Goal Programming and Multiple Objective Linear Programming

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The process of managing an organization presupposes dealing with a range of complex tasks and taking care of complex processes within the company in question. Though a company traditionally strives towards a single goal described in the organization’s vision and mission statements, the necessity to split the aforementioned goal into several basic objectives usually emerges as a result of the multifaceted structure of a company. The process of managing the organization and production processes related to the completion of the objectives under consideration, in its turn, may be based on the principles of linear programming (LP), goal programming (GP) and multiple objective linear programming (MOLP). Being the representation of the same idea, the three types of programming only differ in their complexity and the amount of elements that they embrace.

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The first principle of programming to be ever introduced to management, linear programming, or LP, presupposes a rather basic and simple structure. The nature of the concept is encrypted in the very name of the approach; by definition, linear programming presupposes that every single step taken is supposed to address a single goal. No multiple objectives may be incorporated into the process as long as it is carried out with the help of LP; otherwise, the process becomes extremely convoluted and hard to track down.

Needless to say, shortly after being introduced into the realm of management, LP wore out its welcome. Though it is still used as the key towards solving minor issues, it cannot possibly work in the environment where the completion of more than one objective is required to attain the final goal. Instead, the set of principles suggested in general programming are incorporated into the overall management of the organization.

According to the existing definition, goal programming can be viewed as a modification of linear programming, which provides the environment for accomplishing more complex goals and taking care of more processes than LP does. On a surface, GP is merely a modification of LP, with a few additions that allow for carrying out more complex tasks and performing more detailed assessments. However, a closer look at the specified phenomenon will reveal that it, in fact, has several major modifications, which set the two concepts apart. First and most obvious, the model that is used in the GP differs drastically from the one that the LP method has to offer. Though the GP model is traditionally defined as a simplified and approximated model of the real life process, being solely the “formal description of a real system” (Sen and Nandi 2), it sill involves a much greater amount of elements that make it closer to a real-life company management. Thus, the model in question helps embrace a greater number of factors affecting the company’s operations and, therefore, provide a more credible prognosis for the firm’s future progress, or the lack thereof. More to the point, the specified difference helps identify the potential threats that the company in question may face.

It would be wrong to consider GP a generalization of LP. Instead, GP should be viewed as the extension of the LP possibilities and the exploration of the LP properties once the process in question is transferred and implemented on an entirely different level of management. Therefore, it is more appropriate to assume that GP is not a generalization of LP, but, instead, an attempt to view the grand processes of an organization through the lens of LP.

The difference in the objectives, at which the approaches in question are targeted, has also had its toll on the area of the strategies application. In contrast to the LP, which is traditionally used in the realm of agricultural planning, GP is m ore appropriate for addressing socioeconomic concerns (Sen and Nandi 3). As a result, the solutions that the model suggested by the linear type of programming provide may fit only the agricultural environment and cannot be used for addressing any other type of concerns. GP, on the other hand, has been created for considering the issues faced within the socioeconomic environment and, therefore, should be applied in the specified area. Also known as the GP-OBS model, the above-mentioned approach towards setting goals and implementing them incorporates the use of graphical methods and involves complex mathematic calculations. Unlike LP, the decision making model in which requires less steps to be taken, the GP strategy demands that the clients priorities should be sorted out in accordance with their cost, time and quality, which is quite different from the model suggested by LP and incorporating mainly the information concerning the client’s costs.

In addition, the ways that the GP offers in order to achieve the goals set in the course of the project arrangement differ greatly from those that the LP suggests. For example, the priority structure, which must be followed when setting the GP related goals, differs considerably from that one of the LP approach. To be more specific the GP approach presupposes that the total cost goal, the minimization of the lower limit of the key raw materials, the minimization of the underutilization of the lower limit of the key raw materials and the minimization of the overutilization of the upper limit of the key raw materials (Hassan, Hassan, Yatim and Yusof 47) should occur in the course of the procedure. The specified strategy is quite different from the one suggested in the LP system, where the calculations process is simplified to a great extent. Nevertheless, both LP and GP share a range of similarities, including the use of similar models and graphical methods as a tool for illustrating the key concepts of linear programming. The key difference, therefore, lies in the complexity of the approaches and the related models, as well as the trustworthiness of the assessment results.

