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Basnet, Chuda; Foulds, L. R.; Wilson, John

doi:10.1023/a:1018943910798

Cited by 20 publications

(5 citation statements)

References 4 publications

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“…Arbib et al [17] considered a threedimensional matching model for perishable production scheduling, which was studied under two independent aspects: the relative perishability of products and the feasibility of launching/completion time. Basnet et al [18] described an exact algorithm to solve a scheduling and sequencing problem in the same industry. Chen et al [19] provided a review of literature on the integration of scheduling and lot sizing for perishable food products, and they categorized the papers by the characteristics of the lot sizing and scheduling that were included in their models, as well as the strategies used to model perishability.…”

Section: Literature Reviewmentioning

confidence: 99%

Flexible Job Shop Scheduling Problem with Sequence Dependent Setup Time and Job Splitting: Hospital Catering Case Study

et al. 2021

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This paper aims to study a real case of an optimization problem derived from a hospital supply chain. The present work focuses on developing operational decision support models and algorithms for production process scheduling in hospital catering. The addressed production system is considered as a flexible job shop system. The objective is to minimize the total flow time. A novel mathematical model and two metaheuristics for the production scheduling of multi-product and multi-stage food processes are developed. These methods have proven their effectiveness for the scheduling of operations of the food production processes and allowed significant improvements in the performance of the studied production system.

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Section: Literature Reviewmentioning

confidence: 99%

Flexible Job Shop Scheduling Problem with Sequence Dependent Setup Time and Job Splitting: Hospital Catering Case Study

et al. 2021

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“…In the raw milk transportation industry, Basnet et al (1999) [2] developed a mixed integer program with some non-linear constraints to solve a joint problem of milk-tanker scheduling and sequencing for the New Zealand dairy industry. Following later, Worasan et al (2014) [3] developed a mixed integer programming model to solve the raw milk collection problem in the same industry.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Besides the Tabu search technique that has been wildly used to solve the TTRP, the raw milk transportation problem, others metaheuristics such as genetic algorithm (GA), tabu search (TS), Particle swarm optimization (PSO), Ant Colony Optimization (ACO) and simulated annealing (SA) have been used to solve combinatorial problems [19] The differential evolution (DE) was first introduced in 1997 [20]. Due to the effectiveness of DE, it has been successfully used in many types of combinatorial optimizations such as assembly line balancing [21,22], manufacturing problems [2], transportation problems [23], vehicle routing problems [24,25] and the capacitated VRP with the flexibility of mixing pickup and delivery services [26].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The DE can be improved by adding more steps into the original steps and other techniques such as use various mutation and recombination formula [21,22,28], add effective local search techniques [27], add the best vector reproduction process [29], develop new decoding method which is combined with ant colony optimization [30]. Therefore, in this article, the DE has been improved in following ways so that the effectiveness of DE is increased: (1) provided an effective decoding method; (2) presented new rules of the selection process to let DE escape from the local optimal; and (3) used 3 different local searches so that the DE has intensive search efficiency.…”

Section: Literature Reviewmentioning

confidence: 99%

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Methodology to Solve a Special Case of the Vehicle Routing Problem: A Case Study in the Raw Milk Transportation System

2019

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This research aims to solve the problem of the raw milk collection and transportation system which can be interpreted as a special case of the vehicle routing problem. In the proposed problem, the factory will send the trucks, multiple fleets composed of several compartments, to collect the raw milk from the raw milk farms. The objective of this research is to minimize the total transportation cost and the trucks' and tanks' cleaning costs. The transportation cost directly depends on the fuel usage. The fuel usage occurs during the transportation of the milk and during the waiting times when it arrives at the factory and cannot transfer the raw milk into the production tank. We develop the modified differential evolution algorithm (MDE) to solve the proposed problem. The original process of the Differential Evolution algorithm (DE) has been modified in two folds which are as follows: (1) In the recombination process, the 2nd order of trial vectors has been generated using 3 different strategies and compared with the 1st order trial vector; the better from the 1st and the 2nd order of trial vectors will move to the selection process. (2) The probability function has been used to select the new target vector from one of two sources which are the trial vector and the current target vector so that the worse solution can be accepted in order to increase the diversity of the original DE. The computational result shows that the modified DE (MDE) outperforms the original DE in finding a better solution.

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“…Foulds and Wilson proposed two heuristic algorithms for a variation of the generalized assignment problem arising in the New Zealand dairy industry. Then Basnet et al . described an exact algorithm to solve the scheduling and sequencing problems in the same industry.…”

Section: Literature Reviewmentioning

confidence: 99%

Multistage Production Planning in the Dairy Industry: A Mixed-Integer Programming Approach

Bilgen

Doğan

2015

Ind. Eng. Chem. Res.

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This paper addresses a production planning problem in a multistage production system consisting of continuous processing resources separated by finite-capacity storage tanks, stimulated by a particular case study in the dairy industry. The problem is formulated as a mixed-integer linear programming (MILP) model that incorporates several distinguishing characteristics of dairy production, such as multistage bulk production, shelf-life requirements, intermediate storage, setups, resource speeds, limitations on minimum and maximum lot sizes, and the conservation of flow among various tanks. The objective is to maximize the total profit while determining the quantity of intermediate products and SKUs processed on various resources, the assignment of products to various resources and intermediate storage tanks, the quantity of each SKU sold, lost sales of each SKU, and waiting times. The determinations often reveal production bottlenecks. The efficiency of the proposed model is illustrated through its application to the milk production plant of a leading dairy company. Special features of the proposed model are highlighted through several examples. The computational performance of the MILP model is examined in several test scenarios.

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Cited by 20 publications

References 4 publications

Flexible Job Shop Scheduling Problem with Sequence Dependent Setup Time and Job Splitting: Hospital Catering Case Study

Flexible Job Shop Scheduling Problem with Sequence Dependent Setup Time and Job Splitting: Hospital Catering Case Study

Methodology to Solve a Special Case of the Vehicle Routing Problem: A Case Study in the Raw Milk Transportation System

Multistage Production Planning in the Dairy Industry: A Mixed-Integer Programming Approach

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