Neighborhood Search Procedure Research Articles

Customized simulated annealing based decision algorithms for combinatorial optimization in VLSI floorplanning problem

Modern very large scale integration technology is based on fixed-outline floorplan constraints, mostly with an objective of minimizing area and wirelength between the modules. The aim of this work is to minimize the unused area, that is, dead space in the floorplan, in addition to these objectives. In this work, a Simulated Annealing Algorithm (SAA) based heuristic, namely Simulated Spheroidizing Annealing Algorithm (SSAA) has been developed and improvements in the proposed heuristic algorithm is also suggested to improve its performance. Exploration capability of the proposed algorithm is due to the mechanism of reducing the uphill moves made during the initial stage of the algorithm, extended search at each temperature and the improved neighborhood search procedure. The proposed algorithm has been tested using two kinds of benchmarks: Microelectronics Center of North Carolina (MCNC) and Gigascale Systems Research Center (GSRC). The performance of the proposed algorithm is compared with that of other stochastic algorithms reported in the literature and is found to be efficient in producing floorplans with very minimal dead space. The proposed SSAA algorithm is also found more efficient for problems of larger sizes.

Shift Scheduling Problem in Same-Day Courier Industry

This paper deals with the problem of minimizing the staffing cost of a same-day courier company subject to service-level requirements. The problem has been modeled as an integer program with nonlinear probabilistic constraints. We have devised a heuristic procedure to explore the search space efficiently through an approximated neighborhood evaluation (ANE) model, relying on the estimation (via simulation) of a reduced number of parameters. Computational results show that, compared to traditional neighborhood search procedures, the ANE approach provides significant cost reductions in a typical same-day courier setting.

Tabu Search and Benders Decomposition Approaches for a Capacitated Closed-Loop Supply Chain Network Design Problem

We consider a network design problem in a multiproduct closed-loop supply chain setting consisting of remanufacturing facilities and finite-capacity manufacturing, distribution, and collection facilities that serve a set of retailers. We first present a mixed-integer linear program to determine the optimal locations of the collection centers and remanufacturing facilities along with the integrated forward and reverse flows such that the total cost of facility location, processing, and transportation associated with forward and reverse flows in the network is minimized. Second, we devise two tabu search heuristics—sequential and random neighborhood search procedures—in which we effectively combine search functions using move and exchange neighborhoods to improve efficiency in exploring the solution space. We also suggest a transshipment heuristic to quickly, but effectively, estimate the objective function value (goodness) of a feasible solution in the course of a tabu search. Third, we present a Benders decomposition solution approach that incorporates the tabu search heuristics as well as Benders cuts that are strengthened to facilitate faster convergence and improved computational efficiency, especially for large-scale instances. While the solutions using tabu search heuristics make the applicability of the Benders decomposition approach possible via providing initial upper bounds and facilitating the generation of good initial Benders cuts, the lower bounds obtained by the Benders approach computationally verify the high quality of the tabu search heuristic solutions. We present computational results illustrating the efficient performance of the solution algorithms in terms of both solution quality and time, especially for larger size problems.

A filter-and-fan approach to the job shop scheduling problem

The job shop scheduling problem (JSSP) is a notoriously difficult problem in combinatorial optimization. Extensive investigation has been devoted to developing efficient algorithms to find optimal or near-optimal solutions. This paper proposes a new heuristic algorithm for the JSSP that effectively combines the classical shifting bottleneck procedure (SBP) with a dynamic and adaptive neighborhood search procedure. Our new search method, based on a filter-and-fan (F&F) procedure, uses the SBP as a subroutine to generate a starting solution and to enhance the best schedules produced. The F&F approach is a local search procedure that generates compound moves by a strategically abbreviated form of tree search. Computational results carried out on a standard set of 43 benchmark problems show that our F&F algorithm performs more robustly and effectively than a number of leading metaheuristic algorithms and rivals the best of these algorithms.

Satisfying due-dates in the presence of sequence dependent family setups with a special comedown structure

This paper addresses a static, n-job, single-machine scheduling problem with sequence dependent family setups. The setup matrix follows a special structure where a constant setup is required only if a job from a smaller indexed family is an immediate successor of one from a larger indexed family. The objective is to minimize the maximum lateness (Lmax). A two-step neighborhood search procedure and an implicit enumeration scheme are proposed. Both procedures exploit the problem structure. The enumeration scheme produces optimum solutions to small and medium sized problems in reasonable computational times, yet it fails to perform efficiently in larger instances. Computational results show that the heuristic procedure is highly effective, and is efficient even for extremely large problems.