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The last, but definitely not the least, the Multiple Objective Linear Programming, or MOLP, in its turn, represents a much more complicated level of a company management and incorporates the elements of both GP and LP. The fact that these are the clients that define the multiple tender objectives in the corresponding sections of the documentation is, perhaps, the first difference between MOLP and the remaining two programming approaches to be mentioned (Tan, She, Lu and Shen 657). The specified feature of the MOLP strategy allows for more flexibility and creates the premises for making the company look more trustworthy. In addition, speaking of the core differences between LP, GP and MOLP, it is worth noting that MOLP presupposes the search for the optimum solution for a specific project, whereas LP and GP only suggest the customer to choose from the preexisting set of options. Therefore, in some way, MOLP can be interpreted as a reinvention and a radical improvement of the LP and GP approaches.

Another significant difference between MOLP and the strategies mentioned above is that LP and GP are traditionally viewed as quantitative tools, whereas MOLP can be viewed as a both qualitative and quantitative one: “GP is a quantitative tool enabling decision-maker to come as close as possible to satisfy both various goals (objectives) and constraints in dealing with linear problem” (Tan, She, Lu and Shen 658). An approach that incorporates the evaluation of the numerical factors and the qualitative ones, MOLP helps view the problem from several standpoints and, thus, is more objective than LP or GP. In addition, MOLP includes a well-developed model for defining the contractor’s assets, particularly, the performance of the latter, as well as the contribution that they make to forward the project in question. It should be noted that at this point, MOLP and GP cross again; however, unlike GP, MOLP provides the strategy that helps locate the goal constraints, therefore, making the project goals even more attainable and calculating the possibility of the company’s success more precisely.

On a more general level, the basic difference between GP, LP and MOLP concerns the extent, to which the competitiveness of the bidding contractors can be measured, and the amount of factors that the techniques in question can embrace. Assuming that MOLP requires only the most elaborate tasks to be carried out in order to define the competitiveness of a specific contractor will be quite a stretch. Instead, the MOLP approach provides a “development of using multiple criteria in selecting contractors” (Tan, She, Lu and Shen 657), which means that the method of measurement offered by MOLP is more profound than the ones suggested by LP and GP.

One must admit, though, that there are obvious traces of GP and LP in MOLP. The basic difference between LP, GP and MOLP is, therefore, the level of goal complexity and the amount of elements incorporated into the strategy design. While LP and GP have a very basic set of elements and strategies introduced into it, MOLP represents a very complex structure that reflects the process of an entire organization’s functioning. Hoffman, Schniederjans and Sebora (238) make it quite clear that the MOLP strategy can be utilized for the goals of a considerably larger scale than LP and GP allow for.

Despite the fact that the three types of programming presuppose that different methods of solving specific problems should be chosen, LP, GP and MOLP can be viewed as three constituents of a single entity and three elements that belong to the same domain; therefore, the level that each of the specified phenomena corresponds to, as well as the complexity of the type of programming in question, can be viewed the key distinction of the three. It would be wrong to dismiss the basic programming strategies, such as LP or GP, even for the organizations that incorporate a variety of goals and objectives into their daily operational and organizational processes, though. Each of the specified components is an essential element of a specific level of a company’s functioning, and choosing specific strategies for carrying out the management process at the specified stages is vital for the company’s successful performance.

Works Cited

Hassan, Nasruddin, Khairil Bariyyah Hassan, Siti Salmiah Yatim and Siti Aminah Yusof. “Optimizing Fertilizer Compounds and Minimizing the Cost of Cucumber Production Using the Goal Programming Approach.” American-Eurasian Journal of Sustainable Agriculture 7.2 (2013): 45–49. Print.

Hoffman, James J., Marc J. Schniederjans and Terrence C. Sebora. “A Multi-Objective Approach to CEO Selection.” INFOR 42.4 (2004), 237–255. Print.

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Sen, Nabendu and Manish Nandi. “Goal Programming, Its Application in Management Sectors– Special Attention into Plantation Management: A Review.” International Journal of Scientific and Research Publications 2.9 (2012), 1–6. Print.

Tan, Yongtao, Liyin She, Weisheng Lu and Qiping Shen. “Multiple Objective Bidding Strategy Using Goal Programming Technique”, Management Decision 46.4 (1008), 656–672.

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