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;

Dynamic order acceptance and capacity planning within a multi-project environment.

We present a tactical decision model for order acceptance and capacity planning that maximizes the expected profits from accepted orders, allowing for regular as well as nonregular capacity.We apply stochastic dynamic programming to determine a profit threshold for the accept/reject decision as well as an optimal capacity allocation for accepted projects, both with an eye on maximizing the expected revenues within the problem horizon.We derive a number of managerial insights based on an analysis of the influence of project and environmental characteristics on optimal project selectionand capacity usage.Capacity planning; multi-project; Order acceptance; Stochastic dynamic programming;

Efficient and effective solution procedures for order acceptance and capacity planning.

This paper investigates dynamic order acceptance and capacity planning under limited regular and non-regular resources. Our goal is to maximize the profits of the accepted projects within a finite planning horizon. The way in which the projects are planned affects their payout time and, as a consequence, there investment revenues as well as the available capacity for future arriving projects. In general, project proposals arise dynamically to the organization, and their actual characteristics are only revealed upon arrival. Dynamic solution approaches are therefore most likely to obtain good results. Although the problem can theoretically be solved to optimality as a stochastic dynamic program, real-life problem instances are too difficult to be solved exactly within areas on able amount of time. Efficient and effective heuristics are thus required that supply a response without delay.For this reason, this paper considers both 'single-pass' algorithms as well as approximate dynamic-programming algorithms and investigates their suitability to solve the problem. Simulation experiments compare the performance of our procedures to a firrst-come, first-served policy that is commonly used in practice.Approximate dynamic programming; Capacity planning; multi-project; Order acceptance; Simulation;

Capacity allocation and downsizing decisions in project portfolio management.

This paper aims to gain insight into capacity allocation and downsizing decisions in project portfolio management. By downsizing, we mean reducing the scale or size of a project and thereby changing the project's content. We first determine the amount of critical capacity that is optimally allocated to strategic projects with deterministic or stochastic workloads for a single-period problem when the impact of downsizing is known. In order to solve the multi-period problem, we have modeled the behavior of the portfolio in subsequent periods as a single project for which the return on investment can be estimated. Secondly, we investigate how the scarcity of resources affects the (expected) value of projects. The independent (expected) project value is calculated under the assumption of unlimited capacity; in contrast, the dependent (expected) project value incorporates the resource constraints. We find that the dependent project value is equal to the independent project value when the return on investment of the portfolio is sufficiently low. In addition, we determine the relation between the return on investment of the portfolio and the value of a project and conclude that the impact of resource scarcity on the value of a project cannot be fully captured by the common financial practice of adapting the discount rate with the estimated return on investment.Project portfolio management; Downsizing; Stochastic workload;

Order acceptance and scheduling in a single-machine environment: exact and heuristic algorithms.

In this paper, we develop exact and heuristic algorithms for the order acceptance and scheduling problem in a single-machine environment. We consider the case where a pool consisting of firm planned orders as well as potential orders is available from which an over-demanded company can select. The capacity available for processing the accepted orders is limited and orders are characterized by known processing times, delivery dates, revenues and the weight representing a penalty per unit-time delay beyond the delivery date promised to the customer. We prove the non-approximability of the problem and give two linear formulations that we solve with CPLEX. We devise two exact branch-and-bound procedures able to solve problem instances of practical dimensions. For the solution of large instances, we propose six heuristics. We provide a comparison and comments on the efficiency and quality of the results obtained using both the exact and heuristic algorithms, including the solution of the linear formulations using CPLEX.Order acceptance; Scheduling; Single machine; Branch-and-bound; Heuristics; Firm planned orders;

Dynamic algorithms for order acceptance and capacity planning within a multi-project environment.

Algorithms; Algorithm; Order; Order acceptance; Capacity planning; Planning; multi-project; International; Scheduling; Theory; Applications;

Dynamic order acceptance and capacity planning within a multi-project environment

We present a tactical decision model for order acceptance and capacity planning that maximizes the expected profits from accepted orders, allowing for regular as well as nonregular capacity.We apply stochastic dynamic programming to determine a profit threshold for the accept/reject decision as well as an optimal capacity allocation for accepted projects, both with an eye on maximizing the expected revenues within the problem horizon.We derive a number of managerial insights based on an analysis of the influence of project and environmental characteristics on optimal project selectionand capacity usage.status: publishe

Efficient and effective solution procedures for order acceptance and capacity planning

This paper investigates dynamic order acceptance and capacity planning under limited regular and non-regular resources. Our goal is to maximize the profits of the accepted projects within a finite planning horizon. The way in which the projects are planned affects their payout time and, as a consequence, there investment revenues as well as the available capacity for future arriving projects. In general, project proposals arise dynamically to the organization, and their actual characteristics are only revealed upon arrival. Dynamic solution approaches are therefore most likely to obtain good results. Although the problem can theoretically be solved to optimality as a stochastic dynamic program, real-life problem instances are too difficult to be solved exactly within areas on able amount of time. Efficient and effective heuristics are thus required that supply a response without delay.For this reason, this paper considers both 'single-pass' algorithms as well as approximate dynamic-programming algorithms and investigates their suitability to solve the problem. Simulation experiments compare the performance of our procedures to a firrst-come, first-served policy that is commonly used in practice.status: publishe