Anuj Prakash

FMS scheduling with knowledge based genetic algorithm approach

In this paper a complex scheduling problem in flexible manufacturing system (FMS) has been addressed with a novel approach called knowledge based genetic algorithm (KBGA). The literature review indicates that meta-heuristics may be used for combinatorial decision-making problem in FMS and simple genetic algorithm (SGA) is one of the meta-heuristics that has attracted many researchers. This novel approach combines KB (which uses the power of tacit and implicit expert knowledge) and inherent quality of SGA for searching the optima simultaneously. In this novel approach, the knowledge has been used on four different stages of SGA: initialization, selection, crossover, and mutation. Two objective functions known as throughput and mean flow time, have been taken to measure the performance of the FMS. The usefulness of the algorithm has been measured on the basis of number of generations used for achieving better results than SGA. To show the efficacy of the proposed algorithm, a numerical example of scheduling data set has been tested. The KBGA was also tested on 10 different moderate size of data set to show its robustness for large sized problems involving flexibility (that offers multiple options) in FMS.

Psycho-Clonal algorithm based approach to solve continuous flow shop scheduling problem

This research presents a novel approach to solve m-machine no-wait flow shop problem. A continuous flow shop problem with total flow time as criterion is considered. This paper extends the artificial immune system (AIS) approach by proposing a new methodology termed as Psycho-Clonal algorithm. Proposed algorithm enjoys the flavor of AIS and Maslows need hierarchy theory to evolve a meta heuristic. Numerical simulation with small and large number of jobs with respect to error percentage is reported. The results obtained are compared with the other existing approaches. Numerical simulation has revealed that results obtained using proposed algorithm have significant improvement over others.

Inventory management in a lateral collaborative manufacturing supply chain : a simulation study

Due to global competition, firms are seeking more effective supply chain (SC) collaboration in order to provide quality products with less cost, at the right time and in the right quantity. The present study examines manufacturing SC collaboration on the basis of holding cost, backorder cost and ordering cost. The types of collaboration examined are vertical, horizontal and lateral collaboration. This research emphasises lateral collaboration by determining the impact of inventory policies ((s, S) and (s, Q) inventory policies) on SC performance. For better understanding, a conceptual model is provided that is supported by a numerical example. As the study of SCs is complex in nature, a simulation approach has been employed to show the impact of lateral collaboration on performance measures such as the total cost, which is the sum of several cost components: inventory holding cost, backorder cost and ordering cost. The research is based on two manufacturing SCs where the manufacturer is taken as the collaborative node. To allow more clarity, a separate study on each cost component has been conducted. The laterally collaborative SC was simulated on ARENA 9.0, a simulation package. The results show that the efficacy of lateral collaboration outperforms horizontal collaboration due to having the individual SC members at more liberty to make decisions.

Network optimization in supply chain: A KBGA approach

In this paper, we present a Knowledge Based Genetic Algorithm (KBGA) for the network optimization of Supply Chain (SC). The proposed algorithm integrates the knowledge base for generating the initial population, selecting the individuals for reproduction and reproducing new individuals. From the literature, it has been seen that simple genetic-algorithm-based heuristics for this problem lead to and large number of generations. This paper extends the simple genetic algorithm (SGA) and proposes a new methodology to handle a complex variety of variables in a typical SC problem. To achieve this aim, three new genetic operators-knowledge based: initialization, selection, crossover, and mutation are introduced. The methodology developed here helps to improve the performance of classical GA by obtaining the results in fewer generations. To show the efficacy of the algorithm, KBGA also tested on the numerical example which is taken from the literature. It has also been tested on more complex problems.

A hybrid Tabu sample-sort simulated annealing approach for solving distributed scheduling problem

The distributed scheduling problem has been considered as the allocation of a task to various machines in such a way that these machines are situated in different factories and these factories are geographically distributed. Therefore distributed scheduling has fulfilled various objectives, such as allocation of task to the factories and machines in such a manner that it can utilise the maximum resources. The objective of this paper is to minimise the makespan in each factory by considering the transportation time between the factories. In this paper, to address such a problem of scheduling in distributed manufacturing environment, a novel algorithm has been developed. The proposed algorithm gleans the ideas both from Tabu search and sample sort simulated annealing. A new algorithm known as hybrid Tabu sample-sort simulated annealing (HTSSA) has been developed and it has been tested on the numerical example. To reveal the supremacy of the proposed algorithm over simple SSA and Tabu search, more computational experiments have also been performed on 10 randomly generated datasets.

A biased random key genetic algorithm approach for inventory-based multi-item lot-sizing problem

In this article, we have explored multi-item capacitated lot-sizing problem by addressing the backlogging and associated high penalty costs incurred. At the same time, penalty cost for exceeding the resource capacity has also been taken into account. Penalty cost related to both backlogging and overutilizing capacity has been included in main objective function. The main objective is to achieve such a solution that minimizes the total cost. The ingredients of total cost are the setup cost, production cost, inventory holding cost and aforementioned both the penalty costs. To solve this computationally complex problem, a less explored algorithm “biased random key genetic algorithm” has been applied. To the best of our knowledge, this research presents the first application of biased random key genetic algorithm to a lot-sizing problem. To test the effectiveness of proposed algorithm, extensive computational tests are conducted. The encouraging results show that the proposed algorithm is an efficient tool to tackle such complex problems. A comparative study with other existing heuristics shows the supremacy of proposed algorithm in terms of quality of the solution, number of generation and computational time.

