Tabu Search Heuristic Procedure Research Articles

A New Hybrid Whale Optimization Algorithm for Green Vehicle Routing Problem

In recent years, issues on global warming and climate change have received public attention. One of the causes of this problem is carbon emissions in the transportation sector. Therefore, a proper route can reduce this problem. This article aims to develop the Hybrid Whale Optimization Algorithm (HWOA) to minimize the distribution cost of the Green Vehicle Routing Problem (GVRP). The proposed algorithm is based on the Whale Optimization Algorithm (WOA) algorithm combined with the Tabu search algorithm and local search procedures. In the proposed HWOA algorithm, 10% of the whale search agents were adjusted according to the Tabu Search heuristic procedure. A local search was applied to improve the algorithm's quality. Furthermore, a numerical experiment is carried out to test the proposed algorithm. The experiments are conducted to determine the effect of parameters and various speeds on total distribution cost and computation time. Moreover, The proposed algorithm is also compared to some previous algorithms. The findings showed that HWOA is more competitive in minimizing the total distribution cost of GVRP as compared to other algorithms.

On approximation of the fixed charge transportation problem

In this paper we present a new approximation for computing lower bound for the fixed charge transportation problem (FCTP). The lower bounds thus generated delivered 87% optimal solutions for 56 randomly generated small, up to 6×10 in size, problems in an experimental design. For somewhat larger, 10×10 and 10×15 size problems, the lower bounds delivered an average error of 5%, approximately, using a fraction of CPU times as compared to CPLEX to solve these problems. The proposed lower bound may be used as a superior initial solution with any other existing branch-and-bound method or tabu search heuristic procedure to enhance convergence to the optimal solution for large size problems which cannot be solved by CPLEX due to time constraints.

A tabu search heuristic procedure for the capacitated facility location problem

A tabu search heuristic procedure is developed, implemented and computationally tested for the capacitated facility location problem. The procedure uses different memory structures. Visited solutions are stored in a primogenitary linked quad tree. For each facility, the recent move at which the facility changed its status and the frequency it has been open are also stored. These memory structures are used to guide the main search process as well as the diversification and intensification processes. Lower bounds on the decreases of total cost are used to measure the attractiveness of the moves and to select moves in the search process. A specialized network algorithm is developed to exploit the problem structure in solving transportation problems. Criterion altering, solution reconciling and path relinking are used to perform intensification functions. The performance of the procedure is tested through computational experiments using test problems from the literature and new test problems randomly generated. It found optimal solutions for almost all test problems from the literature. As compared to the heuristic method of Lagrangean relaxation with improved subgradient scheme, the tabu search heuristic procedure found much better solutions using much less CPU time.

A primogenitary linked quad tree approach for solution storage and retrieval in heuristic binary optimization

A data structure, called the primogenitary linked quad tree (PLQT), is used to store and retrieve solutions in heuristic solution procedures for binary optimization problems. Two ways are proposed to use integer vectors to represent solutions represented by binary vectors. One way is to encode binary vectors into integer vectors in a much lower dimension and the other is to use the sorted indices of binary variables with values equal to 0 or equal to 1. The integer vectors are used as composite keys to store and retrieve solutions in the PLQT. An algorithm processing trial solutions for insertion into or retrieval from the PLQT is developed. Examples are provided to demonstrate the way the algorithm works. Another algorithm traversing the PLQT is also developed. Computational results show that the PLQT approach takes only a very tiny portion of the CPU time taken by a linear list approach for the same purpose for any reasonable application. The CPU time taken by the PLQT managing trial solutions is negligible as compared to that taken by a heuristic procedure for any reasonably hard to solve binary optimization problem, as shown in a tabu search heuristic procedure for the capacitated facility location problem. Compared to the hashing approach, the PLQT approach takes the same or less amount of CPU time but much less memory space while completely eliminating collision.

