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.     

Optimal evacuation planning using staging and routing

Evacuation is an important disaster management tool. Evacuating a large region by automobile (the most commonly used mode) is a difficult task, especially as high levels of traffic congestion often form. This paper studies the use of demand-based strategies, specifically, the staging and routing of evacuees. These strategies attempt to manage demand in order to reduce or eliminate congestion. A strategic mixed-integer programming planning model that accounts for evacuation dynamics and congestion is used to study these strategies. The strategies adopted incorporate different evacuee types based on destination requirements and shelter capacity restrictions. The main objective studied is to minimize the network clearance time. We examine the structure of optimal strategies, yielding insights into the use of staging and routing in evacuation management. These insights are then used to develop effective solution procedures. To demonstrate the efficacy of the proposed solution technique, we provide computational experience using a large realistic example based on Virginia Beach.     

Multiple-objective approximation of feasible but unsolvable linear complementarity problems

Using a multiple-objective framework, feasible linear complementarity problems (LCPs) are handled in a unified manner. The resulting procedure either solves the feasible LCP or, under certain conditions, produces an approximate solution which is an efficient point of the associated multiple-objective problem. A mathematical existence theory is developed which allows both specialization and extension of earlier results in multiple-obkective programming. Two perturbation approaches to finding the closest solvable LCPs to a given unsolvable LCP are proposed. Several illustrative examples are provided and discussed.     

An Algorithm for Multiple-objective Non-linear Programming

An interactive algorithm to solve multiple-objective non-linear programming (MONLP) problems is proposed. In each iteration of the proposed algorithm, the decision-maker is presented with a solution and a set of direction (trade-off) vectors indicating possible trade-offs. Using the decision-maker's preferred trade-off vector, a new current solution and the corresponding trade-off vectors are found. The proposed algorithm is illustrated with a numerical example of a replacement model. Finally, the method is compared with four other interactive multiple-objective algorithms.     

Possibilistic linear-programming approach for supply chain networking decisions

Supply chain networking decisions are very important for the medium- and long-term planning success of manufacturing companies. The inputs to supply chain planning models are subject to environmental and system uncertainties. In this paper, a fuzzy set theory-based model is proposed to deal with those uncertainties. For this purpose, a possibilistic linear programming (PLP) model is used to make strategic resource-planning decisions using fuzzy demand forecasts and fuzzy yield rates as well as other inputs such as costs and capacities. The objective of the proposed PLP is to maximize the total profit of the enterprise. The model is applied to Mercedes–Benz Türk, one of the largest bus-manufacturing companies in the world, and conclusions and suggestions for further research are provided.     

On the connections among activity-based costing,mathematical programming models for analyzing strategic decisions,and the resource-based view of the firm

This paper examines connections between data-driven models for analyzing a firm's strategic plans, which use activity-based costing and mathematical programming, and the resource-based view of the firm. After brief reviews of the three disciplines, extensions of activity-based costing methods to mathematical programming models for strategic resource planning are discussed. Applications of these models to supply chain planning in a multi-national food manufacturer, a specialty chemicals company, and a wholesaling/retailing company are presented. The paper concludes by using concepts from the resource-based view of the firm to interpret optimal solutions from mathematical programming models. Extensions to strategic planning under uncertainty using stochastic programming are also discussed briefly.     

Hierarchical production planning and scheduling with random demand and production failure

A multiple-objective hierarchical production planning and scheduling model is developed that integrates aggregate type decisions, family disaggregate decisions, lotsizing and scheduling of the jobs. It is assumed that demand and production failure are subject to uncertainties. Stochastic programming with recourse using a constraint sample approximation method is used to incorporate random demand and production failure into the model. The model evaluates final production plans, updates the demand forecasts and proceeds on a rolling horizon manner. Experimental results show that it is sufficient to generate and incorporate into the aggregate type model a small sample of the stochastic constraints from an infinite set of scenarios. A heuristic scheduling algorithm provides detailed information regarding the progress of jobs through work centers. This information is extremely useful in resolving infeasibilities during the production process. Other features of the model are also reported.     

A tactical model for planning the production and distribution of fresh produce

We present an integrated tactical planning model for the production and distribution of fresh produce. The main objective of the model is to maximize the revenues of a producer that has some control over the logistics decisions associated with the distribution of the crop. The model is used for making planning decisions for a large fresh produce grower in Northwestern Mexico. The decisions obtained are based on traditional factors such as price estimation and resource availability, but also on factors that are usually neglected in traditional planning models such as price dynamics, product decay, transportation and inventory costs. The model considers the perishability of the crops in two different ways, as a loss function in its objective function, and as a constraint for the storage of products. The paper presents a mixed integer programming model used to implement the problem as wells as the computational results obtained from it.     

