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OVERVIEW: R&D spin-outs offer pharmaceutical companies an increasingly attractive mechanism for increasing the effectiveness of R&D activities by allowing companies to maintain a clear focus on core activities and yet still exploit discoveries that are less central to the core business. This study analyzed 42 European R&D spin-outs with regard to background, realization, and further development. Interviews were conducted with key personnel in both parent companies and spinouts to explore the underlying strategy and success factors. Key aspects of successful spin-outs are a clear focus on core activities and a cadre of highly motivated employees. The use of spin-outs has resulted in increased activity along the entire pharmaceutical value chain, producing an increase in drugs introduced to the market in recent years. However, spin-outs are also responsible for the disintegration of established value chains, resulting in higher coordination and transaction costs. Spin-outs offer managers a potential option to refocus their activities and reduce costs. The successful examples and potential pitfalls offered here illustrate best practices for managing spin-outs to maximize R&D productivity. KEYWORDS: Spin-outs, Pharmaceutical industry, Outsourcing, Spin-offs, Drug discovery and development, The number of corporate R&D spin-outs in the pharmaceutical industry has increased significantly in recent years (Ramirez and Duberley 2010; Chemmanur and Yan 2004; Dewdney and Smith 1998). This may [...]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ijpe.2009.08.020 Byline: Yousef Amer, Lee Luong, Sang-Heon Lee Keywords: Supply chain management; Performance measurement; Process control; Order fulfillment; Design for six sigma; Fuzzy logic Abstract: This paper demonstrates a model which uses an adaption of Design for six sigma and fuzzy logic to optimize, monitor and control the order fulfillment process in the supply chain of a global retail firm. Even though the firm has advanced SCM practices in place retail stores suffer from stock outs of popular items and excessive stock of lower selling items. The order fulfillment optimization model presented enhances supply chain integration and collaboration across supply chain partners through effective monitoring and controlling of supply chain variables. It considers the critical to customer requirements at the supply chain design onset making it a useful model for dealing with customer differentiation and channel separation. With 47% of firms only focusing on developing smooth internal processes this model offers opportunities for external supply chain improvements and partial integration across the chain to improve business outcomes. Author Affiliation: School of Advanced Manufacturing and Mechanical Engineering, University of South Australia, Mawson Lakes, SA 5095, Australia Article History: Received 31 December 2008; Accepted 12 August 2009

In this paper, we propose a mixed-integer programming model to select vendors and determine the order quantities. The model considers the stochastic nature of demand, the quality of supplied parts, the cost of purchasing and transportation, the fixed cost for establishing vendors, and the cost of receiving poor quality parts. The model also considers the lead time requirements for the parts.

This paper addresses a problem of assigning electric power repair crews and depots to various locations (cells) in a damaged area during emergency situations such as natural disasters. The problem takes into account the damage levels and consequent demand for various resources at different cells in the area, along with the capacity restrictions of the depots. Two mixed integer quadratic programming models - one for single resource and another for multiple resources allocation are presented in the paper. The objective is to locate the depots, and assigning the crews and other resources to depots to various cells, at a minimum cost. The problem is solved optimally for various dimensions of the problem (e.g., number of cells in the region, number of depots considered and the number of resources) for a limited instance. Key Words: Power restoration, routing, allocation of resources, and mixed integer programming problem.

The design of machines is always done based on the experience obtained by trail and error methods. In this paper, we propose a method of solving an optimal loaded-allocation (OLA) problem of simple beam which is fundamental for the strength factor in design. This model is allocation problem of concentrated load and distributed load for maximized objective function with a nonlinear loaded constraint on the support point. In general, it is difficult to directly solve many optimization problems formulated as nonlinear integer programming (NIP) model. The application of a genetic algorithm (GA) (that uses the stochastic search method) effectively solves the combinatorial problem making it a remarkable new tool for solving any kind of problem. In this paper, we formulate an OLA problem of a simple beam with the concentrated load, distributed load or a combination of both, as a NIP problem and solve it directly by keeping the nonlinear load constraint for support point by using improved GA. We discussed the efficiency of the propose method. keywords: optimal design, genetic algorithm, loaded-allocation problem

This paper reports a tabu-search approach to minimize the cost in hole making processes. Four issues, namely, tool travel scheduling, tool switch scheduling, tool selection, and machining speed specification have been simultaneously addressed in this study. This problem has a structure similar to the Travelling Salesman Problem (TSP) and hence is NP-complete. To provide an efficient solution procedure, a tabu search approach has been proposed. The performance of the proposed approach is tested using an example problem.

This work presents the methodology and the decision support system that apply mathematical programming in a CAD (Computer Aided Design) environment, in order to choose the best portfolio of residential projects. The work also presents a case study where the decision support system was utilized for choosing the best portfolio among 18 residential projects placed in the south region of Brazil. KEYWORDS CAD; mathematical programming; engineering economics; optimization

Productivity is concerned with the effective and efficient utilization of resources in producing goods and/or services. In this study, productivity has been viewed in a broader perspective to present the degree of accomplishment of performance objectives, reconciled optimally through goal-programming. KEYWORDS Goal-Programming, productivity, production, productivity analysis, sub-system.

The development of a software application that optimizes scheduling subject to several dynamic nonlinear variables is described in this study. Twelve different tasks can be scheduled within any given 24 hour period. The tasks scheduled may use one or more facilities up to 48 in number. Facilities may have restrictions on concurrence and maximum power usage. The optimization strategy is developed to minimize both labor and power costs. KEYWORDS Scheduling; optimization; energy cost; labor cost.

