Lakshman S. Thakur

Supplier selection using fuzzy AHP and fuzzy multi-objective linear programming for developing low carbon supply chain

Environmental sustainability of a supply chain depends on the purchasing strategy of the supply chain members. Most of the earlier models have focused on cost, quality, lead time, etc. issues but not given enough importance to carbon emission for supplier evaluation. Recently, there is a growing pressure on supply chain members for reducing the carbon emission of their supply chain. This study presents an integrated approach for selecting the appropriate supplier in the supply chain, addressing the carbon emission issue, using fuzzy-AHP and fuzzy multi-objective linear programming. Fuzzy AHP (FAHP) is applied first for analyzing the weights of the multiple factors. The considered factors are cost, quality rejection percentage, late delivery percentage, green house gas emission and demand. These weights of the multiple factors are used in fuzzy multi-objective linear programming for supplier selection and quota allocation. An illustration with a data set from a realistic situation is presented to demonstrate the effectiveness of the proposed model. The proposed approach can handle realistic situation when there is information vagueness related to inputs.

Infrastructure development for conversion to environmentally friendly fuel

An important concern for any nation wishing to convert to alternate, environmentally friendly energy sources is the development of appropriate fuel distribution infrastructure. We address the problem of optimally locating gas station facilities for developing nations, like India, which are in the process of converting from leaded to unleaded fuel. Importantly, a similar approach may be used in developed countries, which are in the process of converting to automobiles using hydrogen or electrical energy. An integer-programming model with the objective of balancing the perspectives of coverage and cost is presented for this facility location problem. Given the existing network of roads, the model considers the traveling population, the location of existing facilities and the cost of, either converting these facilities to carry unleaded fuel, or of installing new facilities in an attempt to minimize cost and simultaneously maximize coverage of population. We develop a heuristic solution procedure for this problem. The methodology is applied to data sets obtained from Current et al. [J.R. Current, C.S. ReVelle, J.L. Cohon, Decision Sciences 19 (1988) 490] representing the Ohio state limited access highway network, and to the Indian national highway network. Additionally, extensive simulations are carried out in order to examine where our approach compares with the maximum weighted spanning tree approach. This work extends the Maximum Covering/Shortest Path problem (MCSPP) formulated by Current et al. [J.R. Current, C.S. ReVelle, J.L. Cohon, European Journal of Operational Research 21 (1985) 189] to accommodate multiple sources and destinations. � 2002 Elsevier Science B.V. All rights reserved.

Lagrangian relaxation neural networks for job shop scheduling

Manufacturing scheduling is an important but difficult task. In order to effectively solve such combinatorial optimization problems, the paper presents a Lagrangian relaxation neural network (LRNN) for separable optimization problems by combining recurrent neural network optimization ideas with Lagrangian relaxation (LR) for constraint handling. The convergence of the network is proved, and a general framework for neural implementation is established, allowing creative variations. When applying the network to job shop scheduling, the separability of problem formulation is fully exploited, and a new neuron-based dynamic programming is developed making innovative use of the subproblem structure. Testing results obtained by software simulation demonstrate that the method is able to provide near-optimal solutions for practical job shop scheduling problems, and the results are superior to what have been reported in the neural network scheduling literature. In fact, the digital implementation of LRNN for job shop scheduling is similar to the traditional LR approaches. The method, however, has the potential to be implemented in hardware with much improved quality and speed.

