GRASP and statistical bounds for heuristic solutions to combinatorial problems

The quality of a heuristic solution to a NP-hard combinatorial problem is hard to assess. A few studies have advocated and tested statistical bounds as a method for assessment. These studies indicate that statistical bounds are superior to the more widely known and used deterministic bounds. However, the previous studies have been limited to a few metaheuristics and combinatorial problems and, hence, the general performance of statistical bounds in combinatorial optimization remains an open question. This work complements the existing literature on statistical bounds by testing them on the metaheuristic Greedy Randomized Adaptive Search Procedures (GRASP) and four combinatorial problems. Our findings confirm previous results that statistical bounds are reliable for the p-median problem, while we note that they also seem reliable for the set covering problem. For the quadratic assignment problem, the statistical bounds has previously been found reliable when obtained from the Genetic algorithm whereas in this work they found less reliable. Finally, we provide statistical bounds to four 2-path network design problem instances for which the optimum is currently unknown.

Student Thesis Level: Master’s Thesis in Business Administration Corporate Social Responsibility (CSR) networking : A case study on why members join CSR network and why are they sharing their knowledge

Purpose – This research focuses on finding the reasons, why members from different sectors join a cross-sector/multi-stakeholder CSR network and what motivates them to share (or not to share) their knowledge of CSR and their best practices. Design/methodology/approach – Semi-structured interviews were conducted with members of the largest cross-sector CSR network in Sweden. The sample base of 15 people was chosen to be able to represent a wider variety of members from each participating sectors. As well as the CEO of the intermediary organization was interviewed. The interviews were conducted via email and telephone. Findings – The findings include several reasons linked to the business case of CSR such as stakeholder pressure, competitive advantage, legitimacy and reputation as well as new reasons like the importance of CSR, and the access of further knowledge in the field. Further reasons are in line with members wanting to join a network, such as access to contact or having personal contacts. As to why members are sharing their CSR knowledge, the findings indicate to inspire others, to show CSR commitment, to be visible, it leads to business opportunity and the access of others knowledge, and because it was requested. Reasons for not sharing their knowledge would be the lack of opportunity, lack of time and the lack of experience to do so. Originality/value – The research contributes to existing studies, which focused on Corporate Social Responsibility and cross-sector networking as well as to inter-organizational knowledge sharing in the field of CSR.