OR in strategic land-use planning

An important historic strategic application of OR has been in the field of land-use and development plan production. Changes in Government policy and legislation have led to varying levels of interest in plan production. Three post-war cycles of ‘enthusiasm for plans’ can be identified. Whilst the first was rooted very much in the Architectural Design tradition, the second led to significant developments in OR, with far wider application. Subsequent reduced Governmental enthusiasm for ‘Development Plan production’ led to considerable atrophy of relevant skills in the planning community, including those derived from OR. However, the current ‘third period of post-war enthusiasm for planning’, reinforced by environmental concerns, has revived the need for relevant skills. It is suggested that, whilst the deficit in skills and their application remains high, there are some encouraging signs. Moreover, substantial progress in the field of ‘soft OR’ offers opportunities to both the OR and planning communities.     

Simulating the strategic financial planning process

Strategic financial planning should (1) involve the key variables in the corporate growth process, (2) reflect the dynamic interaction in the system and (3) incorporate the dimension of uncertainty. Decision makers need a stochastic model that links the interaction between the investment and financing process for the planning period. The model presented in this paper integrates the investment and financing process by the use of simulation. The measure used to link these two systems is the rate of return required on new investment in order for decision makers to achieve their desired earnings-per-share growth goal. The model provides top management a tool to communicate their expectations to lower levels of management, thereby allowing them to measure and evaluate the impact of various sets of assumptions on the company's strategic plans.     

Multistage stochastic programming in strategic telecommunication network planning

Mobile communication is taken for granted in these days. Having started primarily as a service for speech communication, data service and mobile Internet access are now driving the evolution of network infrastructure. Operators are facing the challenge to match the demand by continuously expanding and upgrading the network infrastructure. However, the evolution of the customer's demand is uncertain. We introduce a novel (long-term) network planning approach based on multistage stochastic programming, where demand evolution is considered as a stochastic process and the network is extended so as to maximize the expected profit. The approach proves capable of designing large-scale realistic UMTS networks with a time horizon of several years. Our mathematical optimization model, the solution approach, and computational results are presented.     

A model for strategic planning under uncertainty

Summary In this paper the multi-period strategic planning problem for a consumer sumer product manufacturing chain is considered. Our discussion is focused on investment decisions which, are economically optimal over the whole planning horizonT, while meeting customer demands and conforming to technological requirements. In strategic planning, time and uncertainty play important roles. The uncertainties in the model are due to different levels of forecast demands, cost estimates and equipment behaviour. The main aim of this paper is to develop and analyse a multiperiod stochastic model representing the entire manufacturing chain, from the acquisitions of raw material to the delivering of final products. The resulting optimization problem is computationally intractable because of the enormous, and sometimes unrealistic, number of scenarios that must be considered in order to identify the optimal planning strategy. We propose two different solution approaches; firstly, we apply a scenario risk analysis giving the related results of experiments on a particular real data set. We then describe and investigate an Integer Stochastic Programming formulation of the problem and propose, as a solution technique, a variation of Benders decomposition method, namely theL-shaped method.     

Linking strategic and tactical forestry planning decisions

In this paper a two-level model and optimization algorithms areintroduced to assist forestry companies in simultaneously considering strategicinvestment and tactical planning decisions. A procedure to reduce thediscrepancy produced in the aggregation and disaggregation process used to linkthese two-level decisions is also presented. This procedure is based onboth cluster analysis over economic parameters defined on the standmacro-units to be harvested and on the information transmittedbottom-up and top-down between the strategic and tactical models. Wealso show a new approach for solving the tactical mixed integer model.     

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.     

A network-based futures method for strategic business planning

Many strategic decisions in business are made in a context which the decision makers perceive as uncertain, complex and opaque. A method, based on Rhyne's field anomaly relaxation technique, is described of generating a network of states which characterise the environment or context in which strategic decisions are to be made. These states represent possible future conditions for the business, and knowledge of them allows improved strategic understanding and decision making to be achieved. This paper describes the method, using a representative real-life application to illustrate the process.     

