Surya Prakash Singh

A mixed-integer non-linear program to model dynamic supplier selection problem

In a highly competitive scenario, suppliers play a vital role in making a business organization successful. Business of any organization is continuous process and therefore the supplier selection is also dynamic in nature. This is quite natural as the organizations demand; suppliers capacity, quality level, lead time, unit part cost and fixed transportation cost of supplier varies with time. Therefore, supplier identified for one period may not necessarily be same for the next period to supply the same set of parts. Hence, the supplier selection problem is highly dynamic in real practice. In this paper, a mixed-integer non-linear program (MINLP) is developed to address the dynamic supplier selection problem (DSSP). To validate the proposed MINLP data are generated randomly. A numerical illustration is also provided to demonstrate the proposed MINLP using LINGO.

Traveling Salesman Problem: an Overview of Applications, Formulations, and Solution Approaches

1.1 Origin The traveling salesman problem (TSP) were studied in the 18th century by a mathematician from Ireland named Sir William Rowam Hamilton and by the British mathematician named Thomas Penyngton Kirkman. Detailed discussion about the work of Hamilton & Kirkman can be seen from the book titled Graph Theory (Biggs et al. 1976). It is believed that the general form of the TSP have been first studied by Kalr Menger in Vienna and Harvard. The problem was later promoted by Hassler, Whitney & Merrill at Princeton. A detailed dscription about the connection between Menger & Whitney, and the development of the TSP can be found in (Schrijver, 1960).

Three-level AHP-based heuristic approach for a multi-objective facility layout problem

In real decision making problems, many conflicting objectives have to be taken into account. With increasing awareness of this, multi-objective problems have become more and more popular. Similarly, the design of the multi-objective facility layout problem (MOFLP) has generally been recognised as an important issue in the modern manufacturing system. The MOFLP is formulated as a quadratic assignment problem (QAP) which is NP-hard and solving MOFLP is a tough problem. A new three-level AHP-based heuristic algorithm for resolving the MOFLP is presented here. It also presents a new normalisation procedure (H-1), and a new heuristic method (H-2) for generating objective weights. The proposed approach consists of three levels. The first level applies AHP to generate paired comparison matrices, the consistency of matrix, to convert inconsistent matrices into consistent ones and then generate a qualitative objective matrix; the second level applies normalisation procedure (H-1) to normalise matrices of qualitative and quantitative objectives and the third level computes the objective weight for qualitative and quantitative objectives. An illustrative example is shown to demonstrate an application of the proposed methodology for solving MOFLP.

Big data in operations and supply chain management: current trends and future perspectives

Abstract Operations and supply chain management encompasses a vast domain and hence provides a myriad of opportunities for huge voluminous data generated from various sources in real time. Such huge data having the requisite properties of big data can be utilised to gain critical and fundamental insights towards optimising the operations and supply chain and thus making effective and efficient decisions. In the recent years, research interest in big data has increased substantially and therefore researchers and practitioners have also tried to tap the capabilities of big data to optimise operations and supply chain management. In this paper, the literature relating to the integration of big data with operations and supply chain management is reviewed. In particular, reviewing past work is primarily focused on three key areas of the operations and supply chain management, namely manufacturing, procurement and logistics where big data has been applied. In addition to reviewing past literature, paper also proposes application of big data in operations and supply chain management.

Formulating and Solving Sustainable Stochastic Dynamic Facility Layout Problem: A Key to Sustainable Operations

Facility layout design, a NP hard problem, is associated with the arrangement of facilities in a manufacturing shop floor, which impacts the performance, and cost of system. Efficient design of facility layout is a key to the sustainable operations in a manufacturing shop floor. An efficient layout design not only optimizes the cost and energy due to proficient handling but also increase flexibility and easy accessibility. Traditionally, it is solved using meta-heuristic techniques. But these algorithmic or procedural methodologies do not generate effective and efficient layout design from sustainable point of view, where design should consider multiple criteria such as demand fluctuations, material handling cost, accessibility, maintenance, waste and more. In this paper, to capture the sustainability in the layout design these parameters are considered, and a new sustainable stochastic dynamic facility layout problem (SDFLP) is formulated and solved. SDFLP is optimized for material handling cost and rearrangement cost using various meta-heuristic techniques. The pool of layouts thus generated are then analyzed by data envelopment analysis to identify efficient layouts. A novel hierarchical methodology of consensus ranking of layouts is proposed which combines the multiple attributes/criteria. Multi attribute decision-making techniques such as technique for order preference by similarity to ideal solution, interpretive ranking process and analytic hierarchy process, Borda–Kendall and integer linear programming based rank aggregation techniques are applied. To validate the proposed methodology data sets for facility size

Heuristic modeling for sustainable procurement and logistics in a supply chain using big data

N=12

Flexible dynamic sustainable procurement model

N=12 for time period