Subir Bhattacharya

An exact depth-first algorithm for the pallet loading problem

Abstract This paper proposes a fast exact algorithm to solve the Pallet Loading Problem (PLP) using depth-first strategy. A new concept called Maximal Breadth Filling Sequence (MBFS) is introduced to bring down the size of the search tree. The algorithm makes use of two pruning rules — lower-bound pruning and state-dominance pruning. Although depth-first search, by itself, requires very little memory, the dominance pruning rule makes effective utilization of the available memory. For large problems, more the memory available, more effective is the dominance pruning. The algorithm has been tested on standard problem sets. It has been found to be quite fast in outputting optimal solutions. Empirical findings are given in detail.

The multiple resource constrained project scheduling problem: A breadth-first approach

We describe a simple breadth-first tree search scheme for minimizing the makespan of a project consisting of a partially ordered network of activities under multiple resource constraints. The method compares quite favourably with existing techniques that employ depth-first or best-first search; in particular, it is able to solve optimally on a Pentium PC running SCO UNIX the entire set of 680 benchmark problems (First Lot: 480, Second Lot: 200) generated by Kolisch et al., 1995. The new algorithm has also been checked out experimentally on additional random test problems at graded levels of difficulty that were generated using two parameters: the threshold, which determined the predecessors of an activity, and the total resource availability of each resource type. The breadth-first scheme can be modified quite readily to do best-first search or to minimize measures other than makespan such as mean flow time and maximum tardiness.

A faster alternative to SSS * with extension to variable memory

The two best known and most frequently referenced minimax search methods for game trees are Alpha-Beta [2] and SSS* [6]. The algorithms are quite dissimilar in structure and properties. Alpha-Beta is a depth-first recursive procedure that needs little memory to execute. Although it evaluates more terminal nodes than SSS*, it generally runs considerably faster owing to its low overhead. In contrast, SSS* is a non-recursive procedure similar in many ways to the best-first search algorithm A*. Although SSS* is superior to Alpha-Beta in number of terminals evaluated, SSS* is seldom used in practice. This is because the improved pruning power of SSS” is more than offset by (see [4]) (a> the enormous storage requirement for the global OPEN list, which is brd/‘l units for a (6, d) uniform tree; (b) the overhead of maintaining OPEN sorted on h-values; (c) the overhead of occasionally purging nodes from OPEN belonging to provably suboptimal solution trees.

A State Task Network model for scheduling operations in cascaded continuous processing units

Abstract The paper proposes a mathematical model based on State Task Network representation for generating optimal schedule for a sequence of n continuous processing units responsible for processing m products. Each product line has fixed capacity storage tanks before and after every unit. Each unit can process only one product line at any point of time. However, the inputs for all the product lines arrive simultaneously at the input side of the first unit, thus giving rise to the possibility of spillage. There can be multiple intermediate upliftments of the finished products. An optimal schedule of the units attempts to balance among spillage, changeover and upliftment failures. The paper develops an MINLP model for the problem. The model has been linearized using standard linearization techniques and the resulting MILP model can output, in reasonable time, optimal schedule for 2 weeks for a 3-unit 3-product scenario encountered by the authors.

Impact of Requirements Discovery Pattern on Software Project Outcome: Preliminary Results

Requirements discovery during project development is known to be the most critical risk in any software project, and managing this is paramount to success in software development. Requirements can change in various ways during the course of a project, and the effect of change on the outcome can differ widely. In this paper we study the effect of different patterns of requirements discovery on a software project. Using a validated model of software process we show that the effect on the total effort, the completion time and the workforce deployment patterns can be counter intuitive as different patterns of change impact software project dynamics in different ways. The insight into the relationship between requirements discovery pattern and project outcome can help managers decide appropriate risk mitigation policy and workforce augmentation plan.

