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Economic incentives, government regulations, and customer perspective on environmental consciousness (EC) are driving more and more companies into product recovery business, which forms the basis for a reverse supply chain. A reverse supply chain consists a series of activities that involves retrieving used products from consumers and remanufacturing (closed-loop) or recycling (open-loop) them to recover their leftover market value. Much work has been done in the areas of designing forward and reverse supply chains; however, not many models deal with the transshipment of products in multiperiods. Linear physical programming (LPP) is a newly developed method whose most significant advantage is that it allows a decision-maker to express his/her preferences for values of criteria for decision-making in terms of ranges of different degrees of desirability but not in traditional form of weights as in techniques such as analytic hierarchy process, which is criticized for its unbalanced scale of judgment and failure to precisely handle the inherent uncertainty and vagueness in carrying out pair-wise comparisons. In this chapter, two multiperiod models are proposed for a remanufacturing system, which is an element of a Reverse Supply Chain (RSC), and illustrated with numerical examples. The first model is solved using mixed integer linear programming (MILP), while the second model is solved using linear physical programming. The proposed models deliver the optimal transportation quantities of remanufactured products for N-periods within the reverse supply chain.

This chapter analyzes the attribute associations, supplied by experts, of top 10 destination brands in Spain. Using a sample of respondents that represents the domestic tourist population, the study examined how they perceive the importance of each of the attributes when selecting a holiday destination. They are rated for all the 10 brands as a whole and for each individually. Comparisons are made between each and the average of all other brands. The application of multidimensional scale method resulted in five distinct groups or competitive sets based on the similarities and disparities of tourists’ ratings of these attributes. For each, the study suggests how these sets are perceived as a whole and in comparison with each other. The chapter offers meaningful relationships between the respondents’ demographic and socioeconomic characteristics and their perceived importance of the destination brands’ attributes.

The raw data are tabulated as given in Table 9.1.

A model of reputation is developed to show how firms operating in concentrated sectors can use the sponsorship of general human capital investments to specifically trained workers as a device of commitment with prospective employees. Employees of firms that operate in concentrated sectors learn skills that are valuable only for a limited number of alternative employers. This gives monopsonistic power to the training firm over the trained workers. Anticipating it, potential employees will be reluctant to work for the firm unless the employer is able to commit oneself’ must be turned back to ‘herself. I argue that human resource policies including the provision of general human capital to workers reduce employers’ commitment costs. Evidence from two representative samples of workers from Spain and the United Kingdom show that, consistent with the predictions of the model, firms from more concentrated sectors are more likely to sponsor their employees’ education.

The field of nonmarket strategy has expanded rapidly over the past 20 years to provide theoretical and practical guidance for managers seeking to influence policymaking. Much of this scholarship has built directly on spatial and “pivotal politics” models of lawmaking. While extremely helpful at identifying crucial targets for lobbying, these models treat all policymakers as identical in their abilities to advance legislative agenda items through various policymaking hurdles. We build upon these earlier models, but include policymakers who vary in their relative effectiveness at advancing measures through the legislative process. We identify how the implications of our model deviate from those of conventional (pivotal politics) analyses. We then present an empirical strategy for identifying effective Lawmakers in the United States Congress, and illustrate the utility of this approach for managers developing nonmarket strategies in legislative institutions, relying on the case of banking and financial services reforms between 2008 and 2011.