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We extend the classical garbage can model to examine how individual differences in ability and motivation will influence organizational performance. We find that spontaneous coordination provided by an organized anarchy is superior when agents are equally competent. The Weberian bureaucracy of planned coordination is effective when problems require specialist knowledge. However, errors in matching problems to specialized agents are a central challenge for bureaucracies. Actual organizations, therefore, combine elements of organized anarchies and bureaucracies. Heterogeneous motivation compounds coordination problems, but is usually less important than competence. Our findings point to matching and interactive learning as fruitful areas for further study.

This chapter examines the prevalence of justiciable civil justice problems experienced by Canadians, the ways in which people respond to them and the consequences of experiencing these kinds of problems. The results show that experiencing justiciable problems is a nearly normal feature of the everyday lives of a large proportion of the population in a modern society. Particularly, important features of justiciable problems are the prevalence of multiple problems, the clustering of justiciable problems and the linkages between justiciable, health and social problems. The results suggest that justiciable problems may be a part of broader patterns of social exclusion. One implication of this research is that access to justice services may not only address legal problems but, by doing so, may have the effect of forestalling processes of social exclusion of which civil law problems are a part.

The purpose of this paper is to provide an effective solution for a complex planning problem encountered in heavy industry. The problem entails selecting a set of projects to produce from a larger set of solicited projects and simultaneously scheduling their production to maximize profit. Each project has a due window inside of which, if accepted, it must be shipped. Additionally, there is a limited inventory buffer where lots produced early are stored. Because scheduling affects which projects may be selected and vice-versa, this is a particularly difficult combinatorial optimization problem.

The authors develop an algorithm based on the Metaheuristic for Randomized Priority Search (Meta-RaPS) as well as a greedy heuristic and an integer programming (IP) model. The authors then perform computational experiments on a large set of benchmark problems over a wide range of characteristics to compare the performance of each method in terms of solution quality and time required.

The paper shows that this problem is very difficult to solve using IP, with even small instances unable to be solved optimally. The paper then shows that both proposed algorithms will in seconds often outperform IP by a large margin. Meta-RaPS is particularly robust, consistently producing the best or very near-best solutions.

The Meta-RaPS algorithm developed enables companies facing this problem to achieve higher profits through improved decision making. Moreover, this algorithm is relatively easy to implement.

This research provides an effective solution for a difficult combinatorial optimization problem encountered in heavy industry which has not been previously addressed in the literature.

The question facing sociology is whether it is a field or a discipline. If it is a field, then there is no need for theorizing. However, if sociology is a discipline, then problem-solving cannot be disentangled from theorizing without a loss of intelligibility – the inability to explain the social as the concept of the discipline. Through the quasi-realism of problem-solving as a course of activity, this chapter presents cognitive sociology as a paradigm appropriate to the concept of the social understood as an ongoing course of activity. In doing so, it is shown how bounded rationality and expertise play a crucial role in how communication interacts with the division of cognitive labor, especially through the idea of representational representationality. Representational representationality is an idea that reveals how the degree of clarity among language, meaning, and thought is relative to the issues of audience and ignorance. Representational representationality is significant because it demonstrates how the relationship among meaning, language, and thought is subject to communicative errors – errors arising from a predicament of intelligibility and not merely arising from issues of computational skill, as described by Herbert Simon's model of bounded rationality and expertise in human problem-solving. The argument that follows from this shows how the means for adapting to ambiguity amounts to the difference between Simon's model and a quasi-real model in terms of its principle of rationality, principle of efficiency, and its cognitive style of problem-solving for deliberate practice. These dimensions are shown to effect what “examples” are good for in the problem-solving process, thereby revealing the politics of expertise. The politics of expertise demonstrates how the conflicts in sociological explanations of strategy are not merely conflicts that can be set aside as a pluralism of values. Rather, the conflicting explanations of theory and theorizing can only be resolved when the situational rationality of sociology as a discipline realizes the quasi-realism of problem-solving as a course of activity.

