A multicriteria decision-making approach to university resource allocations and information infrastructure planning

This paper presents an application of zero-one goal programming (GP) as an aid for resource allocations for information infrastructure planning in a university. The GP model is developed and analyzed to address the dramatic growth in information technology use and network planning. The analytic hierarchy process method is used to assign proper weights to prioritized project goals. Sensitivity analyses are performed to improve the model applicability. The application of the GP model adds insight to the planning functions of the University's information systems.     

Demilitarized zone planning: A multiobjective approach

The demilitarized zone has been used frequently as a means to separate conflicting nations. Imposition of a buffer zone free from military activities is considered to be an effective first step to restore peace between hostile nations. The demilitarized zone is usually patrolled by third-party forces to discourage military hostilities. This paper deals with the problem of allocating the third-party supervisory forces in order to assure effective policing of demilitarized zones where there are multiple and conflicting objectives.     

A probabilistic (max, +) approach for determining railway infrastructure capacity

We consider the problem of determining the capacity of a planned railway infrastructure layout under uncertainties. In order to address the long-term nature of the problem, in which the exact (future) demand of service is unknown, we develop a “timetable”-free approach to avoid the specification of a particular timetable. We consider a generic infra-element that allows a concise representation of many different combinations of infrastructure, safety systems and traffic regimes, such as mixed double and single track lines (e.g., a double track line including a single tunnel tube), and train operations on partly overlapping routes at station yards. We translate the capacity assessment problem for such a generic infra-element into an optimization problem and provide a solution procedure. We illustrate our approach with a capacity assessment for the newly built high-speed railway line in The Netherlands.     

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.     

A genetic approach for strategic resource allocation planning

Enterprises often implement a measurement system to monitor their march towards their strategic goals. Although this way it is possible to assess the progress of each goal, there is no structured way to reconsider resource allocation to those goals and to plan an optimal (or near optimal) allocation scheme. In this study we propose a genetic approach to match each goal with an autonomous entity (agent) with a specific resource sharing behavior. The overall performance is evaluated through a set of functions and genetic algorithms are used to eventuate in approximate optimal behavior’s schemes. To outline the strategic goals of the enterprise we used the balanced scorecard method. Letting agents deploy their sharing behavior over simulation time, we measure the scorecard’s performance and detect distinguished behaviors, namely recommendations for resource allocation.     

Industrial petrochemical production planning and expansion: A multi-objective linear programming approach

This paper considers a strategic model planning for the petrochemical industry. It concerns with the expansion in a firm producing multiple products in several regions of a country. The expansion of the existing facilities and the new ones are considered. It also exists a large amount of interdependencies among the firm’s products, because the output of one particular plant can be used as an input to the production of another plant in the same or different regions and to satisfy the final demand. The decision makers involved in the planning process should identify several objectives. Then, multiple objective programming is used for making trade-offs among the economic and operational factors considered. To define the interval criteria weights into the model we utilized the Analytic Hierarchy Process to bring them closer to the decision makers preferences. This work was sponsored by the Institut National Polytechnique de Toulouse, France, when the author was Associate Professor at the Département Génie des Systèmes Industriels.     

A stochastic programming approach for planning horizons of infinite horizon capacity planning problems

Planning horizon is a key issue in production planning. Different from previous approaches based on Markov Decision Processes, we study the planning horizon of capacity planning problems within the framework of stochastic programming. We first consider an infinite horizon stochastic capacity planning model involving a single resource, linear cost structure, and discrete distributions for general stochastic cost and demand data (non-Markovian and non-stationary). We give sufficient conditions for the existence of an optimal solution. Furthermore, we study the monotonicity property of the finite horizon approximation of the original problem. We show that, the optimal objective value and solution of the finite horizon approximation problem will converge to the optimal objective value and solution of the infinite horizon problem, when the time horizon goes to infinity. These convergence results, together with the integrality of decision variables, imply the existence of a planning horizon. We also develop a useful formula to calculate an upper bound on the planning horizon. Then by decomposition, we show the existence of a planning horizon for a class of very general stochastic capacity planning problems, which have complicated decision structure.     

A communication mix for an event planning: a linear quadratic approach

The communication mix is a relevant decision issue for an organization that plans the advertising campaign for a fixed future event. It is assumed that the objectives of the organization are to minimize the cost of the advertising campaign and to drive the final demand as close as possible to a target value. Two different advertising channels are available: the first affects deterministically the consumers’ demand, whereas the second presents some stochastic aspects which are out of decision-maker’s control. Some recent mathematical developments on the stochastic linear quadratic control problem allow to formulate and solve some interesting instances of the problem. A comparative analysis of the efficiency of deterministic and stochastic controls is done and the optimal feedback policies are discussed. The trade-off between efficiency and risk of an advertising channel is essential to understand the features of the optimal solutions.This study was supported by MIUR and University of Padua.     

Fuzzy pricing,marketing and service planning in a fuzzy inventory model: A geometric programming approach

In the real world markets, demand is influenced by different parameters. Recently, many researchers have been interested in integrated production and marketing planning strategies in inventory models where demand depends on different parameters such as price and/or marketing expenditure. The quality of services that are offered to customers of a product is one of the most important parameters that affects demand in the real markets and has not been considered in development of inventory models. On the other hand, the cost parameters in real inventory systems and other parameters such as price, marketing and service elasticity to demand are imprecise and uncertain in nature. So, the notion of fuzziness can be applied to cope with this uncertainty. In this paper, a new fuzzy profit maximization inventory model with shortages is proposed. The demand is considered as a power function of price, marketing expenditure and service expenditure. Furthermore, unit cost is determined as a power function of order quantity. Since the proposed model is in a fuzzy environment, a fuzzy decision should be made to meet the decision criteria, and the results should be fuzzy. Therefore, the proposed model is formulated and solved using geometric programming and fuzzy optimization techniques to derive an approximation of the results’ membership functions. The model is illustrated with a numerical example and finally a case study is provided for evaluation and validation of the results of model.     

A stochastic programming approach for multi-site aggregate production planning

Production planning problems play a vital role in the supply chain management area, by which decision makers can determine the production loading plan—consisting of the quantity of production and the workforce level at each production plant—to fulfil market demand. This paper addresses the production planning problem with additional constraints, such as production plant preference selection. To deal with the uncertain demand data, a stochastic programming approach is proposed to determine optimal medium-term production loading plans under an uncertain environment. A set of data from a multinational lingerie company in Hong Kong is used to demonstrate the robustness and effectiveness of the proposed model. An analysis of the probability distribution of economic demand assumptions is performed. The impact of unit shortage costs on the total cost is also analysed.