Abstract
This paper considers a production-inventory problem in which the manufacturer participates in an energy buy-back program, which offers probabilistic opportunities with rewards for not using electricity. That is, the manufacturer will get paid for stopping production to save on electricity. The amount rewarded in a period will depend on the electricity market condition at that time. The market condition in any given period is represented by M + 1 states: normal (i.e., nonpeak), peak type 1,..., peak type M, and the reward amount in the period will be O, K 1,..., and K M, respectively. The occurrence of each state in a period is dictated by a known probability distribution. The objective is to determine from the manufacturer's perspective, whether to take such an offer when it arises. Under a mild assumption, we show that in the normal market condition, the production decision is partly a base-stock policy, whereas under peak type m condition, the manufacturer, upon accepting the offer, produces according to an (sm, S) policy, where m = 1,..., M. The numerical experiment demonstrates that the cost savings due to buy-backs can be substantial. It also shows that the always-participating strategy (i.e., the firm shuts down production whenever the buy-back program is activated) can perform much worse than the never-participating strategy. © 2007 IEEE.
| Original language | English |
|---|---|
| Article number | 4266808 |
| Pages (from-to) | 395-406 |
| Journal | IEEE Transactions on Automation Science and Engineering |
| Volume | 4 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - Jul 2007 |
| Externally published | Yes |
Funding
This work was supported in part by HK RGC under Grant 411105, in part by a Distinguished Young Investigator Grant from the National Natural Sciences Foundation of China, and in part by a Grant from the Hundred Talents Program of the Chinese Academy of Sciences.
Research Keywords
- (s, S) policy
- Deregulation in the electricity market
- Dynamic programming
- Production-inventory model
RGC Funding Information
- RGC-funded
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