Supply Chain as a Flexible System: A KM Focused Competence

Supply chains are increasingly needed to work with core competencies of a flexible system enriched with Knowledge Management (KM). An integrated decision making amongst autonomous chain partners focused on suitable knowledge sharing is required to derive superior operational performance from flexibility based core competency in supply chains. Evolving competitive challenges demand a time based competition and an increasing variety of customer needs. In this context, Knowledge Management (KM) can be used as an effective approach to achieve Decision Knowledge Sharing (DKS) leading to improved operational competence. Flexible Supply Chains (FSCs) are more complex and involve multiple autonomous players with varying technical cultures (affects knowledge mindsets), managerial background (affects decision knowledge) and SCM exposures (affects knowledge sharing attitudes). This includes the implementation of decision knowledge enriched supply chains by judicious use of information technology (IT). The development of knowledge based supply chain depends on the nature of knowledge flow in the entire chain. Timely sharing of decision knowledge amongst the chain partners can be very useful. However it requires change in managerial mindsets. Thus there is a need to develop demo models that can encourage chain managers towards greater collaborative-knowledge sharing in the supply chains. This paper presents the application of one such model based on decision knowledge sharing (DKS) for improved supply chain performance. By exploiting Decision Knowledge Sharing (DKS) and flexibility in supply chain structures better operational performance can be achieved. This paper presents a study on the role of different decision knowledge sharing options (i.e. no DKS, partial DKS and full DKS) in a flexible supply chain model based on key dynamic parameters and performance measures. DKS in flexible supply chains has significant potential and needs greater research attention. We attempt to advance the DKS in the context of flexible supply chains. A simulation model of a flexible supply chain based on DKS framework is developed for demo purposes. The key results are highlighted along with industry implications. Here each supply chain node involves decision-making. A seemingly good decision at a stage can be obtained based on local and global knowledge sharing. The cost based performance of alternative DKS with different levels of flexibility is studied. The observations are important for the designers and managers of the FSCs to arrive at appropriate levels of flexibility and judicious level of decision knowledge sharing to attain significant benefits towards operational competence. It is suggested that flexibility and KM are important core-competency that can improve operational performance in supply chains.

Application of knowledge-based artificial immune system (KBAIS) for computer aided process planning in CIM context

In the present era, several manufacturing philosophies like lean manufacturing, total quality management (TQM), etc., have the goal of providing a quality product at reduced cost. In this research paper the process planning problem of a CIM system has been discussed where minimisation of cost of the finished product is considered as the main objective. For determining the cost of the finished product, scrap cost, forgotten by most of the previous researchers, has been considered along with other costs like raw material cost, processing cost, etc. In the present environment of concurrent engineering, optimisation of process planning is an NP-hard problem. To solve this complex problem a noble search algorithm, known as knowledge-based artificial immune system (KBAIS) has been proposed. The nobility of the proposed algorithm is that the inherent capability of AIS has been gleaned and incorporated with the property of the knowledge base. In this problem, the power of knowledge has been used for three stages in the algorithm: initialisation, selection and hyper-mutation. To demonstrate the efficacy of the proposed KBAIS, a bench mark problem has been considered. Intensive computational experiments have also been performed on randomly generated datasets to reveal the supremacy of the proposed algorithm over other existing heuristics.

Performance Analysis of a Flexible Manufacturing System

A typical Flexible Manufacturing System (FMS) has been studied under Planning Design and Control (PDC) strategies. The chief objective is to test the impact of design strategy (routing flexibility) on system performance under planning strategy (alternate system load condition) with control strategies (sequencing and dispatching rules). A computer simulation model is developed to evaluate the effects of aforementioned strategies on the make-span time, which is taken as the system performance measure. Shortest Processing Time (SPT), Maximum Balance Processing Time (MBPT) are the sequencing rules for selecting the part from the input buffer whereas for machine selection the dispatching rules are Minimum Number of parts in the Queue (MINQ), and Minimum queue with Minimum Waiting Time of all parts in the Queue (MQMWT). In this paper, the same manufacturing system is modeled under four different system load conditions. These load conditions are Full Balanced Load (FBL), Balanced Machine Load and Unbalanced Processing Time (BMLUPT), Unbalanced Machine Load and Balanced Processing Time (UMLBPT) and Unbalanced Load (UBL) with respect to machine load and processing time. The result of the simulation shows that there is continuous reduction in make-span with increase in routing flexibility when both machine load and processing times are unbalanced i.e., under UBL system condition.

Modelling of FMS control policy: AIS-based fuzzy expert system

In this paper, an artificial immune system- (AIS-)based fuzzy expert system is developed for a flexible manufacturing system (FMS) to make the real-time decision in the randomly changing marketing and production environments. In FMS context, any direct mathematical relationship between system attributes and performance measures and their effect on controlling policy is not available. The novelty of the paper is that it makes a unique attempt to map an abstract relationship of system attributes with various performance measures. It also provides the estimation of the change required in the self-configured control policy of an FMS. The evolutionary algorithm AIS is used for searching the suboptimal rule base. The efficacy of the proposed system has been shown by an illustrative example.