Interactive Multiple Objective Decision Support for Sales Force Sizing and Deployment*

An interactive decision aid is introduced for the deployment of two sales resources: salespeople and sales support staff. The aid consists of a normative sales resource allocation model with five objectives and an interactive multiple objective programming solution procedure. The specific decision problem addressed involves the assignment of salespeople and sales support people to customer accounts and the allocation of the time they spend on these accounts. The authors contribute to the existing sales resource modeling literature by dealing with the deployment of two sales resources and interactively solving this problem with respect to five short-run and long-run objectives of the firm. This approach differs from existing sales force modeling efforts in which the solution is found noninteractively by optimizing a single sales resource model with respect to a single objective, often short-run sales. An application of the decision aid to the deployment problem of an industrial sales force manager is presented. Furthermore, useful extensions of the basic sales resource allocation model are discussed.     

Optimal and Heuristic Decision Strategies for Integrated Production and Marketing Planning

In this study we examine the effects of integrating production and marketing decisions for a short- to medium-range planning horizon in a profit maximizing firm. We formulate two models for determining price, marketing expenditure, demand or production volume, and lot size for a single product with stable demand when economies of scale are present. The full integration (FI) model simultaneously determines all the decisions involved, while the partial integration (PI) model separates the lot sizing decision from the others, as happens frequently in practice. Geometric programming (GP) techniques and marginal analysis are used to compare FI and PI, and obtain important managerial implications regarding the two models.     

Decomposition algorithms for the integrated process planning and scheduling problem

There are several algorithms to solve the integrated process planning and scheduling (IPPS) problem (i.e., flexible job shop scheduling with process plan flexibility) in the literature. All the existing algorithms for IPPS are heuristic-based search methods and no research has investigated the use of exact solution methods for this problem. We develop several decomposition approaches based on the logic-based Benders decomposition (LBBD) algorithm. Our LBBD algorithm allows us to partition the decision variables in the IPPS problem into two models, master-problem and sub-problem. The master-problem determines process plan and operation-machine assignment, while the sub-problem optimizes sequencing and scheduling decisions. To achieve faster convergence, we develop two relaxations for the optimal makespan objective function and incorporate them into the master-problem. We analyze the performance and further enhance the algorithm with two ideas, a Benders optimality cut based on the critical path and a faster heuristic way to solve the sub-problem. 16 standard benchmark instances available in the literature are solved to evaluate and compare the performances of our algorithms with those of the state-of-the-art methods in the literature. The proposed algorithm either results in the optimal solution or improves the best-known solutions in all the existing instances, demonstrating its superiority to the existing state-of-the-art methods in literature.     

A New Dynamic Programming Decomposition Method for the Network Revenue Management Problem with Customer Choice Behavior

In this paper, we propose a new dynamic programming decomposition method for the network revenue management problem with customer choice behavior. The fundamental idea behind our dynamic programming decomposition method is to allocate the revenue associated with an itinerary among the different flight legs and to solve a single‐leg revenue management problem for each flight leg in the airline network. The novel aspect of our approach is that it chooses the revenue allocations by solving an auxiliary optimization problem that takes the probabilistic nature of the customer choices into consideration. We compare our approach with two standard benchmark methods. The first benchmark method uses a deterministic linear programming formulation. The second benchmark method is a dynamic programming decomposition idea that is similar to our approach, but it chooses the revenue allocations in an ad hoc manner. We establish that our approach provides an upper bound on the optimal total expected revenue, and this upper bound is tighter than the ones obtained by the two benchmark methods. Computational experiments indicate that our approach provides significant improvements over the performances of the benchmark methods.     

