A parallel local search approach to solving the uncapacitated warehouse location problem

The uncapacitated warehouse location problem (UWLP) has been studied by many researchers. It has been solved using various approaches, including branch and bound linear programming, tabu search, simulated annealing, and genetic algorithms. This study presents a new local search (LS) approach to the UWLP that is quite simple and robust and is efficient in some cases. The algorithm was tested against standard OR Library benchmarks and M^* instances, which have already been used to test other approaches. The results show that the only disadvantage of the algorithm is the exponential growth of its computation time with the problem size. However, the multi-search design suggested here enables the algorithm to run under multi-processor or multi-core systems, which are currently provided as part of standard PC configurations.     

A deterministic tabu search algorithm for the capacitated arc routing problem

The capacitated arc routing problem (CARP) is a difficult optimisation problem in vehicle routing with applications where a service must be provided by a set of vehicles on specified roads. A heuristic algorithm based on tabu search is proposed and tested on various sets of benchmark instances. The computational results show that the proposed algorithm produces high quality results within a reasonable computing time. Some new best solutions are reported for a set of test problems used in the literature.     

Solving the uncapacitated facility location problem using tabu search

A tabu search heuristic procedure is developed to solve the uncapacitated facility location problem. Tabu search is used to guide the solution process when evolving from one solution to another. A move is defined to be the opening or closing of a facility. The net cost change resulting from a candidate move is used to measure the attractiveness of the move. After a move is made, the net cost change of a candidate move is updated from its old value. Updating, rather than re-computing, the net cost changes substantially reduces computation time needed to solve a problem when the problem is not excessively large. Searching only a small subset of the feasible solutions that contains the optimal solution, the procedure is computationally very efficient. A computational experiment is conducted to test the performance of the procedure and computational results are reported. The procedure can easily find optimal or near optimal solutions for benchmark test problems from the literature. For randomly generated test problems, this tabu search procedure not only obtained solutions completely dominating those obtained with other heuristic methods recently published in the literature but also used substantially less computation time. Therefore, this tabu search procedure has advantage over other heuristic methods in both solution quality and computation speed.     

Adaptive granular local search heuristic for a dynamic vehicle routing problem

The advance of communication and information technologies based on satellite and wireless networks have allowed transportation companies to benefit from real-time information for dynamic vehicle routing with time windows. During daily operations, we consider the case in which customers can place requests such that their demand and location are stochastic variables. The time windows at customer locations can be violated although lateness costs are incurred. The objective is to define a set of vehicle routes which are dynamically updated to accommodate new customers in order to maximize the expected profit. This is the difference between the total revenue and the sum of lateness costs and costs associated with the total distance traveled. The solution approach makes use of a new constructive heuristic that scatters vehicles in the service area and an adaptive granular local search procedure. The strategies of letting a vehicle wait, positioning a vehicle in a region where customers are likely to appear, and diverting a vehicle away from its current destination are integrated within a granular local search heuristic. The performance of the proposed approach is assessed in test problems based on real-life Brazilian transportation companies.     

Flow shop scheduling with blocking using modified harmony search algorithm with neighboring heuristics methods

The flow shop scheduling with blocking is considered an important scheduling problem which has many real-world applications. This paper proposes a new algorithm which applies heuristic techniques in harmony search algorithm (HSA) to minimize the total flow time. The proposed method is called modified harmony search algorithm with neighboring heuristics methods (MHSNH). To improve the initial harmony memory, we apply two heuristic techniques: nearest neighbor (NN) and constructive modified NEH (MNEH). A modified version of harmony search algorithm evolves to explore and generates a new solution. The newly generated solution is then enhanced by using neighboring heuristics. Lastly, another neighboring heuristic is applied to improve the obtained solution. The proposed algorithm is evaluated using 12 real-world problem instances each with 10 samples. The experimental evaluation is accomplished using two factors: CPU computational time and the number of iterations. For the first factor, comparative evaluation against six well-established methods shows that the proposed method achieves almost the best overall results in six problem instances out of the twelve and yields fruitful results for others. For the second factor, comparative evaluation against twelve well-regarded methods shows that the proposed method achieves the best overall results in three problem instances and obtains very good results in other instances. In a nutshell, the proposed MHSNH is an effective strategy for solving the job shop scheduling problem.     

