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Traditional Indian agri-fresh food supply chain (AFSC) is unorganized and controlled by a large number of intermediaries, causing it to be unsustainable from the farmer (low profitability) to the customer (not getting quality product even after paying a high price). The purpose of this paper is to propose novel strategies for designing a sustainable Indian AFSC. Authors identify shortcomings as well as strengths, weaknesses, opportunities and threats (SWOT) of the Indian agriculture system through the literature survey. To improve overall performance of this chain, authors have developed eighty strategies by incorporating SWOT into TOWS (threat, opportunity, weakness and strength) matrix. They define four key objectives to mitigate the aforementioned problems. In this paper, twelve strategies are shortlisted from existing ones on the basis of fulfilling a maximum number of objectives. The shortlisting of strategies is further verified by evaluating strategies using performance measures (PMs) in sustainability dimensions, which revealed that the twelve chosen strategies dominate over other strategies. The shortlisted strategies are mainly focused on improving the economic dimension of AFSC by satisfying maximum number of objectives and PMs. The chosen strategies pledge for the optimum and most feasible ones to be recommended for managers/researchers towards redesigning the AFSC. The proposed viable strategies are expected to reform the conventional Indian AFSC by designing sustainable supply chain (SSC). The outcomes of this work will provide clear guidelines for researchers and policy makers to make sustainable agriculture.

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PurposeThe purpose of this paper is to study and develop supply chain structure of traditional Indian agri-fresh food supply chain (AFSC). This paper proposes a mathematical model to design a traditional Indian AFSC to minimize total distribution cost and post-harvest losses in the chain.Design/methodology/approachThis paper formulates two mathematical models to structure and represent the flow of products in the existing chain. First, a three-echelon, multi-period, multi-product, mixed-integer linear programming (MILP) model is formulated to minimize the total distribution cost incurred in the chain. Further, the developed formulation is extended by considering the perishability of products in the second model.FindingsA real case study problem of Mandsaur district (India) is solved in LINGO 17.0 package to check the validity of the formulated models. The perishable (second) model of AFSC reports better results in terms of costs and post-harvest losses minimization. The results revealed that 92% of the total distribution cost incurred in the transportation of products from farmers to the hubs.Research limitations/implicationsThis paper includes implications for redesigning an existing supply chain network by incorporating an appropriate transportation strategy from farmers to hubs to minimize transportation inefficiency and enhance the profitability of farmers.Practical implicationsThe formulated AFSC model would help managers and policymakers to identify optimal locations for hubs where required infrastructure would be developed.Originality/valueAccording to the author's best knowledge, this paper is the first to design traditional Indian AFSC by considering the perishability of products.

This paper studies a multi-objective mixture inventory problem for a pharmaceutical distributor. The work starts with a discussion of a mixture inventory model and three objectives, namely the minimization of: 1) ordering and holding costs, 2) number of units that stockout and 3) frequency of stockout occasions. Multi-objective particle swarm optimization (MOPSO) is used to determine the non-dominated solutions and generate Pareto curves for the inventory system. Two variants of MOPSO are proposed, based on the selection of inertia weight. The performance of the proposed MOPSO algorithms is evaluated in comparison with two robust algorithms like non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective cuckoo search (MOCS). The metrics that are used for the performance measurement of the algorithms are error ratio, spacing and maximum spread. Furthermore, the technique of order preference by similarity to ideal solution (TOPSIS) is used to rank the non-dominated solutions and determine the best compromise solution among them. A factorial analysis develops the linear regression expressions of optimal cost, service level measures, lot size and safety stock factor for practitioners. Lastly, the results of the regression equations are compared using a MOPSO–TOPSIS approach and the validity of the developed equations are checked.

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