Budgets for integrating decentralized investment and production planning

Planning for an economic enterprise can be dichotomized into short-run production planning and longer-run investment planning. Usually these problems are treated as if they were separate, if not independent. This paper briefly reviews the separate approaches to optimal production decision making and investment planning, ‘fusing’ these models in order to consider the two issues simultaneously. The resulting ‘fused’ model is used to illustrate several difficulties which result from an intuitive synthesis of the independent solutions of the production problem and the investment problem. An integrated model is presented representing a centralized simultaneous solution for decision variables from the two functional fields. The paper compares and contrasts the synthesis of separate functional models to the decomposition of a simultaneous model of those functional areas. A result of this comparison is a theoretical justification for operating budgets and revenue targets as organizational mechanisms for achieving coordinated plans among decentralized planning units. Further, the set of conditions are identified under which the two approaches to simultaneous decision making are equivalent.     

Linking Product Planning and Process Design Decisions

Manufacturing strategy reflects the goals of the business strategy and directs the manufacturing function in achieving them. In this study, we focus on two decision areas with crucial implications for manufacturing strategy: product planning and process design. Using mathematical programming models as a research tool, we test several conjectures in the manufacturing strategy literature regarding linkages between the two decisions. Our results show that a close integration between these decisions helps control the product offerings, stabilize process requirements, improve process technology choices, and increase net cash flows over time. Using the concept of environmental clusters, we found that a close linkage is more critical when the environment is more complex, less uncertain, and tighter. Based on these findings, we present some managerial implications and suggestions for future research.     

Analysis of forces affecting competitive position of Mexico in supplying U.S. winter melon market

An econometric model representing the United States, Mexico and Caribbean nations melon sectors was estimated to analyze the primary economic forces influencing Mexico’s competitiveness in the U.S. winter melon market, a period when about two-thirds of U.S. consumption is imported. Results show peso-devaluation to be important in the short-run and yield-enhancing technology to be important in the short- and long-run. Increased rates of growth in Mexican yields were about six times more effective at increasing market share than NAFTA provisions which phase-out U.S. tariffs. An accelerated rate of growth in Mexican per capita income was found to reduce melon exports about 75% while higher wages would reduce exports about 20% in the long-run.     

AN EXPLORATORY STUDY OF PATTERNS OF PRIORITIES AND TRADE-OFFS AMONG OPERATIONS MANAGERS

A survey of 15 firms showed that agreement among operations managers on competitive priorities is related to agreement on long-run strategic trade-off decisions and not to agreement on shortrun trade-offs. Furthermore, intended short-run actions were often in conflict with stated competitive priorities. Use of management-by-objectives linked to performance appraisal was related to agreement on competitive priorities.     

Static-dynamic uncertainty strategy for a single-item stochastic inventory control problem

We consider a single-stage inventory system facing non-stationary stochastic demand of the customers in a finite planning horizon. Motivated by the practice, the replenishment times need to be determined and frozen once and for all at the beginning of the horizon while decisions on the exact replenishment quantities can be deferred until the replenishment time. This operating scheme is refereed to as a “static-dynamic uncertainty” strategy in the literature [3]. We consider dynamic fixed-ordering and linear end-of-period holding costs, as well as dynamic penalty costs, or service levels. We prove that the optimal ordering policy is a base stock policy for both penalty cost and service level constrained models. Since an exponential exhaustive search based on dynamic programming yields the optimal ordering periods and the associated base stock levels, it is not possible to compute the optimal policy parameters for longer planning horizons. Thus, we develop two heuristics. Numerical experiments show that both heuristics perform well in terms of solution quality and scale-up efficiently; hence, any practically relevant large instance can be solved in reasonable time. Finally, we discuss how our results and heuristics can be extended to handle capacity limitations and minimum order quantity considerations.     

Decentralization in Linear Programming Models

This paper proposes a decomposition of a linear programming problem based on the structure of the optimal basis matrix. If this matrix contains a zero matrix of appropriate dimensions, the problem may be decomposed into a price-setting problem and a quantity-setting problem. This decomposition is valid for a set of coefficients of the problem to be determined by parametric programming. It can be applied to problems with common constraints or common variables. An application to dairy production planning is discussed and a comparison with the Dantzig-Wolfe decomposition principle is given.     

