Sachin Jayaswal

Efficient algorithm for cell formation with sequence data, machine replications and alternative process routings

Cell formation is an important problem in the design of a cellular manufacturing system. Despite a large number of papers on cell formation being published, only a handful incorporate operation sequence in intercell move calculations and consider alternative process routings, cell size, production volume and allocating units of identical machines into different cells. Modelling the above factors makes the cell formation problem complex but more realistic. The paper develops a model and solution methodology for a problem of cell formation to minimize the sum of costs of intercell moves, machine investment and machine operating costs considering all the factors mentioned above. An algorithm comprised of simulated annealing and local search heuristics has been developed to solve the model. A limited comparison of the proposed algorithm with an optimal solution generated by complete enumeration of small problems indicates that the algorithm produces a solution of excellent quality. Large problems with 100 parts and 50 machine types are efficiently solved using the algorithm.

Product differentiation and operations strategy in a capacitated environment

We study a firm selling two products/services, which are differentiated solely in their prices and delivery times, to two different customer segments in a capacitated environment. From a demand perspective, when both products are available to all customers, they act as substitutes, affecting each others demand. Customized products for each segment, on the other hand, result in independent demand for each product. From a supply perspective, the firm may either share the same capacity or may dedicate a different capacity for each segment. Our objective is to understand the interaction between product substitution and the firms operations strategy (dedicated versus shared capacity), and how this interaction shapes the optimal product differentiation strategy. We show that in a highly capacitated system, if the firm decides to move from a dedicated to a shared capacity setting, it will need to offer more differentiated products, whether the products are substitutable or not. In contrast, when independent products become substitutable, it results in a more homogeneous pricing scheme. Moreover, the optimal response to an increase in capacity cost also depends on the firms operations strategy. In a dedicated capacity scenario, the optimal response is always to offer more homogeneous prices and delivery times. In a shared capacity setting, it is always optimal to quote more homogeneous delivery times, but to increase or decrease the price differentiation depending on whether the status-quo capacity cost is high or low, respectively.

A new formulation and Benders decomposition for the multi-period maximal covering facility location problem with server uncertainty

Facility location problems reported in the literature generally assume the problem parameter values (like cost, budget, etc.) to be known with complete certainty, even if they change over time (as in multi-period versions). However, in reality, there may be some uncertainty about the exact values of these parameters. Specifically, in the context of locating primary health centers (PHCs) in developing countries, there is generally a high level of uncertainty in the availability of servers (doctors) joining the facilities in different time periods. For transparency and efficient assignment of the doctors to PHCs, it is desirable to decide the facility opening sequence (assigning doctors to unmanned PHCs) at the start of the planning horizon. We present a new formulation for a multi-period maximal covering location problem with server uncertainty. We further demonstrate the superiority of our proposed formulation over the only other formulation reported in the literature. For instances of practical size, we provide a Benders decomposition based solution method, along with several refinements. For instances that the CPLEX MIP solver could solve within a time limit of 20 hours, our proposed solution method turns out to be of the order of 150–250 times faster for the problems with complete coverage, and around 1000 times faster for gradual coverage.

Price and lead time differentiation, capacity strategy and market competition

We study a duopoly market in which customers are heterogeneous, and can be segmented as price or time sensitive. Each firm tailors (differentiates) its products/services for the two customer classes solely based on guaranteed lead time and the corresponding price. Our objective is to understand how competition affects price and lead time differentiation of the firms in the presence of different operations strategy (shared versus dedicated capacity), product substitution and asymmetry between the competing firms. Our results suggest that when firms use dedicated resources to serve the two market segments, pure price competition always tends to decrease individual prices as well as price differentiation, irrespective of the market behaviour. Further, the effect of competition is more pronounced when customers are allowed to self-select, thereby introducing substitutability between the two product options. On the other hand, when firms compete in time, in addition to price, the effect of competition on product differentiation depends crucially on the behaviour of the market. Our results further suggest that the firm with a larger market base should always maintain a larger price and lead time differentiation between the two market segments. Similarly, the firm with a capacity cost advantage should also maintain a larger lead time differentiation.

Bandwidth packing problem with queueing delays: modelling and exact solution approach

We present a more generalized model for the bandwidth packing problem with queuing delays under congestion than available in the extant literature. The problem, under Poison call arrivals and general service times, is set up as a network of spatially distributed independent M/G/1 queues. We further present two exact solution approaches to solve the resulting nonlinear integer programming model. The first method, called finite linearization method, is a conventional Big-M based linearization, resulting in a finite number of constraints, and hence can be solved using an off-the-shelve MIP solver. The second method, called constraint generation method, is based on approximating the non-linear delay terms using supporting hyperplanes, which are generated as needed. Based on our computational study, the constraint generation method outperforms the finite linearization method. Further comparisons of results of our proposed constraint generation method with the Lagrangean relaxation based solution method reported in the literature for the special case of exponential service times clearly demonstrate that our approach outperforms the latter, both in terms of the quality of solution and computation times.

Facility location under service level constraints for heterogeneous customers

We study the problem of locating service facilities to serve heterogeneous customers. Customers requiring service are classified as either high priority or low priority, where high priority customers are always served on a priority basis. The problem is to optimally locate service facilities and allocate their service zones to satisfy the following coverage and service level constraints: (1) each demand zone is served by a service facility within a given coverage radius; (2) at least

A cutting plane approach to combinatorial bandwidth packing problem with queuing delays

\alpha ^h

Balancing U-Shaped Assembly Lines with Resource Dependent Task Times: A Simulated Annealing Approach

αh proportion of the high priority customers at any service facility should be served without waiting; (3) at least