Ibrahim H. Osman

Metastrategy simulated annealing and tabu search algorithms for the vehicle routing problem

The vehicle routing problem (VRP) under capacity and distance restrictions involves the design of a set of minimum cost delivery routes, originating and terminating at a central depot, which services a set of customers. Each customer must be supplied exactly once by one vehicle route. The total demand of any vehicle must not exceed the vehicle capacity. The total length of any route must not exceed a pre-specified bound. Approximate methods based on descent, hybrid simulated annealing/tabu search, and tabu search algorithms are developed and different search strategies are investigated. A special data structure for the tabu search algorithm is implemented which has reduced notably the computational time by more than 50%. An estimate for the tabu list size is statistically derived. Computational results are reported on a sample of seventeen bench-mark test problems from the literature and nine randomly generated problems. The new methods improve significantly both the number of vehicles used and the total distances travelled on all results reported in the literature.

Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization

From the Publisher: A meta-heuristic is an iterative master process that guides and modifies the operations of subordinate heuristics to efficiently produce high-quality solutions, and recently, there have been significant advances in the theory and application of meta-heuristics to the approximate solutions of hard optimization problems. Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization comprises a carefully refereed selection of extended versions of the best papers presented at the Second Meta-Heuristics Conference (MIC 97). The selected articles describe the most recent developments in theory and applications of meta-heuristics, heuristics for specific problems, and comparative case studies.

Simulated annealing for permutation flow-shop scheduling

The problem of scheduling jobs in a flow-shop is considered. The job processing order must be the same on each machine and the objective is to minimize the maximum completion time. Simulated annealing is proposed as a heuristic to obtain approximate solutions. Extensive computational tests with problems having up to 20 machines and 100 jobs show simulated annealing to compare favourably with known constructive heuristics and with descent methods.

Metaheuristics: A bibliography

Metaheuristics are the most exciting development in approximate optimization techniques of the last two decades. They have had widespread successes in attacking a variety of difficult combinatorial optimization problems that arise in many practical areas. This bibliography provides a classification of a comprehensive list of 1380 references on the theory and application of metaheuristics. Metaheuristics include but are not limited to constraint logic programming; greedy random adaptive search procedures; natural evolutionary computation; neural networks; non-monotonic search strategies; space-search methods; simulated annealing; tabu search; threshold algorithms and their hybrids. References are presented in alphabetical order under a number of subheadings.

Meta-Heuristics: Theory and Applications

From the Publisher: Meta-heuristics have developed dramatically since their inception in the early 1980s. They have had widespread success in attacking a variety of practical and difficult combinatorial optimization problems. They incorporate concepts based on biological evolution, intelligent problem solving, mathematical and physical sciences, nervous systems, and statistical mechanics. Since the 1980s, a great deal of effort has been invested in the field of combinatorial optimization theory in which heuristic algorithms have become an important area of research and applications. This volume is drawn from the first conference on Meta-Heuristics and contains 41 papers on the state-of-the-art in heuristic theory and applications. The book treats the following meta-heuristics and applications: Genetic Algorithms, Simulated Annealing, Tabu Search, Networks & Graphs, Scheduling and Control, TSP, and Vehicle Routing Problems. It represents research from the fields of Operations Research, Management Science, Artificial Intelligence and Computer Science.

Routing problems: A bibliography

This bibliography contains 500 references on four classical routing problems: the Traveling Salesman Problem, the Vehicle Routing Problem, the Chinese Postman Problem, and the Rural Postman Problem. References are presented alphabetically under a number of subheadings.

Coordinating a two-level supply chain with delay in payments and profit sharing

Achieving effective coordination among suppliers and retailers has become a pertinent research issue in supply chain management. Channel coordination is a joint decision policy achieved by a supplier(s) and a retailer(s) characterized by an agreement on the order quantity and the trade credit scenario (e.g., quantity discounts, delay in payments). This paper proposes a centralized model where players in a two-level (supplier-retailer) supply chain coordinate their orders to minimize their local costs and that of the chain. In the proposed supply chain model the permissible delay in payments is considered as a decision variable and it is adopted as a trade credit scenario to coordinate the order quantity between the two-levels. Computational results indicate that with coordination, the retailer orders in larger quantities than its economic order quantity, with savings to either both players, or to one in the supply chain. Moreover, a profit-sharing scenario for the distribution of generated net savings among the players in the supply chain is presented. Analytical and experimental results are presented and discussed to demonstrate the effectiveness of the proposed model.

