**2. Literature review**

In literature, this kind of optimization problem is classified as the worker assignment prob‐ lem [3]. Applications of this problem are matching employees on work positions, where the required knowledge of work positions is compared to the actual knowledge of known employees [4]. The optimal solution (objective function) depends on the global minimum of the current knowledge deficit or the global maximum of the current knowledge surplus.

In a real environment, production processes are complicated and diverse. Almost every prod‐ uct and its production technology require modification of its objective function or modifica‐ tion of the entire optimization problem. Even if there is production of the same product in different locations, there will be modification needs, despite work standardization efforts. During process execution (over several years), the optimization problem also changes because of expected and unexpected events, such as production errors, economic opportunities and new arrangements. These events are sometimes very important for optimization. In the case of the presence of a more important and/or urgent business event, their importance for opti‐ mization disappears, and their priorities for optimization are changed. Therefore, there are many specific solutions for the worker assignment problem in the literature. Some solutions are case specific while other are made in an attempt to be universally applicable. Depending on the complexity of the worker assignment problem, researchers implement different opti‐ mization methods: mathematic programming models (linear, non‐linear, integer), genetic algorithms and heuristics.

production strategy' (ETO). Products in ETO production have a complex structure and a cus‐ tomer‐specified production that is treated as a project. These projects are generally unique and were never previously executed. Therefore, it is impossible that they be handled with existing standard project activities. Problems with the allocation of employees appear in the first activi‐ ties of the ETO production project, in which activities require a high level of innovation, and the project requires a proper knowledge allocation prior to capacity allocation. Of course, the management needs both allocation views, but the knowledge aspect is more important when dealing with new product or technology changes. The typical question before executing each

Knowledge is an element of the employees and also an element of the activities of business processes [1]. In Make‐to‐Stock (MTS), production activities are highly specialized and require a small set of required knowledge. In ETO production, employees execute many activities with a large set of required knowledge. Due to salary requirements, the human‐resource‐ required knowledge is linked to the work position definitions [2]. The management goal is to optimize the required knowledge of work positions and the current knowledge of employ‐ ees. With every product or process change, the knowledge structure of the work position is changed. If changes are permanent, there will be a continuous searching for new appropriate employees. However, what if the process of change was adjusted so that it took into consider‐ ation currently available knowledge? These employees are the only source that is available at the time a new product requires new knowledge in the process. What if the capacity load of each employee's knowledge and not just the employee's capacity in general were taken into

In literature, this kind of optimization problem is classified as the worker assignment prob‐ lem [3]. Applications of this problem are matching employees on work positions, where the required knowledge of work positions is compared to the actual knowledge of known employees [4]. The optimal solution (objective function) depends on the global minimum of the current knowledge deficit or the global maximum of the current knowledge surplus.

In a real environment, production processes are complicated and diverse. Almost every prod‐ uct and its production technology require modification of its objective function or modifica‐ tion of the entire optimization problem. Even if there is production of the same product in different locations, there will be modification needs, despite work standardization efforts. During process execution (over several years), the optimization problem also changes because of expected and unexpected events, such as production errors, economic opportunities and new arrangements. These events are sometimes very important for optimization. In the case of the presence of a more important and/or urgent business event, their importance for opti‐ mization disappears, and their priorities for optimization are changed. Therefore, there are many specific solutions for the worker assignment problem in the literature. Some solutions are case specific while other are made in an attempt to be universally applicable. Depending

ETO project is: Do we have appropriate knowledge to do that?

218 Knowledge Management Strategies and Applications

consideration?

**2. Literature review**

The following research has been used as a background for the worker assignment problem in this chapter. From the perspective of tasks, Azizi and Liang [5] developed an integrated approach to the worker assignment problem. Their dominant assignment problem includes workforce flexibility acquisition and task rotation. They used a constructive‐search heuristic method and set the objective to minimizing the total cost including the incremental cost of new training cost, flexibility cost and productivity loss cost. The learning effect in the worker assignment model was also the subject of research in a project task scheduling problem [6]. They used a mixed non‐linear integer program, solved by a proposed genetic algorithm. The objective function was to minimize outsourcing costs. From the task perspective, there is opti‐ mization model of task allocation and knowledge worker scheduling [7]. The purpose of this model is to assign knowledge workers to every task and arrange them (the tasks) in order to minimize the total time required to finish all projects. Their optimization is based on the Ant Colony algorithm as an optimization technique [8]. Nembhard [9] uses a heuristic approach for assigning workers to tasks that is based on individual learning rates.