The SATISFACTORY LUBRICATION OF Diesel engines presents some of the most difficult problems encountered by oil technologists. This is especially true of large marine engines, where, due to low speeds and high loads, it is difficult to establish fluid film lubrication. Cylinder lubrication is particularly difficult due to the high temperatures encountered. This problem is more difficult in two‐stroke engines than in four‐stroke engines as, in the former, there is no non‐working stroke during which it is easier to form an oil film on the cylinder walls. Pressure‐charged two‐stroke engines are the most difficult of all to lubricate satisfactorily. The problem is aggravated in engines operating on residual fuel due to the high sulphur content increasing corrosive wear, and to the abrasive ash forming constituents present in such fuels. In addition, the contaminating influences of partially burnt products of combustion on the crankcase oil have to be considered. The ever‐present risk of water leakage into the crankcase oil, either from condensation, or from leakage of the cooling system, influences and often restricts the use of otherwise beneficial additives.

In this study, the authors unpack the micro-level processes of knowledge accumulation (experiential learning) and knowledge application (problem solving) to examine how task allocation structures influence organizational learning. The authors draw on untapped potential of the classical garbage can model (GCM), and extend it to analyze how restrictions on project participation influence differentiation and integration of organizational members’ knowledge and consequently organizational efficiency in solving the diverse, changing problems from an uncertain task environment. To isolate the effects of problem or knowledge diversity and experiential learning, the authors designed three simulation experiments to identify the most efficient task allocation structure in conditions of (1) knowledge homogeneity, (2) knowledge heterogeneity, and (3) experiential learning. The authors find that free project participation is superior when the members’ knowledge and the problems they solve are homogenous. When problems and knowledge are heterogeneous, the design requirement is on matching specialists to problem types. Finally, the authors found that experiential learning creates a dynamic problem where the double duty of adapting the members’ specialization and matching the specialists to problem types is best solved by a hierarchic structure (if problems are challenging). Underlying the efficiency of the hierarchical structure is an adaptive role of specialized members in organizational learning and problem solving: their narrow but deep knowledge helps the organization to adapt the knowledge of its members while efficiently dealing with the problems at hand. This happens because highly specialized members reduce the necessary scope of knowledge and learning for other members during a certain period of time. And this makes it easier for the generalists and for the organization as a whole, to adapt to unforeseen shifts in knowledge demand because they need to learn less. From this nuanced perspective, differentiation and integration may have a complementary, rather than contradictory, relation under environmental uncertainty and problem diversity.

The purpose of this paper is to demonstrate a novel use of a generalized Lennard‐Jones (LJ) force law in Physicomimetics, combined with offline evolutionary learning, for the control of swarms of robots moving through obstacle fields towards a goal. The paper then extends the paradigm to demonstrate the utility of a real‐time online adaptive approach named distributed agent evolution with dynamic adaptation to local unexpected scenarios (DAEDALUS).

To achieve the best performance, the parameters of the force law used in the Physicomimetics approach are optimized, using an evolutionary algorithm (EA) (offline learning). A weighted fitness function is utilized consisting of three components: a penalty for collisions, lack of swarm cohesion, and robots not reaching the goal. Each robot of the swarm is then given a slightly mutated copy of the optimized force law rule set found with offline learning and the robots are introduced to a more difficult environment. The online learning framework (DAEDALUS) is used for swarm adaptation in this more difficult environment.

The novel use of the generalized LJ force law combined with an EA surpasses the prior state‐of‐the‐art in the control of swarms of robots moving through obstacle fields. In addition, the DAEDALUS framework allows the swarms of robots to not only learn and share behavioral rules in changing environments (in real time), but also to learn the proper amount of behavioral exploration that is appropriate.

There are significant issues that arise with respect to “wall following methods” and “local minimum trap” problems. “Local minimum trap” problems have been observed in this paper, but this issue is not addressed in detail. The intention is to explore other approaches to develop more robust adaptive algorithms for online learning. It is believed that the learning of the proper amount of behavioral exploration can be accelerated.

In order to provide meaningful comparisons, this paper provides a more complete set of metrics than prior papers in this area. The paper examines the number of collisions between robots and obstacles, the distribution in time of the number of robots that reach the goal, and the connectivity of the formation as it moves.

This paper addresses the difficult task of moving a large number of robots in formation through a large number of obstacles. The important real‐world constraint of “obstructed perception” is modeled. The obstacle density is approximately three times the norm in the literature. The paper shows how concepts from population genetics can be used with swarms of agents to provide fast online adaptive learning in these challenging environments. In addition, this paper also presents a more complete set of metrics of performance.