A bi-objective genetic algorithm approach to risk mitigation in project scheduling

A problem of risk mitigation in project scheduling is formulated as a bi-objective optimization problem, where the expected makespan and the expected total cost are both to be minimized. The expected total cost is the sum of four cost components: overhead cost, activity execution cost, cost of reducing risks and penalty cost for tardiness. Risks for activities are predefined. For each risk at an activity, various levels are defined, which correspond to the results of different preventive measures. Only those risks with a probable impact on the duration of the related activity are considered here. Impacts of risks are not only accounted for through the expected makespan but are also translated into cost and thus have an impact on the expected total cost. An MIP model and a heuristic solution approach based on genetic algorithms (GAs) is proposed. The experiments conducted indicate that GAs provide a fast and effective solution approach to the problem. For smaller problems, the results obtained by the GA are very good. For larger problems, there is room for improvement

Multiobjective strategies for New Product Development in the pharmaceutical industry

New Product Development (NPD) constitutes a challenging problem in the pharmaceutical industry, due to the characteristics of the development pipeline. Formally, the NPD problem can be stated as follows: select a set of R&D projects from a pool of candidate projects in order to satisfy several criteria (economic profitability, time to market) while coping with the uncertain nature of the projects. More precisely, the recurrent key issues are to determine the projects to develop once target molecules have been identified, their order and the level of resources to assign. In this context, the proposed approach combines discrete event stochastic simulation (Monte Carlo approach) with multiobjective genetic algorithms (NSGAII type, Non-Sorted Genetic Algorithm II) to optimize the highly combinatorial portfolio management problem. In that context, Genetic Algorithms (GAs) are particularly attractive for treating this kind of problem, due to their ability to directly lead to the so-called Pareto front and to account for the combinatorial aspect. This work is illustrated with a study case involving nine interdependent new product candidates targeting three diseases. An analysis is performed for this test bench on the different pairs of criteria both for the bi- and tricriteria optimization: large portfolios cause resource queues and delays time to launch and are eliminated by the bi- and tricriteria optimization strategy. The optimization strategy is thus interesting to detect the sequence candidates. Time is an important criterion to consider simultaneously with NPV and risk criteria. The order in which drugs are released in the pipeline is of great importance as with scheduling problems

Multiobjective strategies for New Product Development in the pharmaceutical industry

New Product Development (NPD) constitutes a challenging problem in the pharmaceutical industry, due to the characteristics of the development pipeline. Formally, the NPD problem can be stated as follows: select a set of R&D projects from a pool of candidate projects in order to satisfy several criteria (economic profitability, time to market) while coping with the uncertain nature of the projects. More precisely, the recurrent key issues are to determine the projects to develop once target molecules have been identified, their order and the level of resources to assign. In this context, the proposed approach combines discrete event stochastic simulation (Monte Carlo approach) with multiobjective genetic algorithms (NSGAII type, Non-Sorted Genetic Algorithm II) to optimize the highly combinatorial portfolio management problem. In that context, Genetic Algorithms (GAs) are particularly attractive for treating this kind of problem, due to their ability to directly lead to the so-called Pareto front and to account for the combinatorial aspect. This work is illustrated with a study case involving nine interdependent new product candidates targeting three diseases. An analysis is performed for this test bench on the different pairs of criteria both for the bi- and tricriteria optimization: large portfolios cause resource queues and delays time to launch and are eliminated by the bi- and tricriteria optimization strategy. The optimization strategy is thus interesting to detect the sequence candidates. Time is an important criterion to consider simultaneously with NPV and risk criteria. The order in which drugs are released in the pipeline is of great importance as with scheduling problems

Multi-objective model for optimizing railway infrastructure asset renewal

Trabalho inspirado num problema real da empresa Infraestruturas de Portugal, EP.A multi-objective model for managing railway infrastructure asset renewal is presented. The model aims to optimize three objectives, while respecting operational constraints: levelling investment throughout multiple years, minimizing total cost and minimizing work start postponements. Its output is an optimized intervention schedule. The model is based on a case study from a Portuguese infrastructure management company, which specified the objectives and constraints, and reflects management practice on railway infrastructure. The results show that investment levelling greatly influences the other objectives and that total cost fluctuations may range from insignificant to important, depending on the condition of the infrastructure. The results structure is argued to be general and suggests a practical methodology for analysing trade-offs and selecting a solution for implementation.info:eu-repo/semantics/publishedVersio

