The aim of the present article is to optimize the robustness objective for the Resource-Constrained Project scheduling Problem (RCPSP) dealing with activity duration uncertainty.The studied robustness consists in minimizing the worst-case performance, referred to as the min-max robustness objective, among a set of initial scenarios.We propose an enhanced GRASP approach as a solution to the given scenario-based robust model.This approach is based on different priority rules in the construction phase and a forwardbackward heuristic in the improvement phase.We investigate two different benchmark data sets, the Patterson set and the PSPLIB J30 set.Experiments show that the proposed enhanced GRASP outperforms the basic procedure, and a based-evolutionary algorithm, in robustness optimization.

The present paper deals with the resource-constrained project scheduling problem with uncertain activity durations. Based on scenarios, we investigate two robust models, the min-max model which focuses on the minimization of the absolute robustness objective and the min-max regret model having the object to minimize the absolute regret. We propose an adaptive robust genetic approach with a sophisticated initial population and a Forward-Backward Improvement heuristic. The proposed algorithm is applied for the PSPLIB J30 data set with modified activity durations. Obtained results show the performance of the genetic algorithm combined with the improvement heuristic compared with the basic version. Different perturbation levels were tested to determine the corresponding performance degradation.

This paper deals with the Resource-Constrained Project scheduling Problem (RCPSP) under activity duration uncertainty.Based on scenarios, the object is to minimize the worst-case performance among a set of initial scenarios which is referred to as the min-max robustness objective.Due to the complexity of the tackled problem, we propose the application of the GRASP method which is qualified as a simple and effective multi-start metaheuristic.The proposed approach incorporates an adaptive greedy function based on priority rules to construct new solutions, and a local search with a forward-backward heuristic in the improvement phase.Two different benchmark data sets are investigated, the Patterson set and the PSPLIB J30 set.Comparative results show that the proposed enhanced GRASP outperforms the basic procedure in robustness optimization.

Recently, many studies are carried out with inspirations from ecological phenomena for developing optimization techniques. The new algorithm that is motivated by a common phenomenon in agriculture is colonization of invasive weeds. In this paper, a modified invasive weed optimization (IWO) algorithm is presented for optimization of multiobjective flexible job shop scheduling problems (FJSSPs) with the criteria to minimize the maximum completion time (makespan), the total workload of machines and the workload of the critical machine. IWO is a bio-inspired metaheuristic that mimics the ecological behaviour of weeds in colonizing and finding suitable place for growth and reproduction. IWO is developed to solve continuous optimization problems that’s why the heuristic rule the Smallest Position Value (SPV) is used to convert the continuous position values to the discrete job sequences. The computational experiments show that the proposed algorithm is highly competitive to the state-of-the-art methods in the literature since it is able to find the optimal and best-known solutions on the instances studied.

In this paper, a modified invasive weed optimization (IWO) algorithm is presented for optimization of multiobjective flexible job shop scheduling problems (FJSSPs) with the criteria to minimize the maximum completion time (makespan), the total workload of machines and the workload of the critical machine.IWO is a bio-inspired metaheuristic that mimics the ecological behaviour of weeds in colonizing and finding suitable place for growth and reproduction.IWO is developed to solve continuous optimization problems that's why the heuristic rule the Smallest Position Value (SPV) is used to convert the continuous position values to the discrete job sequences.The computational experiments show that the proposed algorithm is highly competitive to the state-of-the-art methods in the literature since it is able to find the optimal and best-known solutions on the instances studied.

In this paper, we aim to overcome the difficulty of parameter tuning present in all metaheuristics by proposing an adaptive algorithm for multiobjective flexible job shop scheduling problems (MFSSPs). The adaptive algorithm named TRIBES is a parameter-free PSO that does not require any parameter tuning (swarm size, sociometry...).TRIBES like PSO, is a computational method that mimics the behavior of flying birds and their means of information exchange. The candidate solutions communicate and cooperate with each other, whereas individuals in an evolutionary algorithm compete for survival. Numerical results highlight the effectiveness of the proposed algorithm and show that TRIBES is very competitive to the state-of-the-art methods in the literature since it is able to find the optimal and best-known solutions on the instances studied. The following sections are devoted to the description of our problem, the description of TRIBES, the most important issues in designing the algorithm and the performance results.

We consider the Dynamic Pick-up and Delivery problem with Time Windows (DPDPTW). It is about goods to be carried from a pickup to a delivery location. Real world applications of DPDPTW arise, for example, in courier operations and door-to-door transportation services. Our purpose is to solve efficiently the DPDPTW, taking into account the real time customer requests for transportation services to produce a solution that minimizes the operational costs and fulfills the problem constraints. For this aim, we propose an online K-means based heuristic. The requests are processed one per one, clustered into groups using the K-means algorithm. Routing and scheduling are performed using a dynamic programming algorithm. Computational results show that the proposed method yields good solutions compared to literature construction and insertion methods.

The objective of the present work is to estabilish a robust approach for the resource-constrained project scheduling problem (RCPSP) under uncertainties with the special case of activities durations variability. The proposed approach is based on a Genetic Algorithm (GA) which incorporates both performance and robustness mesures. Several robustness metrics were proposed and discussed in the litterature. We adopt minmax and minmax-regret objectives to study the compromise between the makespan and the robustness criteria. The goal is to analyse and compare robustness values and their impact on the solution quality.