Multi-objective modeling of production and pollution routing problem with time window

Integration of two issues of vehicle routing problem, namely, production routing and pollution routing.Multi-period multi-vehicle production and pollution routing problem with time window is formulated.Multi-objective formulation with the objectives of minimization of cost and minimization of carbon emissions.SLPSO algorithm is enhanced in multi-objective framework.Comparison of the proposed algorithm with NSGA-II through a case study. Production routing and pollution routing problems are two important issues of vehicle routing problem (VRP) of the supply chain planning system. Both determine an optimum path for the vehicle, in addition, production routing problem (PRP) deals with production and distribution whereas pollution routing problem deals with carbon footprint. In this paper, we develop a VRP that simultaneously considers production and pollution routing problems with time window (PPRP-TW). The proposed PPRP-TW is a NP-hard problem concentrating to optimize the routing problem over the periods. A fleet of identical capacitated vehicles leave from a production plant to a set of customers scattered in different locations. The transportation part of PPRP-TW is concerned with carbon footprint. Thus, a multi-objective multi-vehicle PPRP-TW (MMPPRP-TW) is formulated with two objectives: minimization of the total operational cost and minimization of the total emissions (equivalently, minimization of the fuel consumption). A hybrid Self-Learning Particle Swarm Optimization (SLPSO) algorithm in multi-objective framework is proposed to solve the MMPPRP-TW. To establish superior computational efficiency of hybrid SLPSO algorithm, a comparison with the well-known Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is performed.

A big data MapReduce framework for fault diagnosis in cloud-based manufacturing

This research develops a MapReduce framework for automatic pattern recognition based on fault diagnosis by solving data imbalance problem in a cloud-based manufacturing (CBM). Fault diagnosis in a CBM system significantly contributes to reduce the product testing cost and enhances manufacturing quality. One of the major challenges facing the big data analytics in CBM is handling of data-sets, which are highly imbalanced in nature due to poor classification result when machine learning techniques are applied on such data-sets. The framework proposed in this research uses a hybrid approach to deal with big data-set for smarter decisions. Furthermore, we compare the performance of radial basis function-based Support Vector Machine classifier with standard techniques. Our findings suggest that the most important task in CBM is to predict the effect of data errors on quality due to highly imbalance unstructured data-set. The proposed framework is an original contribution to the body of literature, where our proposed MapReduce framework has been used for fault detection by managing data imbalance problem appropriately and relating it to firm’s profit function. The experimental results are validated using a case study of steel plate manufacturing fault diagnosis, with crucial performance matrices such as accuracy, specificity and sensitivity. A comparative study shows that the methods used in the proposed framework outperform the traditional ones.

Modeling a low-carbon garment supply chain

This article attempts to introduce indirect carbon emission and trade-credit concept in a network optimisation model for sustainable supply chain. The proposed model optimises total cost, total direct carbon emission, total indirect emission in the form of embodied carbon footprint of the raw material and total trade-credit amount over the purchased item in a supply chain. The model calculates the total cost by considering purchasing cost, logistics cost, handling cost and manufacturing cost. It attempts to measure the direct emission involved in manufacturing and logistics operations. The model has the capability to consider dissimilar trucks used for transportation according to their operating cost and carbon emission. Multi-objective goal programming is applied to deal with four objectives to find a tradeoff among these objectives. The result suggests that managers should capture the direct as well as the indirect emission which helps in arriving at appropriate strategy for a sustainable supply chain. The effectiveness of the proposed model is demonstrated through a case of a garment supply chain. This model also supports in deciding appropriate goal for carbon emission, supply chain costs, etc.

Optimization-based manufacturing scheduling with multiple resources, setup requirements, and transfer lots

Abstract The increasing demand for on-time delivery of products and low production cost is forcing manufacturers to seek effective schedules to coordinate machines and operators so as to reduce costs associated with labor, setup, inventory, and unhappy customers. This paper presents the modeling and resolution of a job shop scheduling system for J. M. Products Inc., whose manufacturing is characterized by the need to simultaneously consider machines and operators, machines requiring significant setup times, operators of different capabilities, and lots dividable into transfer lots. These characteristics are typical for many manufacturers, difficult to handle, and have not been adequately addressed in the literature. In our study, an integer optimization formulation with a separable structure is developed where both machines and operators are modeled as resources with finite capacities. Setups are explicitly considered following our previous work with additional penalties on excessive setups. By analyzing transfer lot dynamics, transfer lots are modeled by using linear inequalities. The objective is to maximize on-time delivery of products, reduce inventory, and reduce the number of setups. By relaxing resource capacity constraints and portions of precedence constraints, the problem is decomposed into smaller subproblems that are effectively solved by using a novel dynamic programming procedure. The multipliers are updated using the recently developed surrogate subgradient method. A heuristic is then used to obtain a feasible schedule based on subproblem solutions. Numerical testing shows that the method generates high quality schedules in a timely fashion.