Cooperative Search Using Agents for Cardinality Constrained Portfolio Selection Problem

This paper presents an agent-based model to select an investment portfolio with a restriction on the number of stocks in it. Daily movements of all the stocks in the market for the past few years are assumed to be available. The scheme deploys a federally structured consortium of agents in the stock market at the start of the historical period. Each agent starts with a pseudorandom portfolio and follows individual investment strategies as it walks through the past data. The agents are designed to emulate some of the characteristics of human investors-adjusting the weights of the stocks based on its own attitude toward risk, occasionally dropping and adding stocks to the portfolio, etc. Periodically, the agents share information about their performances and can switch portfolios. A final cardinality constrained portfolio is constructed by consolidating individual portfolios arrived at by the agents working on the historical data of the stocks. When tested in real markets of the U.K. and Japan, the model suggested portfolios that were quite competitive to, and frequently better than, the portfolios suggested by the mean-variance models.

Towards a Framework for Assessing Agility

As software houses align themselves to serve clients operating in rapidly changing business environments, they start putting more emphasis on increasing their agility. However as a precursor to this, there is a need within an organization to measure and monitor its agility. The clients also would be interested to know the level of agility of their vendors. While the need for an effective metric is well established, the subjective nature of the parameters involved, and the need for some degree of generality makes the task of assessing agility a difficult one. This paper is an attempt to arrive at a quantifiable, scalable framework to measure the same. Apart from giving a framework which can be used to assess the agility of a company, this paper indicates how a detailed sensitivity analysis can be done with respect to the parameters that define agility.

Mathematical model for scheduling operations in cascaded continuous processing units

The scenario under consideration involves n cascaded continuous processing units responsible for processing m product lines. Each product line needs to be processed by all the units in the same sequence, and has dedicated finite capacity storage tanks before and after every processing unit. A unit can process only one product line at a time. Inputs for all the product lines arrive continuously and simultaneously on the input side of the first unit in the sequence. There are multiple intermediate due dates for the final products. An optimal schedule for the units calls for a trade-off among spillage costs, upliftment failure penalties and changeover costs. A mathematical model is developed for the purpose and the resulting MINLP is linearized using standard techniques. The MILP has been tested using GAMS for three units and three product lines as encountered in a refinery situation. The model could output optimal schedules for a ten day scheduling horizon within reasonable time.

A two pass heuristic algorithm for scheduling ‘blocked out’ units in continuous process industry

Abstract This paper addresses the problem of scheduling cascaded ‘blocked out’ continuous processing units separated by finite capacity storage tanks. The raw materials for the product lines arrive simultaneously on the input side of the first unit. But every unit can process only one product line at a time, thus giving rise to the possibility of spillage of raw material due to limited storage capacity. The need to process multiple product lines and the added constraint of multiple intermediate upliftment dates aggravate the problem. This problem is quite common in petrochemical industry. The paper provides a MINLP (Mixed Integer Non-Linear Programming) formulation of the problem. However, for any realistic scheduling horizon, the size of the problem is too large to be solved by standard packages. We have proposed a depth first branch and bound algorithm, guided by heuristics, to help planners in tackling the problem. The suggested algorithm could output near optimal solutions for scheduling horizons of 30 time periods when applied to real life situations involving 3 units and 3 product lines.

Quo vadis, SaaS a system dynamics model based enquiry into the SaaS industry

SaaS has quickly become a mainstream business phenomenon by delivering a software product as a service. In this study, we examine the system dynamics of the SaaS business model in light of the drivers of growth, the various types of SaaS applications and the various entities in the SaaS value chain. A modeling approach is followed to determine the effects of various drivers of growth in SaaS business model. In addition, our model tries to determine the relative power of the entities in the SaaS value chain under various scenarios chain and attempts to determine when and how bandwagon effects occur in this industry and isolate the factors that contribute to growth or decline of various types of application revenues. The model is also a means to identify the stakeholders in the value network who gain maximum value under different conditions. Our results suggest that the SaaS industry would fall victim to a stronger substitute business model unless SaaS can move more of its resources to work on applications which are more critical and customized to the needs of a client.