Solving the Stochastic Time-Dependent Orienteering Problem with Time Windows

This paper introduces the stochastic time-dependent orienteering problem with time windows. The orienteering problem occurs in logistic situations where an optimal combination of locations must first be selected and then the routing between these selected locations must be optimized. In the stochastic time-dependent variant, the travel time between two locations is a stochastic function that depends on the departure time at the first location. The main contribution of this paper lies in the design of a fast and eective algorithm to solve this challenging problem. To validate the performance and the practical relevance of this proposed algorithm, several experiments were carried out on realistic benchmark instances of varying size and properties. These benchmark instances are constructed based on an actual large road network in Belgium with historic travel time profiles for every road segment.publisher: Elsevier articletitle: Solving the stochastic time-dependent orienteering problem with time windows journaltitle: European Journal of Operational Research articlelink: http://dx.doi.org/10.1016/j.ejor.2016.05.031 content_type: article copyright: © 2016 Elsevier B.V. All rights reserved.status: publishe

Simultaneously scheduling n jobs and the preventive maintenance on the two-machine flow shop to minimize the makespan

Most of the papers that deal with the two-machine flow shop problem assume that the machines are always available during the scheduling period. However, in most real life industrial settings a machine can be unavailable for many reasons. This paper is concerned with the problem of jointly scheduling n immediately available jobs and the preventive maintenance in a two-machine flow shop with the objective of minimizing the makespan. We consider that one of the two machines must be maintained once during the first T periods of the schedule. Only the non-resumable case is studied. We first focus on the particularity of this problem. After we present some properties of the optimal solution then we show that the problem is NP-hard. We last focus on the optimal solutions under some conditions.

A Metaheuristic Solution Approach for the Time-constrained Project Scheduling Problem

In this paper, a metaheuristic solution procedure for the Time-Constrained Project Scheduling Problem is proposed, in which additional resources can be temporarily allocated to meet a given deadline. The problem consists of determining a schedule such that the project is completed on time and that the total additional cost for the resources is minimized. For this problem, an artificial immune system is proposed, in which each solution is represented by a vector of activity start times. A local search procedure, which tries to shift cost causing activities, is applied to each population schedule. Computational experiments are applied to modified resource-constrained project scheduling problem benchmark instances and reveal promising results.status: publishe

Solving the integrated production and imperfect preventive maintenance planning problem

This paper considers the integrated production and imperfect preventive maintenance planning problem. The article provides more details on how Relax-and-Fix/Fix-and-Optimize as well as Dantzig-Wolfe Decomposition and Lagrangian Relaxation techniques were applied and implemented for solving the integrated production and imperfect preventive maintenance planning problem. More experiments were also carried out. The objective of this planning problem is to determine an optimal integrated production and preventive maintenance plan that concurrently minimizes production as well as preventive maintenance costs during a given finite planning horizon. Three solution approaches were investigated and applied to the reformulated version of the problem, and their performances are compared and discussed. The Relax-and-Fix/Fix-and-Optimize method (RFFO) determines first an initial feasible solution, generated by the relax-and-fix heuristic step, which is further improved in the fix-and-optimize step. Dantzig-Wolfe Decomposition (DWD) and Lagrangian Relaxation (LR) techniques are also applied to the same reformulation of the problem and the results of these three approaches are compared in terms of the solution quality as well as CPU time. The computational results obtained for different instances of the integrated production planning and imperfect preventive maintenance planning problem, show that the RFFO method is very efficient and is competitive in term of the solutions quality. It provides quite good solutions to the tested instances with a noticeable improvement in computational time. Dantzig-Wolfe Decomposition (DWD) and Lagrangian Relaxation (LR) methods, on the other hand, exhibit a good enhancement in terms of computational time especially for large instances, however, the quality of solution still requires some more improvements