Combined ship routing and inventory management in the salmon farming industry

Agra, Agostinho; Christiansen, Marielle; Ivarsøy, Kristine S.; Solhaug, Ida Elise; Tomasgård, Asgeir

doi:10.1007/s10479-015-2088-x

Cited by 20 publications

(7 citation statements)

References 27 publications

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“…Constraints (8) establish a capacity on the quantity transported by a vehicle. Constraints (9) and (10) are the routing constraints. Constraints (11) guarantee that a vehicle leaves the producer when there are visits to clients.…”

Section: P R O B L E M S P E C I F I C a T I O N S A N D M A T H E M mentioning

confidence: 99%

“…Complex problems combining production, inventory and routing decisions have received much attention in recent years . For a survey on previous publications see .…”

Section: Introductionmentioning

confidence: 99%

“…The quantities to deliver to each client in each time period as well as the inventory levels can be adjusted to the scenario (known as second-stage decisions). Such assumptions may hold for short planning horizons.Complex problems combining production, inventory and routing decisions have received much attention in recent years [6,9,11,14,16,17,19,20,25,29,33,38,40]. For a survey on previous publications see [4].…”

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confidence: 99%

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An adjustable sample average approximation algorithm for the stochastic production‐inventory‐routing problem

2017

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We consider a stochastic single item production‐inventory‐routing problem with a single producer, multiple clients, and multiple vehicles. At the clients, demand is allowed to be backlogged incurring a penalty cost. Demands are considered uncertain. A recourse model is presented, and valid inequalities are introduced to enhance the model. A new general approach that explores the sample average approximation (SAA) method is introduced. In the sample average approximation method, several sample sets are generated and solved independently in order to obtain a set of candidate solutions. Then, the candidate solutions are tested on a larger sample, and the best solution is selected among the candidates. In contrast to this approach, called static, we propose an adjustable approach that explores the candidate solutions in order to identify common structures. Using that information, part of the first‐stage decision variables is fixed, and the resulting restricted problem is solved for a larger size sample. Several heuristic algorithms based on the mathematical model are considered within each approach. Computational tests based on randomly generated instances are conducted to test several variants of the two approaches. The results show that the new adjustable SAA heuristic performs better than the static one for most of the instances.

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Section: P R O B L E M S P E C I F I C a T I O N S A N D M A T H E M mentioning

confidence: 99%

“…Complex problems combining production, inventory and routing decisions have received much attention in recent years . For a survey on previous publications see .…”

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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An adjustable sample average approximation algorithm for the stochastic production‐inventory‐routing problem

2017

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“…Such complex problems combining production, inventory and routing decisions have been receiving a great attention in recent years with a large increase of the number of publications. Applications to complex supply chain problems can be found, for instance, in [3,4,6,10,13,16,19]. For surveys on complex inventory routing problems covering theoretical and industrial aspects see [1,7,11].…”

Section: Introductionmentioning

confidence: 99%

“…The problem considered in this paper ts in the land transportation mode since for each time period a vehicle routing problem is solved in order to nd the low cost distribution plan for the deliveries assigned to that period. This contrasts with maritime transportation where, in general, a route may take several time periods [3,4,6,10].…”

Section: Introductionmentioning

confidence: 99%

Lagrangian Relaxation Bounds for a Production-Inventory-Routing Problem

Agra

Cerveira

Requejo

2016

Lecture Notes in Computer Science

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Abstract. We consider a single item Production-Inventory-Routing problem with a single producer/supplier and multiple retailers. Inventory management constraints are considered both at the producer and at the retailers, following a vendor managed inventory approach, where the supplier monitors the inventory at retailers and decides on the replenishment policy for each retailer. We assume a constant production capacity. Based on the mathematical formulation we discuss a classical Lagrangian relaxation which allows to decompose the problem into four subproblems, and a new Lagrangian decomposition which decomposes the problem into just a production-inventory subproblem and a routing subproblem. The new decomposition is enhanced with valid inequalities. A computational study is reported to compare the bounds from the two approaches.

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