Enrico Angelelli

The periodic vehicle routing problem with intermediate facilities

Abstract In this paper, we study an extension of the PVRP where the vehicles can renew their capacity at some intermediate facilities. Each vehicle returns to the depot only when its work shift is over. For this problem we propose a tabu search (TS) algorithm and present computational results on a set of randomly generated instances and on a set of PVRP instances taken from the literature.

The Application of a Vehicle Routing Model to a Waste Collection Problem: Two Case Studies

In this paper we suggest a unique model for estimating the operating cost of each of three waste-collection systems. Under the traditional system, which is widely used, waste is typically collected in plastic bags and a three-man crew is needed on each vehicle. The other two systems require a one-man crew for vehicle collecting street containers. The side-loader system with fixed body automatically empties street containers into the vehicle body and empties the load at the disposal site. The side-loader system with demountable body allows the separation of the waste collection phase from transport to the disposal site, since the vehicle body can be demounted. We also present two case studies and show how the estimation of operating costs is a critical issue in decisions regarding the type of system to be used for waste collection.

The Vehicle Routing Problem with Time Windows and Simultaneous Pick-up and Delivery

In this paper we consider the problem of a single depot distribution/collection system servicing a set of customers by means of a homogeneous fleet of vehicles. Each customer requires the simultaneous delivery and pick-up of products to be carried out by the same vehicle within a given time window. Products to be delivered are loaded at the depot and picked-up products are transported back to the depot. The objective is to minimize the overall distance traveled by the vehicles while servicing all the customers. To the best of our knowledge no exact algorithms have been introduced for this problem. We implement a Branch and Price approach based on a set covering formulation for the master problem. A relaxation of the elementary shortest path problem with time windows and capacity constraints is used as pricing problem. Branch and Bound is applied to obtain integer solutions. Known benchmark instances for the VRP with time windows have been properly modified to be used for the experimental analysis.

Kernel search: A general heuristic for the multi-dimensional knapsack problem

In this paper we apply the kernel search framework to the solution of the strongly NP-hard multi-dimensional knapsack problem (MKP). Kernel search is a heuristic framework based on the identification of a restricted set of promising items (kernel) and on the exact solution of ILP sub-problems. Initially, the continuous relaxation of the MKP, solved on the complete set of available items, is used to identify the initial kernel. Then, a sequence of ILP sub-problems are solved, where each sub-problem is restricted to the present kernel and to a subset of other items. Each ILP sub-problem may find better solutions with respect to the previous one and identify further items to insert into the kernel. The kernel search was initially proposed to solve a complex portfolio optimization problem. In this paper we show that the method has general key features that make it appropriate to solve other combinatorial problems using binary variables to model the decisions to select or not items. We adapt the kernel search to the solution of MKP and show that the method is very effective and efficient with respect to known problem-specific approaches. Moreover, the best known values of some MKP benchmark problems from the MIPLIB library have been improved.

Competitive analysis of a dispatch policy for a dynamic multi-period routing problem

We analyze an on-line algorithm (dispatch policy) for a dynamic multi-period routing problem. The objective is to minimize the total cost over all periods. We show that the competitive ratio of this policy for instances with customers located on the non-negative real line is 32.

Kernel Search: a new heuristic framework for portfolio selection

In this paper we propose a new heuristic framework, called Kernel Search, to solve the complex problem of portfolio selection with real features. The method is based on the identification of a restricted set of promising securities (kernel) and on the exact solution of the MILP problem on this set. The continuous relaxation of the problem solved on the complete set of available securities is used to identify the initial kernel and a sequence of integer problems are then solved to identify further securities to insert into the kernel. We analyze the behavior of several heuristic algorithms as implementations of the Kernel Search framework for the solution of the analyzed problem. The proposed heuristics are very effective and quite efficient. The Kernel Search has the advantage of being general and thus easily applicable to a variety of combinatorial problems.

The On-Line Multiprocessor Scheduling Problem with Known Sum of the Tasks

AbstractIn this paper we investigate a semi on-line multiprocessor scheduling problem. The problem is the classical on-line multiprocessor problem where the total sum of the tasks is known in advance. We show an asymptotic lower bound on the performance ratio of any algorithm (as the number of processors gets large), and present an algorithm which has performance ratio at most

Exploring greedy criteria for the dynamic traveling purchaser problem

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