Metaheuristics for the Vehicle Routing Problem with Loading Constraints

We consider a combination of the capacitated vehicle routing problem and a class of additional loading constraints involving a parallel machine scheduling problem. The work is motivated by a real-world transportation problem occurring to a wood-products retailer, which delivers its products to a number of customers in a specific region. We solve the problem by means of two different metaheuristics algorithms: a Tabu Search and an Ant Colony Optimization. Extensive computational results are given for both algorithms, on instances derived from the vehicle routing literature and on real-world instances

A Decision Support System for Earthwork Activities in Construction Logistics

Making decisions in a complex system such as the construction of a highway is a hard task that involves a combinatorial set of possibilities, concerning thousands of interrelated activities and resources over several years. In this paper we describe a decision support system (DSS) developed to assist project managers in decision making for the construction of the Autostrada Pedemontana Lombarda highway, in Italy. The considered problem evaluates the earthwork activities in de- tail and defines the minimum cost earthwork plan satisfying all constraints. The proposed DSS involves the use of linear programming to solve the earthwork pro- blem in a two-phase approach: in the first phase, an aggregate model determines the feasibility of the overall project, whereas in the second phase, disaggregate models determine the actual flows of each material. The DSS gathers the needed informa- tion directly from the master plan commonly used by the company and provides as output a set of visual solutions. The solution are yielded in short times and can be run many times with different data sets supporting a fast evaluation of different decisions. The provided solutions are also optimized and could substitute the previ- ous manual results. The DSS has been proved to be very effective for assisting the project managers of the above highway construction and is currently in use in other project

Metaheuristics for Vehicle Routing Problems with Three-Dimensional Loading Constraints

This paper addresses an important combination of three-dimensional loading and vehicle routing, known as the Three-Dimensional Loading Capacitated Vehicle Routing Problem. The problem calls for the combined optimization of the loading of freight into vehicles and the routing of vehicles along a road network, with the aim of serving customers with minimum traveling cost. Despite its clear practical relevance in freight distribution, the literature on this problem is very limited. This is because of its high combinatorial complexity.We solve the problem by means of an Ant Colony Optimization algorithm, which makes use of fast packing heuristics for the loading. The algorithm combines two different heuristic information measures, one for routing and one for packing. In numerical tests all publicly available test instances are solved, andfor almost all instances new best solutions are found

Two natural heuristics for 3D packing with practical loading constraints

In this paper, we describe two heuristics for the Single Vehicle Loading Problem (SVLP), which can handle practical constraints that are frequently encountered in the freight transportation industry, such as the servicing order of clients; item fragility; and the stability of the goods. The two heuristics, Deepest-Bottom-Left-Fill and Maximum Touching Area, are 3D extensions of natural heuristics that have previously only been applied to 2D packing problems. We employ these heuristics as part of a two-phase tabu search algorithm for the Three-Dimensional Loading Capacitated Vehicle Routing Problem (3L-CVRP), where the task is to serve all customers using a homogeneous fleet of vehicles at minimum traveling cost. The resultant algorithm produces mostly superior solutions to existing approaches, and appears to scale better with problem size. © 2010 Springer-Verlag Berlin Heidelberg

Bone Marrow Aspirate Concentrate for the Treatment of Avascular Meniscus Tears in a One-Step Procedure—Evaluation of an In Vivo Model

Avascular meniscus tears show poor intrinsic regenerative potential. Thus, lesions within this area predispose the patient to developing knee osteoarthritis. Current research focuses on regenerative approaches using growth factors or mesenchymal stem cells (MSCs) to enhance healing capacity within the avascular meniscus zone. The use of MSCs especially as progenitor cells and a source of growth factors has shown promising results. However, present studies use bone-marrow-derived BMSCs in a two-step procedure, which is limiting the transfer in clinical praxis. So, the aim of this study was to evaluate a one-step procedure using bone marrow aspirate concentrate (BMAC), containing BMSCs, for inducing the regeneration of avascular meniscus lesions. Longitudinal meniscus tears of 4 mm in size of the lateral New Zealand White rabbit meniscus were treated with clotted autologous PRP (platelet-rich plasma) or BMAC and a meniscus suture or a meniscus suture alone. Menisci were harvested at 6 and 12 weeks after initial surgery. Macroscopical and histological evaluation was performed according to an established Meniscus Scoring System. BMAC significantly enhanced regeneration of the meniscus lesions in a time-dependent manner and in comparison to the PRP and control groups, where no healing could be observed. Treatment of avascular meniscus lesions with BMAC and meniscus suturing seems to be a promising approach to promote meniscus regeneration in the avascular zone using a one-step procedure

Experimental Analysis of Pheromone-based Heuristic Column Generation using irace

Abstract. Pheromone-based heuristic column generation (ACO-HCG) is a hybrid algorithm that combines ant colony optimization and a MIP solver to tackle vehicle routing problems (VRP) with black-box feasibility. Traditionally, the experimental analysis of such a complex algorithm has been carried out manually by trial and error. Moreover, a full-factorial statistical analysis is infeasible due to the large number of parameters and the time required for each algorithm run. In this paper, we first automatically configure the algorithm parameters by using an automatic algorithm configuration tool. Then, we perform a basic sensitivity analysis of the tuned configuration in order to understand the significance of each parameter setting. In this way, we avoid wasting effort analyzing parameter settings that do not lead to a high-performing algorithm. Finally, we show that the tuned parameter settings improve the performance of ACO-HCG on the multipile VRP and the three-dimensional loading capacitated VRP.

DESIGN AND OPTIMIZATION OF PICKING IN THE CASE OF MULTI-ITEM MULTI-LOCATION MULTI-PALLET CUSTOMER ORDERS

Order picking related costs may account for up to 65% of the total expense of warehouse management. Hence, the implementation of robust design and optimization procedures for planning picking is addressed by researchers and practitioners.In this chapter the case of warehouses served by humans, in picker-to-parts systems, with a discrete picking organization is studied. Specifically, the case of orders including multiple different items, located in different aisles and requiring more than one forklift load to completely satisfy customer requests is analyzed, with the aim of minimizing the time for retrieving an order. Specifically, two aspects are studied:\u2022the grouping of orders into a finite number of forklift missions, by assuring that each required item is picked in the required amount\u2022the optimization of the routing to be followed by handling facilities in accordance with the objective of minimizing the total travelled distance and the computation of the number of handling facilities necessary for serving the warehouse aisles