Formulations and Branch-and-Cut Algorithms for Multivehicle Production and Inventory Routing Problems

Adulyasak, Yossiri; Cordeau, Jean‐François; Jans, Raf

doi:10.1287/ijoc.2013.0550

Cited by 226 publications

(158 citation statements)

References 20 publications

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“…To resolve this symmetry issue, we can add symmetry breaking constraints by computing a unique number for a set of arcs that belong to each route and imposing constraints to rank them. This approach has been successfully applied to several applications (e.g., see Sherali and Smith (2001), Jans (2009), Adulyasak et al (2013)). We use a form of the symmetry breaking constraints which can be easily obtained from the description of the travel time uncertainty.…”

Section: Single Uncapacitated Vehiclementioning

confidence: 99%

Models and Algorithms for Stochastic and Robust Vehicle Routing with Deadlines

Adulyasak

Jaillet

2016

Transportation Science

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We consider the vehicle routing problem with deadlines under travel time uncertainty in the contexts of stochastic and robust optimization. The problem is defined on a directed graph where a fleet of vehicles is required to visit a given set of nodes and deadlines are imposed at a subset of nodes. In the stochastic vehicle routing problem with deadlines (SVRP-D), the probability distribution of the travel times is assumed to be known and the problem is solved to minimize the sum of probability of deadline violations. In the robust vehicle routing problem with deadlines (RVRP-D), however, the exact probability distribution is unknown but it belongs to a certain family of distributions. The objective of the problem is to optimize a performance measure, called lateness index, which represents the risk of violating the deadlines. Although novel mathematical frameworks have been proposed to solve these problems, the size of problem that those approaches can handle is relatively small. Our focus in this paper is the computational aspects of the two solution schemes. We introduce formulations that can be applied for the problems with multiple capacitated vehicles and discuss the extensions to the cases of incorporating service times and soft time windows. Furthermore, we develop an algorithm based on a branch-and-cut framework to solve the problems. The experiments show that these approaches provide substantial improvements in computational efficiency compared to the approaches in the literature.

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Section: Single Uncapacitated Vehiclementioning

confidence: 99%

Models and Algorithms for Stochastic and Robust Vehicle Routing with Deadlines

Adulyasak

Jaillet

2016

Transportation Science

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“…All of the data sets have demands in every period, with exception for case 50 customers. The holding cost for each customer is generated within the range [1,10] and the demands are generated randomly within the range [0,50]. The vehicle capacity is fixed as 100 and the depot is located at (0,0) for all data sets.…”

Section: Mip1mentioning

confidence: 99%

“…Besides, the approach was also tested on a set of single item instances [13] and they outperformed the memetic algorithm suggested by Boudia and Prins [9] and the reactive tabu search developed by Bard and Nananukul [7]. Adulyasak et al [10] improves upon the results of Armentano et al [8] by proposing Adaptative large neighborhood search heuristic to consider binary variables representing the setup and routing variables whilst the continuous variables associated with inventory, production and quantity delivered are handled by solving a network flow problem. The results outperformed all other known heuristics for PIDRP.…”

Section: Introductionmentioning

confidence: 99%

A two-phase iterative procedure for the production, inventory and distribution routing problem

Kyee¹,

Moin²

2016

AIP Conference Proceedings

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Abstract. In this paper, the PIDRP is modelled as a one-to-many distribution system, in which a single warehouse or production facility is responsible for restocking a set of customers whose demands are deterministic and time-varying. The demand can be satisfied from either inventory held at the customer sites or from daily production. A fleet of homogeneous capacitated vehicles for making the deliveries is also considered. Capacity constraints for the inventory are given for each customer and the demand must be fulfilled on time, without delay. The aim of solving the PIDRP model is to minimize the overall cost of coordinating the production, inventory and transportation over a finite planning horizon. We propose an iterative procedure commonly known as MatHeuristic algorithm, an optimization algorithm designed by the interpolation of metaheuristics and mathematical programming techniques, to solve the model. In Phase 1, we construct routes in each period with the assumption that all the demands are satisfied in the given period by Variable Neighborhood Search, then the mixed integer programming is solved in Phase 2 to obtain the production schedules, quantity to be delivered and the inventory levels at the production facility and the customer sites. Based on the output from Phase 2, the routes are improved and the algorithm iterates until some stopping criteria is met. The model is solved by using Concert Technology of CPLEX 12.5 Optimizers with Microsoft Visual C++ 2010. Computational experiment is conducted to test the effectiveness of the algorithm. We observed that our algorithm performs better compared to the best integer solution obtained from CPLEX.

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“…After the importance of considering production, inventory, and routing decisions simultaneously was stressed in [7], the PRP was extended in various ways to consider, for example, multiple plants and heterogeneous fleets of vehicles [8], incapacitated production [9], multiple homogeneous capacitated vehicles [10], demand uncertainty [11], multi-item back-order [12], perishable products [13], and multiscale production [14] in the past decade. The environmental impact of the PRP has seldom…”

Section: Introductionmentioning

confidence: 99%

Reducing Carbon Emissions in a Closed-Loop Production Routing Problem with Simultaneous Pickups and Deliveries under Carbon Cap-and-Trade

Fang

Qiu

2017

Sustainability

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Abstract:The incorporation of reverse logistics into production routing problems can promote and coordinate the implementation of sustainable development for supply chains. This study aims to incorporate reverse logistics into production routing problems and investigate the reduction of carbon emissions under carbon cap-and-trade. Mixed-integer programming models are proposed for the production routing problem with reverse logistics by considering simultaneous pickups and deliveries in vehicle routing subproblems. To solve this problem, we propose a solution method of a branch-and-cut guided search algorithm based on adaptation of known valid inequalities. Computational results highlight the trade-offs among various performance indicators, including emission levels and operational costs of production, inventory holding, fuel consumption, and drivers.

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Formulations and Branch-and-Cut Algorithms for Multivehicle Production and Inventory Routing Problems

Cited by 226 publications

References 20 publications

Models and Algorithms for Stochastic and Robust Vehicle Routing with Deadlines

Models and Algorithms for Stochastic and Robust Vehicle Routing with Deadlines

A two-phase iterative procedure for the production, inventory and distribution routing problem

Reducing Carbon Emissions in a Closed-Loop Production Routing Problem with Simultaneous Pickups and Deliveries under Carbon Cap-and-Trade

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