Asia-Pacific Journal of Operational Research

This paper studies efficiency loss and coordination mechanism in a supply chain with one online retailer and multiple competitive shipping companies in the presence of congestion effects. We build a three-level game between customers, shipping companies and the retailer. The equilibrium market structure is determined. The optimal volume–investment ratio for each shipping company is the same for both centralized and decentralized supply chains, and it is dependent on the delivery time function, independent of competitors’ decisions. The efficiency loss of the decentralized supply chain with one retailer and one shipping company is found to be 1/4, independent of the delivery time function and the demand function. The loss is reduced when competition is introduced into shipping companies and an upper bound of efficiency loss with multiple homogeneous shipping companies is derived. Revenue-sharing contracts may be designed to allow the decentralized supply chain to perform as well as a centralized one. The necessity of coordination is reduced when competition is introduced and the number of competitive companies increases. Finally, we extend the model to consider heterogeneous shipping companies and find that the disparity between shipping companies increases the efficiency loss because in the decentralized supply chain less efficient shipping companies also provide service. This research explicates the relationship options between e-commerce retailers and shipping companies, providing managerial insights for industry practitioners.

Multilevel programming appears in many decision-making situations. Investigation of the main properties of quasiconcave multilevel programming (QCMP) problems, to date, is limited to bilevel programming (only two levels). In this paper, first, we present an extension of the properties of quasiconcave bilevel programming (QCBP) problems for the case when three levels exist. Then, by induction on n (the number of levels), we prove the existence of an extreme point of the polyhedral constraint region that solves the QCMP problem under given conditions. Ultimately, a number of numerical examples are illustrated to verify the results.

The maximum dynamic contraflow problem in transportation networks seeks to maximize the flow from a source to a sink within a given time horizon with a possibility of arc reversals. This may result into blockage of paths of desired length from some node of the network towards the source. In some cases such as the evacuation planning, we may require a path towards the source to move some facilities, for example, emergency vehicles. In this work, we model the problem of saving such a path as a bicriteria optimization problem which minimizes the length of the path and maximizes the dynamic flow with arc reversals. We use the $𝜖$-constraint approach to solve the problem and propose a procedure that gives the set of all Pareto optimal solutions in a single-source-single-sink network with integer inputs. We also present computational performance of the algorithm on a road network of Kathmandu city, and on randomly generated networks. The results are of both theoretical and practical importance.

This study investigates an integrated model for the continuous berth allocation and quay crane scheduling problem by considering quay crane coverage range, ship fuel consumption, and transshipment costs. A nonlinear mixed-integer programming model is proposed. Some nonlinear parts in this model are linearized by approximation approaches. While the objective function aims to minimize waiting costs, it also seeks to minimize fuel consumption costs from the current port to the next port and housekeeping costs generated by transshipment between vessels. A local branching-based solution algorithm is designed to solve the proposed model. Computational experiments are conducted to validate the effectiveness of the proposed scientific programming model and efficiency of the algorithm.

In this paper, we introduce the concept and applications of gH-symmetrical derivative for interval-valued multi-objective functions, which is the generalization of generalized Hukuhara derivative (gH-derivative). By a suitable example it has been shown that gH-symmetrically derivative is an extension of gH-derivative. Furthermore, we apply this new derivative to investigate the Fritz John type optimality conditions for interval-valued multiobjective programming problems. We use LR type of order relation in this context.

Stochastic resource allocation problems (SRAPs) involve determining the optimal configuration of a limited resource to achieve an objective function under given constraints and random effects in manufacturing systems (MSs) and service systems (SSs). The problems are traditionally solved by determining the optimal solution. It is generally preferable to determine as many global optima as possible, or at least a small set of diverse but good candidates, to help the decision-maker rapidly adopt alternative solutions from the set if one solution is unsuitable. However, many local or global optima occur in SRAPs in MSs and SSs due to the interaction between random system factors, such as processing time uncertainty and machine failure rates. Thus, enhancing the searching efficiency of algorithms for SRAPs is a challenge. This study proposes an efficient simulation–optimization approach, called elite-based particle swarm optimization (EPSO), using an optimal replication allocation strategy (ORAS) (i.e., EPSO${}_{\mathrm{\text{ORAS}}})$, to address three types of SRAPs from the literature. Three simulation models were constructed to evaluate the system performance under random factors. We developed a novel EPSO to explore and exploit the solution space. We created an elite group (EG) that includes multiple solutions, and each solution of the EG has a statistically nonsignificant difference from the current optimal solution. The new feature of EPSO updates the velocity and position of the particles in the design space based on multiple global optima from the EG to enhance diversity and prevent premature convergence. We propose an ORAS to allocate a limited number of replications to each solution. Three numerical experiments were performed to verify the effectiveness and efficiency of EPSO${}_{\mathrm{\text{ORAS}}}$ compared with other simulation–optimization approaches, namely particle swarm optimization (PSO) and the genetic algorithm (GA) with both optimal computing budget allocation (OCBA) and the ORAS. The experimental results reveal that the solution quality of EPSO improved compared with that of PSO and GA, and the ORAS provides a more efficient allocation of the number of replications compared with the OCBA in the three experiments. Finally, the proposed approach also provides an elite set at the end of the algorithm, instead of a single optimal solution, to support decision-making.

There have recently been extensive studies on proportional response protocol, which is motivated by the successful BitTorrent system for file sharing over a P2P network. The proportional response protocol has been proved to be strategy-proof against weight cheating attacks and edge cheating attacks, in order to allocate a single type of resource on P2P networks. This strategy-proof property holds due to an elegant combinatorial structure: the bottleneck decomposition of the underlying network structure, and the utility function, defined as the total resources that one agent receives from its neighbors. However, Sybil attacks, under which an agent may form several fictitious players and split its resource among them, have been shown as a more difficult attack to defend against, and thus a strategic agent playing Sybil attacks may result in personal gain.