Distributed Computer System Capacity Planning and Capacity Loading

The availability of micro-, mini-, and super computers has complicated the laws governing the economies of scale in computers and has increased the tendency to decentralize and distribute computing power. The optimal design of such a system requires integration of computers of varying power and a strategy for capacity loading. This paper considers the problem of capacity planning and capacity loading of a distributed computer system as a hierarchy of decisions. A linear programming model is developed for the initial capacity planning problem. A loading model that accounts for variations in arrival and processing rates of the jobs in a dynamic environment is developed to support the operations.     

A PRACTICAL APPROACH TO THE LARGE-SCALE FOREST SCHEDULING PROBLEM*

A large-scale forest cutting schedule problem involving 1166 forest units to be cut over a 24–year planning period is discussed. The problem is formulated as a generalized version of the basic transportation problem. The conversion procedure for such a problem to the standard transportation format is outlined. The proposed approach is then compared with a linear programming decomposition approach on the basis of operating results obtained and computer time required by each approach. It is shown that this new approach will solve a real world problem about 40 times faster than the usual linear programming decomposition approach.     

An integrated supplier selection and inventory problem with multi-sourcing and lateral transshipments

In this research, we consider the supplier selection problem of a firm offering a single product via multiple warehouses. The warehouses face stationary, stochastic demand and replenish their inventory via multiple suppliers, to be determined from a set of candidates, with varying price, capacity, quality, and disruption characteristics. Additionally, the warehouses may simultaneously replenish their inventory from other warehouses proactively. With these characteristics, the problem is a multi-sourcing, supplier selection, and inventory problem with lateral transshipments. Even though the benefits of multi-sourcing and lateral transshipments have been presented in the literature individually to mitigate risks associated with uncertain demand and disrupted supply, the intertwined sourcing and inventory decisions under these settings have not been investigated from a quantitative perspective. We develop a decomposition based heuristic algorithm, powered with simulation. While the decomposition based heuristic determines a solution with supplier selection and inventory decisions, the simulation model evaluates the objective function value corresponding to each generated solution. Experimental results show, contrary to the existing literature, inferior decisions may result when considering the selection of suppliers solely on unit and/or contractual costs. We also evaluate the impact of multi-sourcing with rare but long disruptions compared to frequent but short ones.     

Assessing the impact of uncertainty and the level of aggregation in case mix planning

In this article, we present a framework for evaluating the impact of uncertainty and the use of different aggregation levels in case mix planning on the quality of strategic decisions regarding the case mix of a hospital. In particular, we analyze the effect of modeling (i) demand, (ii) resource use, and (iii) resource availability as stochastic input parameters on the performance of case mix planning models. In addition, the consequences of taking the weekly structure with inactive days without surgeries into account are assessed (iv). The purpose of this paper is to provide a guideline for the decision-maker planning the case mix on the consideration of stochastic aspects and different aggregation levels. We formulate a mixed integer programming model for case mix planning along with different stochastic and deterministic extensions. The value of the different extensions is analyzed using a factorial design. The resulting stochastic models are solved using sample average approximation. Simulation is used to evaluate the strategies derived by the different models using real-world data from a large German hospital. We find that highly aggregated basic case mix planning models can overestimate the objective value by up to 10% and potentially lead to biased results. Refining the problem decreased the gap between projected case mix planning results and simulated results considerably and led to improved solutions.     