An effective optimization-based algorithm for job shop scheduling with fixed-size transfer lots

Abstract Effective scheduling of production lots is of great importance for manufacturing medium to high-volume products that require significant setup times. Compared to traditional entire-lot production, lot splitting techniques divide a production lot into multiple smaller sublots so that each sublot can be “transferred” from one stage of operation to the next as soon as it has been completed. “Transfer lots,” therefore, significantly reduce lead times and lower work-in-process (WIP) inventory. The mathematical modeling, analysis, and control of transfer lots, however, is extremely difficult. This paper presents a novel integer programming formulation with separable structure for scheduling job shops with fixed-size transfer lots. A solution methodology based on a synergistic combination of Lagrangian relaxation, backward dynamic programming (BDP), and heuristics is developed. Through explicit modeling of lot dynamics, transfer lots are handled on standard machines, machines with setups, and machines requiring all transfer lots within a production lot to be processed simultaneously. With “substates” and the derivation of DP functional equations considering transfer lot dynamics, the standard BDP is extended to solve the lot-level subproblems. The recently developed “time step reduction technique” is also used for increased efficiency. It implicitly establishes two time scales to reduce computational requirements without much loss of modeling accuracy and scheduling performance, thus enabling resolution of long-horizon problems within controllable computational requirements. The method has been implemented using object-oriented programming language C++, and numerical tests show that high-quality schedules involving transfer lots are efficiently generated to achieve on-time delivery of products with low WIP inventory.

Error analysis for convex separable programs: Bounds on optimal and dual optimal solutions

Abstract Computable lower and upper bounds on the optimal and dual optimal solutions of a nonlinear, convex separable program are obtained from its piecewise linear approximation. They provide traditional error and sensitivity measures and are shown to be attainable for some problems. In addition, the bounds on the solution can be used to develop an efficient solution approach for such programs, and the dual bounds enable us to determine a subdivision interval which insures the objective function accuracy of a prespecified level. A generalization of the bounds to certain separable, but nonconvex, programs is given and some numerical examples are included.

A big data driven sustainable manufacturing framework for condition-based maintenance prediction

Abstract Smart manufacturing refers to a future-state of manufacturing and it can lead to remarkable changes in all aspects of operations through minimizing energy and material usage while simultaneously maximizing sustainability enabling a futuristic more digitalized scenario of manufacturing. This research develops a big data analytics framework that optimizes the maintenance schedule through condition-based maintenance (CBM) optimization and also improves the prediction accuracy to quantify the remaining life prediction uncertainty. Through effective utilization of condition monitoring and prediction information, CBM would enhance equipment reliability leading to reduction in maintenance cost. The proposed framework uses a CBM optimization method that utilizes a new linguistic interval-valued fuzzy reasoning method for predicting the information. The proposed big data analytics framework in our study for estimating the uncertainty based on backward feature elimination and fuzzy unordered rule induction algorithm prediction errors, is an innovative contribution to the remaining life prediction field. Our paper elaborates on the basic underlying structure of CBM system that is defined by transaction matrix and the threshold value of failure probability. We developed this framework for analysing the CBM policy cost more accurately and to find the probabilistic threshold values of covariate that corresponds to the lowest price of predictive maintenance cost. The experimental results are performed on a big dataset which is generated from a sophisticated simulator of a gas turbine propulsion plant. A comparative analysis confirms that the method used in the proposed framework outpaces the classical methods in terms of classification accuracy and other statistical performance evaluation metrics.