Reflections and observations on various approaches to strategic planning

Recent articles in the Operational Research Quarterly in the United Kingdom have discussed the suitability of optimality as the sole, or even the most appropriate, criterion relevant to decision making. This is perhaps particularly so in the case of strategic planning, in which decisions more likely have to be made in situations of uncertainty. Alternative criteria such as satisficing, robustness and stability have been proposed and the paper reflects on their relevance. This paper draws attention to the fact that strategic planning problems often involve multiple, often noncommensurable, objectives and stresses the importance of engaging decision-makers in an interactive dialogue in such situations. In the concluding observations, examples of two different approaches are given, both within the context of mathematical programming.     

Computer aided decision support system for water strategic planning in Jordan

The Nominal Group Technique and a multi-criteria decision aid software are utilised to develop a decision support system for strategic planning of water resources in Jordan. The system described is novel in that it integrates the various decision analytical management techniques in order to increase the flexibility and efficiency of the decision making process.     

A new approach to tactical and strategic planning in production-distribution networks

Today the most important concern of the managers is to make their firms viable in the competitive trade world. Managers are looking for effective tools for decision making in the complex business world. This paper presents a new mathematical model for strategic and tactical planning in a multiple-echelon, multiple-commodity production-distribution network. In the proposed model, different time resolutions are considered for strategic and tactical decisions. Also expansion of the network is planned based on cumulative net incomes. To illustrate applications of the proposed model as well as its performance based on the solution times, some hypothetical numerical examples have been generated and solved by CPLEX. Results show that in small and medium scale of instances, high quality solutions can be obtained using this solver, but for larger instances, some heuristics has to be designed to reduce solution time.     

A comparison of different modelling approaches to strategic energy planning

In this paper we discuss the role of models in strategic planning, paying special attention to the way perceptions, values and preferences are treated. Starting from an analysis of the specific nature of long term planning we propose two criteria to evaluate the interaction between policy preferences and mathematical models. On basis of these criteria we examine four recent energy studies. The paper concludes with a comparison of these cases and recommendations concerning the interface between models and policy preferences.     

The use of management science techniques in a corporate strategic planning system

This paper will discuss the role that Management Science has played in the Strategic Planning process in Gulf Oil Corporation. Two levels of planning will be covered: planning at the functional Company level and at the Corporate level. Both the problems and rewards of using Management Science techniques in these areas will be discussed and illustrated through models developed at Gulf.Gulf Oil Corporation is organizationally subdivided into functional companies that are each responsible for long-range planning in their respective business areas. Corporate Headquarters is responsible for allocations of capital, debt control, dividend policy and strategy. Models developed for use at these two levels face different organizational, personnel, technical and data problems and these problems recur as the organization and personnel change. For models to be effective, the perceived benefits ensuing from their use must outweigh the problems encountered in their implementation. Three models will be discussed to illustrate these points; a Corporate model designed to examine capital allocations, debt control, dividend policies, and net cash flow; a model to aid in the formulation of a strategic policy in the Synthetic Fuels area; and, a model to help plan capital improvements or divestments in the petroleum business.     

An innovative approach for strategic capacity portfolio planning under uncertainties

This research studies multi-generation capacity portfolio planning problems under various uncertainty factors. These uncertainty factors include price uncertainties, demand fluctuation and uncertain product life cycle. The objective of this research is to develop an efficient algorithm that generates capacity portfolio policies robust to aforementioned uncertainties.     