Packing of Unequal Spheres and Automated Radiosurgical Treatment Planning

We study an optimization problem of packing unequal spheres into a three-dimensional (3D) bounded region in connection with radiosurgical treatment planning. Given an input (R, V, S, L), where R is a 3D bounded region, V a positive integer, S a multiset of spheres, and L a location constraint on spheres, we want to find a packing of R using the minimum number of spheres in S such that the covered volume is at least V; the location constraint L is satisfied; and the number of points on the boundary of R that are touched by spheres is maximized. Such a packing arrangement corresponds to an optimal radiosurgical treatment planning. Finding an optimal solution to the problem, however, is computationally intractable. In particular, we show that this optimization problem and several related problems are NP-hard. Hence, some form of approximations is needed. One approach is to consider a simplified problem under the assumption that spheres of arbitrary (integral) diameters are available with unlimited supply, and there are no location constraints. This approach has met with certain success in medical applications using a dynamic programming algorithm (Bourland and Wu, 1996; Wu, 1996). We propose in this paper an improvement to the algorithm that can greatly reduce its computation cost.     

Logic-based Benders decomposition for an inventory-location problem with service constraints

We study an integrated inventory-location problem with service requirements faced by an aerospace company in designing its service parts logistics network. Customer demand is Poisson distributed and the service levels are time-based leading to highly non-linear, stochastic service constraints and a nonlinear, mixed-integer optimization problem. Unlike previous work in the literature, which propose approximations for the nonlinear constraints, we present an exact solution methodology using logic-based Benders decomposition. We decompose the problem to separate the location decisions in the master problem from the inventory decisions in the subproblem. We propose a new family of valid cuts and prove that the algorithm is guaranteed to converge to optimality. This is the first attempt to solve this type of problem exactly. Then, we present a new restrict-and-decompose scheme to further decompose the Benders master problem by part. We test on industry instances as well as random instances. Using the exact algorithm and restrict-and-decompose scheme we are able to solve industry instances with up to 60 parts within reasonable time, while the maximum number of parts attempted in the literature is 5.     

Codes of ethics and firm size: A stakeholder approach to strategic planning

A firm's capital budgeting and strategic planning decisions have the potential to affect many groups of people called stakeholders. A stakeholder is any group or individual who can affect or is affected by the achievement of the firm's objectives. This study examines whether the presence of a code of ethics that specifically addresses capital budgeting/strategic planning decisions will significantly raise the awareness of social responsibility during the long-run planning process. This study also examines whether firm size is associated with the awareness of social responsibility during the long-run planning process. Support was found that a code of ethics that addresses long-range planning is associated with higher awareness during the planning process. Firm size was not found to be statistically different.     

Projecting consumptions of competing products

In this article a practical methodology is proposed to enable the planner to project the consumption levels of competing products. The author argues that the method is best suited for the determination of long-run consumption trends of such products. It is less effective for the forecasting of short-run consumption of products in dynamic markets. The discussion will be particularly pertinent to companies considering production investment with long lead times to commissioning the plant in question.     

Multiobjective investment planning under uncertainty

In this paper we consider the relevance of various planning methods and decision criteria to multiobjective investment planning under uncertainty. Assuming that a natural reaction to uncertainty is to operate so as to leave open as many good options as possible (as opposed to maximizing subjective expected utility) we argue that the planning process should concentrate on analyzing the effects of the initial decision, and that for this exercise the classical methods of mixed integer programming are inappropriate. We demonstrate how the technique of dynamic programming can be extended to take account of multiple objectives and use dynamic programming as a framework in which we analyze the robustness of an initial decision in the face of various types of uncertainty. In so doing we also analyze the risks involved in both the planning and decision making functions.     

资金推动型投机市?基于新增开户数视角的A股市场实证检验

本文通过协整检验、向量误差修正模型和VEC格兰杰因果检验,使用2005年6月20日至2008年1月11日的新增A股开户数和沪深300指数日交易数据,实证考察了新增开户数和股票价格及成交金额间的长短期关系,在此基础上,进一步探讨了我国股票市场的市场特点。研究结果表明:新增A股开户投资者在开户后10个左右的交易日开始介入市场;新增A股开户数与沪深300指数及其成交金额间存在协整关系,达到了长期均衡;新增A股开户数变化是沪深300指数及其成交金额变化的长期原因,沪深300指数及其成交金额变化是新增A股开户数变化的短期原因;A股市场仍具备资金推动型投机市的典型特征。