Strategic route extension in transit networks

This study proposes a methodology through which transportation analysts and policy makers can use spatial optimization to support strategic planning, with the goal of extending existing service networks. Based on modeling objectives common to many service industries, an approach is developed for integrating geographic information systems (GIS) and spatial optimization modeling in order to extend an existing transit system through prioritizing route and stop additions. Development of a strategic methodology such as this is vital for agencies interested in extending transit networks to accommodate urban growth and development. This is especially true in public transit applications, such as bus route planning, as the future of bus-based public transportation depends on the success of route expansion and modification. The developed approach is applied to the transit system in Columbus, Ohio.     

Inverse DEA under inter-temporal dependence using multiple-objective programming

This paper deals with the inverse Data Envelopment Analysis (DEA) under inter-temporal dependence assumption. Both problems, input-estimation and output-estimation, are investigated. Necessary and sufficient conditions for input/output estimation are established utilizing Pareto and weak Pareto solutions of linear multiple-objective programming problems. Furthermore, in this paper we introduce a new optimality notion for multiple-objective programming problems, periodic weak Pareto optimality. These solutions are used in inverse DEA, and it is shown that these can be characterized by a simple modification in weighted sum scalarization tool.     

Measuring Customer Satisfaction Using a Collective Preference Disaggregation Model

A new model to assess customer satisfaction is developed through this paper. The proposed model is based on the principles of multicriteria analysis, using ordinal regression techniques. The procedure uses survey's data on customer satisfaction criteria and disaggregates simultaneously all the global satisfaction judgments via a linear programming disaggregation formulation. The model provides collective global and partial satisfaction functions as well as average satisfaction indices. These results sufficiently describe customer behavior and they can be used in the strategic planning of an organization. The implementation of the model in three real world applications is used for illustration and for testing the model's reliability. Finally, several extensions and future research in the area of customer satisfaction analysis are discussed.     

Multi-Level Strategic Evaluation of Hospital Plans and Decisions

Strategic decision making in hospitals involves the assessment of linkages between decisions that are typically made in a hierarchical fashion. In hospitals, as in most large organizations, overall system performance is a function of how well the critical decisions are integrated. This paper focuses on the multi-level nature of the decisions and policies that typically need to be evaluated in hospital planning, highlighting that both optimization and simulation approaches may be required. An application involving a large general purpose urban hospital is used to illustrate the interdependency between the levels in the planning hierarchy. An optimization model is formulated to deal with facility layout and capacity allocation while a simulation model is proposed to capture the complexities of hospital operations. The linkages and information feedback between the models are shown to be critical in the design of a system that performs well and facilitates strategic hospital planning.     

Hierarchical facility network planning model for global logistics network configurations

This paper presents a novel hierarchical network planning model for global logistics (GLs) network configurations. The proposed method, which is based on the fundamentals of integer programming and hierarchical cluster analysis methods, determines the corresponding locations, number and scope of service areas and facilities in the proposed GLs network. Therein, a multi-objective planning model is formulated that systematically minimizes network configuration cost and maximizes both operational profit and the customer satisfaction rate. Particularly, potential risk-oriented costs, such as macro-environmental-risk and micro-operational-risk costs are considered in the proposed model. Numerical results indicate that the overall system performance can be improved by up to 11.52% using the proposed approach.     

An integrated simulation-optimization framework for the operational planning of seaport container terminals

In this article the operational planning of seaport container terminals is considered by defining a suitable integrated framework in which simulation and optimization interact. The proposed tool is a simulation environment (implemented by using the Arena software) representing the dynamics of a container terminal. When the system faces some particular conditions (critical events), an optimization procedure integrated in the simulation tool is called. This means that the simulation is paused, an optimization problem is solved and the relative solution is an input for the simulation environment where some system parameters are modified (generally, the handling rates of some resources are changed). For this reason, in the present article we consider two modelling and planning levels about container terminals. The simulation framework, based on an appropriate discrete-event model, represents the dynamic behaviour of the terminal, thus it needs to be quite detailed and it is used as an operational planning tool. On the other hand, the optimization approach is devised in order to define some system parameters such as the resource handling rates; in this sense, it can be considered as a tool for tactical planning. The optimization procedure is based on an aggregate representation of the terminal where the dynamics is modelled by means of discrete-time equations.     