Very large-scale neighborhood search

Neighborhood search algorithms are often the most effective approaches available for solving partitioning problems, a difficult class of combinatorial optimization problems arising in many application domains including vehicle routing, telecommunications network design, parallel machine scheduling, location theory, and clustering. A critical issue in the design of a neighborhood search algorithm is the choice of the neighborhood structure, that is, the manner in which the neighborhood is defined. Currently, the two-exchange neighborhood is the most widely used neighborhood for solving partitioning problems. The paper describes the cyclic exchange neighborhood , which is a generalization of the two-exchange neighborhood in which a neighbor is obtained by performing a cyclic exchange . The cyclic exchange neighborhood has substantially more neighbors compared to the two-exchange neighborhood. This paper outlines a network optimization based methodology to search the neighborhood efficiently and presents a proof of concept by applying it to the capacitated minimum spanning tree problem, an important problem in telecommunications network design.     

A tabu search based memetic algorithm for the max-mean dispersion problem

Given a set V of n elements and a distance matrix ${[d_{\mathit{ij}}]}_{n\times n}$ among elements, the max-mean dispersion problem (MaxMeanDP) consists in selecting a subset M from V such that the mean dispersion (or distance) among the selected elements is maximized. Being a useful model to formulate several relevant applications, MaxMeanDP is known to be NP-hard and thus computationally difficult. In this paper, we present a tabu search based memetic algorithm for MaxMeanDP which relies on solution recombination and local optimization to find high quality solutions. One key contribution is the identification of the fast neighborhood induced by the one-flip operator which takes linear time. Computational experiments on the set of 160 benchmark instances with up to 1000 elements commonly used in the literature show that the proposed algorithm improves or matches the published best known results for all instances in a short computing time, with only one exception, while achieving a high success rate of 100%. In particular, we improve 53 previous best results (new lower bounds) out of the 60 most challenging instances. Results on a set of 40 new large instances with 3000 and 5000 elements are also presented. The key ingredients of the proposed algorithm are investigated to shed light on how they affect the performance of the algorithm.     

Modular grammar specification

We establish a semantics for building grammars from a modularised specification in which modules are able to delete productions from imported nonterminals. Modules have import lists of nonterminals; some or all of an imported nonterminal's productions may be suppressed at import time. There are two basic import mechanisms which (a) reference or (b) clone an imported nonterminal's productions. One of our goals is to allow a precise answer to the question: ‘what character level language does this grammar generate’ in the face of difficult issues such as the mutual embedding of languages that have different whitespace and commenting conventions. Our technique is to automatically generate a character level grammar from grammars written at token level in the conventional way; the grammar is constructed from modules each of which may have its own whitespace convention.     

A random search heuristic for a multi-objective production planning

Real world production planning is involved in optimizing different objectives while considering a spectrum of parameters, decision variables, and constraints of the corresponding cases. This comes from the fact that production managers desire to utilize from an ideal production plan by considering a number of objectives over a set of technological constraints. This paper presents a new multi-objective production planning model which is proved to be NP-Complete. So a random search heuristic is proposed to explore the feasible solution space with the hope of finding the best solution in a reasonable time while extracting a set of Pareto-optimal solutions. Then each Pareto-optimal solution is considered as an alternative production plan in the hand of production manager. Both the modeling and the solution processes are carried out for a real world problem and the results are reported briefly. Also, performance of the proposed problem-specific heuristic is verified by comparing it with a multi-objective genetic algorithm on a set randomly generated test data.     

Adaptive population-based search: Application to estimation of nonlinear regression parameters

Algorithms for the estimation of nonlinear regression parameters are considered. Adaptive population-based search algorithms are proposed and implemented in deriving reliable estimates at a reasonable time with default setting of their controlling parameters. The algorithms are tested on the NIST collection of data sets containing 27 nonlinear regression tasks of various level of difficulty. The experimental results show that both algorithms with competing heuristics are significantly more reliable as compared with the algorithm based on Levenberg-Marquardt optimizing procedure.     

Diagnosability of Discrete Event Systems with Modular Structure

The diagnosis of unobservable faults in large and complex discrete event systems modeled by parallel composition of automata is considered. A modular approach is developed for diagnosing such systems. The notion of modular diagnosability is introduced and the corresponding necessary and sufficient conditions to ensure it are presented. The verification of modular diagnosability is performed by a new algorithm that incrementally exploits the modular structure of the system to save on computational effort. The correctness of the algorithm is proved. Online diagnosis of modularly diagnosable systems is achieved using only local diagnosers. *Olivier Contant is now working at Microsoft Corporation.     

Extending Prolog with modularity,concurrency and meta-rules

This paper presents the main features of an extension to Prolog toward modularity and concurrency—calledCommunicating Prolog Units (CPU)—whose main aim is to allow logic programming to be used as an effective tool for system programming and prototyping. While Prolog supports only a single set of clauses and sequential computations, CPU allows programmers to define different theories (P-unis) and parallel processes interacting via P-units, according to a model very similar to Linda’s generative communication. The possibility of expressingmeta-rules to specify where and how object-level (sub)golas have to be proved, not only enhances modularity, but also increases the expressive power and flexibility of CPU systems.     