An application of a network-based futures method to strategic business planning

An analysis of the possible future development paths for the business environment of a medium-sized company operating in the advertising and media sector has been carried out using extended field anomaly relaxation (EFAR). It indicates that a number of development paths exist for the company, the main issues confronting it being location and share structure. The constraints imposed by the environment on the choice of these two conditioning factors are severe and less freedom of action is evident than was perceived to be present before the analysis. For example, the option of relocating to Central London can be seen clearly to be consistent with only a major share sale amounting to exiting the business. The option of a majority or minority share deal together with a relocation to a single site and retaining a London front office emerged as an attractive option for the company. Certain structural characteristics surrounding the share structure are observed. Development of the situation continued after the analysis was reported, leading to confirmation of the validity of the method.     

Integrating effective flexibility measures into a strategic supply chain planning model

This paper develops models for capacity, product mix, distribution and input supply flexibility and integrates them in a strategic level, mixed integer supply chain (SC) planning model as a way of addressing demand and supply uncertainty, as well as improving market responsiveness. Capacity flexibility is modeled via the SC’s production capacity planning to address budgeted demand and ensure the fulfillment of prospective demand increases when considering various market scenarios. This model selects an optimal number of products from fast moving and extended product range options—based on the product mix flexibility. The model confirms a quick response to a changing marketplace by considering elements like transportation and supply lead time along with the probabilities of stock out options when addressing input supply and distribution flexibility. This paper proposes a solution procedure to solve the model for real world problems, and investigates the sensitivity of the model outputs with respect to changes in flexibility measures.     

Decision support for strategic energy planning: A robust optimization framework

Optimization models for long-term energy planning often feature many uncertain inputs, which can be handled using robust optimization. However, uncertainty is seldom accounted for in the energy planning practice, and robust optimization applications in this field normally consider only a few uncertain parameters. A reason for this gap between energy practice and stochastic modeling is that large-scale energy models often present features—such as multiplied uncertain parameters in the objective and many uncertainties in the constraints—which make it difficult to develop generalized and tractable robust formulations. In this paper, we address these limiting features to provide a complete robust optimization framework allowing the consideration of all uncertain parameters in energy models. We also introduce an original approach to make use of the obtained robust formulations for decision support and provide a case study of a national energy system for validation.     

Multimethodology in series and parallel: strategic planning using hard and soft OR

This paper examines two distinct ways in which hard and soft operational research (OR) methodologies can be combined, in series and in parallel. Multimethodology in series is acknowledged as the simpler and more common approach. Multimethodology in parallel is identified as having the potential to provide significant benefits to projects in political, changing, or ‘wicked’ contexts that multimethodology in series cannot. Observations regarding these approaches to multimethodology are examined in light of an information systems strategic planning project in the Australian public sector. Two distinct methodologies were combined in the project: soft systems methodology and project management. These methodologies are based on the soft and hard paradigms, respectively. However, findings in this paper have the potential to be transferred to combinations of other hard and soft OR methodologies.     

Energy efficiency and risk management in public buildings: strategic model for robust planning

Due to deregulations of the energy sector and the setting of targets such as the 20/20/20 in the EU, operators of public buildings are now more exposed to instantaneous (short-term) market conditions. On the other hand, they have gained the opportunity to play a more active role in securing long-term supply, managing demand, and hedging against risk while improving existing buildings’ infrastructures. Therefore, there are incentives for the operators to develop and use a Decision Support System to manage their energy sub-systems in a more robust energy-efficient and cost-effective manner. In this paper, a two-stage stochastic model is proposed, where some decisions (so-called first-stage decisions) regarding investments in new energy technologies have to be taken before uncertainties are resolved, and some others (so-called second-stage decisions) on how to use the installed technologies will be taken once values for uncertain parameters become known, thereby providing a trade-off between long- and short-term decisions.     

A Lagrangian relaxation approach to simultaneous strategic and tactical planning in supply chain design

We study a multi-echelon joint inventory-location model that simultaneously determines the location of warehouses and inventory policies at the warehouses and retailers. The model is formulated as a nonlinear mixed-integer program, and is solved using a Lagrangian relaxation-based approach. The efficiency of the algorithm and benefits of integration are evaluated through a computational study.