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Abstract

The scheduling of outbound baggage at international airports is a challenging task in the airport industry. The issue is to control the incoming baggage flow in order to balance the workload over the system. The resource consumption of the different activities, which have to be scheduled, are depending on the arrival process of the baggage. Because of high complexity we suggest a decomposition heuristic to tackle this problem.

Abstract

This paper presents different methods for solving parallel machine scheduling problems with precedence constraints and setup times between the jobs. Limited discrepancy search methods mixed with local search principles, dominance conditions and specific lower bounds are proposed. The proposed methods are evaluated on a set of randomly generated instances and compared with previous results from the literature and those obtained with an efficient commercial solver. We conclude that our propositions are quite competitive and our results even outperform other approaches in most cases.

Mots-Clés / Keywords

Abstract

The Generalized Traveling Salesman Problem (GTSP) is a generalization of the well-known Traveling Salesman Problem (TSP), in which the set of nodes is divided into mutually exclusive clusters. The objective of the GTSP consists in visiting each cluster exactly once in a tour, while minimizing the sum of the routing costs. This paper addresses the solution of the GTSP using a Memetic Algorithm procedure. The originality of our approach rests on the crossover procedure that uses a large neighborhood search. This algorithm is compared with other algorithms on a set of 41 standard test problems with up to 442 nodes. The obtained results show that our algorithm is efficient in both solution quality and computation time.

Abstract

This paper deals with production scheduling involving energy constraints, typically electrical energy. We start by an industrial case-study for which we propose a two-step integer/constraint programming method. From the industrial problem we derive a generic problem, the Energy Scheduling Problem (EnSP). We propose an extension of specific resource constraint propagation techniques to efficiently prune the search space for EnSP solving. We also present a branching scheme to solve the problem via tree search. Finally, computational results are provided.

Abstract

This paper proposes an efficient heuristic algorithm for solving a complex batching and scheduling problem in a diffusion area of a semiconductor plant. Diffusion is frequently bottleneck in the plant and also one of the most complex areas in terms of number of machines, constraints to satisfy and the large number of lots to manage. The purpose of this study is to investigate an approach to group lots in batches and to schedule these batches on machines. The problem is modelled through a disjunctive graph formulation. A constructive algorithm is proposed and improvement procedures based on iterative sampling and Simulated Annealing are developed. Computational experiments, carried out on actual industrial problem instances, show the ability of the iterative sampling to signifcantly improve the initial solution. The Simulated Annealing enhances the results of the iterative sampling. The constructive algorithm has been embedded in a software and is currently being used.

Abstract

In this paper we address a real world optimisation problem, the Rail Track Inspection Scheduling Problem (RTISP). This problem consists of scheduling network inspection tasks. The objective is to minimise total deadhead distance. A mixed integer formulation of the problem is presented. A column generation based algorithm is proposed to solve this rich arc routing problem. Its performance is analysed by benchmarking a real world dataset from the French national railway company (SNCF). The efficiency of the algorithm is compared to an enhanced greedy algorithm. Its ability to schedule one year of inspection tasks on a sparse graph with thousand nodes, arcs and edges is assessed.