Multi-mode resource constrained multi-project scheduling and resource portfolio problem

This paper introduces a multi-project problem environment which involves multiple projects with assigned due dates; activities that have alternative resource usage modes; a resource dedication policy that does not allow sharing of resources among projects throughout the planning horizon; and a total budget. Three issues arise when investigating this multi-project environment. First, the total budget should be distributed among different resource types to determine the general resource capacities, which correspond to the total amount for each renewable resource to be dedicated to the projects. With the general resource capacities at hand, the next issue is to determine the amounts of resources to be dedicated to the individual projects. The dedication of resources reduces the scheduling of the projects’ activities to a multi-mode resource constrained project scheduling problem (MRCPSP) for each individual project. Finally, the last issue is the efficient solution of the resulting MRCPSPs. In this paper, this multi-project environment is modeled in an integrated fashion and designated as the resource portfolio problem. A two-phase and a monolithic genetic algorithm are proposed as two solution approaches, each of which employs a new improvement move designated as the combinatorial auction for resource portfolio and the combinatorial auction for resource dedication. A computational study using test problems demonstrated the effectiveness of the solution approach proposed.     

Constant Sustainable Consumption Rate in Optimal Growth with Exhaustible Resources

The problem of optimal growth with an exhaustible resource deposit under R. M. Solow's criterion of maximum sustainable consumption rate, previously formulated as a minimum-resource-extraction problem, is shown to be a Mayer-type optimal-control problem. The exact solution of the relevant firstorder necessary conditions for optimality is derived for a Cobb-Douglas production function, whether or not the constant unit resource extraction cost vanishes. The related problem of maximizing the terminal capital stock over an unspecified finite planning period is investigated for the development of more efficient numerical schemes for the solution of multigrade-resource deposit problems. The results for this finite-horizon planning problem are also important from a theoretical viewpoint, since they elucidate the economic content of the optimal growth paths for infinite-horizon problems.     

A model and approach to the challenge posed by optimal power systems planning

Currently, there is a need to plan and analyze the electric power transmission system in greater detail and over larger geographic areas. Existing models approach the problem from different perspectives. Each model addresses different aspects of and has different approximations to the optimal planning process. In order to scope out the huge challenge of optimal transmission planning, this paper presents a new modeling approach for inter-regional planning and investment in a competitive environment. This modeling approach incorporates the detailed generator, topology and operational aspects found in production cost planning models into a larger framework that can find optimal sets of transmission expansion projects. The framework proposed here can be used in an auction to award investment contracts or as a part of a more general policy analysis. The solution yields the set of transmission projects that have the highest expected benefits, while also representing generic generation expansions under the same objective. The model is a two-stage, mixed-integer, multi-period, N-1-reliable model with investment, unit commitment, and transmission switching. The combination of combinatorial, stochastic and operational elements means this model may be computationally intractable without judicious modelling aggregations or approximations to reduce its size and complexity. Nevertheless we show via a dual problem that analysing the economics and sensitivity of the solution is computationally more straightforward.     

Effective program planning for multiple projects under limited resources

This work deals with an analytical approach to effective planning based upon the relative economics of projects contained in a program. A mathematical model is formulated to maximize the total return over the planning horizon subject to various annual cash flow and resource constraints. Stochastic mixed-integer programming is applied to determine the optimal sequence of project planning activities. To illustrate the use of the proposed procedure, a numerical example is solved using the mathematical programming software package SCICONIC/VM in a VAX-11/750 computer system.     

