Fayez F. Boctor

A Jinear formulation of the machine-part cell formation problem

Abstract The machine-part group formation is an important issue in the design of cellular manufacturing systems. The present paper first discusses some of the alternative formulations of this problem, their advantages and disadvantages, and then suggests a new linear zero-one formulation which seems to have removed most of the disadvantages observed in other models. It will be shown that most of the integrality conditions of the proposed formulation can be relaxed. This considerably improves its computational feasibility and efficiency. Finally, a simulated annealing approach to deal with large-scale problems is also presented.

A tabu search heuristic for the multi-depot vehicle routing problem

This article describes a tabu search algorithm for the multi-depot vehicle routing problem with capacity and route length restrictions. The algorithm is tested on a set of 23 benchmark instances. It is shown to outperform existing heuristics.

Resource-constrained project scheduling by simulated annealing

This paper presents a new adaptation of the simulated annealing algorithm for solving non-preemptive resource-constrained project scheduling problems in which resources are limited but renewable from period to period. This algorithm is able to handle single-mode and multi-mode problems and to optimize different objective functions. Statistical experiments show the efficiency of the proposed algorithm even in comparison to some Tabu search heuristics.

A sweep-based algorithm for the fleet size and mix vehicle routing problem

Abstract This paper presents a new sweep-based heuristic for the fleet size and mix vehicle routing problem. This problem involves two kinds of decisions: the selection of a mix of vehicles among the available vehicle types and the routing of the selected fleet. The proposed algorithm first generates a large number of routes that are serviced by one or two vehicles. The selection of routes and vehicles to be used is then made by solving to optimality, in polynomial time, a set-partitioning problem having a special structure. Results on a set of benchmark test problems show that the proposed heuristic produces excellent solutions in short computing times. Having a fast but good solution method is needed for transportation companies that rent a significant part of their fleet and consequently can take advantage of frequent changes in fleet composition. Finally, the proposed heuristic produced new best-known solutions for three of the test problems; these solutions are reported.

A new and efficient heuristic for scheduling projects with resource restrictions and multiple execution modes

Abstract This paper presents a heuristic procedure for solving non-preemptive resource-constrained project scheduling problems in which resources are limited but renewable from period to period. Associated with each activity is a set of possible durations and the corresponding resource requirements, and the objective is to minimize the overall project duration. Unlike other heuristics that consider schedulable activities separately and schedule only one activity at a time, the heuristic proposed in this paper enumerates some schedulable combinations of activities and chooses from them the one having the best value for an evaluation criterion. It is shown that, based on a set of 240 randomly generated problems, the proposed heuristic outperforms the best heuristics proposed in the open literature up to the moment when this research was done.

A heuristic for the pickup and delivery traveling salesman problem

Abstract This paper deals with the pickup and delivery traveling salesman problem. First we show how to adapt some classical traveling salesman heuristics to solve this problem, then we propose a new and efficient composite heuristic. The proposed heuristic is composed of two phases: a solution construction phase including a local optimization component and a deletion and re-insertion improvement phase. To evaluate its performance, the proposed heuristic was compared to the only available heuristic specially designed to solve this problem, to an adaptation of the most efficient heuristic designed to solve the traveling salesman problem with backhaul, to an adaptation of the farthest as well as to an adaptation of the cheapest insertion methods. Each of these heuristics was followed by our deletion and re-insertion procedure which considerably improved their performance. Results based on a new set of test problems show that the proposed heuristic outperforms all these reinforced heuristics. Scope and purpose In several physical distribution problems, goods must be picked at an origin and delivered to a destination. Examples include the transportation of handicapped persons, the pickup and delivery of fast courier, of some medical supplies, etc. This problem differs from classical transportation problems because we have to deal with precedence constraints between the customers to be visited. This article describes a powerful heuristic for this difficult problem.

An efficient composite heuristic for the symmetric generalized traveling salesman problem

The main purpose of this paper is to introduce a new composite heuristic for solving the generalized traveling salesman problem. The proposed heuristic is composed of three phases: the construction of an initial partial solution, the insertion of a node from each non-visited node-subset, and a solution improvement phase. We show that the heuristic performs very well on 36 TSPLIB problems which have been solved to optimality by other researchers. We also propose some simple heuristics that can be used as basic blocks to construct more efficient composite heuristics.

A FAST COMPOSITE HEURISTIC FOR THE SYMMETRIC TRAVELING SALESMAN PROBLEM.

This article describes a new composite heuristic for the symmetric Traveling Salesman Problem. The heuristic consists of three phases: construction of an initial envelope, insertion of the remaining vertices, and improvement procedure. The heuristic is shown to perform very well both on randomly generated instances and on TSPLIB problems.

A heuristic for the multi-period petrol station replenishment problem

In the multi-period petrol station replenishment problem (MPSRP) the aim is to optimize the delivery of several petroleum products to a set of petrol stations over a given planning horizon. One must determine, for each day of the planning horizon, how much of each product should be delivered to each station, how to load these products into vehicle compartments, and how to plan vehicle routes. The objective is to maximize the total profit equal to the revenue, minus the sum of routing costs and of regular and overtime costs. This article describes a heuristic for the MPSRP. It contains a route construction and truck loading procedures, a route packing procedure, and two procedures enabling the anticipation or the postponement of deliveries. The heuristic was extensively tested on randomly generated data and compared to a previously published algorithm. Computational results confirm the efficiency of the proposed methodology.

Perturbation heuristics for the pickup and delivery traveling salesman problem

This article describes and compares seven perturbation heuristics for the Pickup and Delivery Traveling salesman Problem (PDTSP). In this problem, a shortest Hamiltonian cycle is sought through a depot and several pickup and delivery pairs. Perturbation heuristics are diversification schemes which help a local search process move away from a local optimum. Three such schemes have been implemented and compared: Instance Perturbation, Algorithmic Perturbation, and Solution Perturbation. Computational results on PDTSP instances indicate that the latter scheme yields the best results. On instances for which the optimum is known, it consistently produces optimal or near-optimal solutions.