創新動態中的最佳化能力集合轉化

Abstract

我們的記憶、觀念、想法、判斷、反應（統稱為念頭和思路）雖然是動態的，但經過一段時間以後，如果沒有重大事件的刺激，會漸漸地穩定下來，而停在一個固定的範圍內。這些念頭和思路的綜合範圍，包括它們的動態和組織，就是我們的習慣領域（habitual domain, HD）。當我們面對一個決策問題時，相對應的存在一個能力集合（competence set, CS），包括能使我們得到滿意解答所須的想法、知識、技能、資源等等。因為能力集合是我們習慣領域針對某一決策問題的投影，如果習慣領域僵化了，我們的能力集合便無法擴展，進而會阻礙創新的展現。若沒有持續不斷地擴展、升級我們的習慣領域與能力集合，我們就很有可能走入決策陷阱或做出錯誤的決策而不自知。本論文基於習慣領域理論及能力集合分析，提出運作領域（working domain）的概念以及創新循環模式（innovation dynamics）。此模式可以使我們的運作領域能更加的靈活、有彈性。接著我們將著眼於創新循環模式中能力集合轉化的部份，討論最佳化能力集合調整問題。給定一個目標解，我們提出能力集合調整模式（competence set adjustment model, CSA model）求得生產參數的最佳調整，使得該目標解得以被達成。當目標解無法被達成時，我們透過二分法（bisection algorithm）或模糊線性規劃（fuzzy linear programming）的方法修正此目標解。最後，我們利用多準則多資源水準限制線性規劃模式（multiple criteria and multiple constraint levels linear programming, MC2LP）探討可變生產參數的線性規劃模式。這些參數包括單位利潤、可用資源以及投入產出參數。在這些參數會隨著投資或時間而改變的情況下，我們將探討如何找到動態最佳解使得「接單時虧損，交貨時獲利」（紅色接單、黑色出貨）的目標得以實現。

Habitual domain (HD) is a collection of ways of thinking, coupled with its formation, interaction and dynamics, which can gradually stabilize as time passes. Unless extraordinary events occur, our thinking process will reach a steady state. For a decision problem E, there exists a corresponding competence set (CS) consisting of ideas, knowledge, skills, and resources to successfully solve the problem. Being a projection of our habitual domains with respect to E, our competence set may not be expanded as our habitual domains get ossified. This can conceal our innovation. Therefore, without continuous expanding and upgrading our HD and CS, we may unconsciously step into decision traps and make wrong decisions. Based on HD theory and CS analysis, the dissertation presented here introduces the concepts of working domain and innovation dynamics. The model of innovation dynamics helps our working domain become more flexible and agile. We, in advance, focus on the transformation of competence set in the innovation dynamics to investigate the problem of optimal adjustment of competence set. The program is formulated into a linear programming model called competence set adjustment model (CSA model). By means of the CSA model, we study how to optimally adjust the relevant coefficients so that a given target solution could be attainable. In case the target is unattainable, we may either utilize the bisection method or the fuzzy linear programming techniques to revise the target as to make it a reachable one. Finally, we utilize multiple criteria and multiple constraint levels linear programming (MC2LP) model and its extended techniques to explore the linear programming models with changeable parameters. The parameters include: unit profit, available resources and input-output coefficients of production function. With those parameters changed with capital investment and/or time, we study how to find dynamic best solutions to make "taking loss at the ordering time and making profit at the time of delivery" feasible. For more general cases we also sketch a generalized mathematical programming model with changeable parameters and control variables.