This paper suggested improvement of the searching ability in genetic algorithm on job-shop scheduling with batch splitting. Batch splitting reduces make-span because similar batches which occur by splitting can be in processes simultaneously. However, the number of batches increases as a result of splitting the original batches. It may complicate scheduling problems. So, this paper proposed genetic algorithm on job shop scheduling considered influence by batch splitting. The algorithm generates either of an active schedule or a non-delay schedule according to a gene. It has an influence good for the searching ability because the generating ratio changes dynamically by selection. Numerical experiments show the characteristics of the proposed algorithm.

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The following ranking problem is considered.
(1) There exist m candidates to be ranked with respect to s criteria.
(2) Each candidate has s characteristics and each one corresponds to each criterion. Based on the value of the characteristics, candidates are ranked with respect to the corresponding criterion. But this ranking may change due to the change of value of the characteristics. This change occurs according to the some probability. Therefore total change with respect to all criteria occurs with scenarios.
(3) Our theme is to make the total ranking among candidates considering not only the importance of criteria but the total change of ranking about candidates with respect to all criteria. That is, how to make a consensus formation is the main problem.
First for the fixed scenario case, that is, the fixed preference matrices, we define the preference matrix of candidates with respect to each criterion and distance measure of Cook [W.D.Cook. EJOR 172 (2006) 369-385] based on the matrix. Next we extend the Cook distance measure toward the weighted distance measure and then formulate the equivalent assignment problem. The third we solve the assignment problem and based on the optimal assignment, that is, optimal ranking of candidate is found. The fourth we struggle to aggregate the result of each scenario case and make the final suitable ranking considering the risk. We also show our procedure can apply other ranking model, especially the case that preference matrix includes ambiguity in place of randomness. Finally we conclude our results and discuss the further research problem including the efficient solution method for the assignment problem corresponding to the extended weighted distance.

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Business Continuity Management (BCM) aims to enable on an organization to continue to business operations, even when it is afflicted by untoward situations, such as earthquake. In BCM, the organization is required to implement Chronologic Impact Analysis (CIA) as a part of Business Impact Analysis (BIA). In CIA, the organization must divide the period after business disruption into intervals and analyze the impact of the disruption or the decline of operational capability within each interval. In most cases, the period-dividing method in CIA is implemented using the “Irregular Incremental Method (IIM)” (named by the author). The IIM has a demerit that often makes execution of CIA difficult. This study proposes an algorithm to improve this difficulty. The algorithm uses Current Recoverable Time (CRT) as the input data, and cluster analysis to develop the period intervals. In addition, a case study is implemented to prove the efficiency of this method.

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In recent years, almost every manufacturing site has been supported by a lot of part-time, temporary, or mid-career personnel, given the poor state of the economies all over the world. However, expert managers of front-line workers have to design more complex methods of production planning, when urgent orders due to uncertain elements like a disaster are taken into consideration. Therefore, this paper proposes a sustainable production planning model using an inference methodology. The method was suggested by Mahadevan et al. in the field of architecture, and some cases of its effectiveness have been shown. In this paper, we try to incorporate that method into our sustainable production planning model. First, a work element that overflows into another process is assumed to be a “Dummy Moving Element (DME)”. Next, DME estimation analysis and improvement using Bayesian estimation are discussed. Finally, the effectiveness of our model is verified by a numerical experiment.

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This paper focuses on consumer preference between time and air fare for Korean customers for short haul flights. The main focus is on the preferences of customers based on what air fare they would pay depending on departure time of flights. The growing complexity of consumer choice in fare, time and service as a result of liberalization in the airline industry has driven a revaluation of the traditional market segment. The new competitive prices have thrown out a challenge to the airlines to find innovative ways for attracting passengers with a mix fare discount, greater frequency and level of service. This paper focus on consumer preference to buy one airline offer over another and what consumers really value? In short, how much does it affect consumer choice while preferring one airlines departure time and fare over another airline? Using conjoint analysis, we found that fare differentiation depending on the departure time may be effective to generate more revenues in airline markets.

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