Prakash G. Awate

Short term portfolio optimization for load serving entity considering multiple exchanges and ramping constraints

The typical requirement of supply-demand balance in electricity market necessitates for an LSE to incorporate corrective measures in short term. In case of surplus, either few of the contracts may have to be backed down and/or excess power has to be sold in short term market(s). In contrast, deficit scenario has to be managed by procuring additional requirement again from short term market(s). The volatility, and hence risk, associated with prices in such market motivates LSEs to look for an optimal strategy based on portfolio management. While doing so, in addition to conventional approach, one has to comply by regulations as well as factor for technical constraints. In this paper, we model prevention of arbitrage and ramping constraints akin to unit commitment problem.

Optimizing loading policies at a bottleneck facility in an assembly line with random rejects

Abstract This work relates to an actual loading problem at a bottleneck facility in a large refrigerator manufacturing plant. Items are loaded in specific combinations called phases which then move along different flow lines encountering random rejections till assembly. To control shortages and inventory holdings periodically, a heuristic convex program is formulated with two forms of loss function: (a) piecewise linear and (b) quadratic. For all problem versions, the items’ buffer state space admitted partitioning into polyhedral regions, with a particular form of loading policy being optimal in each region. The shapes of these regions depend on the loss function, leading to certain constraints on the parameters in the loss function. In a related paper (presented at the XXIX ORSE Convention, IIT-Bombay, Mumbai, December), numerous modifications to the policies derived here take into account the actual time-varying behaviour in several elements of the system.

Analysis of propagation of stochastic errors through an MRP system, and an industrial case study

This work arose out of a case study in production planning in a switchgear factory. The aspect considered here concerns variability of load on a component manufacturing shop which happened to be a brazing shop. The component shop manager wanted to know what variability in his shop load could be attributed to propagation of sales forecasting errors, assembly production errors and component production errors, through the Materials Requirements Planning system which was the basic mechanism loading his shop. Knowing this would help him to determine whether some of the load variability faced by his shop was due to any other causes in the production planning system; such as manual adjusting of loads to match shop capacities, subjective decisions on lot sizing, lack of coordination between production planning and materials procurement etc. Primarily our interest here is in showing how exactly deviations in stocks and in monthly requirements in the relevant MRP system can be represented as moving sums of the basic monthly stochastic errors impinging on the system at all levels. In obtaining such a linear representation of the system, capacity limitations and stock-outs are not considered; i.e. capacities and base stocks are assumed sufficiently large. As the production shop made a large number of components, with essentially independent demands, it was assumed that the requirement modifications on account of minor capacity problems would not overwhelm the basic effects of propagation of errors through the MRP system. A very limited amount of data was available for the case study. However, it did indicate scope for improvement. Towards the end we indicate this briefly.

An extension of modified-operational-due-date priority rule incorporating job waiting times and application to assembly job shop

The well-known priority dispatching rule MOD (Modified Operational Due Date) in job shop scheduling considers job urgency through ODD (Operational Due Date) and also incorporates SPT (Shortest Processing Time)-effect in prioritising operationally late jobs; leading to robust behaviour in Mean Tardiness (MT) with respect to tightness/looseness of due dates. In the present paper, we study an extension of the MOD rule using job-waiting-time based discrimination among operationally late jobs to protect long jobs from excessive delays by incorporating an ‘acceleration property’ into the scheduling rule. Formally, we employ a weighted-SPT dispatching priority index of the form: (Processing time)/(Waiting time)α for operationally late jobs, while the priority index is ODD for operationally non-late jobs; and the latter class of jobs has a lower priority than the former class.In the context of Assembly Job Shop scheduling, some existing literature includes considerable focus around the concept of ‘Staging Delay’, i.e., waiting of components or sub-assemblies for their counterparts for assembly. Some existing approaches attempt dynamic anticipation of staging delay problems and re-prioritisation of operations along converging branches. In the present paper, rather than depending on such a centralised and largely backward scheduling approach, we consider a partially decentralised approach, endowing jobs with a priority index yielding an ‘acceleration property’ based on a ‘look-back’ in terms of waiting time, rather than ‘look-ahead’. For the particular case, in our proposed rule, whenα is set at zero and when all jobs at a machine are operationally late, our rule agrees with MOD as both exhibit the SPT effect.In simulation tests of our priority scheme for assembly job shops, in comparison with leading heuristics in literature, we found our rule to be particularly effective in: (1) minimising conditional mean tardiness, (2) minimising 99-percentile-point of the tardiness distribution, through proper choice ofα. We also exploit an interesting duality between the scheduling and queueing control versions of the problem. Based on this, some exact and heuristic analysis is given to guide the choice ofα, which is also supported by numerical evidence.

Cyclic machine loading, product allocation and lot sizing in an asbestos cement sheet factory

Abstract In an asbestos cement sheet factory a number of products can be produced on certain non-identical alternative machines. The cyclic planning problem considered deals with the case of constant product demand rates and the determination of (i) the fraction of demand of any item to be allocated to any machine, and (ii) a reorder interval between two batch productions of an item on a machine, restricted to 1, 2, or 4 months, so as to minimize the cost per unit time due to setups, inventories, and variable costs of production. Machine capacities are limited. This problem is partly akin to Caie and Maxwells hierarchical machine load planning in their first level of cyclic planning. However, our problem is not a particular case of theirs because we have to allow product splitting among machines, especially because the number of products in our case is not large. Consequently, our approach to the problem is quite different altogether in terms of both bounding and branching schemes in a branch and bound approach proposed in this paper. On the other hand, our approach concerns single stage production and does not deal with a production flow network as considered by Caie and Maxwell. Numerical results on real data are presented. This cyclic planning is helpful in determining the sequence of setup decisions over time on each machine in a four monthly cycle. The batch quantities can then be adjusted to take care of dynamic variations in demand to a reasonable extent, as in the previous work of Caie and Maxwell. The use of LP in this context can be valid to a certain extent, which is indicated.

Heuristic Algorithms for Dynamic Capacitated Lotsizing for Production Flow Networks with Setup Times

New heuristics are provided for the Multi Stage Capacitated Lot Sizing Problem (MSCLSP) with setup times for general convergent-divergent product structures. We attempt to find cost effective feasible schedules allowing end item backlogs as last resort, while strictly forbidding capacity violations and internal backlogs. The heuristics are of two types — A and B, differentiated by the order in which (resource, time period) cells are scheduled. Variants are obtained by different choices of strategies used in the subroutines of A and B. On Comparison with the most generally applicable one in the literature (Tempelmeier and Derstroff [17] — T&D), the heuristics developed here perform better. The multi-item shift strategies proposed by T&D prove to be very valuable components in our heuristics. Inferences based on the results obtained are also provided.

Despatching and Dynamic Machine Loading in a Wire Rope Factory: A Case Study

Scheduling of production in a wire rope factory is complicated by several features: (a) the simultaneous requirement for two types of limited resource, machines and bobbins; (b) multi‐stage production with normally two or three stranding and one or two closing operations; (c) queuing at the closing machines; the typical job splits into sub‐batches when passing from the stranding to the closing operation; these sub‐batches usually queue at the closing operations which, being faster than stranding operations, generally receive work from several queues; (d) alternative choices in the selection of machines and bobbin sizes for any given stranding or closing operation; (e) the presence of random elements in the timing of machine breakdowns and repairs. In this case study factory in a developing country, the existing control of production flows was ad hoc rather than according to a specified method. The management needed to know whether a scientific scheduling approach could significantly improve the low utilis...