Solving the p-median problem with pos/neg weights by variable neighborhood search and some results for special cases

The p-median problem with positive and negative weights has been introduced by Burkard and Krarup [Computing 60 (1998) 193]. In this paper we discuss some special cases of this problem on trees and propose a variable neighborhood search procedure for general networks, which is in fact a modification of the one proposed by Hansen and Mladenovic [Locat. Sci. 5 (1997) 207] for the p-median. We also compare the results with those obtained by a Tabu search procedure.

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 simple filter-and-fan approach to the facility location problem

The design of effective neighborhood search procedures is a primary issue for the performance of local search and advanced metaheuristic algorithms. Several recent studies have focused on the development of variable depth neighborhoods that generate sequences of interrelated elementary moves to create more complex compound moves. These methods are chiefly conceived to produce an adaptive search as the number of elementary moves in a compound move may vary from one iteration to another depending on the state of the search. The objective is to achieve this goal with modest computational effort. Although ejection chain methods are currently the most advanced methods in this domain, they usually require more complex implementations. The filter-and-fan (F&F) method appears as an alternative to ejection chain methods allowing for the creation of compound moves based on an effective tree search design. This paper reports the first implementation of the F&F approach to the uncapacitated facility location problem (UFLP). We examine a simple version of the F&F method in this study, and explore two search strategies under this framework. The results obtained on a set of 105 standard benchmark problems from the literature demonstrate that this simple but well-structured approach is highly effective for providing optimal and near-optimal solutions for the UFLP in a very short computation time, and indeed can be advantageously compared with the most advanced metaheuristic procedures in solving these problems.

New Multiobjective Metaheuristic Solution Procedures for Capital Investment Planning

Capital investment planning is a periodic management task that is particularly challenging in the presence of multiple objectives as trade-offs have to be made with respect to the preferences of the decision-makers. The underlying mathematical model is a multiobjective combinatorial optimization problem that is NP-hard. One way to tackle this problem is first to determine the set of all efficient portfolios and then to explore this set in order to identify a final preferred portfolio. In this study, we developed heuristic procedures to find efficient portfolios because it is impossible to enumerate all of them within a reasonable computation time for practical problems. We first added a neighborhood search routine to the Pareto Ant Colony Optimization (P-ACO) procedure to improve its performance and then developed a Tabu Search procedure and a Variable Neighborhood Search procedure. Step-by-step descriptions of these three new procedures are provided. Computational results on benchmark and randomly generated test problems show that the Tabu Search procedure outperforms the others if the problem does not have too many objective functions and an excessively large efficient set. The improved P-ACO procedure performs better otherwise.

Efficient neighborhood search for the Probabilistic Pickup and Delivery Travelling Salesman Problem

AbstractIn this article the probabilistic Pickup and Delivery Travelling Salesman Problem is studied and an efficient neighborhood search procedure is developed. This procedure requires O(n3) computations for the evaluation of the neighborhood of a given solution while the straightforward approach has an O(n5)complexity. © 2005 Wiley Periodicals, Inc. NETWORKS, Vol. 45(4), 195–198 2005

Tabu search algorithms for water network optimization

In this paper we propose a tabu search algorithm to find the least-cost design of looped water distribution networks. The mathematical nature of this optimization problem, a nonlinear mixed integer problem, is at the origin of a multitude of contributions to the literature in the last 25 years. In fact, exact optimization methods have not been found for this type of problem, and, in the past, classical optimization methods, like linear and nonlinear programming, were tried at the cost of drastic simplifications. Tabu search is a valuable heuristic technique for solving problems cast in combinatorial form. This is based on the human memory process and uses an iterative neighborhood search procedure in an attempt to avoid becoming trapped in local optima. The use of such a heuristic procedure to solve the aforementioned problem needs particular tailoring to produce high quality solutions. In this paper we present the essential features of the algorithm and the results obtained when it is applied to some of the classical water distribution network case studies appearing in the literature. The results are very promising and demonstrate the usefulness of tabu search algorithms in solving this kind of optimization problem.

Simulated annealing for resource-constrained scheduling

We describe an application of several procedures to a resource-constrained scheduling problem. Basic and augmented neighborhood search procedures are first developed, using a unique way to generate neighborhoods. We then present a simulated annealing procedure which can be viewed as an evolution of the neighborhood search procedures. Computational results indicate that the procedures grow in power as they evolve, with the simulated annealing procedure providing the best results, and further, that it is a viable approach for a very difficult scheduling problem.