Time-dependent discrete road network design with both tactical and strategic decisions

This paper aims to model and investigate the discrete urban road network design problem, using a multi-objective time-dependent decision-making approach. Given a base network made up with two-way links, candidate link expansion projects, and candidate link construction projects, the problem determines the optimal combination of one-way and two-way links, the optimal selection of capacity expansion projects, and the optimal lane allocations on two-way links over a dual time scale. The problem considers both the total travel time and the total CO emissions as the two objective function measures. The problem is modelled using a time-dependent approach that considers a planning horizon of multiple years and both morning and evening peaks. Under this approach, the model allows determining the sequence of link construction, the expansion projects over a predetermined planning horizon, the configuration of street orientations, and the lane allocations for morning and evening peaks in each year of the planning horizon. This model is formulated as a mixed-integer programming problem with mathematical equilibrium constraints. In this regard, two multi-objective metaheuristics, including a modified non-dominated sorting genetic algorithm (NSGA-II) and a multi-objective B-cell algorithm, are proposed to solve the above-mentioned problem. Computational results for various test networks are also presented in this paper.     

Information resource planning for a health-care system using an AHP-based goal programming method

This paper presents a goal programming (GP) model which aids in allocating a health-care system's information resources pertinent to strategic planning. The model is developed based on the data obtained from a major health-care system in the United States. The overall objective is to design and evaluate a model for effective information resource planning in a health-care system. The proposed model: (1) utilizes a GP approach to reflect the multiple, conflicting goals of the health-care system; (2) employs a GP solution process to reflect multi-dimensional aspects of the resource allocation planning; and (3) allows for some degree of flexibility of decision-making with respect to resource allocation. The goals are decomposed and prioritized with respect to the corresponding criteria using the analytic hierarchy process (AHP). The model result is derived and discussed. This GP model facilitates decision-making planning process and managerial policy in health-care information resources planning and similar planning settings.     

A multiple-tree search procedure for the resource-constrained project scheduling problem

For over three decades, researchers have sought effective solution procedures for PERT/CPM types of scheduling problems under conditions of limited resource availability. This paper presents a procedure for this problem which takes advantage of the emerging technology provided by multiple parallel processors to find and verify an optimal schedule for a project under conditions of multiple resource constraints. In our approach, multiple solutions trees are searched simultaneously in the quest for a minimum duration schedule. Global upper and lower bound information in common memory is shared among processors, enabling one or several processors to prune potentially significant portions of its search tree based upon bounds discovered by a processor using a different search tree. Computational experience is reported both for problems in which resources are available in constant amounts per period, as well as the much more difficult problem in which the resources available are allowed to vary over the schedule horizon (e.g., travel, sick leave, assignment to other tasks or projects, and so forth). The modular multiple-tree search procedure described in this paper is quite general, permitting most types of existing serial search strategies to be adapted to this approach where multiple processors are available.     

The economic lot scheduling problem under extended basic period and power-of-two policy

The economic lot scheduling problem schedules the production of several different products on a single machine over an infinite planning horizon. In this paper, a nonlinear integer programming model is used to determine the optimal solution under the extended basic period and power-of-two policy. A small-step search algorithm is presented to find a solution which approaches optimal when the step size approaches zero, where a divide-and-conquer procedure is introduced to speed up the search. Further a faster heuristic algorithm is proposed which finds the same solutions in almost all the randomly generated sample cases.     

The structure of optimal solutions for harvesting a renewable resource

We consider the problem of optimal harvesting of a renewable resource whose dynamics are governed by logistic growth and whose payoff is proportional to the harvest. We consider both the case of a finite and an infinite time horizon and analyse the structure of the optimal solutions and their dependence on the parameters of the model. We show that the optimal policy can only have one of three structures: (1) maximal harvesting effort until the resource is depleted, (2) zero harvesting during an initial time interval followed by a subsequent switch to maximal harvesting effort, or (3) a singular solution, which corresponds to an intermediate level of harvesting, accompanied by the most rapid approach path. All three scenarios emerge, with minor variations, with finite and infinite time horizons, depending on the particular combination of parameters of the system. We characterize the conditions under which the singular solution is optimal and present suggestions for designing an optimal and sustainable harvesting strategy. Recommendations for Resource Managers :

• We have rigorously explored a standard optimal harvesting model and its steady states.
• We show that three different types of solutions may emerge: (i) maximal harvesting eventually leading to a complete depletion of the stock; (ii) maximal harvesting with a potential period of idleness leading to a positive stock; (iii) an initial phase of either no or full harvesting followed by a period of intermediate harvesting intensity leading to a positive stock (singular solution).
• With some modifications, similar results hold for a finite planning horizon.
• Which of these three scenarios emerges in the finite horizon case depends not only on the parameter values but also on the length of the planning horizon.