The impact of wind uncertainty on the strategic valuation of distributed electricity storage

The intermittent nature of wind energy generation has introduced a new degree of uncertainty to the tactical planning of energy systems. Short-term energy balancing decisions are no longer (fully) known, and it is this lack of knowledge that causes the need for strategic thinking. But despite this observation, strategic models are rarely set in an uncertain environment. And even if they are, the approach used is often inappropriate, based on some variant of scenario analysis—what-if analysis. In this paper we develop a deterministic strategic model for the valuation of electricity storage (a battery), and ask: “Though leaving out wind speed uncertainty clearly is a simplification, does it really matter for the valuation of storage?”. We answer this question by formulating a stochastic programming model, and compare its valuation to that of its deterministic counterpart. Both models capture the arbitrage value of storage, but only the stochastic model captures the battery value stemming from wind speed uncertainty. Is the difference important? The model is tested on a case from Lancaster University’s campus energy system where a wind turbine is installed. From our analysis, we conclude that considering wind speed uncertainty can increase the estimated value of storage with up to 50 % relative to a deterministic estimate. However, we also observe cases where wind speed uncertainty is insignificant for storage valuation.     

Vector and Parametric Programming

Vector programming seeks to identify efficient solutions of multiple-objective problems. It is closely related to parametric programming and three examples of this relationship, using objective weights, target (or goal) levels and weighted norms, are described. In each case, the use of the relationship to develop algorithms for determining the efficient set or to obtain characterizations of efficiency is investigated.     

Towards an optimal model of food nutrition structure relating to production development for the region of the middle-lower reach of the Yangtze in China

In this paper, the present situation of the food production, resource structure, food consumption structure and food nutrition structure of the Jiangnan province, which is located in the Middle-Lower Reach of the Yangtze in China (MLRYC), is analysed systematically; and a data store system about nutrition ingredients of 125 kinds of food [1,2] common in this region is built. From these, we set up a food nutrition objective system, and a corresponding multiple-objective programming [3] mathematical model about food nutrition structure optimization. Then, we obtain a series of optimal models of food nutrition structure which are suitable for this region for different seasons, different groups of people and different expenditure capacity, and also, we propose some strategies to adjust food nutrition structure.     

A Non-linear Multi-criteria Programming Approach for Determining County Emergency Medical Service Ambulance Allocations

In this paper an integer, non-linear mathematical programming model is developed to allocate emergency medical service (EMS) ambulances to sectors within a county in order to meet a government-mandated response-time criterion. However, in addition to the response-time criterion, the model also reflects criteria for budget and work-load, and, since ambulance response is best described within the context of a queueing system, several of the model system constraints are based on queueing formulations adapted to a mathematical programming format. The model is developed and demonstrated within the context of an example of a county encompassing rural, urban and mixed sectors which exhibit different demand and geographic characteristics. The example model is solved using an integer, non-linear goal-programming technique. The solution results provide ambulance allocations to sectors within the county, the probability of an ambulance exceeding a prespecified response time, and the utilization factor for ambulances per sector.     

Planning logistics operations in the oil industry

In this paper we apply stochastic programming modelling and solution techniques to planning problems for a consortium of oil companies. A multiperiod supply, transformation and distribution scheduling problem—the Depot and Refinery Optimization Problem (DROP)—is formulated for strategic or tactical level planning of the consortium's activities. This deterministic model is used as a basis for implementing a stochastic programming formulation with uncertainty in the product demands and spot supply costs (DROPS), whose solution process utilizes the deterministic equivalent linear programming problem. We employ our STOCHGEN general purpose stochastic problem generator to ‘recreate’ the decision (scenario) tree for the unfolding future as this deterministic equivalent. To project random demands for oil products at different spatial locations into the future and to generate random fluctuations in their future prices/costs a stochastic input data simulator is developed and calibrated to historical industry data. The models are written in the modelling language XPRESS-MP and solved by the XPRESS suite of linear programming solvers. From the viewpoint of implementation of large-scale stochastic programming models this study involves decisions in both space and time and careful revision of the original deterministic formulation. The first part of the paper treats the specification, generation and solution of the deterministic DROP model. The stochastic version of the model (DROPS) and its implementation are studied in detail in the second part and a number of related research questions and implications discussed.