Options
C Rajendran

A performance analysis of dispatching rules and a heuristic in static flowshops with missing operations of jobs
16-06-2001, C Rajendran, Ziegler, Hans
An experimental investigation of the performance of dispatching rules and a heuristic for scheduling in static flowshops with missing operations is undertaken in this study. The measure of performance is the minimization of total flow time of jobs. Permutation schedules are generated by using the heuristic for scheduling. General schedules, which can be permutation or non-permutation schedules, are obtained by using dispatching rules. Four dispatching rules, including a new dispatching rule, are considered. Two types of flowshops are studied: one with no missing operations of jobs and another with missing operations of jobs. In the latter type of flowshops, jobs with varying number of missing operations are considered. An extensive investigation of the performance of the dispatching rules and the heuristic is carried out. It is observed that the heuristic minimizes total flow time of jobs more than dispatching rules up to a certain level of missing operations of jobs in flowshops, after which dispatching rules perform better. The performance of the heuristic and the dispatching rules in terms of minimizing the makespan as a secondary measure is also reported. © 2001 Elsevier Science B.V.

Ant-colony algorithms for permutation flowshop scheduling to minimize makespan/total flowtime of jobs
01-06-2004, C Rajendran, Ziegler, Hans
The problem of scheduling in permutation flowshops is considered with the objective of minimizing the makespan, followed by the consideration of minimization of total flowtime of jobs. Two ant-colony optimization algorithms are proposed and analyzed for solving the permutation flowshop scheduling problem. The first algorithm extends the ideas of the ant-colony algorithm by Stuetzle [Proceedings of the 6th European Congress on Intelligent Techniques and Soft Computing (EUFIT '98), vol. 3, Verlag Mainz, Aachen, Germany, 1998, p. 1560], called max-min ant system (MMAS), by incorporating the summation rule suggested by Merkle and Middendorf [Proceedings of the EvoWorkshops 2000, Lecture Notes in Computer Science No. 1803, Springer-Verlag, Berlin, 2000, p. 287] and a newly proposed local search technique. The second ant-colony algorithm is newly developed. The proposed ant-colony algorithms have been applied to 90 benchmark problems taken from Taillard [European Journal of Operational Research 64 (1993) 278]. First, a comparison of the solutions yielded by the MMAS and the two ant-colony algorithms developed in this paper, with the heuristic solutions given by Taillard [European Journal of Operational Research 64 (1993) 278] is undertaken with respect to the minimization of makespan. The comparison shows that the two proposed ant-colony algorithms perform better, on an average, than the MMAS. Subsequently, by considering the objective of minimizing the total flowtime of jobs, a comparison of solutions yielded by the proposed ant-colony algorithms with the best heuristic solutions known for the benchmark problems, as published in an extensive study by Liu and Reeves [European Journal of Operational Research 132 (2001) 439], is carried out. The comparison shows that the proposed ant-colony algorithms are clearly superior to the heuristics analyzed by Liu and Reeves. For 83 out of 90 problems considered, better solutions have been found by the two proposed ant-colony algorithms, as compared to the solutions reported by Liu and Reeves. © 2003 Elsevier B.V. All rights reserved.

Heuristics for scheduling in a flowshop with setup, processing and removal times separated
01-01-1997, C Rajendran, Ziegler, Hans
The problem of scheduling in a flowshop, where setup, processing and removal times are separable, is considered with the objective of minimizing makespan. Heuristic algorithms are developed by the introduction of simplifying assumptions into the scheduling problem under study. An improvement method is incorporated in the heuristics to enhance the quality of their solutions. The proposed heuristics and an existing heuristic are evaluated by a large number of randomly generated problems. The results of an extensive computational investigation for various values of parameters are presented. © 1997 Taylor & Francis Ltd.

An ant colony algorithm for scheduling in flowshops with sequence-dependent setup times of jobs
01-09-2006, Gajpal, Yuvraj, C Rajendran, Ziegler, Hans
The problem of scheduling in flowshops with sequence-dependent setup times of jobs is considered and solved by making use of ant colony optimization (ACO) algorithms. ACO is an algorithmic approach, inspired by the foraging behavior of real ants, that can be applied to the solution of combinatorial optimization problems. A new ant colony algorithm has been developed in this paper to solve the flowshop scheduling problem with the consideration of sequence-dependent setup times of jobs. The objective is to minimize the makespan. Artificial ants are used to construct solutions for flowshop scheduling problems, and the solutions are subsequently improved by a local search procedure. An existing ant colony algorithm and the proposed ant colony algorithm were compared with two existing heuristics. It was found after extensive computational investigation that the proposed ant colony algorithm gives promising and better results, as compared to those solutions given by the existing ant colony algorithm and the existing heuristics, for the flowshop scheduling problem under study.