In this paper, we examine the optimal design problem of system reliability with uncertain coefficients and formulate it as an interval programming model. Genetic algorithms are applied to the problem. The basic idea of the proposed method is first to transform the interval programming model into an equivalent bicriteria programming model and then to find the Pareto solutions of the bicriteria programming problem using genetic algorithms. Numerical examples are given to demonstrate the efficiency of the proposed approach. Keywords: Reliability optimization, interval programming, bicriteria programming, and genetic algorithm.

This paper describes the use of evolutionary programming (EP) integrated with a simulation model of a manufacturing system to determine the minimum number of kanbans and corresponding production trigger values required to meet demand. For this problem, a new two step heuristic is developed based on EP and the classical kanban sizing equation used by Toyota. The procedure is illustrated with an applied problem and the results indicate that the new heuristic provides good solutions to the problem. KEYWORDS Evolutionary programming; simulation; kanban sizing problem.

After combining a production and inventory problem with an equipment replacement problem, a model is developed that provides an optimal solution for all conditions presented. The model has two dimensions and is based on the Markov Decision Process. Also included in the analysis are two sets of conditions necessary for the combined optimal policies to display simple monotonic structure.

Research into linear ordering problems evidencing NP-hard combinatorial optimization results in the development of a new algorithm that employs cutting plane procedures, branch-and-bound methodologies and heuristic programming. The algorithm has applications that include triangulation of input-output matrices, analysis of single-machine scheduling, aggregation of individual preferences, and archeological seriation. The algorithm is functional for the triangulation of matrices that are 60 entries square, within acceptable time limits.

A multiperiod problem of finite but arbitrary length is analyzed. The problem is defining the optimal assembly policy and reordering of components for an assembly process that involves two parts in the production of a finished product with random (stochastic) demand. The functional equation approach to dynamic programming is employed in the problem's solution. The contention is that this constitutes the first analysis of an MRP assembly system, for which the products evidence stochastic demand.

Vehicle routing problems, which involve the distribution of delivery vehicles and assignment of routes to vehicles to optimize delivery, are addressed with an integer linear programming algorithm. The integer linear programming employs constraint relaxation and the formulation of subtour elimination constraints. The program developed has two versions for application, depending upon distance as a factor when determining optimal scheduling and routing of vehicles.

Reduced setup costs in a manufacturing environment are shown to improve production levels and quality control procedures, resulting in more and better products. The relationship between lot size (number of items produced in a single batch) and quality of the items produced is examined; it is shown that defective products could be produced repeatedly by production systems that are allowed to function 'out of control'. Smaller lot sizes result in reduced economic impact when defects occur (fewer items to rework or replace). Three quality improvement methodologies are also examined; they are: (1) reducing the likelihood of out-of-control situations, (2) reducing setup expenses, and (3) combinations of reductions in setup and loss of control.

Optimal production policies related to hydrocarbon reservoirs are identified using numerical optimization and simulation methodologies. In the research, a single-phased, two-dimensional reservoir is simulated for use in studying dry gas and gas storage reservoirs, calculating methods for offsetting well deliverability constraints, managing groundwater problems, and determining the applicability of mathematical programming models. This research is part of the process of developing efficient means for recovering oil from producing reserves in an efficient manner.

Scheduling of several activities for periodic processing by a single facility is determined for problems with infinite horizons. The scheduling problem is stated as an integer linear programming (ILP) problem, and solved using partitioning based upon Chinese Remainder Theorems. By partitioning the original problem into smaller model subset problems, optimal scheduling can be determined with relative ease. For example, partitioning allows the solution of a scheduling problem involving ten activities without recourse to computerization.

Clothing sizes and measurements associated with various forms of clothing are analyzed mathematically to identify the optimal number of sizes, such that clothing and apparel sales are maximized with a minimum of purchaser discomfort due to improper fit. The optimization problem is solved for normal population distributions related to body types and sizes, and the solution's necessary conditions for optimization are also identified.

Deterministic demand in a single-item inventory model with random order pricing that is assigned at the outset of each operating period is modeled, and optimal inventory control and ordering procedures are identified. The optimal policies identified minimize expected costs and satisfy all inventory demands. These policies are shown to be a function of critical price levels, equations for which are provided. The critical price level strategy is then applied to models with both finite and infinite horizons, and the strategy is also demonstrated to have applications for optimizing average costs.

Polynomial algorithms are developed that provide optimal solutions for planning the network and processing speeds of computer processors in a multiprocessor environment. The algorithms are developed after consideration of various scheduling problems, optimal speeds for processors given the condition that processor prices are a function of their speed, and optimal scheduling in a preemptive setting. The problem for which the algorithms are the solution is stated such that all projects to be completed by the multiprocessor system are separate from and independent of each other, as are the speeds of the individual processors. The problem is observed from the standpoints of completion time optimization and deadline optimization.

Discrete time shortage cost systems of inventory are analyzed from the standpoints of two parameters, the reorder level and the order up to level. The analysis seeks to identify nearly optimal inventory management policies with a minimum of computational effort. Three methods for determining near optimality are discussed, one of which involves heuristic programming. The methods are applied to various inventory problems and their results are compared numerically. These comparisons and error levels associated with each method are detailed.

The problem researched is identifying the number of rectangular pieces that can be cut from a single large rectangle, such that the value of the cut pieces is maximized. The solution obtained involves Lagrangean relaxation of 0-1 integer programming methods to formulate possible models for the problem and evaluation of solutions to these models, based on tree search procedures. Reduction tests for the originally stated problem and the problems resulting from the Lagrangean relaxation are also provided. The combination of Lagrangean relaxation and tree search methodologies facilitates problem solution of relatively moderate sized problems.