There are also worker assignment models originating in production layout and shifts. McDonald et al. [10] developed a worker assignment model to evaluate a lean manufactur‐ ing cell, using a binary integer programming model that is solved using a branch‐and‐bound approach. The objective of this model is to minimize net present costs (initial training costs, incremental training costs, inventory costs and cost of poor quality). Previously, a model of worker assignment considering technical and human skills in cellular manufacturing was developed [11]. It is classified as mixed‐integer programming problem. The objective of the model is to maximize profit, where profit has three components: productivity, quality costs and training costs. Ingolfsson et al. [12] combined integer programming and the randomiza‐ tion method to schedule employees by using an integer programming heuristic to generate schedules; they used the randomization method to compute service levels. They described a method to find low cost shift schedules with a time‐varying service level that is always above a specified minimum.

There are worker‐assigning models that deal with the satisfaction of workers. Brusco and Johns [13] defined a model of staffing a multi‐skilled workforce with varying levels of pro‐ ductivity. They applied integer linear programming model with the objective of minimizing workforce staffing costs subject to the satisfaction of minimum labour requirements across the planning horizon of a single work shift. Mohan [14] created a model of scheduling part‐time personnel with availability restrictions and preferences to maximize employee satisfaction. He proposed an integer programming model to maximize employee satisfaction (while con‐ sidering their seniority and availability) and to meet the demand requirements for each shift. A branch‐and‐bound algorithm was used for this.

From the perspective of competencies [15], there is a competence‐driven staff assignment approach that is based on a stochastic working status model. This model seeks to minimize employee wages and maximize strategic gains of the company from the increment of desirable competencies. The authors used a genetic algorithm as the optimization method. Competencies are also used in a model that seeks to maximize a weighted average of economic gains from projects and strategic gains from the increment of desirable competencies. As a sub‐problem, the scheduling and staff assignment for a candidate set of selected projects is also optimized [16]. The authors used non‐linear mixed‐integer program formulation for the overall problem and then proposed heuristic solution techniques composed of a greedy heuristic for the sched‐ uling and staff assignment, and alternative 'meta' heuristics for the project selection.

Recent studies are showing that the worker assignment problem is still important subject of research. Grosse et al. [17] designed a framework for integrating human factors into plan‐ ning models. Crawford et al. [18] showed application of worker assignment problem in proj‐ ect scheduling and they innovated optimization approach using hyper‐cube framework. A similar problem that discuses assignment of health care staff to tasks using fuzzy evalua‐ tion method was presented by Mutingi et al. [19]. Olivella et al. [20] gave emphasis on the cross‐training goals, while Senjuti et al. [21] optimized the assignment of tasks to workers by proposing efficient adaptive algorithms. Current efforts are dealing with additional variables in creating the perfect optimization framework (knowledge, cross‐training, etc.), or in finding the best optimization algorithms for solving worker assignment problem. They still assume that tasks are allocated to workers as 'they are'. Our effort was to study the effect of task redef‐ inition in the meaning of splitting tasks on smaller parts with the goal of better knowledge alignment. From the organizational view, especially when the creative job must be done (like in ETO companies), the list of required tasks is created according to the available knowledge of workers, and the new definition of tasks is a subject of optimization output. This was our main theoretical issue that is described as real business example as follows:


This is a typical example of a company that needs to increase the use of its internal sources. Many cases have been found in practice in ETO companies in which the management solved the problem of outgoing knowledge with reorganization of internal employees rather than with the simple extension of employees' existing capacities, for example, overtime work [22]. We also set two assumptions that were not subjects of this research: first, we accepted that in ETO production, business processes are constantly changing and, therefore, knowledge requirements are also changing. Second, because these are simulations, the relation between knowledge and the process efficiency was accepted: if employees have proper knowledge for the execution of activities, then these activities are performed faster. This has an impact on better efficiency of the whole process if that activity is simultaneously a process bottleneck [23].
