Centralized versus market-based approaches to mobile task allocation problem: State-of-the-art

Centralized approach has been adopted for finding solutions to resource allocation problems (RAPs) in many real-life applications. On the other hand, market-based approach has been proposed as an alternative to solve the problem due to recent advancement in ICT technologies. In spite of the existence of some efforts to review the pros and cons of each approach in RAPs, the studies cannot be directly applied to specific problem domains like mobile task allocation problem which is characterised with high level of uncertainty on the availability of resources (workers). This paper aims to review existing studies on task allocation problems(TAPs) focusing on those two approaches and their comparison and identify major issues that need to be resolved for comparing the two approaches in mobile task allocation problems. Mobile Task Allocation Problem (MTAP) is defined and its problematic structures are explained in relation with task allocation to mobile workers. Solutions produced by each approach to some applications and variations of MTAP are also discussed and compared. Finally, some future research directions are identified in order to compare both approaches in function of uncertainty emerging from the mobile nature of the MTAP

Strategic Planning and Project Selection for IT Portfolio Management

Software project proposals, solicited from various sources across an organization, could significantly vary in strategic value, overlap in functionality, and assume conflicting technical infrastructure. Without a holistic approach toward project selection and planning, the resulting project portfolio will likely incur undue risk while delivering poor return on investment. We propose a two-stage optimization procedure. In the first stage, project characteristics such as strategic alignment, perceived benefits, cost, and risk are considered to maximize portfolio value. In the second stage, inter-project dependencies and team expertise are used to determine how projects are assigned to programs and in what sequence they should be carried out. Future extension on the proposed optimization procedure is also discussed

Project portfolio management: capacity allocation, downsizing decisions and sequencing rules.

This paper aims to gain insight into capacity allocation, downsizing decisions and sequencing rules when managing a portfolio of projects. By downsizing, we mean reducing the scale or size of a project and thereby changing the project's content. In previous work, we have determined the amount of critical capacity that is optimally allocated to concurrently executed projects with deterministic or stochastic workloads when the impact of downsizing is known. In this paper, we extend this view with the possibility of sequential processing, which implies that a complete order is imposed on the projects. When projects are sequenced instead of executed in parallel, two effects come into play: firstly, unused capacity can be shifted to later projects in the same period; and secondly, reinvestment revenues gain importance because of the differences in realization time of the sequenced projects. When project workloads are known, only the second effect counts; when project workloads are stochastic, however, the project's capacity usage is uncertain so that unused capacity can be shifted to later projects in the same period. In this case, both effects need to be taken into account. In this paper, we determine optimal sequencing rules when the selection and capacity-allocation decisions for a set of projects have already been made. We also consider a combination of parallel and sequential planning and we perform simulation experiments that confirm the appropriateness of our capacity-allocation methods.Project portfolio management; Downsizing; Sequencing;

A Multiagent Evolutionary Algorithm for the Resource-Constrained Project Portfolio Selection and Scheduling Problem

A multiagent evolutionary algorithm is proposed to solve the resource-constrained project portfolio selection and scheduling problem. The proposed algorithm has a dual level structure. In the upper level a set of agents make decisions to select appropriate project portfolios. Each agent selects its project portfolio independently. The neighborhood competition operator and self-learning operator are designed to improve the agent’s energy, that is, the portfolio profit. In the lower level the selected projects are scheduled simultaneously and completion times are computed to estimate the expected portfolio profit. A priority rule-based heuristic is used by each agent to solve the multiproject scheduling problem. A set of instances were generated systematically from the widely used Patterson set. Computational experiments confirmed that the proposed evolutionary algorithm is effective for the resource-constrained project portfolio selection and scheduling problem

Efficient Heuristics for Scheduling Tasks on a Flo Shop Environment to Optimize Makespan

In modern manufacturing the trend is the development of Computer Integrated Manufacturing, CIM technologies which is a computerized integration of manufacturing activities (Design, Planning, Scheduling and Control) produces right products at right time to react quickly to the global competitive market demands. The productivity of CIM is highly depending upon the scheduling of Flexible Manufacturing System (FMS). Shorting the make span leads to decreasing machines idle time which results improvement in CIM productivity. Conventional methods of solving scheduling problems based on priority rules still result schedules, sometimes, with significant idle times. To optimize these, this paper model the problem of a flow shop scheduling with the objective of minimizing the makes pan. The work proposed here deal with the production planning problem of a flexible manufacturing system. This paper model the problem of a flow shop scheduling with the objective of minimizing the makes pan. The objective is to minimize the make span of batch-processing machines in a flow shop. The processing times and the sizes of the jobs are known and non-identical. The machines can process a batch as long as its capacity is not exceeded. The processing time of a batch is the longest processing time among all the jobs in that batch. The problem under study is NP-hard for makespan objective. Consequently, comparison based on Gupta’s heuristics, RA heuristic’s, Palmer’s heuristics, CDS heuristics are proposed in this work. Gantt chart was generated to verify the effectiveness of the proposed approaches

Project Portfolio Selection with the Maximization of Net Present Value

Projects scheduling by the project portfolio selection, something that has its own complexity and its flexibility, can create different composition of the project portfolio. An integer programming model is formulated for the project portfolio selection and scheduling.Two heuristic algorithms, genetic algorithm (GA) and simulated annealing (SA), are presented to solve the problem. Results of calculations show that the algorithm performance of GA is better than SA in project portfolio selection to maximize the NPV of the project portfolio

A robust R&D project portfolio optimization model for pharmaceutical contract research organizations

Pharmaceutical drug Research and Development (R&D) outsourcing to contract research organizations (CROs) has experienced a significant growth in recent decades and the trend is expected to continue. A key question for CROs and firms in similar environments is which projects should be included in the firm?s portfolio of projects. As a distinctive contribution to the literature this paper develops and evaluates a business support tool to help a CRO decide on clinical R&D project opportunities and revise its portfolio of R&D projects given the existing constraints, and financial and resource capabilities. A new mathematical programming model in the form of a capital budgeting problem is developed to help revising and rescheduling of the project portfolio. The uncertainty of pharmaceutical R&D cost estimates in drug development stages is captured to mimic a more realistic representation of pharmaceutical R&D projects, and a robust optimization approach is used to tackle the uncertain formulation. An illustrative example is presented to demonstrate the proposed approach

Order Acceptance and Scheduling: A Taxonomy and Review

Over the past 20 years, the topic of order acceptance has attracted considerable attention from those who study scheduling and those who practice it. In a firm that strives to align its functions so that profit is maximized, the coordination of capacity with demand may require that business sometimes be turned away. In particular, there is a trade-off between the revenue brought in by a particular order, and all of its associated costs of processing. The present study focuses on the body of research that approaches this trade-off by considering two decisions: which orders to accept for processing, and how to schedule them. This paper presents a taxonomy and a review of this literature, catalogs its contributions and suggests opportunities for future research in this area