MPS - Decision Support System for Multiobjective Project Scheduling

The report presents a decision support system (DSS) for multiobjective project scheduling under multiple-category resource constraints. It handles quite a general class of nonpreemptive scheduling problems with renewable, nonrenewable and doubly-constrained resources, multiple performing modes of activities, precedence constraints in the form of an activity network and multiple project performance criteria of time and cost type. The DSS has been implemented on a microcomputer compatible with IBM PC, and called MPS. It is based on three kinds of heuristics: parallel priority rules, simulated annealing and branch-and-bound. The last algorithm can even yield exact solutions when sufficient processing time is available. Some parts of the MPS are interactive, in particular, the search for a best compromise schedule. Graphical facilities enable a thorough evaluation of feasible schedules. The report starts with a methodological guide presenting the problem formulation and the three heuristics. Then, the general scheme of the MPS is given together with an executive guide. An expanding menu and all its options are described and illustrated with a simple example. The last part presents a real problem solving consisting in scheduling 40 farm activities

A survey of variants and extensions of the resource-constrained project scheduling problem

The resource-constrained project scheduling problem (RCPSP) consists of activities that must be scheduled subject to precedence and resource constraints such that the makespan is minimized. It has become a well-known standard problem in the context of project scheduling which has attracted numerous researchers who developed both exact and heuristic scheduling procedures. However, it is a rather basic model with assumptions that are too restrictive for many practical applications. Consequently, various extensions of the basic RCPSP have been developed. This paper gives an overview over these extensions. The extensions are classified according to the structure of the RCPSP. We summarize generalizations of the activity concept, of the precedence relations and of the resource constraints. Alternative objectives and approaches for scheduling multiple projects are discussed as well. In addition to popular variants and extensions such as multiple modes, minimal and maximal time lags, and net present value-based objectives, the paper also provides a survey of many less known concepts. --project scheduling,modeling,resource constraints,temporal constraints,networks

Relevance and Applicability of Multi-objective Resource Constrained Project Scheduling Problem: Review Article

Resource-Constrained Project Scheduling Problem (RCPSP) is a Non Polynomial (NP) - Hard optimization problem that considers how to assign activities to available resources in order to meet predefined objectives. The problem is usually characterized by precedence relationship between activities with limited capacity of renewable resources. In an environment where resources are limited, projects still have to be finished on time, within the approved budget and in accordance with the preset specifications. Inherently, these tend to make RCPSP, a multi-objective problem. However, it has been treated as a single objective problem with project makespan often recognized as the most relevant objective. As a result of not understanding the multi-objective dimension of some projects, where these objectives need to be simultaneously considered, distraction and conflict of interest have ultimately lead to abandoned or totally failed projects. The aim of this article is to holistically review the relevance and applicability of multi-objective performance dimension of RCPSP in an environment where optimal use of limited resources is important

Deadline Constrained Cloud Computing Resources Scheduling through an Ant Colony System Approach

Cloud computing resources scheduling is essential for executing workflows in the cloud platform because it relates to both execution time and execution cost. In this paper, we adopt a model that optimizes the execution cost while meeting deadline constraints. In solving this problem, we propose an Improved Ant Colony System (IACS) approach featuring two novel strategies. Firstly, a dynamic heuristic strategy is used to calculate a heuristic value during an evolutionary process by taking the workflow topological structure into consideration. Secondly, a double search strategy is used to initialize the pheromone and calculate the heuristic value according to the execution time at the beginning and to initialize the pheromone and calculate heuristic value according to the execution cost after a feasible solution is found. Therefore, the proposed IACS is adaptive to the search environment and to different objectives. We have conducted extensive experiments based on workflows with different scales and different cloud resources. We compare the result with a particle swarm optimization (PSO) approach and a dynamic objective genetic algorithm (DOGA) approach. Experimental results show that IACS is able to find better solutions with a lower cost than both PSO and DOGA do on various scheduling scales and deadline conditions

Short Software Descriptions

This paper briefly presents the software for interactive decision support that was developed in 1990-1991 within the Contracted Study Agreement between the System and Decision Sciences Program at IIASA and several Polish scientific institutions, namely: Institute of Automatic Control (Warsaw University of Technology); Institute of Computing Science (Technical University of Poznaii); Institute of Informatics (Warsaw University); and Systems Research Institute of the Polish Academy of Sciences. This Contracted Study Agreement has been a continuation of the same type of activity conducted since 1985. Therefore many of the software packages are actually improved versions of the programs developed in 1985-1989. The theoretical part of the results developed within this scientific activity is presented in the IIASA Collaborative Paper CP-90-008 by A. Ruszczynski, T. Rogowski and A.P. Wierzbicki entitled "Contributions to Methodology and Techniques of Decision Analysis (First Stage)." Detailed descriptions of the methodology and the user guide for each particular software package are published in separate Collaborative Papers. Each software package described here is available in executable form for non-profit educational and scientific purposes, however, any profit-oriented or commercial application requires a written agreement with IIASA. Inquires about the software should be directed to the System and Decision Sciences Program at IIASA, Methodology of Decisions Analysis Project