Optimal electric energy subscription policy for multiple plants with undertain demand | |
| Author | Puvarin Nilrangsee |
| Call Number | AIT Diss. no.ISE-09-05 |
| Subject(s) | Electricity--Planning Electric power distribution |
| Note | A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Engineering Industrial System Engineering and Management, School of Engineering and Technology |
| Publisher | Asian Institute of Technology |
| Series Statement | Dissertation ; no. ISE-09-05 |
| Abstract | The research proposes a new optimization model to generate aggregate production planning by considering electricity cost. The new Time Of Switching (TOS) electricity type is introduced by switching over Time Of Day (TOD) and Time Of Use (TOU) to minimize the electricity cost. The customer de mand is uncertainty in the real world; the triangular fuzzy demand is used to cover the fluctuation of customer demand. The new Dynamic inventory model is proposed to adjust level of ending inventory from lower bound by considering plant capacity during time of lower cost of electricity. The constraint for minimum hour limitation of plant running per one start up event is introduced to minimize plants idle time. Furthermore the new model of Optimal Weight Moving Average Factor (OWMAF) is proposed for customer demand forecasting to reduce the forecasting error. Model application is illustrated for multiple cement mill plants, multiple cement product types, and multiple electricity types. The mathematical model was solved by two methods, Simplex Method (SM) with Excel Spreadsheet Solver (ESS) tool and Genetic Algorithm (GA). In SM method, the mathematical model was formulated in excel spreadsheet format. Then the spreadsheet linear solver technique was used as a tool to solve the model. A simulation running on part of the system in a test for six months shows the optimal solution could save more than 60% of the actual electricity cost. For convenience of user interfacing the GA programming is encoded by using Microsoft Visual Basic and Excel. A simulation running on part of the system in a test for six months shows the optimal solution could save more than 60% of the actual electricity cost. The SM algorithm is exact and we apply it to 7 plants, 3 types of cement, 2 types of electricity, and 24 hours planning horizon with reasonable response. However for more plants, cement types, electricity types, and time slots we get fast solution times by GA and no more solution by ESS. The dissertation contributes to several fields such as “Production Planning”, “Optimal Electricity use”, “Fuzzy Theory Application”, and “Genetic Algorithm Application” |
| Year | 2009 |
| Corresponding Series Added Entry | Asian Institute of Technology. Dissertation ; no. ISE-09-05 |
| Type | Dissertation |
| School | School of Engineering and Technology (SET) |
| Department | Department of Industrial Systems Engineering (DISE) |
| Academic Program/FoS | Industrial Systems Engineering (ISE) |
| Chairperson(s) | Bohez, Erik L. J.; |
| Examination Committee(s) | Manukid Parnichkun ;Weerakorn Ongsakul; |
| Degree | Thesis (Ph. D.) - Asian Institute of Technology, 2009 |