Management tools for optimal allocation of wood fibre to conventional log and bio-energy markets in Ireland: a case study

A set of tools are described for optimal allocation of wood fibre at an operational planning level. These were applied to a case study in Ireland. Allocation was based on optimising net value recovery (delivered price minus harvesting and transportation costs) while meeting market demands and operational constraints (mainly crew capability and productivity limits). Two new models were developed to predict harvesting costs and transportation costs for Irish forest conditions. A new model was developed to link Sitka spruce biomass expansion factors to optimal log-making algorithms so that log and bio-energy product yields could be estimated for individual harvest areas. An existing operational allocation model based on a tabu search heuristic procedure was used. The case study included 16 forest harvest areas and 12 processing plants (saw logs, pallet logs, stakes, pulp, bio-energy slash bundles, etc.). New terrestrial lidar scanning procedures were used to obtain representative stem profiles from over 4,000 trees for the 16 forests. We demonstrated that optimal allocation of bio-energy and log products, while complex, can be achieved through the use of appropriate management tools.

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.

Layout optimization considering production uncertainty and routing flexibility

The unequal area facility layout problem is studied considering both production uncertainty and routing flexibility. Stochastic production quantities are introduced under the assumption of dependent demands and incorporated into the usual objective of material handling cost minimization. Concurrently, alternative routings are considered based on production quantity. An efficient simulation approach is used to estimate the resulting department pairwise flows, both their expected values and variances. Using these estimates, a tabu search heuristic procedure performs the layout design optimization.

Multiproduct production planning in the presence of work-force learning

This paper explores the multiproduct production planning problem in the presence of work-force learning (MPPL). In this setting the work-force productivity reflects a learning-curve effect which can either increase or decrease (learn or forget) as a function of previous production volume. This research advances the understanding of MPPL along a number of dimensions. We formulate MPPL as a nonlinear mixed-integer programming problem and establish problem complexity, demonstrating that it is strongly NP-hard. We also discuss some important economic properties of production planning in the presence of learning. We then define a branch-and-bound algorithm for MPPL, as well as a tabu-search heuristic (TSH). In addition, we consider a previously defined heuristic for MPPL and demonstrate that its performance can be arbitrarily bad. Computational experiments on a large set of test problems were performed to assess the performance of the algorithm and the TSH procedure, and also to study the behavior of MPPL problems. The results of the experiments indicate that despite the underlying problem complexity, the algorithm is able to solve reasonably large problems, thus providing a basis for evaluating the heuristic solution quality. The TSH consistently obtains high-quality solutions to the test problems, suggesting that the tabu-search methodology offers an effective approach to complex production planning problems. MPPL problem difficulty is seen to vary with the number of periods in the planning horizon, the relative degree of labor intensity, and the relative demand behavior of products.

A tabu search heuristic procedure for the fixed charge transportation problem

We develop a tabu search approach for the fixed charge transportation (FCT) problem using recency based and frequency based memories, together with two strategies for each of the intermediate and long term memory processes, making use of a network based implementation of the simplex method as the local search method. Our approach is evaluated computationally on randomly generated problems of different sizes and of different ranges of magnitude of fixed costs relative to variable costs. Comparisons are made with two leading methods previously proposed, one in the category of exact methods and one in the category of heuristic methods. Our tabu search procedure obtains optimal or near-optimal solutions more than a thousand times faster than the exact solution algorithm for simple problems, and thoroughly dominates the exact algorithm on all dimensions for more complex problems. Compared to the heuristic approach, on very small and easy test problems the tabu search procedure required about the same amount of solution time and found solutions at least as good. However, for larger problems and for problems with higher fixed relative to variable costs, the tabu search procedure was 3–4 times faster than the competing heuristic, and found significantly better solutions in all cases.

A Tabu Search Heuristic Procedure for Solving the Transportation Problem with Exclusionary Side Constraints

A new heuristic procedure for the transportation problem with exclusionary side constraints is developed and implemented. Tabu search, a meta-heuristic method, is used to guide the search to follow a path selectively to prevent from being trapped at local optimal solutions in order to find a global optimal or near global optimal solution. The simplex method on a graph is employed to lead the search from one solution to an adjacent solution in order to take advantage of the underlying network structure of the problem. In the procedure, net changes in cost and in infeasibility are used to measure the attractiveness of a move, and strategic oscillation is used to implement the intensification and diversification functions. A computational experiment was conducted to test the performance of the heuristic procedure and substantial computational results are reported. These results show that the new heuristic procedure finds very good quality solutions and outperforms an existing heuristic procedure in terms of both solution quality and CPU time.