An efficient variable neighborhood search heuristic for very large scale vehicle routing problems

In this paper, we present an efficient variable neighborhood search heuristic for the capacitated vehicle routing problem. The objective is to design least cost routes for a fleet of identically capacitated vehicles to service geographically scattered customers with known demands. The variable neighborhood search procedure is used to guide a set of standard improvement heuristics. In addition, a strategy reminiscent of the guided local search metaheuristic is used to help escape local minima. The developed solution method is specifically aimed at solving very large scale real-life vehicle routing problems. To speed up the method and cut down memory usage, new implementation concepts are used. Computational experiments on 32 existing large scale benchmarks, as well as on 20 new very large scale problem instances, demonstrate that the proposed method is fast, competitive and able to find high-quality solutions for problem instances with up to 20,000 customers within reasonable CPU times.     

A tree search algorithm for the container loading problem

This paper presents a binary tree search algorithm for the three dimensional container loading problem (3D-CLP). The 3D-CLP is about how to load a subset of a given set of rectangular boxes into a rectangular container, such that the packing volume is maximized. In this algorithm, all the boxes are grouped into strips and layers while three constraints, i.e., full support constraint, orientation constraint and guillotine cutting constraint are satisfied. A binary tree is created where each tree node denotes a container loading plan. For a non-root each node, the layer set of its left (or right) child is obtained by inserting a directed layer into its layer set. A directed layer is parallel (or perpendicular) to the left side of the container. Each leaf node denotes a complete container loading plan. The solution is the layer set whose total volume of the boxes is the greatest among all tree nodes. The proposed algorithm achieves good results for the well-known 3D-CLP instances suggested by Bischoff and Ratcliff with reasonable computing time.     

A scatter search approach to the optimum disassembly sequence problem

One of the most difficult problems faced by OR researchers in the reverse logistics field consists in determining an efficient disassembly sequence for a product at its end of life. In this paper, a scatter search (SS) metaheuristic is presented aiming to deal with the optimum disassembly sequence problem for the case of complex products with sequence-dependent disassembly costs, and assuming that only one component can be released at each time. The algorithm was tested over a set of 48 products consisting of 25, 50, 75 and 100 right-angled parallelepiped components, connected by different kinds of joints. A feasible disassembly sequence, consisting on the reverse of the assembly sequence, is always usable. Comparing by using some statistical analysis the costs of the sequence proposed by the SS algorithm with those corresponding to the reverse order, efficiency of the metaheuristic is shown up.     

A tabu search algorithm for the heterogeneous fixed fleet vehicle routing problem

In the heterogeneous fixed fleet vehicle routing problem there are different types of vehicles and a given number of vehicles of each type. The resolution of this problem consists of assigning the customers to the existing vehicles and, in relation to each vehicle, defining the order of visiting each customer for the delivery or collection of goods. The objective is to minimize the total costs, satisfying customers’ requirements and visiting each customer exactly once. In this paper a tabu search algorithm is proposed and tested on several benchmark problems. The computational experiments show that the proposed algorithm produces high quality solutions within an acceptable computation time. Four new best solutions are reported for a set of test problems used in the literature.     

A tree search procedure for the container relocation problem

In the container relocation problem (CRP) n items are given that belong to G different item groups (g=1,…,G). The items are piled up in up to S stacks with a maximum stack height H. A move can either shift one item from the top of a stack to the top of another one (relocation) or pick an item from the top of a stack and entirely remove it (remove). A move of the latter type is only feasible if the group index of the item is minimum compared to all remaining items in all stacks. A move sequence of minimum length has to be determined that removes all items from the stacks. The CRP occurs frequently in container terminals of seaports. It has to be solved when containers, piled up in stacks, need to be transported to a ship or to trucks in a predefined sequence. This article presents a heuristic tree search procedure for the CRP. The procedure is compared to all known solution approaches for the CRP and turns out to be very competitive.     

Computational experiments with heuristics for two nature reserve site selection problems

Nature reserve site selection has become critical as the human population grows and environmentalists seek ways to preserve species and their natural habitats. Simple tabu searches are developed and tested for two reserve site selection models—the maximal covering species problem and the maximal expected covering problem. The testbed is an Oregon terrestrial vertebrate data set composed of 426 species and 441 hexagonal sites. In addition, an extension to a linearized version of the maximal expected covering problem is proposed and tested.