Heuristics for the generalised assignment problem: simulated annealing and tabu search approaches

The generalised assignment problem (GAP) is the problem of finding a minimum cost assignment of a set of jobs to a set of agents. Each job is assigned to exactly one agent. The total demands of all jobs assigned to any agent can not exceed the total resources available to that agent. A review of exact and heuristic methods is presented. Aλ-generation mechanism is introduced. Different search strategies and parameter settings are investigated for theλ-generation descent, hybrid simulated annealing/tabu search and tabu search heuristic methods. The developed methods incorporate a number of features that have proven useful for obtaining optimal and near optimal solutions. The effectiveness of our approaches is established by comparing their performance in terms of solution quality and computional requirement to other specialized branch-and-bound tree search, simulated annealing and set partitioning heuristics on a set of standard problems from the literature.ZusammenfassungDas verallgemeinerte Zuordnungsproblem (GAP) besteht darin, eine Menge von Aufträgen einer Menge von Agenten kostenminimal zuzuordnen. Jeder Auftrag wird genau einem Agenten zugeordnet; die Summe der Anforderungen der einem Agenten zugeordneten Aufträge ist durch die diesem zur Verfügung stehenden Ressourcen begrenzt. Die Arbeit gibt eine Übersicht über exakte und heuristische Lösungsverfahren zum GAP. Es wird einλ-Generierungs-Mechanismus beschrieben, wobei verschiedene Suchstrategien (ein Hybridverfahren aus Simulated Annealing und Tabu Search sowie reine Tabu Search-Verfahren) sowie Parameterkonstellationen untersucht werden. Die entwickelten Methoden beinhalten eine Anzahl von Eigenschaften, die sich für die Erzielung von optimalen Lösungen sowie guten Näherungen als geeignet erwiesen haben. Die Effektivität der Ansätze wird über den Vergleich hinsichtlich Lösungsqualität und Berechnungsanforderungen mit anderen speziellen Verfahren wie Branch und Bound, Simulated Annealing sowie Partitionierungs-Heuristiken bei Anwendung auf Standardprobleme aus der Literatur gezeigt.

A variable neighbourhood search algorithm for the open vehicle routing problem

In the open vehicle routing problem (OVRP), the objective is to minimise the number of vehicles and then minimise the total distance (or time) travelled. Each route starts at the depot and ends at a customer, visiting a number of customers, each once, en route, without returning to the depot. The demand of each customer must be completely fulfilled by a single vehicle. The total demand serviced by each vehicle must not exceed vehicle capacity. Additionally, in one variant of the problem, the travel time of each vehicle should not exceed an upper limit. An effective variable neighbourhood search (VNS) heuristic for this problem is proposed. The neighbourhoods are based on reversing segments of routes (sub-routes) and exchanging segments between routes. Computational results on sixteen standard benchmark problem instances show that the proposed VNS is comparable in terms of solution quality to the best performing published heuristics.

Capacitated clustering problems by hybrid simulated annealing and tabu search

Abstract The capacitated clustering problem (CCP) is the problem in which a given set of weighted objects is to be partitioned into clusters so that the total weight of objects in each cluster is less than a given value (cluster ‘capacity’). The objective is to minimize the total scatter of objects from the ‘centre’ of the cluster to which they have been allocated. A simple constructive heuristic, a λ-interchange generation mechanism, a hybrid simulated annealing (SA) and tabu search (TS) algorithm which has computationally desirable features using a new non-monotonic cooling schedule, are developed. A classification of the existing SA cooling schedules is presented. The effects on the final solution quality of the initial solutions, the cooling schedule parameters and the neighbourhood search strategies are investigated. Computational results on randomly generated problems with size ranging from 50 to 100 customers indicate that the hybrid SA/TS algorithm out-performs previous simulated annealing algorithms, a simple tabu search and local descent algorithms.