An effective heuristic for the CLSP with set-up times

The problem of multi-item, single level, capacitated, dynamic lot-sizing with set-up times (CLSP with set-up times) is considered. The difficulty of the problem compared with its counterpart without set-up times is explained. A lower bound on the value of the objective function is calculated by Lagrangian relaxation with subgradient optimisation. During the process, attempts are made to get good feasible solutions (ie. upper bounds) through a smoothing heuristic, followed by a local search with a variable neighbourhood. Solutions found in this way are further optimised by solving a capacitated transshipment problem. The paper describes the various elements of the solution procedure and presents the results of extensive numerical experimentation.     

A simple and efficient tabu search heuristic for solving the open vehicle routing problem

The open vehicle routing problem (VRP) is an immediate variant of the standard vehicle routing problem where the vehicle need not return to the depot after servicing its last customer. In this paper, we present results on an implementation of the attribute-based hill climber heuristic to the open VRP. The attribute-based hill climber heuristic is a parameter-free variant of the tabu search principle and has shown to be highly effective for the standard vehicle routing problem.     

A tabu search heuristic for ship routing and scheduling

The purpose of this paper is to solve a planning problem faced by many shipping companies dealing with the transport of bulk products. These shipping companies are committed to carrying some contract cargoes and will try to derive additional revenue from optional spot cargoes. An efficient tabu search algorithm has been developed to ensure quick decision support for the planners. The solutions generated by the tabu search heuristic are compared with those produced by a previously published multi-start local search heuristic. Computational results show that the tabu search heuristic yields optimal or near-optimal solutions to real-life instances within reasonable time. For large and tigthly constrained cases, the tabu search heuristic provides much better solutions than the multi-start local search heuristic. A version of the tabu search heuristic will be integrated as an improved solver in a prototype decision support system used by several shipping companies.     

A tabu search heuristic for the generalized minimum spanning tree problem

This paper describes an attribute based tabu search heuristic for the generalized minimum spanning tree problem (GMSTP) known to be NP-hard. Given a graph whose vertex set is partitioned into clusters, the GMSTP consists of designing a minimum cost tree spanning all clusters. An attribute based tabu search heuristic employing new neighborhoods is proposed. An extended set of TSPLIB test instances for the GMSTP is generated and the heuristic is compared with recently proposed genetic algorithms. The proposed heuristic yields the best results for all instances. Moreover, an adaptation of the tabu search algorithm is proposed for a variation of the GMSTP in which each cluster must be spanned at least once.     

A new tabu search heuristic for the open vehicle routing problem

In this paper, another version of the vehicle routing problem (VRP)—the open vehicle routing problem (OVRP) is studied, in which the vehicles are not required to return to the depot, but if they do, it must be by revisiting the customers assigned to them in the reverse order. By exploiting the special structure of this type of problem, we present a new tabu search heuristic for finding the routes that minimize two objectives while satisfying three constraints. The computational results are provided and compared with two other methods in the literature.     

A unified tabu search heuristic for vehicle routing problems with time windows

This paper presents a unified tabu search heuristic for the vehicle routing problem with time windows and for two important generalizations: the periodic and the multi-depot vehicle routing problems with time windows. The major benefits of the approach are its speed, simplicity and flexibility. The performance of the heuristic is assessed by comparing it to alternative methods on benchmark instances of the vehicle routing problem with time windows. Computational experiments are also reported on new randomly generated instances for each of the two generalizations.     

A tabu search heuristic for ship routing and scheduling with flexible cargo quantities

This paper presents a planning problem faced by many shipping companies dealing with the transport of bulk products. These shipping companies typically have a certain amount of contract cargoes that they are committed to carry, while trying to maximize their profit from optional spot cargoes. The cargo quantities are often flexible within an interval. Therefore, interwoven with the routing and scheduling decisions, the planner also has to decide the optimal cargo quantities. A tabu search algorithm embedding a specialized heuristic for deciding the optimal cargo quantities in each route is proposed to solve the problem. Computational results show that the heuristic gives optimal or near-optimal solutions to real-life cases of the problem within reasonable time. It is also shown that utilizing the flexibility in cargo quantities gives significantly improved solutions.     

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 tabu search heuristic for the vehicle routing problem with private fleet and common carrier

This paper describes a tabu search heuristic for a vehicle routing problem where the owner of a private fleet can either visit a customer with one of his vehicles or assign the customer to a common carrier. The owner’s objective is to minimize the variable and fixed costs for operating his fleet plus the total costs charged by the common carrier. The proposed tabu search is shown to outperform the best approach reported in the literature on 34 benchmark instances with a homogeneous fleet.     

A tabu search algorithm for solving economic lot scheduling problem

The economic lot scheduling problem has driven considerable amount of research. The problem is NP-hard and recent research is focused on finding heuristic solutions rather than searching for optimal solutions. This paper introduces a heuristic method using a tabu search algorithm to solve the economic lot scheduling problem. Diversification and intensification schemes are employed to improve the efficiency of the proposed Tabu search algorithm. Experimental design is conducted to determine the best operating parameters for the Tabu search. Results show that the tabu search algorithm proposed in this paper outperforms two well known benchmark algorithms.     

Backbone guided tabu search for solving the UBQP problem

We propose a backbone-guided tabu search (BGTS) algorithm for the Unconstrained Binary Quadratic Programming (UBQP) problem that alternates between two phases: (1) a basic tabu search procedure to optimize the objective function as far as possible; (2) a strategy using the TS notion of strongly determined variables to alternately fix and free backbone components of the solutions which are estimated likely to share values in common with an optimal solution. Experimental results show that the proposed method is capable of finding the best-known solutions for 21 large random instances with 3000 to 7000 variables and boosts the performance of the basic TS in terms of both solution quality and computational efficiency.     

