scholarly journals A solution algorithm for integrated production-inventory-routing of perishable goods with transshipment and uncertain demand

2021 ◽  
Author(s):  
Peide Liu ◽  
Ayad Hendalianpour ◽  
Jafar Razmi ◽  
Mohamad Sadegh Sangari
Keyword(s):  
Blood Platelets ◽  
Solution Algorithm ◽  
Uncertain Demand ◽  
Inventory Level ◽  
Optimization Approach ◽  
Inventory Routing ◽  
Solution Quality ◽  
Heuristic Solution ◽  
Distribution Management ◽  
Supply And Distribution

AbstractSupply and distribution management of blood products is a challenging task due to their short lifespan. The problem is even more sophisticated considering uncertain demand for these products. This paper addresses integrated inventory-routing of blood in a supply chain network consisting of a single supplier and a group of blood centers. Transshipment among blood centers is allowed to decrease the cost of excess inventory and shortage of goods. A mathematical model is developed that decides on the optimal quantity of supplied blood, delivery plan, inventory level, and quantity of products transshipped between blood centers with the objective of minimizing total costs. In addition, a robust optimization approach is adopted to deal with uncertainty in demand. Since the proposed model is NP-hard, a heuristic solution algorithm is developed that improves solution quality by determining the most efficient change in vehicle routes in each search stage. The efficiency of the proposed algorithm is examined in a set of numerical experiments and using data from a real case of supply and distribution management of blood platelets. The results indicated that allowing transshipment reduces the need for supply capacity at the supplier, product shortage, inventory level, and the total cost.

scholarly journals Correction to: A solution algorithm for integrated production-inventory-routing of perishable goods with transshipment and uncertain demand

2021 ◽  
Author(s):  
Peide Liu ◽  
Ayad Hendalianpour ◽  
Jafar Razmi ◽  
Mohamad Sadegh Sangari
Keyword(s):  
Solution Algorithm ◽  
Uncertain Demand ◽  
Inventory Routing ◽  
Integrated Production ◽  
Perishable Goods ◽  
Production Inventory

A correction to this paper has been published: https://doi.org/10.1007/s40747-021-00311-2

scholarly journals Enhanced coverage by integrating site interdependencies in capacitated EMS location models

2021 ◽  
Author(s):  
Matthias Grot ◽  
Tristan Becker ◽  
Pia Mareike Steenweg ◽  
Brigitte Werners
Keyword(s):  
Discrete Event ◽  
Mathematical Formulation ◽  
Optimization Approach ◽  
Solution Quality ◽  
Similar System ◽  
Event Simulation ◽  
Simulation Based ◽  
Average System ◽  
Simplifying Assumptions ◽  
A Site

AbstractIn order to allocate limited resources in emergency medical services (EMS) networks, mathematical models are used to select sites and their capacities. Many existing standard models are based on simplifying assumptions, including site independency and a similar system-wide busyness of ambulances. In practice, when a site is busy, a call is forwarded to another site. Thus, the busyness of each site depends not only on the rate of calls in the surrounding area, but also on interactions with other facilities. If the demand varies across the urban area, assuming an average system-wide server busy fraction may lead to an overestimation of the actual coverage. We show that site interdependencies can be integrated into the well-known Maximum Expected Covering Location Problem (MEXCLP) by introducing an upper bound for the busyness of each site. We apply our new mathematical formulation to the case of a local EMS provider. To evaluate the solution quality, we use a discrete event simulation based on anonymized real-world call data. Results of our simulation-optimization approach indicate that the coverage can be improved in most cases by taking site interdependencies into account, leading to an improved ambulance allocation and a faster emergency care.

A Benders Decomposition Approach for the Multivehicle Production Routing Problem with Order-up-to-Level Policy

2020 ◽  
Author(s):  
Zhenzhen Zhang ◽  
Zhixing Luo ◽  
Roberto Baldacci ◽  
Andrew Lim
Keyword(s):  
Benders Decomposition ◽  
Valid Inequalities ◽  
Planning Horizon ◽  
Set Partitioning ◽  
Master Problem ◽  
Inventory Level ◽  
Inventory Routing ◽  
Routing Problem ◽  
Decomposition Approach ◽  
Production Routing

