small examples, this paper also reveals problem properties and repositioning strategies in bike sharing systems with multiple types of bikes.published_or_final_versio