A hybrid tabu search based heuristic for the periodic distribution inventory problem with perishable goods

Most of the research on integrated inventory and routing problems ignores the case when products are perishable. However, considering the integrated problem with perishable goods is crucial since any discrepancy between the routing and inventory cost can double down the risk of higher obsolescence costs due to the limited shelf-life of the products. In this paper, we consider a distribution problem involving a depot, a set of customers and a homogeneous fleet of capacitated vehicles. Perishable goods are transported from the depot to customers in such a way that out-of-stock situations never occur. The objective is to simultaneously determine the inventory and routing decisions over a given time horizon such that total transportation cost is minimized. We present a new “arc-based formulation” for the problem which is deemed more suitable for our new tabu search based approach for solving the problem. We perform a thorough sensitivity analysis for each of the tabu search parameters individually and use the obtained gaps to fine-tune the parameter values that are used in solving larger sized instances of the problem. We solve different sizes of randomly generated instances and compare the results obtained using the tabu search algorithm to those obtained by solving the problem using CPLEX and a recently published column generation algorithm. Our computational experiments demonstrate that the tabu search algorithm is capable of obtaining a near-optimal solution in less computational time than the time required to solve the problem to optimality using CPLEX, and outperforms the column generation algorithm for solving the “path flow formulation” of the problem in terms of solution quality in almost all of the considered instances.     

A tabu search heuristic using genetic diversification for the clustered traveling salesman problem

The clustered traveling salesman problem is an extension of the classical traveling salesman problem where the set of vertices is partitioned into clusters. The objective is to find a least cost Hamiltonian cycle such that the vertices of each cluster are visited contiguously and the clusters are visited in a prespecified order. A tabu search heuristic is proposed to solve this problem. This algorithm periodically restarts its search by merging two elite solutions to form a new starting solution (in a manner reminiscent of genetic algorithms). Computational results are reported on sets of Euclidean problems with different characteristics.     

A tabu search heuristic for the dynamic transportation of patients between care units

The problem studied in this paper stems from a real application to the transportation of patients in the Hospital Complex of Tours (France). The ambulance central station of the Hospital Complex has to plan the transportation demands between care units which require a vehicle. Some demands are known in advance and the others arise dynamically. Each demand requires a specific type of vehicle and a vehicle can transport only one person at a time. The demands can be subcontracted to a private company which implies high cost. Moreover, transportations are subject to particular constraints, among them priority of urgent demands, disinfection of a vehicle after the transportation of a patient with contagious disease and respect of the type of vehicle needed. These characteristics involve a distinction between the vehicles and the crews during the modeling phase. We propose a modeling for solving this difficult problem and a tabu search algorithm inspired by Gendreau et al. (1999). This method supports an adaptive memory and a tabu search procedure. Computational experiments on a real-life instance and on randomly generated instances show that the method can provide high-quality solutions for this dynamic problem with a short computation time.     

A tabu search heuristic for the split delivery vehicle routing problem with production and demand calendars

The purpose of this article is to propose a tabu search heuristic for the split delivery Vehicle Routing Problem with Production and Demand Calendars (VRPPDC). This new problem consists of determining which customers will be served by a common carrier, as well as the delivery routes for those served by the private fleet, in order to minimize the overall transportation and inventory costs. We first model this problem and then propose a simple decomposition procedure that can be used to provide a starting solution. Next, we introduce a new tabu search heuristic and we describe two new neighbor reduction strategies. Finally, we present the results of our extensive computational tests. According to these tests, our reduction strategies are efficient not only at reducing computing time but also at improving the overall solution quality.     

Erratum: A new tabu search heuristic for the open vehicle routing problem

Correction to: Journal of the Operational Research Society (2005) 56, 267–274. doi:10.1057/palgrave.jors.2601817     

A heuristic for vehicle fleet mix problem using tabu search and set partitioning

The vehicle fleet mix problem is a special case of the vehicle routing problem where customers are served by a heterogeneous fleet of vehicles with various capacities. An efficient heuristic for determining the composition of a vehicle fleet and travelling routes was developed using tabu search and by solving set partitioning problems. Two kinds of problems have appeared in the literature, concerning fixed cost and variable cost, and these were tested for evaluation. Initial solutions were found using the modified sweeping method. Whenever a new solution in an iteration of the tabu search was obtained, optimal vehicle allocation was performed for the set of routes, which are constructed from the current solution by making a giant tour. Experiments were performed for the benchmark problems that appeared in the literature and new best-known solutions were found.     

A heuristic search based on diversity for solving combinatorial problems

Journal of Heuristics - In this paper we propose a novel heuristic search for solving combinatorial optimization problems which we call Diverse Search (DS). Like beam search, this constructive...     

An incremental tabu search heuristic for the generalized vehicle routing problem with time windows

This paper describes an incremental neighbourhood tabu search heuristic for the generalized vehicle routing problem with time windows. The purpose of this work is to offer a general tool that can be successfully applied to a wide variety of specific problems. The algorithm builds upon a previously developed tabu search heuristic by replacing its neighbourhood structure. The new neighbourhood is exponential in size, but the proposed evaluation procedure has polynomial complexity. Computational results are presented and demonstrate the effectiveness of the approach.     

A priority list based heuristic for the job shop problem: part 2 tabu search

Following a recent paper by the same author, a priority list-based tabu search heuristic is compared with the leading schedule-based tabu search heuristic of Nowicki and Smutnicki. More search neighbourhoods are required to achieve a given average makespan, but each priority list neighbourhood is searched much faster than the corresponding neighbourhood in the space of feasible schedules. Priority list-based tabu search therefore outperforms schedule-based tabu search in terms of elapsed CPU time.