Scheduling to minimize the sum of weighted flowtime and weighted tardiness of jobs in a flowshop with sequence-dependent setup times
16-09-2003, C Rajendran, Ziegler, Hans
Efficient heuristics for scheduling jobs in a static flowshop with sequence-dependent setup times of jobs are presented in this paper. The objective is to minimize the sum of weighted flowtime and weighted tardiness of jobs. Two heuristic preference relations are used to construct a good heuristic permutation sequence of jobs. Thereafter, an improvement scheme is implemented, once and twice, on the heuristic sequence to enhance the quality of the solution. An existing heuristic, a random search procedure and a greedy local search are used as benchmark methods for relatively evaluating the proposed heuristics. An extensive performance analysis has shown that the proposed heuristics are computationally faster and more effective in yielding solutions of better quality than the benchmark procedures. © 2002 Elsevier Science B.V. All rights reserved.

Minimum cost berth allocation problem in maritime logistics: new mixed integer programming models
01-06-2019, Jos, Bobin Cherian, Harimanikandan, M., Rajendran, Chandrasekharan, Ziegler, Hans
The berth allocation problem (BAP) involves decisions on how to allocate the berth space and to sequence maritime vessels that are to be loaded and unloaded at a container terminal involved in the maritime logistics. As the berth is a critical resource in a container terminal, an effective use of it is highly essential to have efficient berthing and servicing of vessels, and to optimize the associated costs. This study focuses on the minimum cost berth allocation problem (MCBAP) at a container terminal where the maritime vessels arrive dynamically. The objective comprises the waiting time penalty, tardiness penalty, handling cost and benefit of early service completion of vessels. This paper proposes three computationally efficient mixed integer linear programming (MILP) models for the MCBAP. Through numerical experiments, the proposed MILP models are compared to an existing model in the literature to evaluate their computational performance. The computational study with problem instances of various problem characteristics demonstrates the computational efficiency of the proposed models.

A comparative study of periodic-review order-up-to (T, S) policy and continuous-review (s, S) policy in a serial supply chain over a finite planning horizon
01-07-2014, Sethupathi, P. V.Rajendra, C Rajendran, Ziegler, Hans
In this paper, we consider a serial supply chain (SC) operating with deterministic and known customer demands and costs of review or orders, holding, and backlog at every installation over a finite planning horizon. We present an evaluation of two order policies: Periodic-review order-up-to S policy (i.e., (T, S) policy), and (s, S) policy. We first present a mathematical programming model to determine optimal re-order point and base-stock for every member in the SC. By virtue of the computational complexity associated with the mathematical model, we present genetic algorithms (GAs) to determine the order policy parameters, s and S for every stage. We compare the performances of GAs (for obtaining installation s and S) with the mathematical model for the periodic-review order-up-to (T, S) policy that obtains in its class optimal review periods and order-up-to levels. It is observed that the (s, S) policy emerges to be mostly better than the (T, S) policy.

An ant-colony algorithm to transform jobshops into flowshops: A case of shortest-common-supersequence stringology problem
06-09-2012, Rajendran, Suchithra, C Rajendran, Ziegler, Hans
In this work we address the problem of transforming a jobshop layout into a flowshop layout with the objective of minimizing the length of the resulting flowline. This problem is a special case of the well-known classical Shortest Common Supersequence (SCS) stringology problem. In view of the problem being NP-hard, an ant-colony algorithm, called PACO-SFR, is proposed. A new scheme of forming an initial supersequence of machines (i.e., flowline) is derived from a permutation of jobs, followed by the reduction in the length of the flowline by using a concatenation of forward reduction and inverse reduction techniques, machine elimination technique and finally an adjacent pair-wise interchange of machines in the flowline. The proposed ant-colony algorithm's performance is relatively evaluated against the best known results from the existing methods by considering many benchmark jobshop scheduling problem instances. © 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering.