Previous efforts have been made to show that an agent may generate a gain, but with limited gains by Sybil attacks on several special networks, including trees, cliques, and rings. This paper is the first to study the agent’s incentives by adopting a Sybil attack on general networks. The main contribution is to prove that any agent cannot obtain more than three times as much as the revenue when it plays honestly.

Companies have started to use a business-to-business (B2B) spot market in combination with their traditional long-term procurement contracts to procure intermediate goods. This study investigates whether or not the market players should share their demand forecast information with one another in a supplier — manufacturer supply chain and assess the benefits of sharing information in the presence of the B2B spot market. First, the supplier and manufacturer make forecast on the demand, and during this period they select an information-sharing arrangement, that is, whether to share information or not. Then, the supplier sets the wholesale price and the manufacturer submits an order after observing the wholesale price. Both the supplier and manufacturer can trade their intermediate goods in a B2B spot market. We find that the manufacturer can infer the supplier’s demand forecast from the wholesale price in the non-information-sharing case, but the supplier cannot enjoy such an advantage. We also find that information sharing benefits both the supplier and the manufacturer, if and only if demand and spot price are positively correlated and the supplier’s expectation of the manufacturer’s forecast is medium. By contrast, obtaining more demand forecast information can hurt supply chain players. Information sharing benefits the manufacturer but hurts the supplier when the supplier’s expectation of the manufacturer’s forecast is high. However, when the supplier’s expectation of the manufacturer’s forecast is low, information sharing benefits the supplier but hurts the manufacturer.

Multidimensional scaling (MDS) is to recover a set of points by making use of noised pairwise Euclidean distances. In some situations, the observed Euclidean distances may contain large errors or even missing values. In such cases, the order of the distances is far more important than their magnitude. Non-metric multidimensional scaling (NMDS) is then to deal with this problem by taking use of the ordinal information. The challenge of NMDS is to tackle the large number of ordinal constraints on distances (for $n$ points, this will be of $O(n^4)$), which will slow down existing numerical algorithms. In this paper, we propose an ordinal weighted Euclidean distance matrix model for NMDS. By designing an ordinal weighted matrix, we get rid of the large number of ordinal constraints and tackle the ordinal constraints in a soft way. We then apply our model to image ranking. The key insight is to view the image ranking problem as NMDS in the kernel space. We conduct extensive numerical test on two state-of-the-art datasets: FG-NET aging dataset and MSRA-MM dataset. The results show the improvement of the proposed approach over the existing methods.

In today’s intensive competition conditions, it is an inevitable necessity for the companies operating in the e-commerce sector to manage their physical distribution processes efficiently and effectively. The most important step in e-commerce logistics processes is the last step delivery operations in the city. In order for these operations to be carried out quickly and efficiently, delivery vehicles and personnel must be optimally led. These problems, referred to as vehicle routing problem (VRP) in the literature, include determining the least cost routes that vehicles will cover to meet customer needs. In this study, the urban delivery problems of the enterprises operating in the online retail sector are examined. In line with these problems, VRP for urban e-commerce deliveries has been modified; open, multi-depot, distribution aggregation, time-window VRP is discussed. A mathematical model for the modified VRP has been developed and a new heuristic algorithm consisting of two stages has been developed for large-scale problems. The developed algorithm was tested on three different cases. The results are compared with the solution of the nearest neighbor method and the performance of the proposed algorithm is presented.

This paper investigates an $(S,s)$ continuous-review perishable inventory model with a stock-dependent Poisson demand process, full backordering (with an extension for lost sales) and uncertainty in lead time and shelf life. Four types of costs are considered: a fixed cost of an order and each outdated item; age-dependent costs of an item (i.e., holding and salvage costs), given by a function of its remaining shelf life; and a delay cost of a backlogged demand unit, which is a function of its delay duration.

Applying the supplementary variable technique, we obtain the joint probability-density function of the number of items in the system and the remaining time and thereby obtain the optimal parameters minimizing the long-run average total cost. Numerical experiments show that supply chain profits are enhanced by integrating the age components into replenishment decisions, and ignoring the shelf age- and delay-dependent costs may result in a substantial loss (up to 25%). It further appeared that estimating the lead-time distribution by an exponential one is significantly more costly, in particular as the c.v. differs from 1. In contrast, an exponential shelf life may provide a good heuristic for other shelf-life distributions.

Iterative multi-attribute reverse auctions in practice create certain difficulties for both the buyer and participating bidders. While the buyer faces the problem of creating the right attribute weights, the bidders have difficulty in adjusting the attribute values in each round. In this paper, we present an iterative multi-attribute reverse auction mechanism based on integrated data envelopment analysis (DEA) and best–worst method (BWM) with an objective of reducing the intervention of the buyer in the determination of the winner and also easing up the preference elicitation process. Unlike the typical scoring auctions, the proposed mechanism does not require the buyer to estimate the characteristics of the participating sellers in order to determine the optimal scoring function. As there will be no other intervention from the buyer during the winner determination process, the proposed method makes the procurement process impartial and corruption-free. Besides solving the buyer’s problem, the proposed mechanism is also associated with an optimal bid determination method (OBDM) to assist the sellers in formulating improvised bids in iterative rounds of the auction. Simulation experiments show that the proposed OBDM benefits both the buyer and sellers. For the buyer, it provides higher expected utility and attribute values as per his preferences; for the seller, it gives a better expected profit and a higher probability of winning.