Renaud Masson

The Dial-A-Ride Problem with Transfers

The Dial-A-Ride Problem with Transfers (DARPT) consists in defining a set of routes that satisfy transportation requests of users between a set of pickup points and a set of delivery points, in the presence of ride time constraints. Users may change vehicles during their trip. This change of vehicle, called a transfer, is made at specific locations called transfer points. Solving the DARPT involves modeling and algorithmic difficulties. In this paper we provide a solution method based on an Adaptive Large Neighborhood Search (ALNS) metaheuristic and explain how to check the feasibility of a request insertion. The method is evaluated on real-life and generated instances. Experiments show that savings due to transfers can be up to 8% on real-life instances.

An iterated local search heuristic for multi-capacity bin packing and machine reassignment problems

This paper proposes an efficient Multi-Start Iterated Local Search for Packing Problems (MS-ILS-PPs) metaheuristic for Multi-Capacity Bin Packing Problems (MCBPP) and Machine Reassignment Problems (MRP). The MCBPP is a generalization of the classical bin-packing problem in which the machine (bin) capacity and task (item) sizes are given by multiple (resource) dimensions. The MRP is a challenging and novel optimization problem, aimed at maximizing the usage of available machines by reallocating tasks/processes among those machines in a cost-efficient manner, while fulfilling several capacity, conflict, and dependency-related constraints. The proposed MS-ILS-PP approach relies on simple neighborhoods as well as problem-tailored shaking procedures. We perform computational experiments on MRP benchmark instances containing between 100 and 50,000 processes. Near-optimum multi-resource allocation and scheduling solutions are obtained while meeting specified processing-time requirements (on the order of minutes). In particular, for 9/28 instances with more than 1000 processes, the gap between the solution value and a lower bound measure is smaller than 0.1%. Our optimization method is also applied to solve classical benchmark instances for the MCBPP, yielding the best known solutions and optimum ones in most cases. In addition, several upper bounds for non-solved problems were improved.

Efficient feasibility testing for request insertion in the pickup and delivery problem with transfers

The Pickup and Delivery Problem with Transfers (PDPT) consists of defining a set of minimum cost routes in order to satisfy a set of transportation requests, allowing them to change vehicles at specific locations. In this problem, routes are strongly interdependent due to request transfers. Then it is critical to efficiently check if inserting a request into a partial solution is feasible or not. In this article, we present a method to perform this check in constant time.

Optimization of a city logistics transportation system with mixed passengers and goods

In this paper, we propose a mathematical model and an adaptive large neighborhood search to solve a two-tiered transportation problem. This problem arises in a prospective study that aims at designing an innovative distribution system for goods in congested city cores. In the first tier, goods are transported in city buses from a consolidation and distribution center to a set of bus stops. The main idea is to use the buses spare capacity to drive the goods to the city core. In the second tier, final customers are distributed by a fleet of near-zero emissions city freighters. This system requires transferring the goods from buses to city freighters at the bus stops. We model the corresponding optimization problem as a variant of the pickup and delivery problem with transfers and solve it with an adaptive large neighborhood search. To evaluate its results, lower bounds are calculated with a column generation approach. The algorithm is assessed on data sets derived from a field study in the medium-sized city of La Rochelle in France.

A branch-and-cut-and-price approach for the pickup and delivery problem with shuttle routes

The main objective of this communication is to show how realistic-sized instances of the Pickup and Delivery Problem with Transfers (PDPT) can be solved to optimality with a branch-and-cut-and-price method, under realistic hypotheses. We introduce the Pickup and Delivery Problem with Shuttle routes (PDP-S) which is a special case of the PDPT relying on a structured network with two categories of routes. {\em Pickup routes} visit a set of pickup points independently of their delivery points and end at one delivery point. {\em Shuttle routes} are direct trips between two delivery points. We propose a path based formulation of the PDP-S and solve it with a branch-and-cut-and-price algorithm. We show that this approach is able to solve real-life instances with up to 87 transportation requests.

A multi-criteria large neighbourhood search for the transportation of disabled people

This paper addresses the problem of optimizing the transportation of disabled persons from home to specialized centres or schools. It is modelled as a Dial-a-ride problem (DARP), where several people share the same destination. Particular emphasis is placed on the objective function in order to consider several potentially conflicting interests. We propose a multi-criteria model from Multi-attribute Utility Theory based on the Choquet integral. The resulting multi-criteria DARP is then solved with a large neighbourhood search algorithm. This method includes classical destroy and repair heuristics as well as new operators exploiting the shared destination feature and criterion-specific operators. The algorithm is evaluated on a set of 14 real-world instances in the field of health care logistics, with up to 200 requests and 51 destination points.

A two-stage solution method for the annual dairy transportation problem

The annual dairy transportation problem involves designing the routes that collect milk from farms and deliver it to processing plants. The demands of these plants can change from one week to the next, but the collection is fixed by contract and must remain the same throughout the year. While the routes are currently designed using the historical average demand from the plants, we show that including the information about plants demands leads to significant savings. We propose a two-stage method based on an adaptive large neighborhood search (ALNS). The first phase solves the transportation problem and the second phase ensures that the optimization of plant assignment is performed. An additional analysis based on period clustering is conducted to speed up the resolution.

The PrePack Optimization Problem

The goal of packing optimization is to provide a foundation for decisions related to inventory allocation as merchandise is brought to warehouses and then dispatched. Major retail chains must fulfill requests from hundreds of stores by dispatching items stored in their warehouses. The demand for clothing items may vary to a considerable extent from one store to the next. To take this into account, the warehouse must pack “boxes” containing different mixes of clothing items. The number of distinct box types has a major impact on the operating costs. Thus, the PrePack problem consists in determining the number and contents of the box types, as well as the allocation of boxes to stores. This paper introduces the PrePack problem and proposes CP and MIP models and a metaheuristic approach to address it.