The generalized assignment problem is a classical combinatorial optimization problem known to be NP-hard. It can model a variety of real world applications in location, allocation, machine assignment, and supply chains. Researchers have studied the problem since the late 1960s, and computer codes for practical applications emerged in the early 1970s. We propose a new algorithm for this problem ...

In service oriented scenarios, applications are created by composing atomic services and exposing the resulting added value logic as a service. When several alternative service providers are available for composition, quality of service (QoS) properties such as execution time, cost, or availability are taken into account to make the choice, leading to the creation of QoS-aware composite web ser...

A frame relay service offers virtual private networks to customers by provisioning a set of long-term private virtual circuits (PVCs) between customer endpoints on a large backbone network. During the provisioning of a PVC, routing decisions are made without any knowledge of future requests. Over time, these decisions can cause inefficiencies in the network and occasional offline rerouting of t...

In a previous paper, a simple genetic algorithm (GA) was developed for finding (approximately) the minimum makespan of the n-job, m-machine permutation flowshop sequencing problem (PFSP). The performance of the algorithm was comparable to that of a naive neighborhood search technique and a proven simulated annealing algorithm. However, recent results have demonstrated the superiority of a tabu ...

Railway scheduling is a complex task of rail operators that involves the generation of a conflict-free train timetable. This paper presents a discrete-event simulation-based optimization approach for solving the train timetabling problem to minimize total weighted unplanned stop time in a hybrid single and double track railway networks. The designed simulation model is used as a platform for ge...

Many successful metaheuristics employ intelligent procedures to obtain high quality solutions for optimization problems. Intelligence emerges in these metaheuristics via memory and learning. Meta-RaPS (Metaheuristic for Randomized Priority Search) which can produce promising solutions is classified as a memoryless metaheuristic. To improve its performance, Q learning and Path Relinking (PR) are...

We study a class of vehicle-routing problems (VRP) with simultaneous pickup and delivery (VRPSPD). In VRPSPDs, each customer may require a certain quantity of goods delivered from the depot and a quantity of goods to be picked up and returned to the depot. Besides the standard VRPSPD, we address (i) the VRPSPD with time limit (VRPSPDTL), which imposes a time limit on the routes of the transport...

Recently, in the field of project scheduling problems the concept of partially renewable resources has been introduced. Theoretically, it is a generalization of both renewable and non-renewable resources. From an applied point of view, partially renewable resources allow us to model a large variety of situations that do not fit in classical models, but can be found in real problems in timetabli...

The set k-covering problem is a variant of the classical set covering problem, in which each object is required to be covered at least k times. We describe a hybrid Lagrangean heuristic, named LAGRASP, which combines subgradient optimization and GRASP with path-relinking to solve the set kcovering problem. Computational experiments carried out on 135 test instances show experimentally that by p...

The procurement of transportation services via large-scale combinatorial auctions involves a couple of complex decisions whose outcome highly influences the performance of the tender process. This paper examines the shipper’s task of selecting a subset of the submitted bids which efficiently trades off total procurement cost against expected carrier performance. To solve this bi-objective winne...