Roberto Musmanno

Real-time vehicle routing: Solution concepts, algorithms and parallel computing strategies

Abstract In real-time fleet management, vehicle routes are built in an on-going fashion as vehicle locations, travel times and customer requests are revealed over the planning horizon. To deal with such problems, a new generation of fast on-line algorithms capable of taking into account uncertainty is required. Although several articles on this topic have been published, the literature on real-time vehicle routing is still disorganized. In this paper the research in this field is reviewed and some issues that have not received attention so far are highlighted. A particular emphasis is put on parallel computing strategies.

A queuing network model for the management of berth crane operations

This paper focuses on the optimal management of container discharge/loading at any given berthing point, within a real maritime terminal. Productivity maximization of expensive resources, as rail-mounted berth cranes, should be matched with the vessel requirement of minimizing waiting times with an adequate rate of service completion. To this practical problem, a queuing network model is proposed. Due to its complexity, discrete-event simulation appears as the most appropriate approach to model solution. To get a systematic representation of real constraints and policies of resource allocation and activity scheduling, an event graph (EG)-based methodology has been exploited in simulator design. Alternative policies issued by the operation manager can be inserted in a suitable panel-like view of the queuing network model and then compared by means of simulation, to evaluate the average measures for all berth cranes, such as throughput and completion time. Numerical experiments for simulator validation against real data are encouraging. Some decisions on both straddle carrier assignment to berth cranes and hold assignment and sequencing upon the same crane could be improved by the proposed manager-friendly simulation tool.

Review: Operations research in solid waste management: A survey of strategic and tactical issues

Solid waste management (SWM) is an increasingly complex task, absorbing a huge amount of resources and having a major environmental impact. Computerized systems based on operations research techniques can help decision makers to achieve remarkable cost savings as well as to improve waste recovery. Nevertheless, the literature is quite scattered and disorganized. The objective of this paper is to present an updated survey of the most relevant operations research literature on SWM, mainly focusing on strategic and tactical issues. In addition to providing an extensive bibliographic coverage, we describe the relationships between the various problems, and outline future research.

An Adaptive Large Neighbourhood Search Heuristic for the Capacitated Arc-Routing Problem with Stochastic Demands

The capacitated arc-routing problem with stochastic demands (CARPSD) is an extension of the well-known capacitated arc-routing problem (CARP) in which demands are stochastic. This leads to the possibility of route failures whenever the realized demand exceeds the vehicle capacity. This paper presents the CARPSD in the context of garbage collection. It describes an adaptive large-scale neighbourhood search heuristic for the problem. Computational results show the superiority of this algorithm over an alternative solution approach.

Parallel asynchronous label-correcting methods for shortest paths

We develop parallel asynchronous implementations of some known and some new label-correcting methods for finding a shortest path from a single origin to all the other nodes of a directed graph. We compare these implementations on a shared-memory multiprocessor, the Alliant FX/80, using several types of randomly generated problems. Excellent (sometimes superlinear) speedup is achieved with some of the methods, and it is found that the asynchronous versions of these methods are substantially faster than their synchronous counterparts.

Robust scheduling of parallel machines with sequence-dependent set-up costs

In this paper we propose a robust approach for solving the scheduling problem of parallel machines with sequence-dependent set-up costs. In the literature, several mathematical models and solution methods have been proposed to solve such scheduling problems, but most of which are based on the strong assumption that input data are known in a deterministic way. In this paper, a fuzzy mathematical programming model is formulated by taking into account the uncertainty in processing times to provide the optimal solution as a trade-off between total set-up cost and robustness in demand satisfaction. The proposed approach requires the solution of a non-linear mixed integer programming (NLMIP), that can be formulated as an equivalent mixed integer linear programming (MILP) model. The resulting MILP model in real applications could be intractable due to its NP-hardness. Therefore, we propose a solution method technique, based on the solution of an approximated model, whose dimension is remarkably reduced with respect to the original counterpart. Numerical experiments conducted on the basis of data taken from a real application show that the average deviation of the reduced model solution over the optimum is less than 1.5%.

THE CAPACITATED PLANT LOCATION PROBLEM WITH MULTIPLE FACILITIES IN THE SAME SITE

Abstract In this paper, we introduce the capacitated plant location problem (CPLP) with multiple facilities in the same site (CPLPM), a special case of the classical CPLP where several facilities can be opened in the same site. Applications of the CPLPM arise in a number of contexts, such as the location of polling stations. Although the CPLPM can be modelled and solved as a standard CPLP, this approach usually performs very poorly. In this paper we describe a novel Lagrangean relaxation and a tailored Lagrangean heuristic that overcome the drawbacks of classical procedures. These algorithms were used to solve a polling station location problem in Italy. Computational results show that the average deviation of the heuristic solution over the lower bound is less than 2%. Scope and purpose This paper deals with a location problem that is of utmost importance for many public and private organizations. The problem aims at determining a set of capacitated facilities (warehouses, plants, polling stations, etc.) in such a way that the sum of facility construction costs and transportation costs is minimised. Unlike previous papers we allow multiple facilities in the same site. As classical lower and upper bounding procedures perform very poorly in this case, we devised a novel Lagrangean relaxation and a tailored Lagrangean heuristic. Our study was motivated by a real-world application arising in Italian municipalities where one has to locate polling stations and to assign voters to polling stations.

Parallel algorithms to solve two-stage stochastic linear programs with robustness constraints

Abstract In this paper we present a parallel method for solving two-stage stochastic linear programs with restricted recourse. The mathematical model considered here can be used to represent several real-world applications, including financial and production planning problems, for which significant changes in the recourse solutions should be avoided because of their difficulty to be implemented. Our parallel method is based on a primal-dual path-following interior point algorithm, and exploits fruitfully the dual block-angular structure of the constraint matrix and the special block structure of the matrices involved in the restricted recourse model. We describe and discuss both message-passing and shared-memory implementations and we present the numerical results collected on the Origin2000.

Tabu Search Heuristics for the Arc Routing Problem with Intermediate Facilities under Capacity and Length Restrictions

This paper deals with the Arc Routing Problem with Intermediate Facilities under Capacity and Length Restrictions(CLARPIF), a variant of the classical Capacitated Arc Routing Problem(CARP), in which vehicles may unload or replenish at intermediate facilities and the length of any route may not exceed a specified upper bound. Three heuristics are developed for the CLARPIF: the first is a constructive procedure based on a partitioning approach while the second and the third are tailored Tabu Search procedures. Computational results on a set of benchmark instances with up to 50 vertices and 92 required edges are presented and analyzed.

The Arc Routing and Scheduling Problem with Transshipment

This article introduces theArc Routing and Scheduling Problem with Transshipment (ARPT), a particularArc Routing Problem whose applications arise in garbage collection. In the ARPT,the demand is collected by specially equipped vehicles, taken to a transfer station, shredded or compacted and, finally, transported to a dump site by means of high-capacity trucks. A lower bound, based on a relaxation of an integer linear formulation of the problem, is developed for the ARPT. A tailored Tabu Search heuristic is also devised. Computational results on a set of benchmark instances are reported.