To avoid the user to tune the parameters, a lot of effort in metaheuristics have been put to propose “adaptive” algorithms. For such algorithms, we have only to define the problem and the stopping criterion. This paper presents a new attempt involving the use of TRIBES, which is an autonomous adaptive search heuristic, for the well-known flexible job shop scheduling problem with the objective of minimizing the makespan or the total duration of schedule. TRIBES, a metaphor for different sized groups of people moving in an unknown area/region, looking for a “good” place, is a parameter-free Particle Swarm Optimization (PSO) algorithm that does not need any parameter tuning (swarm size, sociometry, ...). Numerical results highlight the effectiveness of the proposed algorithm and show that TRIBES in which a local search engine has been included is promising for solving combinatorial optimization problems, which are of significant importance in the manufacturing sector.

Vehicle Routing and Flexible Job Shop Scheduling Problems (VRP and FJSSP) are two common hard combinatorial optimization problems that show many similarities in their conceptual level [2, 4]. It was proved for both problems that solving techniques like exact methods fail to provide good quality solutions in a reasonable amount of time when dealing with large scale instances [1, 5, 14]. In order to overcome this weakness, we decide in the favour of meta heuristics and we focalize on evolutionary algorithms that have been successfully used in scheduling problems [1, 5, 9]. In this paper we investigate the common properties of the VRP and the FJSSP in order to provide a new controlled evolutionary approach for the CVRP optimization inspired by the FJSSP evolutionary optimization algorithms introduced in [10].

In this paper, we investigate in the applicability of the Contract Net Protocol negotiation (CNP) in the field of the dynamic transportation. We address the optimization of the Dynamic Pickup and Delivery Problem with Time Windows also called DPDPTW. This problem is a variant of the Vehicle Routing Problem (VRP) that may be described as the problem of finding the least possible dispatching cost of requests concerning the picking of some quantity of goods from a pickup to a delivery location while most of the requests continuously occur during the day. The use of contract nets in dynamic and uncertain domains such as ours has been proved to be more fruitful than the use of centralized problem solving [9].We provide a new automated negotiation based on the CNP. Negotiation process is adjusted to deal intelligently with the uncertainty present in the concerned problem.

Different similarities and analogies have been investigated between the travel salesman problem (TSP) and the one machine sequencing problem, and common heuristics and bounds were also established for both of them. Recently, works focalized on possible analogies between variants of vehicle routing problems (VRP) and parallel machine scheduling problems in Beck, J-C, et al., (2003). In this paper, we present different analogies between two complex combinatorial optimization problems: the VRP and the flexible job shop scheduling problem (FJSSP) and use it for the calculation of new VRP lower bounds in Carlier, J. et al., (1988).

This paper deals with the real-time regulation of traffic within a disrupted transportation system. We outline the necessity of a decision support system that detects, analyzes, and resolves the unpredicted disturbances. Due to the distributed aspects of transportation systems, we present a multi-agent approach for the regulation process. Moreover, this approach also includes an evolutionary algorithm that is based on an original genetic coding representing the decisions on a set of vehicles and stops affected by the disturbance. This set constitutes, in fact, the space-time horizon of the regulation process. The evolutionary algorithm then treats the regulation problem as an optimization and provides the regulator with relevant decisions that can result in a partial reconfiguration of the network.

The most important disadvantage of urban bus networks (UBN) is their close dependence on diverse random phenomena that can alter the initial preestablished schedules of the buses. In fact, any changes occurring in the global urban traffic, in the demand, in the equipment or in the stuff, may certainly cause disturbances within the network traffic. Moreover, these disturbances affect the waiting time of the customers at the stops, the transit operations and also the duration of the different trips on the buses. This article deals with the traffic regulation in an UBN, especially with the transit operations. After introducing the considered disturbances, we state the different criteria that have to be optimized. The regulation approach by evolutionary algorithms is then exposed and some simulation results are presented.

Due to several external influences that affect the real-time transportation systems traffic, it is necessary to design a decision-support tool that provides some appropriate decisions to each disturbance. In this paper, this real-time regulation problem is handled as a rerouting and rescheduling problem that is proved to be NP-hard. Its resolution is in fact based on an evolutionary algorithm that integrates routing and scheduling processes to endow the regulator with immediate applicable decisions resulting, if necessary, in a partial reconfiguration of the network.

This paper deals with an Agent-based Model of a Decision-Support System for an Urban Transport Network. The present Multi-Agent Decision-Support System (MADSS) assists the regulator in the search of the appropriate regulation decisions in case of a bus traffic disturbance. It is composed of a Supervision Module and a Regulation Module that rely on the information given by the Automatic Vehicle Monitoring system (AVM). The supervision Module represents the temporal axis of the network by the agents of type BUS and the spatial axis by the agents STOP. These agents cooperate in order to detect as soon as possible the disturbances. The regulation Module contains the agents INCIDENT that identify the disturbances, the agents ZONEPERT that analyze them and the agents ZONEREG that generate regulation decisions via an anytime evolutionary approach. Hence, through the cooperative aspects of the agent interactions, the MADSS is able to perform a coherent and preventive regulation process.