A general variable neighborhood search-based solution approach for the location-inventory-routing problem with distribution outsourcing

A hybrid optimization method to design a sustainable resilient supply chain in a perishable food industry

To integrate the location, inventory, and routing (LIR) problems arising in designing a resilient sustainable perishable food supply network (RSPFSN), a bi-objective optimization model is developed. To improve the resiliency and sustainability of the RSPFSN, a dynamic pricing strategy is used to cope with the disrupting events, along with minimizing the total cost and CO2 emission of the whole network. One of the important features of the proposed model is taking into account the effects of route disruptions and traffic conditions on the deterioration of products. To solve the mixed-integer nonlinear bi-objective optimization model, a novel hybrid method is developed using the Heuristic Multi-Choice Goal Programming and Utility Function Genetics Algorithm (HMCGP-UFGA). To improve resiliency, the dynamic pricing strategy, considering the traffic condition, can lead to around a 20% improvement in both cost and CO2 emission, based on the results of our case study in a dairy supply chain. Besides, the results of sensitivity analysis display the high flexibility of the proposed approach for various problems.

Multi-Stage Multi-Product Production and Inventory Planning for Cold Rolling under Random Yield

This paper studies a multi-stage multi-product production and inventory planning problem with random yield derived from the cold rolling process in the steel industry. The cold rolling process has multiple stages, and intermediate inventory buffers are kept between stages to ensure continuous operation. Switching products during the cold rolling process is typically very costly. Backorder costs are incurred for unsatisfied demand while inventory holding costs are incurred for excess inventory. The process also experiences random yield. The objective of the production and inventory planning problem is to minimize the total cost including the switching costs, inventory holding costs, and backorder costs. We propose a stochastic formulation with a nonlinear objective function. Two lower bounds are proposed, which are based on full information relaxation and Jensen’s inequality, respectively. Then, we develop two heuristics from the proposed lower bounds. In addition, we propose a two-stage procedure motivated by newsvendor logic. To verify the performance of the proposed bounds and heuristics, computational tests are conducted on synthetic instances. The results show the efficiency of the proposed bounds and heuristics.

A variable neighborhood search for the last-mile delivery problem during major infectious disease outbreak

During major infectious disease outbreak, such as COVID-19, the goods and parcels supply and distribution for the isolated personnel has become a key issue worthy of attention. In this study, we propose a delivery problem that arises in the last-mile delivery during major infectious disease outbreak. The problem is to construct a Hamiltonian tour over a subset of candidate parking nodes, and each customer is assigned to the nearest parking node on the tour to pick up goods or parcels. The aim is to minimize the total cost, including the routing, allocation, and parking costs. We propose three models to formulate the problem, which are node-based, flow-based and bilevel programing formulations. Moreover, we develop a variable neighborhood search algorithm based on the ideas from the bilevel programing formulations to solve the problem. Finally, the proposed algorithm is tested on a set of randomly generated instances, and the results indicate the effectiveness of the proposed approach.

LIRP optimization of cold chain logistics in satellite warehouse mode of supermarket chains

Aiming at the problem of high cost in cold chain logistics of fresh products home-delivery in supermarket chain in the new retail era, the paper constructs the model of Location Inventory Routing Problem (LIRP) optimization in Satellite Warehouse mode in view of customer satisfaction with the broken line soft time windows. The model minimizes the total cost of the cold chain logistics system of supermarket chain through the location allocation, inventory optimization, the determination of distribution service relationship between Satellite Warehouse and customer, and the constraint of time penalty cost. Then, the paper designed an improved ant colony optimization to solve the LIRP model of supermarket chain. Finally, the simulation in MATLAB verifies and analyzes the validity of the model and algorithm. Therefore, LIRP optimization model in Satellite Warehouse mode can effectively improve the operational efficiency of fresh products home-delivery in the supermarket chain and thus reduce the logistics cost.

Demand management to cope with routes disruptions in location-inventory-routing problem for perishable products

In today's competitive world, with the increase in the complexity of supply chains, supply chain vulnerability to disruptions has increased. In this research, a multi-period location-inventory-routing (LIR) problem of perishable products is investigated under the disruption of routes in some periods. To make a resilient supply chain, two types of pricing namely dynamic pricing and disruptive pricing are applied to manage demands along with location, inventory, and routing decisions. In this regard, an integrated LIR model is developed considering disruption in routes, price-sensitive demand, and a product with a certain life-time. In this model, the price of retailers is a descending function of the time and product lifetime. The proposed model is devised as a mixed-integer non-linear programming model that maximizes the total profit of the supply chain. Due to the NP-hard nature of the problem, the research has developed an efficient genetic algorithm to solve large-sized problems. Computational experiments conducted indicating that the projected GA has an average gap of less than 2.66% from the optimal solution within a reasonable time. The performance of the integrated model, the efficiency of the proposed resilient strategy, and the impact of shelf-life are investigated in a case study. Results revealed that the integrated model for dynamic pricing and LIR decisions enjoys 79.33% improvement in the total expected profit for the supply chain under disruption compared to static pricing. As expected, by increasing the product's shelf-life, the profit of the supply chain increases in all pricing policies. It should be noted that applying the dynamic pricing policy, compared to the product's lifetime, enjoys a greater impact on supply chain profit under disruption. Moreover, there is a necessity to choose an appropriate pricing policy for markets with a different value of price elasticity.

A Performance Study of the Impact of Different Perturbation Methods on the Efficiency of GVNS for Solving TSP

The purpose of this paper is to assess how three shaking procedures affect the performanceof a metaheuristic GVNS algorithm. The first shaking procedure is generally known in the literatureas intensified shaking method. The second is a quantum-inspired perturbation method, and thethird is a shuffle method. The GVNS schemes are evaluated using a search strategy for both Firstand Best improvement and a time limit of one and two minutes. The formed GVNS schemes wereapplied on Traveling Salesman Problem (sTSP, nTSP) benchmark instances from the well-knownTSPLib. To examine the potential advantage of any of the three metaheuristic schemes, extensivestatistical analysis was performed on the reported results. The experimental data shows that for aTSPinstances the first two methods perform roughly equivalently and, in any case, much better thanthe shuffle approach. In addition, the first method performs better than the other two when usingthe First Improvement strategy, while the second method gives results quite similar to the third.However, no significant deviations were observed when different methods of perturbation were usedfor Symmetric TSP instances (sTSP, nTSP).

Unconventional GVNS for Solving the Garbage Collection Problem with Time Windows

GVNS, which stands for General Variable Neighborhood Search, is an established and commonly used metaheuristic for the expeditious solution of optimization problems that belong to the NP-hard class. This paper introduces an expansion of the standard GVNS that borrows principles from quantum computing during the shaking stage. The Traveling Salesman Problem with Time Windows (TSP-TW) is a characteristic NP-hard variation in the standard Traveling Salesman Problem. One can utilize TSP-TW as the basis of Global Positioning System (GPS) modeling and routing. The focus of this work is the study of the possible advantages that the proposed unconventional GVNS may offer to the case of garbage collector trucks GPS. We provide an in-depth presentation of our method accompanied with comprehensive experimental results. The experimental information gathered on a multitude of TSP-TW cases, which are contained in a series of tables, enable us to deduce that the novel GVNS approached introduced here can serve as an effective solution for this sort of geographical problems.