A hybrid scatter search. Electromagnetism meta-heuristic for project scheduling.

In the last few decades, several effective algorithms for solving the resource-constrained project scheduling problem have been proposed. However, the challenging nature of this problem, summarised in its strongly NP-hard status, restricts the effectiveness of exact optimisation to relatively small instances. In this paper, we present a new meta-heuristic for this problem, able to provide near-optimal heuristic solutions. The procedure combines elements from scatter search, a generic population-based evolutionary search method, and a recently introduced heuristic method for the optimisation of unconstrained continuous functions based on an analogy with electromagnetism theory, hereafter referred to as the electromagnetism meta-heuristic. We present computational experiments on standard benchmark datasets, compare the results with current state-ofthe-art heuristics, and show that the procedure is capable of producing consistently good results for challenging instances of the resource-constrained project scheduling problem. We also demonstrate that the algorithm outperforms state-of-the-art existing heuristics.Algorithms; Effectiveness; Electromagnetism; Functions; Heuristic; Project scheduling; Scatter; Scatter search; Scheduling; Theory;

Feasibility and dominance rules in the electromagnetism-like algorithm for constrained global optimization

This paper presents the use of a constraint-handling technique, known as feasibility and dominance rules, in a electromagnetismlike (ELM) mechanism for solving constrained global optimization problems. Since the original ELM algorithm is specifically designed for solving bound constrained problems, only the inequality and equality constraints violation together with the objective function value are used to select points and to progress towards feasibility and optimality. Numerical experiments are presented, including a comparison with other methods recently reported in the literature

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.

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.

THE DISCRETE TIME/COST TRADE-OFF PROBLEM UNDER VARIOUS ASSUMPTIONS EXACT AND HEURISTIC PROCEDURES

Time/cost trade-offs in project networks have been the subject of extensive research since the development of the critical path method (CPM) in the late 50s. Time/cost behaviour in a project activity basically describes the trade-off between the duration of the activity and its amount of non-renewable resources (e.g. money) committed to it. In the discrete version of the problem (the discrete time/cost trade-off problem), it is generally accepted that the trade-off follows a discrete non-increasing pattern, i.e. expediting an activity is possible by allocating more resources (i.e. at a larger cost) to it. However, due to its complexity (the problem is known to be NP hard (see De et al. (1997)), the problem has been solved for relatively small instances. In this paper, we elaborate on three extensions of the well-known discrete time/cost trade-off problem in order to cope with more realistic settings: time/switch constraints, work continuity constraints and net present value maximization. We give an extensive literature overview of existing procedures for these problem types, and present an exact solution approach for the work continuity version, which is not being investigated yet. Moreover, we discuss a new meta-heuristic approach in order to provide near-optimal heuristic solutions for the different problems. We present computational results for the problems under study by comparing the results for both exact and heuristic procedures. We demonstrate that the heuristic algorithms produce consistently good results for two versions of the discrete time/cost trade-off problem.