The production routing problem (PRP) arises in the applications of integrated supply chain which jointly optimize the production, inventory, distribution, and routing decisions. The literature on this problem is quite rare due to its complexity. In this paper, we consider the multivehicle PRP (MVPRP) with order-up-to-level inventory replenishment policy, where every time a customer is visited, the quantity delivered is such that the maximum inventory level is reached. We propose an exact Benders’ decomposition approach to solve the MVPRP, which decomposes the problem as a master problem and a slave problem. The master problem decides whether to produce the product, the quantity to be produced, and the customers to be replenished for every period of the planning horizon. The resulting slave problem decomposes into a capacitated vehicle routing problem for each period of the planning horizon where each problem is solved using an exact algorithm based on the set partitioning model, and the identified feasibility and optimality cuts are added to the master problem to guide the solution process. Valid inequalities and initial optimality cuts are used to strengthen the linear programming relaxation of the master formulation. The exact method is tested on MVPRP instances and on instances of the multivehicle vendor-managed inventory routing problem, a special case of the MVPRP, and the good performance of the proposed approach is demonstrated.

Multi-Objective Optimization in Multi-Attribute and Multi-Unit Combinatorial Reverse Auctions

2017 ◽  
Vol 26 (05) ◽  
pp. 1760016 ◽  
Author(s):  
Shubhashis Kumar Shil ◽  
Samira Sadaoui
Keyword(s):  
Quality Measures ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Solution Quality ◽  
Winner Determination ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Trading Constraints ◽  
Significant Superiority

This study introduces an advanced Combinatorial Reverse Auction (CRA), multi-units, multiattributes and multi-objective, which is subject to buyer and seller trading constraints. Conflicting objectives may occur since the buyer can maximize some attributes and minimize some others. To address the Winner Determination (WD) problem for this type of CRAs, we propose an optimization approach based on genetic algorithms that we integrate with our variants of diversity and elitism strategies to improve the solution quality. Moreover, by maximizing the buyer’s revenue, our approach is able to return the best solution for our complex WD problem. We conduct a case study as well as simulated testing to illustrate the importance of the diversity and elitism schemes. We also validate the proposed WD method through simulated experiments by generating large instances of our CRA problem. The experimental results demonstrate on one hand the performance of our WD method in terms of several quality measures, like solution quality, run-time complexity and trade-off between convergence and diversity, and on the other hand, it’s significant superiority to well-known heuristic and exact WD techniques that have been implemented for much simpler CRAs.

Heuristic approaches for operational aircraft maintenance routing problem with maximum flying hours and man-power availability considerations

2017 ◽  
Vol 117 (10) ◽  
pp. 2142-2170 ◽  
Author(s):  
Abdelrahman E.E. Eltoukhy ◽  
Felix T.S. Chan ◽  
S.H. Chung ◽  
Ben Niu ◽  
X.P. Wang
Keyword(s):  
Airline Industry ◽  
Solution Algorithm ◽  
The Other ◽  
Computational Time ◽  
Aircraft Maintenance ◽  
Solution Quality ◽  
Routing Problem ◽  
Content Type ◽  
Other Hand ◽  
Availability Constraints

Purpose The purpose of this paper is twofold. First, to propose an operational model for aircraft maintenance routing problem (AMRP) rather than tactical models that are commonly used in the literature. Second, to develop a fast and responsive solution method in order to cope with the frequent changes experienced in the airline industry. Design/methodology/approach Two important operational considerations were considered, simultaneously. First one is the maximum flying hours, and second one is the man-power availability. On the other hand, ant colony optimization (ACO), simulated annealing (SA), and genetic algorithm (GA) approaches were proposed to solve the model, and the upper bound was calculated to be the criteria to assess the performance of each meta-heuristic. After attempting to solve the model by these meta-heuristics, the authors noticed further improvement chances in terms of solution quality and computational time. Therefore, a new solution algorithm was proposed, and its performance was validated based on 12 real data from the EgyptAir carrier. Also, the model and experiments were extended to test the effect of the operational considerations on the profit. Findings The computational results showed that the proposed solution algorithm outperforms other meta-heuristics in finding a better solution in much less time, whereas the operational considerations improve the profitability of the existing model. Research limitations/implications The authors focused on some operational considerations rather than tactical considerations that are commonly used in the literature. One advantage of this is that it improves the profitability of the existing models. On the other hand, identifying future research opportunities should help academic researchers to develop new models and improve the performance of the existing models. Practical implications The experiment results showed that the proposed model and solution methods are scalable and can thus be adopted by the airline industry at large. Originality/value In the literature, AMRP models were cast with approximated assumption regarding the maintenance issue, while neglecting the man-power availability consideration. However, in this paper, the authors attempted to relax that maintenance assumption, and consider the man-power availability constraints. Since the result showed that these considerations improve the profitability by 5.63 percent in the largest case. The proposed operational considerations are hence significant. Also, the authors utilized ACO, SA, and GA to solve the model for the first time, and developed a new solution algorithm. The value and significance of the new algorithm appeared as follow. First, the solution quality was improved since the average improvement ratio over ACO, SA, and GA goes up to 8.30, 4.45, and 4.00 percent, respectively. Second, the computational time was significantly improved since it does not go beyond 3 seconds in all the 12 real cases, which is considered much lesser compared to ACO, SA, and GA.

