Optimal wholesale facilities location within the fruit and vegetables supply chain with bimodal transportation options: An LP-MIP heuristic approach

Etemadnia, Hamideh; Goetz, Stephan J.; Canning, Patrick; Tavallali, M. Sadegh

doi:10.1016/j.ejor.2015.01.044

Cited by 95 publications

(52 citation statements)

References 23 publications

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“…Furthermore, two industrial case studies of Greek dairy industry have been effectively solved using proposed approach. Moreover, Etemadnia et al (2015) developed the mixed integer linear programming (MILP) model for minimization of total network cost containing transportation and facility location cost with two potential shipment modes for the design of the optimal hub logistic network for efficient transfer of food from production region to consumption region. To minimize the handling cost of Canadian wheat supply chain under the new declaration system, Ge, Gray and Nolan (2015) developed the analytic and agent-based simulation models assuming individual behavior and farmers as well as handlers as rational and learning individual, respectively.…”

Section: Related Workmentioning

confidence: 99%

An MINLP model to support the movement and storage decisions of the Indian food grain supply chain

Mogale

Kumar

Tiwari

2018

Control Engineering Practice

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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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Section: Related Workmentioning

confidence: 99%

An MINLP model to support the movement and storage decisions of the Indian food grain supply chain

Mogale

Kumar

Tiwari

2018

Control Engineering Practice

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“…Formally, this study falls within the scope of strategic planning for facility location along supply chains, where mathematical programming models have proven effective for solving this type of problem (Alumur and Kara, ; Apland and Andersson, ; Cortignani and Severini, ). Previous studies conduct some computational experiments to identify the optimal hub numbers and locations in food supply chain systems (Etemadnia et al., , ; Faminow and Sarhan, ). While these studies contribute to the analysis of local and regional food systems, they impose strong simplifying assumptions on the operational level.…”

Section: Introductionmentioning

confidence: 99%

Effects of scale economies and production seasonality on optimal hub locations: the case of regional fresh produce aggregation

Canning

Goetz

et al. 2018

Agricultural Economics

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Interest in supporting local and regional food systems is rising and food hubs have attracted considerable attention among Federal, State, and local policymakers. This study explores the problem of endogenous hub location in fresh produce value chains in the Northeastern United States. To overcome limitations in the literature, we incorporate the effects of economies of scale and production seasonality into our models. Three experimental models are designed to examine the effects of alternatively applying yearly, quarterly, and monthly data on model solutions. We explicitly assess how interactions of scale economies and seasonality influence the structure and spatial attributes of an optimal regional produce aggregation hub system. The three models generate marketed different solutions and in many respects they lead to different conclusions about developing local/regional supply chains. The monthly model allows for production seasonality and actual hub operation cycle frequency and thus leads to more efficient hub solution with rich policy implications.JEL classifications: C61, C63, P25, Q18, R58

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“…The location network (depots, customers and arcs) that incurs the minimum total distance/lowest total cost is regarded as an optimal solution. Many researchers (Dantrakul et al, 2014;Etemadnia et al, 2015;Guastaroba & Speranza, 2014;Rahmani & MirHassani, 2014) have proposed solving techniques such as mathematical techniques (heuristic and optimization techniques) only. However, with some complex problems such as selecting sites for hazardous material waste disposal, choosing sites for nuclear power plants, location selection for infectious waste disposal, these problems are very important decisions because they are costly and hard to reverse.…”

Section: Related Literaturementioning

confidence: 99%

Solving a multi-objective location routing problem for infectious waste disposal using hybrid goal programming and hybrid genetic algorithm

Wichapa¹,

Khokhajaikiat²

2018

10.5267/j.ijiec

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Infectious waste disposal remains one of the most serious problems in the medical, social and environmental domains of almost every country. Selection of new suitable locations and finding the optimal set of transport routes for a fleet of vehicles to transport infectious waste material, location routing problem for infectious waste disposal, is one of the major problems in hazardous waste management. Determining locations for infectious waste disposal is a difficult and complex process, because it requires combining both intangible and tangible factors. Additionally, it depends on several criteria and various regulations. This facility location problem for infectious waste disposal is complicated, and it cannot be addressed using any standalone technique. Based on a case study, 107 hospitals and 6 candidate municipalities in UpperNortheastern Thailand, we considered criteria such as infrastructure, geology and social & environmental criteria, evaluating global priority weights using the fuzzy analytical hierarchy process (Fuzzy AHP). After that, a new multi-objective facility location problem model which hybridizes fuzzy AHP and goal programming (GP), namely the HGP model, was tested. Finally, the vehicle routing problem (VRP) for a case study was formulated, and it was tested using a hybrid genetic algorithm (HGA) which hybridizes the push forward insertion heuristic (PFIH), genetic algorithm (GA) and three local searches including 2-opt, insertion-move and interexchange-move. The results show that both the HGP and HGA can lead to select new suitable locations and to find the optimal set of transport routes for vehicles delivering infectious waste material. The novelty of the proposed methodologies, HGP, is the simultaneous combination of relevant factors that are difficult to interpret and cost factors in order to determine new suitable locations, and HGA can be applied to determine the transport routes which provide a minimum number of vehicles and minimum transportation cost under the actual situation efficiently in this case.

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Optimal wholesale facilities location within the fruit and vegetables supply chain with bimodal transportation options: An LP-MIP heuristic approach

Cited by 95 publications

References 23 publications

An MINLP model to support the movement and storage decisions of the Indian food grain supply chain

An MINLP model to support the movement and storage decisions of the Indian food grain supply chain

Effects of scale economies and production seasonality on optimal hub locations: the case of regional fresh produce aggregation

Solving a multi-objective location routing problem for infectious waste disposal using hybrid goal programming and hybrid genetic algorithm

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