Growing food demand, environmental degradation, post-harvest losses and the dearth of resources encourage the decision makers from developing nations to integrate the economic and environmental aspects in food supply chain network design. This paper aims to develop a bi-objective decision support model for sustainable food grain supply chain distribution system considering an entire network of procurement centres, central, state and district level warehouses, and fair price shops. The model seeks to minimize the cost and carbon dioxide emission simultaneously. The model covers several problem peculiarities such as multi-echelon, multi-period, multi-modal transportation, emission caused due to various motives, heterogeneous capacitated vehicles and limited availability, multiple sourcing and distribution, and capacitated warehouses. Several different realistic problem instances are solved using the two Pareto based multi-objective algorithms. Sensitivity analysis results imply that the decision makers should establish the sufficient number of warehouses in each producing and consuming states by maintaining the suitable balance between the two objectives. Multiple policymakers like Food Corporation of India, logistics providers and state government agencies will be benefited from this research study.

Food supply chains are receiving increased attention due to the rapid depletion of natural resources, increasing quality standards and rising food safety and security concerns regarding contamination and fraud. Implementing sustainability practices in food supply chains is believed to overcome emerging challenges at both regional and global levels. However, limited studies address sustainability implementation concerns particularly in cold food supply chains.This study aims to contribute to this evident research gap by addressing the major factors hindering sustainability implementation in these networks by considering a case of UK artisan supply chain. Survey data from the UK artisan cheese producers are utilized to identify and prioritise barriers for implementing sustainability following a fuzzy analytic hierarchy process and sensitivity analysis. The analysis identified several key barriers including initial investment cost, firm size and unawareness of government regulations. The internal barriers significantly dominate implementation of sustainability practices in comparison to the external barriers.Lack of consensus regarding the concept of sustainability by different stakeholders was observed to be an issue negatively affecting the level of integration in SMEs. The findings will be highly useful for food and dairy SME's to gain competitive advantage through the successful implementation of sustainability practices.

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...

This research investigates the multi-period multi-modal bulk wheat transportation and storage problem in a two-stage supply chain network of Public Distribution System (PDS). The bulk transportation and storage can significantly curtail the transit and storage losses of food grains, which leads to substantial cost savings. A mixed integer non-linear programming model (MINLP) is developed after studying the Indian wheat supply chain scenario, where the objective is to minimize the transportation, storage and operational cost of the food grain incurred for efficient transfer of wheat from producing states to consuming states. The cost minimization of Indian food grain supply chain is a very complex and challenging problem because of the involvement of the many entities and their constraints such as seasonal procurement, limited scientific storages, varying demand, mode of transportation and vehicle capacity constraints. To address this complex and challenging problem of food grain supply chain, we have proposed the novel variant of Chemical Reaction Optimization (CRO) algorithm which combines the features of CRO and Tabu search (TS) and named it as a hybrid CROTS algorithm (Chemical reaction optimization combined with Tabu Search). The numerous problems with different sizes are solved using the proposed algorithm and obtained results have been compared with CRO. The comparative study reveals that the proposed CROTS algorithm offers a better solution in less computational time than CRO algorithm and the dominance of CROTS algorithm over the CRO algorithm is demonstrated through statistical analysis.

The global food insecurity, malnourishment and rising world hunger are the major hindrances in accomplishing the zero hunger sustainable development goal by 2030. Due to the continuous increment of wheat production in the past few decades, India received the second rank in the global wheat production after China. However, storage capacity has not been expanded with similar extent. The administrative bodies in India are constructing several capacitated silos in major geographically widespread producing and consuming states to curtail this gap. This paper presents a multi-period single objective mathematical model to support their decisionmaking process. The model minimizes the silo establishment, transportation, food grain loss, inventory holding, carbon emission, and risk penalty costs. The proposed model is solved using the variant of the particle swarm optimization combined with global, local and near neighbor social structures (GLNPSO) along with traditional PSO. The solutions obtained through two metaheuristic algorithms are compared with the optimal solutions. The impact of supply, demand and capacity of silos on the model solution is investigated through sensitivity analysis. Finally, some actionable theoretical and managerial implications are discussed after analysing the obtained results.

2018. An MINLP model to support the movement and storage decisions of the Indian food grain supply chain. Control Engineering Practice 70 , pp. Abstract:This paper addresses the novel three stage food grain distribution problem of Public Distribution System (PDS) in India which comprises of farmers, procurement centers, base silos and field silos. The Indian food grain supply chain consists of various activities such as procurement, storage, transportation and distribution of food grain. In order to curb transportation and storage losses of food grain, the Food Corporation of India (FCI) is moving towards the modernized bulk food grain supply chain system. This paper develops a Mixed Integer Non-Linear Programming (MINLP) model for planning the movement and storage of food grain from surplus states to deficit states considering the seasonal procurement, silo capacity, demand satisfaction and vehicle capacity constraints. The objective function of the model seeks to minimize the bulk food grain transportation, inventory holding, and operational cost. Therein, shipment cost contains the fixed and variable cost, inventory holding and operational cost considered at the procurement centers and base silos. The developed mathematical model is computationally complex in nature due to nonlinearity, the presence of numerous binary and integer variables along with a huge number of constraints, thus, it is very difficult to solve it using exact methods. Therefore, recently developed, Hybrid Particle-Chemical Reaction Optimization (HP-CRO) algorithm has been employed to solve the MINLP model. Different problem instances with growing complexities are solved using HP-CRO and the results are compared with basic Chemical Reaction Optimization (CRO) and Particle Swarm Optimization (PSO) algorithms. The results of computational experiments illustrate that the HP-CRO algorithm is competent enough to obtain the better quality solutions within reasonable computational time.

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The food grain supply chain problem of the Public Distribution System (PDS) of India is addressed in this paper to satisfy the demand of the deficit Indian states. The problem involves the transportation of bulk food grain by capacitated vehicles from surplus states to deficit states through silo storage. A mixed integer non-linear programming (MINLP) model is formulated which seeks to minimize the overall cost including bulk food grain shipment, storage, and operational cost. The model incorporates the novel vehicle preference constraints along with the seasonal procurement, silo storage, vehicle capacity and demand satisfaction restrictions. The management of Indian food grain supply chain network is more intricate and difficult issue due to many uncertain interventions and its chaotic nature. To tackle the aforementioned problem an effective meta-heuristic which based on the strategy of sorting elite ants and pheromone trail updating called Improved Max-Min Ant System (IMMAS) is proposed. The solutions obtained through IMMAS is validated by implementing the Max-Min Ant System (MMAS). A sensitivity analysis has been performed to visualize the effect of model parameters on the solution quality. Finally, the statistical analysis is carried out for confirming the superiority of the proposed algorithm over the other.