The impact of various activity assumptions on the lead-time and resource utilization of resource-constrained projects

The well-known resource-constrained project scheduling problem (RCPSP) schedules project activities within the precedence and renewable resource constraints while minimizing the total lead-time of the project. The basic problem description assumes non-pre-emptive activities with fixed durations, and has been extended to various other assumptions in literature. In this paper, we investigate the effect of three activity assumptions on the total lead-time and the total resource utilization of a project. More precisely, we investigate the influence of variable activity durations under a fixed work content, the possibility of allowing activity pre-emption and the use of fast tracking to decrease a project's duration. We give an overview of the procedures developed in literature and present some modifications to existing solution approaches to cope with our activity assumptions under study. We present computational results on a generated dataset and evaluate the impact of all assumptions on the quality of the schedule

Meta-heuristic resource constrained project scheduling: solution space restrictions and neighbourhood extensions

The resource-constrained project scheduling problem (RCPSP) has been extensively investigated during the past decades. Due to its strongly NP-hard status and the need for solving large realistic project instances, the recent focus has shifted from exact optimisation procedures to (meta-) heuristic approaches. In this paper, we extend some existing state-of-the-art RCPSP procedures in two ways. First, we extensively test a decomposition approach that splits problem instances into smaller sub-problems to be solved with an (exact or heuristic) procedure, and re-incorporates the obtained solutions for the sub-problems into the solution of the main problem, possibly leading to an overall better solution. Second, we study the influence of an extended neighbourhood search on the performance of a meta-heuristic procedure. Computational results reveal that both techniques are valuable extensions and lead to improved results

Pre-emptive resource-constrained project scheduling with setup times

Resource-constrained project scheduling with activity pre-emption assumes that activities are allowed to be interrupted and restarted later in the schedule at no extra cost. In the current paper, we extend this pre-emptive scheduling problem with setup times between activity interruptions and the possibility to fast track pre-emptive subparts of activities. The contribution of the paper is twofold. First, we present an optimal branch-and-bound procedure for the pre-emptive resource-constrained project scheduling problem with setup times and fast tracking options. Second, we test the impact of these pre-emptive extensions to the quality of the schedule from a lead-time point-of-view

Avoiding congestion in freight transport planning: a case study in Flanders

A substantial increase in transport intensity for passenger and freight traffic has been observed during the last decades and research confirms that this trend will continue in the years to come. Economic centres have turned into heavily congested areas. The freight transport sector incurs excessive waiting times on the road as well as at intermediate stops (e.g. sea terminals, loading or unloading points). This may cause economic losses and environmental damages. Waiting times may be avoided by taking into account congestion in freight transport planning. Vehicle routing problems arise when several pickup and delivery operations need to be performed, mainly by truck, over relatively short distances [1]. Congestion leads to uncertain travel times on links and uncertain waiting times at pickup or delivery locations. Peak hours may be avoided on congested road segments by changing the order in which customers are served. On the other hand, time slots at customer sites may be renegotiated, creating more flexibility to avoid congestion on the road and at customer stops. The objective of this paper is to estimate the benefits of taking congestion into account in transport planning and to quantify the impact of delivery restrictions on transport costs. A highly congested road network raises the need for robust vehicle routing decisions. Current traffic conditions give rise to uncertain travel times. The reliability of travel time on a route is one of the dominant factors affecting route and departure time choices in passenger transport [2]. Similarly, in freight transport the reliability of travel times may be taken into account when planning vehicle routes. In this paper congestion is modelled as time-dependent travel times. These travel times take into account the dynamics of the time lost due to congestion using the Bureau of Public Roads (BPR) function, which is commonly-used for relating travel times to increases in travel volume [3]. The Time Dependent Vehicle Routing Problem (TDVRP) will be studied as a deterministic planning problem taking into account peak hour traffic congestion. Solution methods for the TDVRP have been focused on heuristic approaches [4, 5, 6, 7]. Kok [8] applies a restricted dynamic programming heuristic to solve a TDVRP. In this paper a heuristic algorithm will be presented to solve problem instances of realistic size. Next, this algorithm will be applied to perform a sensitivity analysis to identify which congestion avoiding strategies have a large influence on the objective function. Shippers may adapt the way they plan their transport as a strategy to avoid congestion. For example, time windows at customer locations may be renegotiated, departure times at the depot may be questioned or the assignment of customers to routes and the order in which customers are served may be changed. The proposed methodology will be demonstrated with a Flemish case study

A Bi-Population Based Genetic Algorithm for the Resource-Constrained Project Scheduling Problem

The resource-constrained project scheduling problem (RCPSP) is one of the most challenging problems in project scheduling. During the last couple of years many heuristic procedures have been developed for this problem, but still these procedures often fail in finding near-optimal solutions for more challenging problem instances. In this paper, we present a new genetic algorithm (GA) that, in contrast of a conventional GA, makes use of two separate populations. This bi-population genetic algorithm (BPGA) operates on both a population of left-justified schedules and a population of right-justified schedules in order to fully exploit the features of the iterative forward/backward local search scheduling technique. Comparative computational results reveal that this procedure can be considered as today's best performing RCPSP heuristic

A Bi-Population Based Genetic Algorithm for the Resource-Constrained Project Scheduling Problem, February 2005

Abstract. The resource-constrained project scheduling problem (RCPSP) is one of the most challenging problems in project scheduling. During the last couple of years many heuristic procedures have been developed for this problem, but still these procedures often fail in finding near-optimal solutions for more challenging problem instances. In this paper, we present a new genetic algorithm (GA) that, in contrast of a conventional GA, makes use of two separate populations. This bi-population genetic algorithm (BPGA) operates on both a population of left-justified schedules and a population of right-justified schedules in order to fully exploit the features of the iterative forward/backward local search scheduling technique. Comparative computational results reveal that this procedure can be considered as today’s best performing RCPSP heuristic.

A finite capacity production scheduling procedure for a belgian steel company

We present a multi-objective finite-capacity production scheduling algorithm for an integrated steel company located in Belgium. The two-stage optimization model takes various company-specific constraints into account and optimizes various, often conflicting, weighted objectives. A first machine assignment stage determines the routing of an individual order through the network while a second scheduling stage makes a detailed timetable for each operation for all orders. The procedure has been tested on randomly generated data instances sampled from real-life data from the steel company. We report promising computational results and illustrate the flexibility of the optimization model with respect to the various weights in the multi-objective function