scholarly journals Multi-objective inventory routing with uncertain demand using population-based metaheuristics

2016 ◽  
Vol 23 (3) ◽  
pp. 205-220 ◽  
Author(s):  
Zhiwei Yang ◽  
Michael Emmerich ◽  
Thomas Bäck ◽  
Joost Kok ◽  
Zhiwei Yang ◽  
...  
Keyword(s):  
Population Based ◽  
Uncertain Demand ◽  
Inventory Routing ◽  
Multi Objective

scholarly journals Adjustable Robust Optimization for Planning Logistics Operations in Downstream Oil Networks

Processes ◽  
2019 ◽  
Vol 7 (8) ◽  
pp. 507
Author(s):  
Lima ◽  
Relvas ◽  
Barbosa-Póvoa ◽  
Morales
Keyword(s):  
Robust Optimization ◽  
Oil Industry ◽  
Computational Effort ◽  
Optimization Techniques ◽  
Planning Problem ◽  
Optimization Approach ◽  
Solution Quality ◽  
Inaccurate Information ◽  
Computational Performance ◽  
Adjustable Robust Optimization

The oil industry operates in a very uncertain marketplace, where uncertain conditions can engender oil production fluctuations, order cancellation, transportation delays, etc. Uncertainty may arise from several sources and inexorably affect its management by interfering in the associated decision-making, increasing costs and decreasing margins. In this context, companies often must make fast and precise decisions based on inaccurate information about their operations. The development of mathematical programming techniques in order to manage oil networks under uncertainty is thus a very relevant and timely issue. This paper proposes an adjustable robust optimization approach for the optimization of the refined products distribution in a downstream oil network under uncertainty in market demands. Alternative optimization techniques are studied and employed to tackle this planning problem under uncertainty, which is also cast as a non-adjustable robust optimization problem and a stochastic programing problem. The proposed models are then employed to solve a real case study based on the Portuguese oil industry. The results show minor discrepancies in terms of network profitability and material flows between the three approaches, while the major differences are related to problem sizes and computational effort. Also, the adjustable model shows to be the most adequate one to handle the uncertain distribution problem, because it balances more satisfactorily solution quality, feasibility and computational performance.

A Non-Dominant Sorting Genetic Algorithm for Optimization of a Product Design and Selection of Its Suppliers

2020 ◽  
Vol 19 (01) ◽  
pp. 167-188
Author(s):  
Oulfa Labbi ◽  
Abdeslam Ahmadi ◽  
Latifa Ouzizi ◽  
Mohammed Douimi
Keyword(s):  
Genetic Algorithm ◽  
Product Design ◽  
Computational Time ◽  
Optimization Approach ◽  
Nsga Ii ◽  
Solution Quality ◽  
Holding Costs ◽  
Weighted Sum Method ◽  
The Impact ◽  
Selection Of

The aim of this paper is to address the problem of supplier selection in a context of an integrated product design. Indeed, the product specificities and the suppliers’ constraints are both integrated into product design phase. We consider the case of improving the design of an existing product and study the selection of its suppliers adopting a bi-objective optimization approach. Considering multi-products, multi-suppliers and multi-periods, the mathematical model proposed aims to minimize supplying, transport and holding costs of product components as well as quality rejected items. To solve the bi-objective problem, an evolutionary algorithm namely, non-dominant sorting genetic algorithm (NSGA-II) is employed. The algorithm provides a set of Pareto front solutions optimizing the two objective functions at once. Since parameters values of genetic algorithms have a significant impact on their efficiency, we have proposed to study the impact of each parameter on the fitness functions in order to determine the optimal combination of these parameters. Thus, a number of simulations evaluating the effects of crossover rate, mutation rate and number of generations on Pareto fronts are presented. To evaluate performance of the algorithm, results are compared to those obtained by the weighted sum method through a numerical experiment. According to the computational results, the non-dominant sorting genetic algorithm outperforms the CPLEX MIP solver in both solution quality and computational time.

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