     

Multi-product production planning: A goal programming approach

Cost minimization multi-product production problems with static production resource usage and internal product flow requirements have been solved by linear programming (LP) with input/output analysis. If the problem is complicated by interval resource estimates, interval linear programming (ILP) can be used. The solution of realistic problems by the above method is cumbersome. This paper suggests that linear goal programming (LGP) can be used to model a multi-product production system. LGP's unique modeling capabilities are used to solve a production planning problem with variable resource parameters. Input/output analysis is used to determine the technological coefficients for the goal constraints and is also used to derive an information sub-model that is used to reduce the number of variable resource goal constraints. Preliminary findings suggest that the LGP approach is more cost-efficient (in terms of CPU time) and in addition provides valuable information for aggregate planning.     

Short-term liner ship fleet planning with container transshipment and uncertain container shipment demand

This paper proposes a short-term liner ship fleet planning problem by taking into account container transshipment and uncertain container shipment demand. Given a liner shipping service network comprising a number of ship routes, the problem is to determine the numbers and types of ships required in the fleet and assign each of these ships to a particular ship route to maximize the expected value of the total profit over a short-term planning horizon. These decisions have to be made prior to knowing the exact container shipment demand, which is affected by some unpredictable and uncontrollable factors. This paper thus formulates this realistic short-term planning problem as a two-stage stochastic integer programming model. A solution algorithm, integrating the sample average approximation with a dual decomposition and Lagrangian relaxation approach, is then proposed. Finally, a numerical example is used to evaluate the performance of the proposed model and solution algorithm.     

Multi-objectives Tabu Search based algorithm for progressive resource allocation

Military course of action planning involves time and space synchronization as well as resource and asset allocation. A mission could be seen as a defined set of logical ordered tasks with time and space constraints. The resources to task rules require that available assets should be allocated to each task. A combination of assets might be required to execute a given task. The couple (task, resources) constitutes an action. This problem is formulated as a multi-objectives scheduling and resource allocation problem. Any solution is assessed based on a number of conflicting and heterogeneous objectives. In fact, military planning officers should keep perfecting the plan based on the Commander’s criteria for success. The scheduling problem and resource allocation problem are considered as NP-Hard Problems [A. Guitouni, B. Urli, J.-M. Martel, Course of action planning: A project based modelling, Working Paper, Faculté des sciences de l’ administration, Université Laval, Québec, 2005]. This paper is concerned with the multi-objectives resource allocation problem. Our objective is to find adequate resource allocation for given courses of action schedule. To optimize this problem, this paper investigates non-exact solution methods, like meta-heuristic methods for finding potential efficient solutions. A progressive resource allocation methodology is proposed based on Tabu Search and multi-objectives concepts. This technique explores the search space so as to find a set of potential efficient solutions without aggregating the objectives into a single objective function. It is guided by the principle of maximizing the usage of any resource before considering a replacement resource. Thus, a given resource is allocated to the maximum number of tasks for a given courses of action schedule. A good allocation is a potential efficient solution. These solutions are retained by applying a combination of a dominance rule and a multi-criteria filtering method. The performance of the proposed Pareto-based approach is compared to two aggregation approaches: weighted-sum and the lexicographic techniques. The result shows that a Pareto-based approach is providing better solutions and allowing more flexibility to the decision-maker.     

