Grain silo location-allocation problem with dwell time for optimization of food grain supply chain network

Abstract

In the last few decades, production and procurement of food grain in India have steadily increased, however, storage capacity has not increased proportionally. The government of India (GOI) is establishing the various capacitated silos across the country to bridge this storage capacity gap. This paper presents a novel integrated multi-objective, multi-modal and multiperiod mathematical model for grain silo location-allocation problem with Dwell time to support the decision-making process of GOI. Two conflicting objectives-minimization of total supply chain network cost and total lead time (transit and dwell time) are simultaneously optimized using two Pareto based multi-objective algorithms with calibrated parameters. Road transportation Surplus state silos and warehouses Rail transportation Deficit state silos and warehouses District level warehouses Fair Price Shops Consumers Road transportation Road transportation 4heterogeneous capacitated vehicles and their limited availability at each echelon, multiple sourcing, multi-modal transportation, geographically dispersed surplus and deficit states, capacitated base and field silos, and vehicle capacity constraints, etc. The waiting time of food grain stock at SGA warehouses must be reduced to avoid -deterioration of food grain quality, an increase of carry-over charges and food grain losses. Therefore, the new DT function is introduced for calculating the waiting time of food grain at procurement centers with the consideration of administrative activities, vehicles used for shipment between procurement centers and base silos and availability of base silos storage capacity. Third, to provide the compromise solution to the FCI and GOI, the model is solved using the recently developed multi-objective evolutionary algorithm called non-dominated chemical reaction optimization (NCRO) algorithm and compared the results with the well-known non-dominated sorting genetic algorithm (NSGA-II). Even though the NCRO algorithm is not original, to the best of our knowledge, it has not been used for any practical problems. Therefore, we feel that the application of the NCRO algorithm with calibrated parameters to solve a grain silo locationallocation problem and the comparison between NCRO and NSGA-II results for this problem can be one of the contributions. Finally, sensitivity analysis is conducted considering the eight parameters to obtain the managerial insights and practical implications for the effective decision-making process of food grain supply chain.The rest of the paper is structured in the following manner. Section 2 provides the comprehensive review of the relevant existing literature. The problem delineation is given in Section 3. Section 4 illustrates the mathematical model of the problem including assumptions and notations. Section 5 deals with solution methodologies used for solving the model. Section 6 reports and discusses the computational results. Finally, the conclusion and some future extensions are given in Section 7. Background and prior related workThe l...

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“…In another research, Mogale et al developed an integrated multi-objective, multi-modal and multi-period model for the grain silo location-allocation problem with dwell time. They further compared their results to those of NSGA-II algorithm [37]. Mogale et al formulated a Mixed-Integer Non-Linear Programming (MINLP) model for planning the food grain storage and movement from surplus states to deficit states considering the seasonal procurement, silo capacity, demand satisfaction, and vehicle capacity constraints.…”