Giuseppe Paletta

Deterministic Order-Up-To Level Policies in an Inventory Routing Problem

We consider a distribution problem in which a set of products has to be shipped from a supplier to several retailers in a given time horizon. Shipments from the supplier to the retailers are performed by a vehicle of given capacity and cost. Each retailer determines a minimum and a maximum level of the inventory of each product, and each must be visited before its inventory reaches the minimum level. Every time a retailer is visited, the quantity of each product delivered by the supplier is such that the maximum level of the inventory is reached at the retailer. The problem is to determine for each discrete time instant the retailers to be visited and the route of the vehicle. Various objective functions corresponding to different decision policies, and possibly to different decision makers, are considered. We present a heuristic algorithm and compare the solutions obtained with the different objective functions on a set of randomly generated problem instances.

A HEURISTIC FOR THE PERIODIC VEHICLE ROUTING PROBLEM

The paper describes a model for the optimal management of periodic deliveries of a given commodity. The goal is to schedule the deliveries according to feasible combinations of delivery days and to determine the scheduling and routing policies of the vehicles in order to minimize over the planning horizon the maximum number of vehicles simultaneously employed, i.e., the fleet size. Heuristic algorithms are proposed and computational experience is reported.

Minimizing the Total Cost in an Integrated Vendor--Managed Inventory System

In this paper we consider a complex production-distribution system, where a facility produces (or orders from an external supplier) several items which are distributed to a set of retailers by a fleet of vehicles. We consider Vendor-Managed Inventory (VMI) policies, in which the facility knows the inventory levels of the retailers and takes care of their replenishment policies. The production (or ordering) policy, the retailers replenishment policies and the transportation policy have to be determined so as to minimize the total system cost. The cost includes the fixed and variable production costs at the facility, the inventory costs at the facility and at the retailers and the transportation costs, that is the fixed costs of the vehicles and the traveling costs. We study two different types of VMI policies: The order-up-to level policy, in which the order-up-to level quantity is shipped to each retailer whenever served (i.e. the quantity delivered to each retailer is such that the maximum level of the inventory at the retailer is reached) and the fill-fill-dump policy, in which the order-up-to level quantity is shipped to all but the last retailer on each delivery route, while the quantity delivered to the last retailer is the minimum between the order-up-to level quantity and the residual transportation capacity of the vehicle. We propose two different decompositions of the problem and optimal or heuristic procedures for the solution of the subproblems. We show that, for reasonable initial values of the variables, the order in which the subproblems are solved does not influence the final solution. We will first solve the distribution subproblem and then the production subproblem. The computational results show that the fill-fill-dump policy reduces the average cost with respect to the order-up-to level policy and that one of the decompositions is more effective. Moreover, we compare the VMI policies with the more traditional Retailer-Managed Inventory (RMI) policy and show that the VMI policies significantly reduce the average cost with respect to the RMI policy.

Analysis of the maximum level policy in a production-distribution system

We consider a production-distribution system, where a facility produces one commodity which is distributed to a set of retailers by a fleet of vehicles. Each retailer defines a maximum level of the inventory. The production policy, the retailers replenishment policies and the transportation policy have to be determined so as to minimize the total system cost. The overall cost is composed by fixed and variable production costs at the facility, inventory costs at both facility and retailers and routing costs. We study two different types of replenishment policies. The well-known order-up to level (OU) policy, where the quantity shipped to each retailer is such that the level of its inventory reaches the maximum level, and the maximum level (ML) policy, where the quantity shipped to each retailer is such that the inventory is not greater than the maximum level. We first show that when the transportation is outsourced, the problem with OU policy is NP-hard, whereas there exists a class of instances where the problem with ML policy can be solved in polynomial time. We also show the worst-case performance of the OU policy with respect to the more flexible ML policy. Then, we focus on the ML policy and the design of a hybrid heuristic. We also present an exact algorithm for the solution of the problem with one vehicle. Results of computational experiments carried out on small size instances show that the heuristic can produce high quality solutions in a very short amount of time. Results obtained on a large set of randomly generated problem instances are also shown, aimed at comparing the two policies.

The period traveling salesman problem: a new heuristic algorithm

A new, simple and effective heuristic algorithm has been developed for the period traveling salesman problem. Computational results obtained from the test problems taken from the literature indicate that the algorithm compares well in terms of accuracy with other existing algorithms, finding a larger number of best solutions. Moreover, its average percentage error and its worst ratio of solution to the best-known solution are smaller than those of the other existing algorithms.

Parallel machine scheduling to minimize the makespan with sequence dependent deteriorating effects

A new unrelated parallel machine scheduling problem with deteriorating effect and the objective of makespan minimization is presented in this paper. The deterioration of each machine (and therefore of the job processing times) is a function of the sequence of jobs that have been processed by the machine and not (as considered in the literature) by the time at which each job is assigned to the machine or by the number of jobs already processed by the machine. It is showed that for a single machine the problem can be solved in polynomial time, whereas the problem is NP-hard when the number of machines is greater or equal than two. For the last case, a set of list scheduling algorithms and simulated annealing meta-heuristics are designed and the effectiveness of these approaches is evaluated by solving a large number of benchmark instances.

An improved heuristic for the period traveling salesman problem

We propose a heuristic algorithm for the solution of the period traveling salesman problem Computational results obtained on the classical test instances of the literature show that the total distance obtained by the algorithm is not worse than the best-known total distance in 95% of the instances and is strictly better in 18 of the 40 tested instances.

A multiperiod traveling salesman problem: heuristic algorithms

Abstract This paper deals with a particular traveling salesman problem in which the cities must be visited on a periodic basis over a given M -day time period. Two heuristic algorithms, embedding a procedure for finding a shortest path on a layered network, are developed. Computational results are also reported for ten test problems drawn from the literature.

A heuristic for the periodic rural postman problem

The periodic rural postman problem (PRPP) is variant of the classical rural postman problem whose applications arise in garbage collection and street sweeping. In the PRPP each required arc/edge of a graph must be visited a given number of times over an m-day planning period in such a way that service days are equally spaced. The PRPP amounts to select a service day combination for each required arc/edge and to determine a postman tour for each day of the planning period. The objective is to minimize the total distance travelled. In this paper a simple but effective heuristic for the undirected PRPP is presented. Extensive computational results indicate that the algorithm is capable of providing high quality solutions. To our knowledge this is the first methodological paper devoted to a periodic arc routing problem.

Optimization of join strategies in distributed databases

Abstract The paper presents a structured approach to the problem of minimizing the join cost in a relational distributed environment. A tree model is used to represent a query and a set of tree equivalence classes for query representation is identified corresponding to the space of all the feasible strategies to execute the query. The optimal strategy is then chosen by a dynamic programming approach which exploits the properties of the tree model, although the computational complexity remains exponential in the size of the problem.