The value of information sharing in a serial supply chain with AR(1) demand and non-zero replenishment lead times
01-12-2016, Sabitha, Devarajulu, C Rajendran, Kalpakam, S., Ziegler, Hans
This paper analyzes the value of information sharing, in terms of reduction in the demand variance and average (on-hand) inventory level, in a single product multi-stage (i.e., serial) supply chain with non-zero replenishment lead times. An order-one auto-regressive, AR(1), process characterizes the customer demand. We quantify the reduction in the demand variance and average (on-hand) inventory level in a multi-stage supply chain considering two information sharing scenarios: (1) supply-chain-wide information sharing; and (2) Vendor-Managed Inventory (VMI). In contrast to the related existing literature on a three-stage supply chain with non-zero replenishment lead times, we prove for a multi-stage supply chain that there exists no difference, in terms of the expectation and the variance of total demand over lead time, between supply-chain-wide information sharing and VMI. When there is an instantaneous replenishment across all firms, our analytical results are in agreement with the existing literature on a multi-stage supply chain. Further, we show that the value of information sharing is always greater than or equal to those obtained by an existing study. We also observe that the variance of total demand over lead time is reflected in the variance of the inventory level, derived using inventory balance equation. Furthermore, we carry out a comparative study with respect to the benefits of information sharing under three different supply chain settings, and find that the value of information sharing is more for upstream firms, when demand correlation over a period is high or when lead times are high or both.

A multi-objective ant-colony algorithm for permutation flowshop scheduling to minimize the makespan and total flowtime of jobs
01-12-2009, C Rajendran, Ziegler, Hans
The problem of scheduling in permutation flowshops is considered with the objectives of minimizing the makespan and total flowtime of jobs. A multi-objective ant-colony algorithm (MOACA) is proposed. The salient features of the proposed multi-objective ant-colony algorithm include the consideration of two ants (corresponding to the number of objectives considered) that make use of the same pheromone values in a given iteration; use of a compromise objective function that incorporates a heuristic solution's makespan and total flowtime of jobs as well as an up per bound on the makespan and an upper bound on total flowtime of jobs, coupled with weights that vary uniformly in the range [0, 1]; increase in pheromone intensity of trails by reckoning with the best solution with respect to the compromise objective function; and updating of pheromone trail intensities being done only when the ant-sequence's compromise objective function value is within a dynamically updated threshold level with respect to the best-known compromise objective function value obtained in the search process. In addition, every generated ant sequence is subjected to a concatenation of improvement schemes that act as local search schemes so that the resultant compromise objective function is improved upon. A sequence generated in the course of the ant-search process is con sidered for updating the set of heuristically non-dominated solutions. We consider the benchmark flowshop scheduling problems proposed by Taillard (1993), and solve them by using twenty variants of the MOACA. These variants of the MOACA are obtained by varying the values of parameters in the MOACA and also by changing the concatenation of improvement schemes. In order to benchmark the proposed MOACA, we rely on two recent research reports: one by Minella et al. (2008) that re ported an extensive computational evaluation of more than twenty existing multi-objective algorithms available up to 2007; and a study by Framinan and Leisten (2007) involving a multi-objective iterated greedy search algorithm, called MOIGS, for flowshop scheduling. The work by Minella concluded that the multi-objective simulated annealing algorithm by Varadharajan and Rajendran (2005), called MOSA, is the best performing multi-objective algorithm for permutation flowshop scheduling. Framinan and Leisten found that their MOIGS performed better than the MOSA in terms of generating more heuristically non-dominated solutions. They also obtained a set of heuristically non-dominated solutions for every benchmark problem instance provided by Taillard (1993) by consolidating the solutions obtained by them and the solutions reported by Varadharajan and Rajendran. This set of heuristically non-dominated solutions (for every problem instance, up to 100 jobs, of Taillard's benchmark flowshop scheduling problems) forms the reference or benchmark for the present study. By considering this set of heuristically non-dominated solutions with the solutions given by the twenty variants of the MOACA, we form the net heuristically non-dominated solutions. It is found that most of the non-dominated solutions on the net non-dominated front are yielded by the variants of the MOACA, and that in most problem instances (especially in problem instances exceeding 20 jobs), the variants of the MOACA con tribute more solutions to the net non-dominated front than the corresponding solutions evolved as benchmark solutions by Framinan and Leisten, thereby proving the effectiveness of the MOACA. We also pro vide the complete set of heuristically non-dominated solutions for the ninety problem instances of Taillard (by consolidating the solutions obtained by us and the solutions obtained by Framinan and Leisten) so that researchers can use them as benchmarks for such research attempts. © 2009 Springer-Verlag Berlin Heidelberg.