Efficient GRASP+VND and GRASP+VNS metaheuristics for the traveling repairman problem

The traveling repairman problem is a customer-centric routing problem, in which the total waiting time of the customers is minimized, rather than the total travel time of a vehicle. To date, research on this problem has focused on exact algorithms and approximation methods. This paper presents the first metaheuristic approach for the traveling repairman problem

An Algorithm for the Cycled Shortest Path Problem

For a network with cycle, where at least one cycle exists, the Floyd- Warshall algorithm is probably the most used algorithm to determine he least cost path between every pair of nodes on this network, i.e. the solution for the shortest path problem with cycle. In this paper, a new algorithm for this problem which requires less computational effort than the Floyd-Warshall algorithm has been developed Furthermore, it can be shown that the basis of our algorithm is much easier to be learnt and understood which might be an advantage for educational puposes. A small example validates our algorithm and shows its implementation

The coupled task scheduling problem : an improved mathematical program and a new solution algorithm

The general single machine coupled task scheduling problem with the objective function of minimizing the makespan, which is strongly NP-hard, aims to schedule a set of coupled task jobs on one machine such that the completion time of the last job is minimized. We propose a new mixed-integer program (MIP) for the problem. We also propose a relax-and-solve (R&amp;S) matheuristic algorithm as the solution method. We show that the new MIP outperforms the available models and improves the quality of solutions. Also, the proposed MIP significantly improves the average gap to the best known feasible solution of an existing binary search algorithm. We show that our R&amp;S matheuristic produces new best solutions for almost 50% of the instances.</p

A binary search algorithm for the general coupled task scheduling problem

The coupled task scheduling problem aims to schedule a set of jobs, each with at least two tasks and there is an exact delay period between two consecutive tasks, on a set of machines to optimize a performance criterion. We study the problem of scheduling a set of coupled jobs to be processed on a single machine with the objective of minimizing the makespan, which is known to be strongly NP-hard. We obtain competitive lower bounds for the problem through different procedures, including solving 0-1 knapsack problems. We obtain an upper bound by applying a heuristic algorithm. We then propose a binary search heuristic algorithm for the coupled task scheduling problem. We perform extensive computational experiments and show that the proposed method is able to obtain quality solutions. The results also indicate that the proposed solution method outperforms the standard exact solver Gurobi.</p

The gradual minimum covering location problem

The minimum covering location problem with distance constraints deals with locating a set of undesirable facilities on a geographical map, where there is a given minimum distance between any pair of located facilities. A covering radius is defined within which the population node is fully covered and beyond that it is not covered at all. This setting may not be applicable in practice because usually the coverage gradually decreases with an increase in the distance. Additionally, undesirable facilities may have a cooperative adverse impact on the nearby population. We introduce the gradual coverage to the problem that extends the classic definition of the coverage and is more suitable for modelling real-world applications. We name the problem the gradual minimum covering location problem with distance constraints (GMCLPDC). We propose a mixed-integer program for GMCLPDC where cooperation of facilities is also considered. We propose a threshold accepting heuristic as the solution method. We conduct computational experiments on instances with up to 10,000 nodes. The outcomes indicate that the heuristic delivers quality solutions and outperforms the solver Gurobi. We also show an application of our model in Sydney metropolitan area.</p

Statistical Control of Time and Cost Performance Indices in Construction Projects: A Case Study

The earned value management is a powerful and important technique in analyzing and controlling the project performance. While it allows exact measurement of project progress, it lets corrective actions in a timely manner. In fact, the earned value allows project managers to find out any project time and cost deviations by calculating the performance indices. In this paper, to improve the applicability of the traditional earned value technique, we develop an integrated approach by combining statistical quality control charts with traditional earned value technique, to monitor and control project time and cost performances. The results applied to a real construction project compete favorly against traditional approache

Heuristics for flights arrival scheduling at airports

We develop an efficient matheuristic algorithm for the aircraft landing problem (ALP). The ALP aims to schedule aircraft landings such that the total deviation from target arrival times is minimized. We propose a relax-and-solve (R&amp;S) algorithm that operates by performing a set of “relax” and “solve” iterations. The relax procedure destructs a sequence of aircraft landings, and the solve procedure re-constructs a complete sequence and schedules the aircraft landings. We compare the proposed algorithm and the state-of-the-art algorithm for the ALP and also the solver CPLEX, and show that our algorithm obtains all best-known solutions within one minute, even for instances including 500 aircraft. Those characteristics of the algorithm are very important for practical settings. In particular, the typical short time window available for planning the aircraft landings at busy airports demands quick delivery of quality landing schedules (or updating the current schedule), and fast and effective algorithms are therefore paramount.</p

A meta-heuristic to solve the just-in-time job-shop scheduling problem

Just-in-time job-shop scheduling (JIT-JSS) is a variant of the job-shop scheduling problem, in which each operation has a distinct due-date and any deviation of the operation completion time from its due-date incurs an earliness or tardiness penalty. We develop a variable neighbourhood search (VNS) algorithm to solve JIT-JSS. The algorithm operates by decomposing JIT-JSS into smaller problems, obtaining optimal or near-optimal sequences of performing the operations for those smaller problems, and generating a schedule, i.e., determining the completion time of the operations, for JIT-JSS. The algorithm uses several neighbourhood structures, including the new relaxation neighbourhoods developed in this study, to obtain a quality sequence. The relaxation neighbourhoods partially destruct (relax) the sequence and then re-construct (sequence) certain operations. Differing from the classical neighbourhoods, in which manipulations are performed either randomly or myopically, the moves in the new neighbourhoods are made with reference to other operations, so their impacts on the whole sequence are well considered. By solving a set of 72 benchmark instances, ranging from 10 to 20 jobs and 20 to 200 operations, and comparing the outcomes of the proposed algorithm with the state-of-the-art solution methods in the literature, we obtain new best solutions for nearly 57% of the instances, including new best solutions for 80% of the instances with 20 jobs. The computational results demonstrate the efficacy of the proposed VNS algorithm.</p

Optimal location of workstations in tandem automated-guided vehicle systems

The way workstations are located in a tandem automated-guided vehicle (AGV) systems affect the total lateness of the system. So far, almost all studies have focused on either minimizing the total flow or minimizing the total AGV transitions in each zone. This study presented a novel approach to locate the workstations in a tandem AGV zones by developing a new mixed-integer programming (MIP) formulation. The objective is to minimize total waiting time of all workstations which is equivalent to minimizing the total lateness of each zone. Lateness is defined as the total idle time of a workstation waiting to be supplied by an AGV. The proposed MIP formulation is very competitive and has the capability to solve instances of up to 25 workstations to optimality in a reasonable amount of time