Prescriptive analytics for human resource planning in the professional services industry

In this paper, we examine human resource planning decisions made at firms that sell contract-based consulting projects. High levels of uncertainty in deals and revenue forecasts make it challenging for consulting firms to hire the right people to staff their projects. We present a human resource planning model using concepts from robust optimization to allow companies to dynamically make hiring decisions that maximize profit while remaining as flexible as possible, and demonstrate potential profit improvements through simulation on real data.     

Tertiary Education Faculty Planning: An Application of a Substantially New Direct Control Model

In this paper, an absorbing Markov chain representing the stocks and flows of undergraduate students in a tertiary education faculty is established.In order to assist faculty planners in achieving specific goals, a direct control model in fixed time is developed that allows a partial specification of the distribution of students within the system based on the concept of stated ratios. The attainment and maintenance of this objective, together with a given system size for each planning period, is attempted through recruitment control, as this is the most appropriate control mechanism in an educational institution. The unique specification of the distributional objective makes for considerable ease of planning.The development of a useful sensitivity analysis modus operandi, along with a new efficient solution algorithm to the direct control model, further enhances its applicability for faculty planning.     

Progressive Basic Decision CPM

PERT and CPM are two valuable tools for the planning, scheduling and controllingof projects, but they do not on their own deal with the interdependence between planning resource utilization or direct cost expenditure and sclheduling with its overhead cost (penalty cost and/or reward bonus) implications. Several approaches to overcome this deficiency are possible, this paper dealing with one known as Decision CPM. The most recent approach to the Decision CPM problem known to the authors leads to relatively large integer programming problems which are solved as such. This paper considers a restricted formulation of the Decision CPM problem in an integer programming framework, and shows how it may be solved relatively easily witlhout resort to integer programming methods. Extensions of the restricted model are also considered briefly, some of which pose no difficulties, while others suggest the need for a hybrid of other approaches and that described here.     

Modeling and solution of COA development based on timed influence net and game theory

The development of a course of action (COA) is one of key steps in operation planning. Considering the conflict game, resource restriction, and the influence of execution time, this paper establishes a COA development model based on the timed influence net and game theory. The given problem is solved by transforming it into a standard matrix game model. An example is provided to illustrate this model and its solution.     

Resource allocation in light of a stochastic stream of opportunities

The problem of resource allocation over time in a world of uncertainty has proven to be formidable indeed. Efforts to date have either removed the essential dynamics from the formulation or have produced complex backward optimization expressions of questionable usefulness. We deal with the development of an optimal strategy for the situation where potential projects arrive in accordance with a specified stochastic process. A project, once revealed, possesses a nonstochastic cash flow stream. The investor seeks to maximize his long-run rate of return (μ). The resulting (implicit) expression involving μ requires numerical solution in general. However, under certain conditions, a direct solution is possible. An illustrative example is presented.     

Urban rapid transit network capacity expansion

This paper examines a multi-period capacity expansion problem for rapid transit network design. The capacity expansion is realized through the location of train alignments and stations in an urban traffic context by selecting the time periods. The model maximizes the public transportation demand using a limited budget and designing lines for each period. The location problem incorporates the user decisions about mode and route. The network capacity expansion is a long-term planning problem because the network is built over several periods, in which the data (demand, resource price, etc.) are changing like the real problem changes. This complex problem cannot be solved by branch and bound, and for this reason, a heuristic approach has been defined in order to solve it. Both methods have been experimented in test networks.     

A PRAGMATIC MULTILEVEL APPROACH TO LARGE-SCALE RENEWABLE RESOURCE OPTIMIZATION: A TEST CASE

In recent years, public renewable resource management agencies have devoted substantial effort to local planning. This paper analyzes and evaluates a multilevel approach to utilizing these local planning analyses as an input in developing large–scale (national) resource management plans. This approach is evaluated in a test case with a two–level prototype model that is built from a global model that serves as a standard of comparison. The two–level prototype performs well at the highest level of analysis (involving aggregated variables) but is less reliable in terms of its implications for the lower level planning units.